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Q1:DIRECTIONS Aims to evaluate the skills achieved by the students during the first part of the course- Cyber Risk Modeling and Analysis Tools. SUBMISSION The student must submit the notebook filled with the answers to the questions. He must use screenshots to support his explanation. Avoid using weak answers, and spend time explaining your answer correctly. It is compulsory to send the Bayesian Network models in the UNBBayes or GENIE format. The FCL file must be written and executable by JLogicFuzzy. Read the rubric to understand what is required to submit. Problem Statement Based on Alexandre B. Barreto, Paulo C.G. Costa, Cyber-ARGUS - A mission assurance framework, Journal of Network and Computer Applications, Volume 133, 2019, Pages 86-108, ISSN 1084-8045, https://doi.org/10.1016/j.jnca.2019.02.001. A. Inference Model The use of cyberspace as a platform for military operations has been growing at impressive rates. Yet, it is still a relatively new area with considerable research challenges. Security techniques are not sufficiently effective in protecting IT systems, and most fail to address the correlation between actions and effects across multiple domains. In other words, identifying how actions performed in the cyber domain affect the mission goals is yet an unsolved problem. It must include the previous approach: an adequate assessment of the correlation between cyber and physical behaviors performed holistically and allowing tasks to be evaluated in real-time. Existing tools and methodologies cannot provide this information level and are unsuitable to support complex cyber threat assessment in real situations. Despite the relatively large body of research on the subject, this significant gap still exists. The most common approach found in the research literature is to determine how vulnerabilities can be exploited by the enemy (e.g. (Jajodia et al., 2005; Jajodia et al., 2011; Jakobson, 2011a; Buckshaw et al., 2005)) - the threat-centric (Silva and Jacob, 2018). It usually involves the generation of an attack graph (Sheyner et al., 2002), which includes vulnerabilities and exploit strategies. The impact assessment is then calculated using evidence the analyst extracts from the environment, which in most real scenarios is impracticable due to the computational cost of solving the graphs. Another limitation is the requirement of knowing the attack path in advance, which is rarely possible since attacks against critical infrastructure always attempt to explore new attack paths and unknown vulnerabilities (i.e., zero-day attacks). A more recent approach that avoids this problem involves measuring the impact of cyber-attacks on a mission, mission-centric approach (Silva and Jacob, 2018). The idea is to define how the mission can be impacted through the analysis of the effects produced by the interactions of offensive (enemy) and defensive plans (Musman et al., 2011a) under the premise that it is easier to design one's mission and its restrictions than to know the enemy behavior. This approach is focused on effects and does not require detecting attacks or attackers but only understanding the possible effects of their actions on the mission. The mission is modeled to measure the impact, and all critical components are identified and monitored. Musman studied using this perspective (Musman et al., 2010, 2011a, 2011b; Musman and Temin, 2015). However, that body of research focuses on assessing how to leverage impact estimations to support the designing of security architectures and not on exploring and developing specific and scalable approaches based on the concept. A new approach is provided by the Cyber-ARGUS Framework, which provides a scalable way of modeling the mission from a holistic perspective, both in its planning and execution phases, establishing the connection of entities within the mission and the cyber domains, and enabling the impact assessment of cyber events to be measured within the mission context (i.e., within the operations domain). An essential contribution is that the framework allows cyber impact assessment of an ongoing mission to be achieved without the need to know the individual actions of the attacker. Figure 1 - The overall model includes a mission process model, a cyber adversary process model, a cyber defender process model from (Kott et al., 1710).The framework has a mission-centric approach, which requires the cyber and mission concepts to be defined and the connection between the two domains to be explicitly expressed. Figure 1 shows a pictorial representation of how Cyber-Argus understands the mission and its concepts. There, you can see different perspectives represented in layers. The first layer represents the mission and the required tasks to perform it successfully. The second layer represents the required services for each layer, where OT and IT systems could provide the services. Finally, the last layer is the cyber domain layer. However, this representation is relatively abstract. To be usable with a computer system, the concept of the impact graph must be adapted, as presented by Jakobson (2011b). The adapted graph (impact graph) includes all relationships between tasks, services, and nodes, resulting in a structure that makes it easier to assess the consequences generated when a node is compromised (see Figure 2). The impact graph is a type of dependency network. A dependency network approach provides a system-level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the network's nodes (when the network structure is analyzed) and provides an essential step towards inference of causal activity relations between the network nodes (when analyzing the network activity). Kenett, Dror Y., et al. "Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market." PloS one 5.12 (2010): e15032. Figure 2 presents an adaptation of Jakobson's representation of the impact graph. At the top is the Intra-Mission Layer, which is compounded by a mission goal and a set of tasks required to accomplish it. In the example, you have tasks T1 and T2 that both are required to accomplish (AND gateway) successfully to accomplish the mission M1 has accomplished in a thriving state. Also, you can see in the Intra-Mission Dependency layer that tasks T3 and T4, which are required (at least one with successful performance – OR gateway) to task T2, can be performed successfully. You have the services required for each task at the Intra-Service Dependency layer. There, you can see individual services required to accomplish the tasks, complex one compound by two or more services (using OR/AND gateways); it is not represented in the Figure, but you can have a service that depends on other services. Finally, you have the last layer, the Intra-Asset Dependency or the Cyber Layer, which is compounded by the cyber-nodes and has the same topology design properties as the concepts in the other layers. Figure 2 - Impact Graph. Note that the Impact Graph represents the mission, and this step aims to assess the influence between the nodes in the graph. In other words, assess the interdependence between the various components of this mission representation. This assessment is performed using a Reasoning Model. The Reasoning Model can be implemented via four main approaches. The first involves using algebraic expressions to model the relations, a typical example being a linear regression model. There are two main issues with this approach. One is that such models require generating statistical historical data, which in most cases does not exist. Even in the few cases where it exists, its success in predicting cyber-attacks is fragile as these tend not to follow predefined patterns. The second issue is related to the need to interpret evidence by an analyst, which makes it more complicated when an arid mathematical model is used (Daniels et al., 2008). A second approach for building a Reasoning Model is the use of a black box approach, such as Neural Networks (e.g., Deep Learning). However, in addition to the pitfalls of the previous approach, there is the problem of the intrinsic nature of the mapping between the input and output of such models. That is, there is no clear way of building a narrative that explains the model’s results and, thus, no way of making the interrelationships in the graph explicit or explainable. Markov models for security risk are a third alternative, such as the one developed by Kim et al. (2007). However, using Markov processes to propagate Impact Assessment highlights the technique's weakness of its inability to represent non-monotonic dependencies. For instance, in this technique, two independent variables must be directly connected by an edge merely because some other variable depends on both (Pearl, 1986). The last alternative, used by Cyber-Argus, is Bayesian Networks (BNs). This probabilistic graphical technique represents a set of random variables and their joint probability distribution via a Directed Acyclic Graph (DAG) (Russell and Norvig, 2009). BNs are cognitively meaningful and directly interpretable. Unlike traditional rule-based systems, BNs employ a coherent calculus to manage the uncertainty and absorb evidence as it accrues (cf. (Daniels et al., 2008)). In contrast to other classical statistic approaches, BNs provide backward inference (i.e., allowing for what-if analyses). Fenton lists some of the advantages of BNs, such as a) explicitly model causal factors; b) provide reasoning from effect to cause and vice-versa; c) reduce the burden of parameter acquisition; d) overturn previous belief in light of new evidence; e) make predictions with incomplete data; f) combine diverse types of evidence including both subjective beliefs and objective data and arrive at decisions based on visible, auditable reasoning (Fenton and Neil, 2013). Given that Cyber-Argus's main idea is to interpret how events in the cybernetic layer influence the performance of elements at the mission level and vice versa, it is required to represent the cyber node's health. In other words, it is the ability to provide the required resources and services within a certain level of quantity, quality, effectiveness, and cost. At Cyber-Argus, the node health is measured by the Infrastructure Capacity (IC). Unlike the reference paper, in this Exam, we will simplify the reasoning model, making it more straightforward than the one used in the paper. In the approach used in this activity, the IC is calculated by a fuzzy inference box. Using fuzzy sets in cybersecurity has several advantages, particularly in dealing with uncertainty, imprecision, and complexity in risk assessment and decision-making processes. In the particular case of cyber nodes, making some inferences useful for the other layers is challenging. Fuzzy sets are excellent for modeling uncertain and imprecise data, often expressed in natural language terms like "high risk," "moderate impact," or "low likelihood." Also, Fuzzy logic can handle incomplete and noisy data more gracefully than other methods. It does not rely heavily on complete datasets, which are often difficult to obtain in cybersecurity. We use the Fuzzy Inference engine presented in Figure 3 to calculate the IC. There, you can see that you calculate IC based on five inputs collected from cyber sensors and, based on a set of rules, infer the node resilience, which will be used to infer the reliability of the following layers. Figure 3 - IC Calculation. Each input variable has its membership representation in Table 1, where it is presented the functions that transform the crips value from the sensor measurements to a fuzzy value. Variable Membership Function Threat Exploitation (likelihood) Likely Triangular (a=10, b=8, c=6) Possible Gaussian (c = 5, s=2, m=2) Unlikely Triangular (a=5, b=7, c=0) Failure Logins Attempt (#number) Low Triangular (a=0, b=30, c=50) Medium Trapezoidal (a=20, b=30, c=50, d=300) High Triangular (a=200, b=400, c=1000) CPU Load (%) Low Triangular (a=0, b=20, c=30) Medium Trapezoidal (a=20, b=30, c=40, d=60) High Triangular (a=50, b=70, c=100) Memory Load (%) Low Triangular (a=0, b=20, c=30) Medium Trapezoidal (a=20, b=30, c=40, d=60) High Triangular (a=50, b=70, c=100) Interface IN/OUT (%) Low Triangular (a=0, b=20, c=30) Medium Trapezoidal (a=20, b=30, c=40, d=60) High Triangular (a=50, b=70, c=100) Finally, you have the target variable described in Table 2. Node Resilience (Boolean) Reliable Gaussian (c = 5, s=2, m=2) No-reliable Gaussian (c = 7, s=2, m=2) The model is based on these rules to support the inference of node resilience. The first one is that node resilience is no-reliable when all of its inputs are high, and the threat of exploitation is likely. The second rule is that when the threat exploitation is possible, or the other variables are high, the node resilience is no-reliable. The node resilience is reliable when the threat exploitation is unlikely and the Failure Logins Attempt is low. When one of the CPU, memory, and IO interfaces load is low, the node resilience is reliable. Finally, the inference model uses a center-of-gravity approach, and the aggregation is a MAX function. After you calculate the IC and check whether it is reliable, you must propagate this information to the other layers, allowing you to infer whether the mission is reliable (achievable) or not. To perform this task, we use a Bayesian Network that must implement the dependencies described in Jacobson’s impact graph (see Figure 2). To restrict complexity, all BN nodes are binary, and they can have two possible states: reliable and non-reliable. We will use only simple connections (one parent) or Noisy-OR connections (several parents) to implement the required dependencies. Using Noisy-OR, we assume that each parent influences the target node as a conditional independence assumption. B. Study Case Greenfield City operates a large municipal water treatment plant (WTP) that provides clean drinking water to approximately 500,000 residents. The WTP uses an automated control system, Supervisory Control and Data Acquisition (SCADA), to monitor and control water purification and distribution processes. This system includes sensors, programmable logic controllers (PLCs), and human-machine interfaces (HMIs). Figure 4 contains a simplistic version of this service's topology. The cyber layer is provided for two different devices: smart pumps and valves. The intelligent pumps push water from the river and insert it into the water purification system. The valves regulate the flow of purification water to the city. Both components are IoT devices that process capacity and you use this end device to measure the cyber environment. The next layer, the intra-service layer, contains the required services to support the two main tasks of the water system: water purification and water flow distribution. Figure 4 - Greenfield City Service. You know that the Coagulation service is reliable 80% of the time when pump1 or pump2 is reliable. The Sedimentation Service is reliable at 70%, while the Coagulation Service is reliable. The filtration service is reliable 90% of the time, while the sedimentation service is reliable. The disinfection service is 70% reliable when the filtration service is reliable. Finally, the purification system is reliable 85% of the time when the disinfection service is reliable. When one of these services or the pumps is unreliable, the system moves to an unreliable state. In the other process, you know that flow regulation is reliable 98% of the time, where one of the valves is reliable. The water flow distribution is reliable 75% of the time when the flow regulation is reliable. They have the same behavior as the previous process: unreliable when one component is unreliable. The Greenfield City Service is reliable 60% of the time when purification and water flow distribution are reliable, 45% when only one of the processes is reliable, and not reliable when both are not reliable. C. Exam Tasks Task 1. Create an FCL file that implements the node fuzzy inference engine used to infer the IC value from the cyber nodes. Task 1 – 30 points FCL file The file was submitted. 1.0 Rules The rules are implemented correctly (no errors). 5.0 Membership Functions The membership functions were corrected and implemented (4 points for variable). Max 24 points Task 2. Create a Bayesian Network that implements the topology proposed to the Greenfield City Service. The BN must implement triggers when you have OR gateways. Task 2 – 42 points BN file The file was submitted. 1.0 BN Topology The topology is completed and correct. 5.0 CPT elicitations The CPTs are corrected based on the information provided in the document. 2 points for each CPT. Max 16 points Noisy-OR design The noisy-OR trigger is correctly applied, and the correspondent CPTs are corrected. 20 points Task 3—The system monitors the states of pump1, pump2, valv1, and valv2. Based on the following information, answer: What is the membership of each cyber node (pump1, pump2, valv1, and valv2) – uses linguistic variables. For example, pump1 is a very likely threat exploited, almost possibly exploited; it is a very high CPU load, etc. What is the final state of Greenfield City Service? Observation: You must use JFuzzyLogic to infer the values of the Fuzzy engine process and UNBBAYES or Gennie to the BN. You must explain the results. Pump1 Threat_exploitation (7), CPU_load(80%), Memory Load (90%), IN/OUT Load(80%), FailureLoginsAttempt(100) Pump2 Threat_exploitation (4), CPU_load(20%), Memory Load (10%), IN/OUT Load(30%), FailureLoginsAttempt(5) Valv1 Threat_exploitation (8), CPU_load(98%), Memory Load (89%), IN/OUT Load(90%), FailureLoginsAttempt(200) Valv2 Threat_exploitation (4), CPU_load(100%), Memory Load (10%), IN/OUT Load(30%), FailureLoginsAttempt(800) Task 3 – 28 points Fuzzification Process Students demonstrate (using screenshots) to demonstrate the result calculation from the tools, and he explains the results using linguistic variables. 5.0 Defuzzification Process Students demonstrate (using screenshots) to demonstrate the result calculation from the tools, and he explains the results using linguistic variables. 5.0 BN propagation Students demonstrate the execution of BN (using screenshots). 7.0 BN result explanation Students explain the result of the BN propagation, the input node (cyber), and the final result at the target node (mission). 8.0 BN result The result is correct. 3.0See Answer
Q2: Teesside University International Study Centre Study Group TUISC - Assessment Brief - Digital Futures Summative Assessment Title of the assignment: Digital Futures Research Report International Foundation Year - Computing, Engineering, & Programme: Science Assessment type ECA Academic Year: 2023-2024 (ICA/ECA): Module Title: Digital Futures Module Code: Summative Report: Assignment Format & Maximum Word Count: 2000-word written report on research project 100% Weighting: Submission Date/Time and Location/Method: Feedback date: Tutor/s Marking: Cameron Dowse/ Mike Jaques Resubmission Date: Detailed Brief for Individual/Group Assessment Purpose of the assignment: This assignment will assess students' ability to report on the findings of their research, as well as provide context for their findings, reflect on the process, and adequately reference their work. Context of the assignment: Students will need work individually to write a report of no longer than 2000 words (+10%) (excluding references and any title / cover page), writing about their research question of their choice within the realms of Digital technology. The report will be split into six sections: ● Introduction • Literature Review • Analysis and Discussion • Conclusions • Self-Evaluation • References The report will include a title page, and each section will need a subtitle. Introduction This section will require the students to introduce the report, briefly explaining what the report will be about and introducing the project question, as well as detailing the Teesside University International Study Centre Study Group structure of the report. It should be clearly stated in this section what the aims of the project are. This section will be worth around 5% of the total mark and should be around 100 words. Literature Review This section requires students to write a literature review that will provide valuable background information on their project question as well as provide justification for their research. For this section, the students will need to begin by explaining information that will provide the reader with the necessary information to help them to understand the background of the project. The literature review should also include a justification for the project. This should explain why the research project is useful and worthwhile. This could include reasons like filling a gap in current research, creating a new process or idea based on existing research, performing a new critical analysis of existing research, or building on new research. Students should be able to say why their research project is worthwhile, providing an explanation of what the intended contribution of their research is. This section should be around 600 words and will be worth 25% of the total mark for this assessment. • Analysis and Discussion For this section, students will detail the findings of their project and outline the research that justifies / supports those findings. This section will expand on the background foundational concepts introduced in the literature review section. This is where most of the critical analysis of data will take place, and where students will need to explain the reasoning behind their main conclusions / outcomes. As such, there should be a critical discussion about what the findings imply, and how they relate to the existing literature. Students should also discuss the questions raised by their project for future research that could build on their research. There should also be some critical analysis of the limitations on the students' research as part of this, including why those limits existed and how they could be overcome in future. This section should be around 600 words and will be worth 30% of the total mark for this assignment. • Conclusions Here, students should summarise each of the main conclusions of their project, and briefly explain each one. It would be useful in this section for the students to relate their conclusions to their project aims and original research question. This section should be around 200 words long and is worth around 10% of the mark for this assessment. Teesside University International Study Centre Study Group Self-Evaluation In this section, students will look back on their project and perform a critical analysis on the skills they developed and the challenges they faced. In their evaluation, students will have to discuss skills they developed over the course of this module. These skills should be stated, as well as an explanation of why the skill is important and how the student developed it over the course of the project. Students should also evaluate what went well and what went less well over the course of their project. Students should discuss at least one aspect of their project that went well, explaining why it went well and how that experience will benefit them in the future. They should also discuss one aspect of their project that went less well, explaining why it went less well, how they overcame the challenges involved, and how that experience will be beneficial to them in the future. This section should be around 500 words and will be worth 25% of the total mark for this assessment. • References Finally, the report should end with a list of references for any sources the student used when writing their report. Students should note that anywhere where they have taken information from another source, they should cite that source with a reference. Students should use the IEEE referencing system when doing so. Students are reminded that academic misconduct (in forms including, but not limited to plagiarism, collusion, and contract cheating / purchase commissioning) will not be tolerated in any of their assessments. Students' ability to adequately provide proper references for their work will be worth 5% of the total mark for this assessment. Assessment Criteria Learning Outcomes: Personal and Transferable Skills Construct reasoned argument, evaluate relevant information, and exercise critical judgement on innovative technology. Learning Outcomes: Research, Knowledge and Cognitive Skills Apply concepts theories and methods used in the study of digital innovation to the analysis of ideas, practices, and issues. Develop an understanding of the relative and appropriate contribution that digital innovation can make in the work environment. Learning Outcomes: Professional Skills Display appropriate knowledge and understanding of historical and contemporary issues within digital innovation. Teesside University International Study Centre Study Group Display appropriate knowledge and understanding of historical and contemporary issues within innovation. Feedback/Marking Criteria for this assignment (Rubric) The report will be marked out of 100, with marks given according to the below rubric: Criterion: Introduction Literature Review >80% Report gives a clear, in-depth explanation of the purpose of their report and project aims. Report outlines 80%-60% Report explains the purpose of their report and project aims, but some things may be unclear. The report's structure is the report's structure in outlined. detail. demonstrates a Report Report demonstrates an excellent good background knowledge of the theory surrounding the chosen project question. Excellent justification of the project is provided, including a detailed explanation of contribution the research is making. Justification of project is based on and related strongly to existing research. The connections between this project and existing research are explained clearly in the report. background knowledge of the theory surrounding the chosen project question. Good justification of the project is provided, including some explanation of contribution the project is making, which may be lacking in detail. Justification of project is based on and related to existing research. Connections between this project and existing research are explained in the report, but this lacks clarity. 60%-40% Report gives partial, unclear explanation of report's purpose. Aims may be unclear or missing Outline of report's structure is vague. Report describes background knowledge of the theory surrounding the chosen project question, although this lacks clarity/ detail. Limited justification of the project is provided, and explanation of contribution the project is making may be limited. Basis of project on existing research is unclear. Connections between this project and existing research are limited. <40% Report gives little to no explanation of report's purpose. Aims may be unclear or missing. Outline of the report's structure is mostly / entirely missing. Weighting 5% Report contains 25% very limited description of the theory surrounding the chosen project question. Limited justification of the project is provided, and explanation of contribution the project is making may be limited. Basis of project on existing research is unclear. Connections between this project and existing research are limited. Analysis and Discussion Report presents results/findings appropriately, clearly, and in- depth. Report demonstrates an excellent, detailed understanding of how analysis was carried out and the results/ findings were obtained. Reasoning/ analysis displays a high level of Report generally presents results / findings appropriately, however there may be a few places where clarity and depth are lacking. Report demonstrates an understanding of how analysis was carried out and the results / findings were obtained. Report presents results/ findings, but clarity and depth are generally lacking. Report demonstrates a limited understanding of how analysis was carried out and the results / findings were obtained. Reasoning/ analysis displays some critical Report presents results findings, 30% in a basic way that is unclear. Report demonstrates very limited/no understanding of how analysis was carried out and the results / findings were obtained. Criticality is absent from reasoning/ analysis. Teesside University International Study Centre Study Group Conclusions Evaluation Referencing critical analysis throughout. Findings are related well to sources. Report explains at least 3 conclusions from the project. Conclusions are related to project aims. Explanation of each conclusion and its relation to the project question is clear. Student critically analyses multiple skills covered over the course of the project, analysing each one in terms of its current and future usefulness. Student explains in detail at least 1 aspect of their project that went well, along with critical analysis of how it went well and how it will be beneficial to them in the long- term. Student explains in detail at least 1 aspect of their research project that went less well, along with an explanation of how the challenges involved were overcome and a critical analysis of how to improve in the future. Report supplies a wide range of reliable, relevant references supporting Reasoning/ analysis displays a good level of critical analysis but there may be some places where criticality is lacking. Findings are generally related to sources although there may some gaps/ errors. Report explains up to 3 conclusions from the project. Conclusions are generally, but not all related to project aims. Explanation of each conclusion and its relation to the project question is present but may sometimes be unclear. Student analyses at least 1 skill covered over the course of the project, analysing each one in terms of its current and future usefulness. Student explains 1 aspect of their project that went well, along with an analysis of how it went well and how it will be beneficial to them in the long-term. Student explains at least 1 aspect of their research project that went less well, along with an explanation of how the challenges involved were overcome and an analysis of how to improve in the future. Criticality in the above analyses may be limited. Report supplies a range of references supporting claims. Findings are not analysis, but related to sources. criticality is generally lacking. Findings are often not related to sources. Report states up to 3 conclusions from the project. Conclusions are generally not related to project aims. Explanation of each conclusion and its relation to the project question is unclear. Student analyses 1 skill covered over the course of the project, with limited analysis of its current and future usefulness. Student describes 1 aspect of their project that went well, with limited analysis of how it will be beneficial to them in the long-term. Student describes 1 aspect of their research project that went less well, with limited explanation of how the challenges involved were overcome or an analysis of how to improve in the future. Criticality in the above analyses is largely absent. Report states less than 3 conclusions from the project. Conclusions are not related to project aims. Explanation of each conclusion and its relation to the project question is missing. Student analyses up to 1 skill covered over the course of the project, with little to no analysis in terms of its current and future usefulness. Student states 1 aspect of their studies that went well. Student states 1 aspect of their studies that could be improved upon. Criticality is absent. 10% 25% References are very limited, not relevant, and are from unreliable sources. 5%See Answer
Q3: Artificial Intelligence Solving Problems by Searching Objective: The goal of this programming assignment is to implement and compare three search algorithms - Depth-First Search (DFS), Breadth-First Search (BFS), and A* algorithm – in solving a specific problem. This assignment will provide understanding and implementing different search strategies using Python and evaluating their performance. Problem Statement: Consider a 2D grid representing a maze. The objective is to find the optimal path from the starting point (S) to the goal point (G) using the specified search algorithms. The maze consists of open cells (.) representing valid paths and obstacles (X) representing blocked areas. Maze Representation: ● Example Maze: S '.' represents an open cell. 'X' represents an obstacle. 'S' represents the starting point. 'G' represents the goal point. Tasks: 1. Maze Generation: G O Implement a function to generate a random maze with a size of 10 X 10 and density of obstacles using Python. O The maze should have a defined starting point (S) and a goal point (G). O 2. Depth-First Search (DFS): Each run of the algorithms should produce a different maze configuration in terms of starting point, goal point and obstacle location. However, in each run, all 3 algorithms should use the same maze. Maze configuration changes from run to run but remains same within each run of three algorithms. o O Implement the Depth-First Search algorithm in Python to find a path from the starting point to the goal point. O Visualize the explored paths and the final solution. 3. Breadth-First Search (BFS): O Implement the Breadth-First Search algorithm in Python to find a path from the starting point to the goal point. O Visualize the explored paths and the final solution. 4. A* Algorithm: 5. Performance Evaluation: ● Implement the A* algorithm in Python with an appropriate heuristic function to find an optimal path from the starting point to the goal point. Visualize the explored paths and the final solution. O 7. Report: O O Compare the performance of DFS, BFS, and A* algorithm in terms of: Solution Path Length Number of Nodes Expanded Time Execution 6. Visualization: ■ Implement a visualization tool in Python to display the maze, explored paths, and the final solution for each algorithm. The visualization should be interactive and highlight the progress of the search. Write a report documenting your Python implementation, including: Overview of the problem and maze generation. Description of each implemented algorithm. Results of the performance evaluation. Visualizations and observations. ■ ■ Note: the exact behavior of each run depends on the algorithm's determinism, the characteristics of the maze, and any random or heuristic elements involved. It's essential to consider these factors when interpreting and analyzing the results of multiple runs on the same size of the maze. You can gain valuable insights by investigating the reasons behind different paths and variations in algorithm behavior. In your report, provide a clear and concise summary in the following format. Summary of Algorithm Performance (10 Runs) Depth-First Search (DFS) Failures: [Number of failures out of 10 runs] ● Breadth-First Search (BFS) ● ● ● A* Algorithm Successes: [Number of successes out of 10 runs] Average Solution Path Length: [Average length of successful runs] Average Nodes Expanded: [Average number of nodes expanded in successful runs] Average Execution Time: [Average execution time in seconds for successful runs] ● Note: Provide additional details on each failure, such as the specific run number and any observations or insights gained from analyzing failures. Failures: [Number of failures out of 10 runs] Successes: [Number of successes out of 10 runs] Average Solution Path Length: [Average length of successful runs] Average Nodes Expanded: [Average number of nodes expanded in successful runs] Average Execution Time: [Average execution time in seconds for successful runs] Submission Guidelines: ● Failures: [Number of failures out of 10 runs] Successes: [Number of successes out of 10 runs] Average Solution Path Length: [Average length of successful runs] Average Nodes Expanded: [Average number of nodes expanded in successful runs] Average Execution Time: [Average execution time in seconds for successful runs] ● Grading Criteria: ● Submit the Python source code in zip format along with any necessary instructions for running the program. Include a README file with details on how to install dependencies, compile, and execute the Python code. Submit the report in a pdf or word format. Grading Rubric: Correctness and functionality of the implemented algorithms in Python. Quality of visualization and interactivity in Python. Accuracy and completeness of the performance evaluation. Clarity and organization of the report. 1. Maze Generation (10 points): [2 points]: Implementation of a function to generate a random maze in Python. [2 points]: Maze includes a starting point (S), a goal point (G), open cells (.), and obstacles (X). [2 points]: Maze generation allows customization of size and obstacle density. [2 points]: Starting point (S) and goal point (G) are correctly placed. • generation is efficient and produces varied mazes. 2. Depth-First Search (DFS) Implementation (15 points): ● 3. Breadth-First Search (BFS) Implementation (15 points): ● ● 4. A* Algorithm Implementation (15 points): ● ● [5 points]: Correct implementation of DFS algorithm in Python. [5 points]: DFS finds a valid path from starting point to goal point. [3 points]: Visualization of explored paths during DFS. [2 points]: Visualization of the final solution path using DFS. 5. Performance Evaluation (20 points): [8 points]: Accurate comparison of DFS, BFS, and A* in terms of solution path length. [8 points]: Accurate comparison of DFS, BFS, and A* in terms of the number of nodes expanded. [4 points]: Discussion of time complexity for each algorithm. 6. Visualization Quality (15 points): ● [5 points]: Correct implementation of BFS algorithm in Python. [5 points]: BFS finds a valid path from starting point to goal point. [3 points]: Visualization of explored paths during BFS. [2 points]: Visualization of the final solution path using BFS. ● [2 points]: Maze ● [5 points]: Correct implementation of A* algorithm in Python. [5 points]: A* finds an optimal path from starting point to goal point. [3 points]: Visualization of explored paths during A*. [2 points]: Visualization of the final optimal solution path using A*. 7. Report (25 points): [5 points]: Clear and interactive visualization tool implementation in Python. [5 points]: Visualization accurately reflects the progress of the search algorithms. [5 points]: Visualization enhances understanding of the maze exploration process. [5 points]: Overview of the problem and maze generation in Python. [5 points]: Description of each implemented algorithm in Python. [5 points]: Results of the performance evaluation in Python. [5 points]: Visualizations and observations presented in a clear and organized manner. [5 points]: Overall quality, coherence, and professionalism of the report. To record the performance of each algorithm, run each algorithm 10 times each with a difference maze configuration. Record the performance of each run of the algorithm using the following table: 10x10 Maze Performance Table (10 Runs) 1 2 10 Average 1 2 10 2 : Total Points: 100 Run Algorithm Solution Path Length [nodes] [nodes] 10 Average Average BFS 1 Note: DFS [length] DFS [length] DFS DFS BFS [length] BFS [length] BFS : [length] [avg_length] A* A* [length] A* [length] A* [length] [nodes] [nodes] [avg_length] [nodes] [nodes] [length] [avg_length] Nodes Expanded [time] Pass [time] Fail [nodes] [avg_nodes] [time] Pass [time] Fail [nodes] [avg_nodes] [time] Pass [time] Fail [nodes] [avg_nodes] Execution Time Status [time] [avg_time] [time] [avg_time] [time] [avg_time] Pass Pass Pass Replace [length], [nodes], [time], [avg_length], [avg_nodes], and [avg_time] with actual values obtained during each run and their averages. In the "Execution Time" column, use the actual execution time values for each run.See Answer
Q4:1. (100 points) Please make your new bidirectional uniform cost search algorithm with clear steps for finding a path as short as possible from a city to another city in a map. Please clearly show how your new algorithm finds a short path from Arad to Giurgiu in Romania map step by step (tree expansion steps must be shown clearly in detail). Please make best effort to write a perfect solution by typing in your clear solution. Arad 75, 118 71 Oradea Zerind 140 Timisoara 70 Midterm Exam Spring 2024 CSc 4810 Artificial Intelligence Undergraduate Student Name: 75 Dobreta 151 Lugoj Sibiu Me hadia 120 80 Rimnicu Vilcea 97 Fagaras 146 Pitesti 138 Cralova 211 101 Neamt Q 87 85 ♫ 90 Giurgiu Bucharest lasi 92 142 Urziceni 98 Vaslui Hirsova 86 Eforie Straight-line distance b Bucharest Arad Bucharest Craiova Dobreta Eforie Fagaras Giurgiu Hirsova Iasi Lugoj Mehadia Neamt Oradea Pitesti Rimnicu Vilcea Sibiu Timisoara Urziceni Vaslui Zerind 366 0 160 242 161 176 77 151 226 244 241 234 3.80 10 193 253 329 80 199 374See Answer
Q5: ual: camberwell chelsea wimbledon Unit Assessment Brief BSc (Hons) Creative Computing Unit Title: Creative Making: Art and Artificial Intelligence Unit Leader: Programme Administration Contact: Unit code: IU000120 Unit credit: 20 credits Unit duration: Year / Level: 3/6 Unit briefing date] Unit introduction Please read the Learning Materials that accompany this document. This may include project briefs, unit guidelines, glossary, additional reading lists or event and presentation information. This information will be published together on Moodle. In this unit we explore the application of machine learning techniques to creative practice and consider the cultural history of machine creativity. Creative practice based on ideas of artificial intelligence is not new but recent developments in computing power and machine leaning approaches have seen renewed interest in creative practice that both addresses and uses artificial intelligence. You will use computational approaches that leverage machine learning techniques to produce creative practice and explore the cultural idea of Al in a broad context. This practice may leverage current sound and image processing techniques, but you are also encouraged to explore how machine learning approaches may be integrated into creative practice more generally. This unit will also help you to situate Al arts practice in the context of the discourse of contemporary art and media arts practice. Learning outcomes and assessment criteria ual: camberwell chelsea wimbledon On completion of this unit, you will be able to LO1, Produce complex creative Al arts practice (Realisation) LO2, Use common approaches to Al arts practice (Knowledge) LO3, Recognise critical issues in Al arts practice (Enquiry) Assessment Criteria Your work in this unit will be marked against the UAL assessment criteria, which are designed to give you clear feedback on your achievement. The full assessment criteria descriptions can be found on the UAL Assessment webpage. ual: Assessment Criteria | Level 6 F E D с B A Enquiry ?? Engagement in practice informed Little or no by critical analysis and evaluation evidence Insufficient evidence Satisfactory evidence Good evidence Very good Excellent evidence evidence of diverse, complex concepts and ideas Knowledge Critical analysis of a range of practical, theoretical and/or Little or no evidence Insufficient evidence Satisfactory evidence Good evidence Very good Excellent evidence evidence technical knowledge(s) Process Experiment and evaluate methods, results and their Little or no evidence Insufficient evidence Satisfactory evidence Good evidence Very good Excellent evidence evidence implications Communication Demonstrating clarity and depth. Synthesis of diverse intentions, Little or no evidence Insufficient evidence Satisfactory evidence Good evidence Very good Excellent evidence evidence contexts and arguments appropriate to your audiences Realisation Meeting appropriate standards of professional production Little or no evidence Insufficient evidence Satisfactory evidence Good evidence Very good evidence Excellent evidence What you have to produce Holistic - This unit is assessed holistically (100% of the unit). Assessment will be against the specified marking criteria Assessment Description, Portfolio of work: documenting creative outcomes. This will also include a research weblog and/or sketchbook documenting iterative design and development process specifically. (100% Holistic) 2 ual: camberwell chelsea wimbledon Submission information Submission date and time: Holistic assessment (Portfolio of work): By Tuesday 30 January 2024 6:00pm (18:00) GMT Adjusted assessment submission date and time: By Tuesday 13 February 2024 6:00pm (18:00) GMT Adjusted Assessment (AA) is applicable to students with Individual Support Agreements. To confirm that you intend to use adjusted assessment, please email standard deadline. two weeks in advance of the Submission method: Anonymous marking: Zip folder via Moodle: A Zip folder (100MB max) containing a link to your GIT code repository and Readme PDF. The repository must include your portfolio work, documentation, research weblog and/or sketchbook outlining your iterative design and development process, and a Readme file which provides a description and comments on your work, exported as a PDF and submitted in the Zip No: It is not possible for this unit to be marked anonymously. However, all unit assessments are internally moderated to maintain fairness in assessment. (delete as appropriate) Date to expect feedback by: 20/02/2024 You will receive feedback online via Assessment Feedback. Please note grades and feedback are indicative until confirmed following the Exam Board. Submission queries: Please contact the submission deadline. Further information 3 in advance of ual: camberwell chelsea wimbledon • Completion of 3 small projects and reflections: Please complete 3 small projects using what you learned in class. Examples will be developed in class, and you could build from there or start something completely new. Please always be mindful of plagiarism. Your portfolio will be presented with documentation. • • • Students must complete 3 projects. These projects must constitute 2 'Creative projects' and 1 ‘Critical response' (examples below) The 'Critical response' should be between 500 and 1000 words and include citations (not included in word count), though does not have to be written in an academic 'tone' Students can apply skills and tools learnt in other units in this assignment Examples of projects could be but not limited to: 1. Use of AI/ML tools to produce an audio, visual or written piece such as work featuring generative AI/ML creation using a data set. (Creative project) 2. Use of government or other open-source data repositories to produce visual artwork or tell a story using AI/ML tools such as Runway ML. (Creative project) 3. Pick a societal issue and create a narrative based creative artifact (story, comic, design fiction) which explore how AI/ML could impact on this societal issue or raise awareness of it. (Creative project) 4. Pick a story or piece of media (literature, film, even computer game) that uses Al or robotics as a narrative device. Produce a critical/creative response such as a video essay, blog post, podcast that explores the role played by AI/ML in the narrative and how it reflects the potential of Al as it exists in the world. (Critical response). 5. Pick an existing example of a work, service, project, platform, or tool within the creative industries and re-imagine how an AI/ML tool could supplement or replace a human agent in this context. Produce a critical/creative response such as a video essay, blog post, podcast that explores the impact of the use of Al in this design/industrial context. (Critical response). Documentation: each of your creative projects will be presented with a 500-word documentation (or single essay or 1000 words) covering your iterative design approach, development process, ethical issues you might have considered, research and personal reflection. As usual, if you would like to provide different documentation (video, podcast, recorded presentation) this can be agreed upon with your tutor and as usual, we remain open to a creative way to produce documentation. If you provide a PDF and want to link to videos, please use QR codes (not URL links). Reading and resource list Essential Reading 4 ual: camberwell chelsea wimbledon Boden, M.A. (1998) 'Creativity and artificial intelligence', Artificial Intelligence, 103(1), pp. 347-356. Broeckmann, A. (2016) Machine Art in the Twentieth Century. MIT Press. Dewey, J. (2005) Art as Experience. Penguin. Kaplan, J. (2016) Artificial Intelligence: What Everyone Needs to Know. Oxford University Press. Kodratoff, Y. (2014) Introduction to Machine Learning. Elsevier. McCormack, J. and d'Inverno, M. (2012) Computers and Creativity. Springer Science & Business Media. Further Reading Aztiria, A., Augusto, J.C. and Orlandini, A. (2017) State of the Art in Al Applied to Ambient Intelligence. IOS Press. Bentley, P.J. and Corne, D.W. (2002) Creative Evolutionary Systems. Morgan Kaufmann. Géron, A. (2017) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc. Millington, I. and Funge, J. (2016) Artificial Intelligence for Games. CRC Press. Pattanayak, S. (2017) Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python. Apress. Periodicals Artificial Intelligence Web Ref: https://www.creativeapplications.net Support CCW and UAL has a range of services that can support you with your studies. Useful contacts for advice and guidance include: CCW Academic Support Contact: 5See Answer
Q6: Unit introduction This unit explores the ethical issues raised by computationally intelligent systems and by both current and future technologies. It also explores the personal ethics frameworks that software developers need to develop to make informed choices about the scope and use of their work. The behaviour of computationally intelligent agents that might include autonomous vehicles, military or medical robots and computer vision systems for predictive policing raises ethical question at multiple levels. Issues around the implicit basis of algorithms that underpin such technologies are becoming increasingly apparent and how to recognise these issues is an increasingly necessary skill for computational professionals of all types. You will explore the current ethical debates around such technologies and useful approaches to conceptualising ethical issues in a proactive way. Learning outcomes and assessment criteria On completion of this unit, you will be able to L01, Identify complex ethical issues in relation to computational technology (Enquiry) L02, Discuss approaches to mitigating algorithm bias (Enquiry) L03, Recognise personal ethical agency as a computational practitioner (Knowledge) What you have to produce Assessment Description, Portfolio of work: documenting the outcomes for the research on ethical issues in computational practice. (100%) Further Information With this unit you have the option of how to present your work and the number of elements you can present to create your portfolio. Some examples can be: 1) One essay (2000 words) 2) A 20' presentation supported by slides, mindmap, or research poster 3) An original artistic exploration that has ethical computational challenges (in this case the practice-based work must be accompained by 1000 words explaining the project and ethical implications). If you have other ideas, please discuss them with the tutors as the composition of your portfolio must be discussed well before the deadline. If instead you are resubmitting or working for a resit, please stick with one of the options above. Your portfolio can focus on: · a specific AI/computational artwork/project/platform (including your own if relevant) with ethical implications or a specific AI/computational Ethics problem. Before starting to write you will be guided to choose the focus of your portfolio. If you choose to focus on a specific AI artwork/project, you will need to link this practice to the theory around ethics explained during the lectures and workshops. All lectures in this unit are examples of essays that could work as submission. Please always be mindful of plagiarism. During the unit we will develop skills around creating your own structure for the essay. Usually an essay should have: Introduction, Body, Conclusion, Bibliography. Essential Reading Boddington, P. (2017). Towards a Code of Ethics for Artificial Intelligence. Springer. Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press. Lin, P., Abney, K. and Jenkins, R. (2017). Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence. Oxford University Press. Reed, B. (2015). The Drone Papers. The Incercept_ https://theintercept.com/dronepapers/ Kearns, M. and Roth, A. (2019). The ethical algorithm: The science of socially aware algorithm design. Oxford University Press. Liebman, S. (2018). Mapping Word Embeddings with Word2vec. [online] Medium. Available at: <https://towardsdatascience.com/mapping-word- embeddingswithword2vec- 99a799dc9695>See Answer
Q7: PHIL225 Ethics and Artificial Intelligence - Course Project, Spring 2024 Upcoming due dates: • • • • . In-class annotated bibliography work: Wk. 10 (after the break – Thursday) *Draft* Project 'proposal': Week 12 (Tuesday) Project consultation time, in-person: Weeks 12 (Thursday) & 14 (as needed) Project Work and Reflection Page: Week 15 (Tuesday, online only) Project 'Poster Presentation': Week 15 (Thursday, in class) Project Work and Reflection, and Poster Presentation: Project task: to understand and address the current impact of A.I. on society What you and your fellow students will be doing: • Each student will work on a project (individually or as a pair/team) aimed at understanding a particular form/application of A.I. used in the world today and addressing both the benefits and challenges it presents. But in particular, you will be asked to address creatively a particular ethical problem/dilemma this form of A.I. presents us with. Then, through your work, you will propose some kind of meaningful solution to the dilemma presented or at least some constructive ideas for how to moderate the negative impacts of the A.I. you've chosen to discuss. At various points, students will be assessed on their work, creative skills, project relevance, and research skills. Regular deadlines for major work relating to each phase can be seen below. Project Work/Reflection Page (10%, 5%), and Poster Presentation (5%) NOTE: In the interest of your time and effort, I am combining the original elements of the PROPOSAL and the PORTFOLIO (as listed in the syllabus) 1) Project Work (written) (approximately 600 words only; that's approx. 1.5 to 2 pages; sources cited page/bibliography not included in this word count) Your proposal should: II) Content: 1. Summarize what you learned from your research about the form of A.I. your group/pair has chosen to focus on, considering the following: What are the benefits and drawbacks (challenges) this form of A.I. presents us with? What are the various challenges that exist with regard to solving the particular problems it creates? To what extent is this problem already being addressed or 'worked on'? Finally, which groups/individuals benefit most from this use of A.I. and which are harmed/disadvantaged most? 2. Describe the creative solution/constructive ideas that you intend to develop for the problems/dilemmas presented and how and why it will contribute in some way to solving the problem/situation. What does your creative solution entail or how does it work? 3. Describe the difficulties you anticipate facing in your development of the project/suggesting of creative ideas in order for it to succeed/be a workable solution. 4. Describe the target audience/'customer' you are addressing in your project/solution/ideas (beyond our class itself). Is it a particular segment of society or a specific profession that will be able to take up and ‘actualize' your solution/suggestions? Or, are everyday people going to benefit from and engage in your solution the most? Project Reflection (written) (approx. 300-400 words only; that's approx. I page) Content - answer the following questions as part of your reflection: I. How did your solution/ideas for how to make this application of A.I. 'better' arise out of your research? In other words, in what specific ways does your solution/ideas respond to knowledge you gained about your particular application of A.I.? 2. How do you think your solution/ideas can contribute in some meaningful way to the problems A.l. poses us with? 3. What challenges did you face in coming up with creative ideas on how to deal with the 'negatives' of A.I.? 4. Describe a topic/reading/discussion that was part of our course that relates to your solution/ideas in some way and explain the connection you see between them. (For example, how were you inspired by or motivated by what we read/discussed?) III) Presentation (POSTER form) Here's what you'll be asked to present/include in your poster and your informal presentation: a. Explain rationale behind your solution/ideas and how they arose out of your research, and your intellectual exchanges with your partner/classmates/professor b. Show preparedness and familiarity with project topic C. Present your solution/ideas in some creative, “deliverable” way, meaning in a way that really comes across clearly to the class/'audience' d. Explain how you hope your solution/ideas contribute would contribute to more beneficial, 'ethical' A.I. General comments: a. Formatting: This proposal must be formatted in MLA Manuscript Format, which means that it should have I- inch top, bottom, right and left margins; be double-spaced and set in 12pt. Times New Roman font. (Rubric for grading of the Proposal can be found overleaf.) Citation of sources: Any/all citations of sources used in the writing of your proposal should also be in MLA format (i.e. sources you yourself consulted as part of your research into the problem and culture you're focusing on, and your research into what solutions to this problem are already being attempted in the world today). If you refer to ANY sources for ideas, statistics, etc. in your writing you must cite them properly (i.e. in text citations and sources cited page). Failing to do so will be considered plagiarism. PHIL225 - Project Work and Reflection Points Project work and Reflection Page (80 points) 72-80 (A) Exceeds Expectations All aspects of the assignment thoroughly and thoughtfully 64-71 (B) Meets Expectations All aspects of the assignment are addressed in detail 56-63 (C) Mostly Meets Expectations Most aspects of the assignment are addressed in detail 48-55 (D) Minimally Meets Expectations Minimal aspects of the assignment are addressed 0-54 (F) Fails to Meet Expectations Fails to address most or any aspects of the assignment addressed Describes research and proposed solutions/ideas with an unusual degree of complexity, critical analysis, originality and detail Describes research and proposed solution/ideas with a strong degree of complexity and critical analysis Describes research and proposed solution/ideas with some degree of complexity and acceptable analysis Describes research and proposed solution/ideas with little or no support, complexity, or analysis Description of research and/or the proposed solution/ideas is weak, largely, or completely irrelevant Reflection: ALL questions addressed thoroughly, thoughtfully and with exceptional complexity ALL questions addressed thoughtfully and in detail Most (but NOT all) questions addressed in detail and thoughtfully Questions addressed minimally, with some parts lacking detail Fails to address most of the questions in any detail Grammar, mechanics, vocabulary, organization, (20 points) 18-20 Mastery of writing conventions and mechanics Errors few and minor Sophisticated unity and coherence Seamless integration of research 16-17 Strong use of writing conventions and mechanics Contains errors but does not impede understanding Strong coherence Sound integration of research 14-15 Competency in writing conventions and mechanics Contains several errors, but does not impede the overall understanding Adequate Coherence Mostly correct integration of research 12-13 Below-average competency in writing conventions and mechanics Contains several errors which prevent understanding of some points Lacking unity or coherence Poor integration of research 0-11 Lacks minimal competency in writing conventions and mechanics Contains so many errors that understanding is extremely difficult or prevented No apparent plan or organization Irrelevant or no integration of research Citation 0 -2 -4 -6 -8 Deductions Incorrect use of citations (if used) Formatting 0 -2 -4 -6 -8 Deductions Does not follow instructions Proofreading 0 -2 -4 -6 -8 Deductions Careless errors Rubric for the POSTER Points 100-90 89-80 Content All aspects of the (100 points) All aspects of the directions addressed directions fully addressed Original, thought- provoking, complex ideas presented; made ideas come alive for the class Interesting and relevant ideas presented 79-70 Most aspects of the directions addressed Acceptable ideas presented though perhaps lacking detail/creativity in some parts 69-60 Major aspects of the directions not addressed Only minimal answers presented Weak/largely irrelevant ideas Presented or not connected clearly to topic 59-0 Fails to address the directions; not well- prepared Weak or completely irrelevant ideas presented/unconnected to topic/n11:07 create in them in A1 or A2 size - whichever you prefer, but please NO SMALLER than A2. FOCUS OF YOUR POSTER? You should incl what are, in your view, the highlights of your project work, such as the most impactful advice/solutions you propose to counter the negative uses/effects of the form of Al you've chosen as your focus. Please also make it visually appealing and include the following (text-boxes could be useful, where relevant): 1. Explain rationale behind your solution/ideas/advice and how ideas arose out of your research, and your intellectual exchanges with any of your classmates/professor/class discussions 2. Show preparedness (have the poster ready!) and familiarity with project topic by including valuable details in the poster 3. Present your solution/ideas/advice in some creative, "deliverable" way, meaning in a way that really comes across clearly to the class/'audience' in your poster; in other words, be creative with your poster! 4. Explain how you hope your solution/ideas/advice would contribute to more beneficial, ‘ethical' A.I. today and in the future AO 841 x 1189mm 5. 6 ft A1 594 x 841mm A2 420 x 594mm A3 297 x 420mm A4 210 x 297mm aud.blackboard.com/nSee Answer
Q8: Application of Artificial Intelligence (AI) in Wind Energy System with a Case Study Keywords: Wind Energy, Artificial Intelligence, Wind Turbine, Wind Mill, Genetic Algorithm, Wind Speed, Current Output Abstract Renewable energy is the fastest growing source of clean energy worldwide. The employment of wind energy is expected to increase dramatically over the next few years. There is a good source of wind power on the highways due to the movement of vehicles. A small windmill could utilize the wind power generated by passing vehicles and produce electricity that can power the lights on the highway. This paper presents the application of artificial intelligence to predict the current output from a small windmill placed on the highway. The results show a good concurrence between the experimental and predicted values. Introduction The main energy source is from fossil fuels, which is extensively used to meet the demand. The usage of fossil fuels directly harms the clean environment and also leads to global warming. Fossil fuels are non-renewable and get depleting, which makes people to focus on renewable energy sources. All over the world harnessing of solar and wind as a sustainable source of energy gained popularity to curtail the heavy dependency on fossil fuels and also to counter the global warming. When wind energy is used to produce electricity, less pollution from conventional power plants will be released into the environment. The need of concentrating on renewable energy resources has increased, particularly in the wake of the Gulf of Mexico oil leak and the Japanese nuclear accident. The installed wind energy capacity reached 196,630 MW globally in 2010, with 37,642 MW added in that year, according to the World Energy Association's report on wind energy for 2010. Enhancing wind farm design and layout; boosting wind turbine accessibility, dependability, and efficiency; streamlining the upkeep, assembly, and installation of offshore and onshore turbines and their substructures; showcasing massive wind turbine prototypes and expansive, interconnected wind farms, etc. are the main research areas that should be prioritized in the wind energy industry [1]. The first wind-powered generator was invented by Charles F. Brush, an electrical pioneer from America, and it produced energy in his backyard. He built a windmill that was 40 tons in weight and stood 60 feet tall. The actual wind mill measured 56 feet in diameter. The wind mill had a total of 144 separate blades. 500 revolutions per minute was the turbine's peak rotational speed. Everything in his basement was wired up to 408 batteries. With this technology, he was able to power his entire house, including the lab. Up until 1909, his wind mill operated for 20 years [2]. The wind turbine's size is determined by its intended use. Typically, tiny turbines have a power output between 20 and 100 kW. The 20- to 500-watt "micro" turbines are smaller and have a wider range of uses, including the charging of sailboat and recreational vehicle batteries. Water pumping is one use for turbines ranging from one to ten kW. Grain mills and water pumps have been powered by wind for millennia. While mechanical windmills remain a cost-effective and practical choice for water pumping in wind-free regions, farmers and ranchers are discovering that wind-electric pumping offers greater versatility and doubles the volume of water pumped for the same initial outlay. Furthermore, mechanical windmills have to be positioned straight above the well, which could not maximize the wind resources that are available. Electric cables can be used to link wind-electric pumping systems to the pump motor, which can be installed where the best wind resource is available. Depending on how much power you wish to create, household turbines can range in size from 400 watts to 100 kW (100 kW for extremely big loads). An average household consumes around 10,000 kWh (kilowatt-hours) of power year, or 830 kWh each month. To significantly meet this requirement, a wind turbine with a rating of between 5 and 15 kW would be needed, depending on the typical wind speed in the region. If the average yearly wind speed in the area is 14 miles per hour (6.26 meters per second), a 1.5 kW wind turbine can supply the energy needed for a house that uses 300 kWh per month. Automatic overspeed- governing mechanisms are included in most turbines, which prevent the rotor from spinning uncontrollably in extremely strong winds [3]. Harrous and Ahshan [4], [5] developed a hybrid solar/wind system for his home. The hybrid system consists of Bergey XL-1, a 1000-watt wind generator mounted on a tower 104 feet tall along with 300 watts of solar, which is a stand-alone system with batteries. The battery bank is a 220-amp system made up of eight 6-volt batteries wired as a 24-volt system. The system runs incandescent lights and a well pump at the barn, as well as water through heaters. The cost of the complete system was around $ 10,000 including equipment, trenching for wires, building permit, etc. For wind energy uses, there must be open space or accessible coastlines for wind energy plants. Saudi Arabia is a large nation with extensive coastlines and open spaces. In the majority of these locations, the wind speed is sufficiently high to make using wind energy cost-effective. Saudi Arabian authorities will invest billions in this potential field of electricity since they understand the value of renewable energy, particularly wind energy. Despite its vast oil reserves, Saudi Arabia is very interested in actively participating in the development of new technologies for the exploitation and use of renewable energy sources. Despite Saudi Arabia's substantial wind resource potential, there are several obstacles to its development. These comprise the resource's erratic nature, its seasonal and diurnal fluctuations, its isolated geographic position, and the electrical grid infrastructure required to transfer wind energy to load regions. Significant technological obstacles must be overcome in order to fully utilize Saudi Arabia's wind potential. The energy balance between the needed load and the generated power, as well as the matching of the wind turbine and location with an appropriate economic position, remain a significant problem. By matching the locations and wind turbines, the researcher created an extensive computer program that does all the calculations and optimization needed to precisely build the Saudi Arabian wind energy system [6]. Eltamaly et al [7] built and examined the dynamic performance of a novel wind turbine producing system using a thyristor inverter. The system is basically based on shaft generators, which are highly reliable and produce high-quality power output and are frequently employed in big ships. It was looked into if this innovative method could provide low-distortion electric power at a steady frequency even when the natural wind's velocity fluctuated. Additionally, a dynamic model was created, and it was discovered to have good agreement with the system's experimental and simulated results. Zemamou et al [8] investigated the remarkable performance of savonius wind turbines and how they might be used as an alternative to normal wind turbines to extract valuable energy from air streams. Some benefits of employing this kind of machine include its straightforward design, high starting and full operation moment, ability to receive wind from any direction, minimal noise and angular velocity when operating, and reduced wear on moving components. There have been many suggested modifications for this gadget over time. Another benefit of employing such a machine is the range of possible rotor designs. The performance of a Savonius rotor is impacted by each configuration. The performance of a Savonius rotor is influenced by air flow, geometric, and operational factors. For the majority of settings, the quoted range for the highest averaged power coefficient is between 0.05 and 0.30. The usage of stators has also been shown to result in performance increases of up to 50% for the tip speed ratio of the highest averaged power coefficient. Renewable energy technologies affect how household power demands are met. Since most of the energy produced by fossil fuels is used in buildings and their unchecked use is linked to environmental risks, global warming, and the possibility of their depletion, it will be advantageous to replace the conventional energy generation system with renewable energy sources [9]. Globally, there is a growing need to transition from fossil fuels to renewable energy sources. The main causes of this transformation are the lack of fossil fuels and their detrimental consequences on the environment, particularly the climate. As a result, interest in renewable energy sources such as solar, wind, and wave energy is growing around the world [10]. Converters for multiphase generators, back-to-back linked converters, passive generator-side converters, and converters without an intermediary dc-link for high-power wind energy conversion systems (WECS) are all included in the low and medium voltage category. The series/parallel connection of wind turbine ac/dc output terminals and high voltage ac/dc gearbox are taken into consideration while evaluating the onshore and offshore wind farm layouts [11]. Artificial Intelligence (AI) in Wind Energy Systems The majority of wind farms are situated in isolated areas or several miles offshore, thus it is vital to monitor their mechanical parts for maintenance in order to keep them from breaking down mechanically and perhaps cutting themselves off from the electricity grid [12]. Machine learning algorithms, particularly artificial neural network ANNs, are commonly used to process gathered data. The ANN's structure is inspired by real neurons, with basic processing units coupled by weighted linkages. It contains three major layers: input, concealed, and output. Furthermore, the number of hidden layers may be increased to construct the deep neural network (DNN) architecture [13]. The Artificial Neural Network (ANN), Backpropagation Neural Network (BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Genetic Algorithm (GA) are some of the most often used and proven AI approaches. The level of technology today and potentially uses tried-and-true methods to create AI-powered renewable energy systems, particularly solar energy systems. To ascertain the state and progress of AI approaches in the field of renewable energy systems (RES), particularly solar power systems, a number of peer-reviewed journal publications were analyzed [14]. The physical methods forecast wind energy using meteorological data, such as topography, atmospheric pressure, and ambient temperature; the hybrid methods combine the advantages of multiple single forecasting models to obtain the final prediction results through various weighting strategies; and the intelligent methods process and optimize the integration of external and internal big data to estimate future wind energy. The statistical approaches anticipate wind energy time series by an assessment of the probability distribution and random process of the samples. Since intelligent approaches and AI-based hybrid methods are more efficient at analyzing the complex connections present in huge data sets, they are essential for increasing energy efficiency, decreasing energy usage, and allowing real-time decision-making in the wind energy business. [15]. Case Study A small wind mill was fabricated using wind turbine mounted inside the tube, generator and a battery pack. The fabricated tubular wind mill was flexible and can withstand turbulence. The turbine inside the tube rotates in the direction of wind turbulence. Standard generator system was used which can deliver a power output of 1 kW along with a maintenance free battery pack, inverter and charge controller. Figure 1 shows the schematic diagram of the wind mill. Experimental data was recorded by keeping the wind mill on road side platform based on vehicular movements for 7 days. Duration of data recording on each day varies from 30 to 180 min. Fig 1. Schematic Diagram of Wind Mill Artificial Neural Networks (ANN) use genetic algorithms that make use of adaptive heuristic search methods. A genetic algorithm is a better method for achieving the global optimum's convergence. Chromosome initialization is the first step in the genetic algorithm's operation, after which fitness is assessed using an objective function [16]. Chromosomes are genetically propagated by first selecting the most fit individuals and then using operators such as crossover and mutation. A multi-objective solution from the optimization toolkit and a genetic algorithm were used to optimize the process output variable models [17]. Ten neurons or nodes made up the hidden layer, the output current serving as the dependent output neuron, and time and wind speed serving as independent input variables were used to create the ANN network model [18]. Ten neurons made up the hidden layer of the neural network, which was trained until the mean squared error between the target and model output was as little as possible. The comparison between the goal values of the present output and the output values of the ANN network model is displayed in Figure 2 [19]. A high correlation coefficient value across training, validation, testing, and overall comparison shows that the model can accurately forecast the wind mill's current production value. The comparison output variables between the experimental investigations and the ANN-GA projected values are displayed in Table 1 [20], [21]. Output 0.95 Target + 21 Output =0.87*Target +63 Training: R=0.96054 490 ° Data Fit 480 470 Y T 460 450 440 430 440 460 480 Target Test: R=0.99205 Output = 0.96*Target + 21 Validation: R=0.99924 480 470 о Data Fit Y T 460 450 440 430 430 440 450 460 470 480 Target 490 480 470 ° Data Fit ........Y=T All: R=0.97457 460 450 ° 440 Output =0.9*Target +46 490 о Data Fit 480 470 Y T 460 о 450 0 440 ° ° 440 450 460 470 480 490 Target 430 440 460 480 Target Fig 2. ANN network model output values and the target values of the current output Table 1. Experimental data and ANN-GA (Genetic Algorithm) Predicted Data Current [Amp] (Predicted Wind Speed Current [Amp] Time [m/s] (Experimental) from ANN) 30 6.44 460 459.562 60 6.58 470 469.965 90 6.86 490 487.948 120 6.72 480 475.955 150 6.58 470 470.052 180 6.78 485 485.718 30 6.3 450 450.116 60 6.37 455 455.799 90 6.52 466 466.029 120 6.47 462 463.786 150 6.44 460 460.033 180 6.59 471 471.153 30 6.88 492 489.558/n I have a research paper and I want to transfer it into a clear and engaging PowerPoint presentation. I will be presenting in a conference so I need it to look professional and elegant. PRESENTATION FORMAT: The format for your paper presentation will be oral presentation (power point). Attached video in which professor has explained what is required 1st page will be TITLE. Presentation will be for 15 Minutes (20 slides minimum) + speaker notes requiredSee Answer
Q9: INSTRUCTIONS Need to create the chatbot using Microsoft copilot He done the ppt and word part, he want poster as well Student have idea to create the chatbot and he attached it in the reference section./nSee Answer
Q10: CS 404, Spring 2024 Artificial Intelligence Assignment 1: Solving Color-Maze Puzzle using A* Search Due Sunday, 17 March, 11:30pm Color-Maze puzzle is a single-agent grid-game played on a rectangular board that includes a maze. Initially, the agent is located on a single maze cell. The agent can move in four cardinal directions: up, down, right or left. Once a direction is chosen, the agent moves in that direction until it reaches a wall at once, and colors all the cells it travels over. Once a cell is colored, its color does not change. The goal is to color all the cells of the maze by moving the agent over them, while minimizing the total distance traveled by the agent. This game is available at: https://www.mathplayground.com/logic_color_maze. Please implement in Python an A* search algorithm to solve the following version of the Color-Maze puzzle. Input is a rectangular game board with a single agent, represented as a grid where each grid cell is uniquely marked with 0, X or S: • O denotes the cells that are empty and that need to be colored, • X denotes the cells occupied by the walls, and • S denotes the cell occupied by the agent. For instance, in Figure 1 (right) describes the game board depicted in Figure 1 (left). OOOKOS 0 0 0 X 0 0 Χ 0 0 0 X X X ○ × × × ×0 ○ × × × ×O ○ × × × 0 0 X X X 0 0 X 0 X 0 X 0 X X X 0 0 0 Figure 1: An example puzzle (left) and its representation in the given format (right). Output is an alternating sequence of states and moves, that illustrates how the agent colors all the cells such that the total distance traveled by the agent is minimized. For instance, Figure 2 (resp. Figure 3) shows step by step how the agent can color the cells of a given maze, where the total distance traveled by the agent is 6 (resp. 5). In both solutions, the agent takes the same number of moves but the total distance traveled by the agent is smaller in Figure 3. Therefore, the sequence of states and actions illustrated in Figure 3 should be returned as the output. CS 404, Spring 2024 Artificial Intelligence 111 Figure 2: The agent moves right, down, and then up, and travels 6 units of distance in total. What to do The assignment consists of four parts: Figure 3: The agent moves right, up, and then down, and travels 5 units of distance in total. 1. (20 points) Model the Color-Maze puzzle as a search problem: Specify the states, suc- cessor state function, initial state, goal test, and step cost function.¹ 2. (20 points, provided that part 1 is completed) Extend your search model for Color-Maze to apply A* search:¹ (a) Find an inadmissible heuristic function h₁ and illustrate with an example that h₁ is not admissible. (b) Find an admissible heuristic function h₂ and prove that h₂ is admissible. (c) Discuss whether h₂ is monotone or not. 3. (20 points, provided that part 2 is completed) Implement in Python the A* search algo- rithm studied in class, 2 to solve the Color-Maze puzzle. Make sure that your implemen- tation displays the solution (i.e., an alternating sequence of states and actions) as well as the total distance travelled. 4. (40 points, provided that part 3 is completed) (a) Define 3 difficulty levels (e.g., easy, normal, difficult) for a Color-Maze puzzle instance, and prepare a benchmark set of at least 15 Color-Maze puzzle instances of 3 difficulty levels (i.e., 5 easy, 5 normal, 5 difficult instances), on a game board of size 12 x 12.³ (b) Evaluate your A* implementations experimentally over these benchmark instances, and summarize the results of your experiments in a table that shows, for each puzzle instance, the number of cells in the maze, the difficulty level, the total distance traveled by the agent, with heuristic h₁ vs. h2, the total number of expanded nodes, with heuristic h₁ vs. h2, the CPU time and the memory consumption, with heuristic h₁ vs. h2. (c) Discuss the results presented in the table: - What do you observe about the scalability of the A* search algorithm? How does the time and memory consumption increase as the input size increases? Are these observations surprising or expected, considering the asymptotic time and space complexity of the algorithm? Please explain. How does the A* search algorithm explore the search space, with h₁ vs. with h₂? Are these observations surprising or expected, considering the ad- missibility/monotonicity features of the functions? Please explain. ¹Reminder: The step cost function and the heuristic function return positive numbers > € > 0, at every non-goal state. 2 Attention: Some A* implementations available online are incorrect or incomplete. Make sure that your own implementations match the algorithms taught in class. 3 Attention: In the demos, you are expected to show one example for each difficulty level. 2 CS 404, Spring 2024 Submit Artificial Intelligence • A pdf copy of a description of 4 · your formulations of the Color-Maze puzzle (i.e., search model and heuristic func- tions), and experimental evaluation of your A* search implementation on the Color-Maze in- stances (i.e., table and discussion). A zip file containing the following: - Your Python code for the algorithm, with comments describing your solution. Several test boards you created for the 4'th part of the assignment, and the corre- sponding solutions found by your implementations. In each one of the deliverables above, please include your name and student id. Demos If your submitted implementation runs correctly and you can demonstrate it with your test instances successfully (without any bugs), then you will be invited to make a demo of your implementations. The 3'rd and the 4'th parts of the assignment will be graded at the demos.5 The demos are planned for the week following the deadline and will be scheduled later on. Collaboration You are allowed to work with another classmate. In that case, each team should submit one report in pdf, and one zip file at SUCourse+. Both team members should be present at the demos, if the team would like to make a demo of their implementation. 4Suggestion: You can use Overleaf for editing in LATEX: https://www.overleaf.com/. 5 Attention: If your implementation does not run correctly with any of your instances, you will not be invited to the demos and thus no credit will be given to the 3'rd and the 4'th parts. 3See Answer
Q11: Ethical Considerations in Using Artificial Intelligence and Integrating Al Technologies for Personalised Health Care. The incorporation of artificial intelligence (AI) into personalized medicine is a topic at the forefront of innovation and ethical research. As Al systems become more advanced, they have great potential to revolutionize personalized medicine by providing treatments and interventions. However, this potential includes a variety of ethical considerations that must be carefully explored to ensure that the deployment of Al technologies benefits all stakeholders without violating individual rights and exacerbating existing inequalities. One of the main ethical considerations is data privacy and consent issues. The effectiveness of Al in healthcare depends on the availability of personal health data. Ensuring the confidentiality of this data and obtaining patient consent for its use is of utmost importance. This requires strong data protection and clear information about the use, storage and sharing of patient data (Mittelstadt, 2019). Fraud and fairness in Al algorithms represent another important ethical issue. Al systems tend to reflect or amplify social networks in the data they are trained on, which can make a difference in health outcomes. Addressing these vulnerabilities and ensuring that Al technologies are developed and deployed in a way that promotes justice is essential to the ethical integration of Al into health care (Rajkomar et al., 2018). In the context of Al in healthcare, accountability raises important ethical questions. The complexity of Al systems and the sometimes impossible decision-making process complicates accountability, especially when Al- driven decisions lead to adverse patient outcomes. It is important to establish a clear framework for responsibilities, including how decisions are made and who is responsible when problems arise (Luxton, 2014). Additionally, the potential for reduced generosity in the age of Al is also a major concern. Al can improve the efficiency and accuracy of healthcare delivery, but there is a risk that an over-reliance on technology will undermine the human care element at the core of the patient-provider relationship. It is important to ensure that Al acts as a tool to enhance, rather than replace, human judgment and empathy (Blease et al., 2019). Navigating the ethical terrain of integrating Al into personalized medicine requires the collaboration of a wide range of stakeholders, including technologists, medical professionals, ethicists, and policymakers. By considering these ethical considerations early on, Al can be used to improve healthcare outcomes while maintaining high ethical standards. The basis of Al 's contribution to personalized medicine lies in its ability to process large amounts of health data to provide personalized treatment. But this requires careful consideration of data privacy and informed consent. According to Mittelstadt (2019), strong encryption methods and early data management models are needed to ensure that patients are fully informed about the use, storage and sharing of their data. Implementing comprehensive data protection laws and ethical guidelines is critical to maintaining patient trust and privacy in the digital age. For example, Europe's GDPR is a benchmark for data privacy law, emphasizing the need for clear consent and giving individuals control over their personal data (European Commission, 2018). Compromises in Al algorithms raise serious ethical issues and may even pose health risks. Rajkomar et al. (2018) argue that to reduce these risks, it is important to develop Al systems with appropriate reasoning integrating diverse data sets that reflect a broad patient population. Algorithms must be continuously monitored and adjusted to ensure that current settings are not maintained or new ones are introduced. Collaborative and multidisciplinary efforts can help define and refine algorithmic settings, thereby promoting equitable health outcomes for all individuals, regardless of background. The opaque nature of Al algorithms undermines accountability, especially when decisions affect health. Luxton (2014) highlights the need to define responsibilities between Al developers, healthcare providers and regulators. Establishing clear protocols for Al decision-making processes and ensuring that people are present to interpret and reject Al recommendations when necessary is critical to maintaining accountability. In addition, the development of ethical standards and legal frameworks will help clarify responsibility and professional liability in the event of adverse consequences arising from Al interventions. As Al technologies become more integrated into healthcare delivery, it becomes increasingly difficult to maintain the human touch in patient- provider interactions. Blase et al. (2019) highlight the importance of ensuring that Al tools enhance, rather than replace, the love and compassion at the heart of healthcare. Teaching healthcare professionals to properly integrate Al insights while focusing on patient care can preserve the human element in the digital age. This approach ensures that Al acts as a support tool, improving diagnostic accuracy and personalizing treatment without compromising human judgment and human value. . • - References Mittelstadt, B. (2019). Principles alone cannot guarantee ethical Al. Nature Machine Intelligence, 1, 501–507. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866–872. Luxton, D. D. (2014). Artificial intelligence in psychological practice: Current and future applications and implications. Professional Psychology: Research and Practice, 45(5), 332–339. Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019). Artificial intelligence and the future of primary care: Exploratory qualitative study of UK general practitioners' views. Journal of Medical Internet Research, 21(3), e12802. European Commission. (2018). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation)./n 1.0 Introduction With the rapid development of social media and the internet, celebrities and stars I have gained unprecedented influence and appeal. Through social media platforms, celebrities can engage in direct interaction with their fans, sharing their lives, product recommendations, and personal opinions (Chung & Cho, 2017). For example, the well-known American singer Taylor Swift has 92.47 million followers on Twitter are her fans (Dixon, 2023). This close interactive relationship provides fans with more opportunities for engagement and consumption, thereby fueling the rapid growth of the fan economy. In support of this argument, Liang (2022, p.332) said the 'fan economy' is, in its broadest sense, referring to the value and revenue generated via interactions between individual fans (especially “super fans") and fan communities, with the artists/stars (and their production studios and programs) that they follow." The North American fan economy is deeply rooted in the entertainment and sports industries, with fans following their favorite celebrities, movies, TV shows and sports teams (Campo & Ryan, 2008). This passionate fan culture translates into a huge economic impact, with fans buying merchandise, attending live events, and actively participating in fan communities through social media platforms. Blockbuster films, TV shows, and musical performers have benefited from the North American fan economy, which has also encouraged expansion in sectors including merchandising, event planning, and digital content creation (Johnson, 2013). Likewise, China's enormous population and developing digital environment support its fan economy (Li, 2022). Chinese fans have formed a huge fan community around stars, idol groups and popular TV series. These enthusiastic fans actively support their idols by purchasing related merchandise, attending fan mee Al: Good paraphrasing and participating in various fan events (Li, 2022). The Chinese fan economy has become a powerful market force, shaping the success of entertainment companies and influencing product endorsements and brand partnerships (Hou, 2019). While there are some similarities between the fan economies in North America and China in the area of fan culture and economic influence, there are also some obvious distinctions. Lucas (2020) points out that there are some notable differences between the fan economies in the US and China in terms of market size. With a historical tradition and a solid star culture, the North American fan economy has an unbreakable relationship to the developed entertainment industry (Sternheimer, 2011) that started long before China's fan economy began. In contrast, the Chinese fan economy is thriving in the digital age, using social media platforms and influencer marketing to expand fan engagement and drive economic growth (Jia, hung, & Zhang, 2018). This report aims to compare the North American fan economy with the Chinese fan economy, and analyze their similarities and differences in terms of development background, market size, business model, and cultural influence. 41 It will do this by answering the next three research questions: 1) What are the similarities and differences between the fan economy business models in the North America and China? 2) How do the fan economies of the North America and China differ in terms of marketing size? What causes this discrepancy, and why? 3) To what extent has the growing fan economy positively affected the mindset of citizens of the North America and China in the last decade? 2.0 Comparison of the Impact of the Northern American Fan Economy and the Chinese Fan Economy 2.1 What are the main similarity and difference between the fan economy business models in the North America and China? 2.1.1: Similarities date To data, a number of studies have indicated that "Star-centric" is a major common feature of the fan economy in North America and China (eg. Chung & Cho, 2017; Liang, 2022; Dixon, 2023). This may mean that the core of the fan economy revolves around well-known stars, artists and public figures, whose popularity and influence play an important role in fan enthusiasm and participation. Pop music icons like Taylor Swift draw throngs of devoted followers in North America. Her audience regularly purchases her CDs, goes to her shows, and promotes her music on social media. Taylor Swift engages with fans through brand collaborations, the release of limited-edition items, and other activities that further stoke their passion and support (Junes, 2023). Uos - Critical Analysis UOS: Add synthesising In China, Mi Yang (5) is a high-profile film and television star. Her popularity and influence have attracted a large number of fans and become an important driving force for the fan economy. Fans of Yang Mi actively participate in the promotion of her works, buy her peripheral products, and share her updates on social media. Her fans also expressed their support for her through online and offline activities, such as organizing movie viewing events and voting for her (Ban, 2023). Therefore, the celebrity's popularity and influence not only attract a large number of fans, but also bring business opportunities and growth space for the fan economy. At the same time, the interaction and relationship between celebrities and fans also promotes the development of the fan economy, forming an economic model of interaction, support, and common growth. 2.1.2: Differences S: Good INTERPRETATION 40 There are differences in the profit model between the fan economy in North America and China. In North America, the main sources of income for the fan economy include merchandise sales, concert tours, and endorsement partnerships. At the same time, Schaefer, Parker, & Kent, (2010) propose that the sports industry has huge UOS: Good synthesising North America, attracting a large number of passionate fans. Likewise, Mocarski & Billings (2014) hold the view that sports stars realize profits by signing sponsors, selling peripheral products for fans, and selling event tickets. Supporting this view, Mocarski & Billings (2014) write that LeBron James, a basketball player for the NBA, gets money through endorsement deals with well-known companies in addition to displaying excellent abilities on the court. While supporting his squad, his supporters may purchase basketball shoes, jerseys, and other memorabilia. In China, the profit model of fan economy pays more attention to e-commerce live broadcasts, social media advertisements and digital rewards. E-commerce live broadcasting is a rising profit method in China's fan economy. Celebrities sell products live on e-commerce platforms, attracting fans to buy and earn commissions from them (Lucas, 2020). For example, Chinese internet celebrity Jiaqi Li (*) has attracted a large number of followers by sharing beauty product recommendations and shopping tips on Taobao which is a Chinese online shopping platform similar to Amazon, and earns income through brand partnerships and endorsements. In addition, Wong & Dobson (2019) point out that digital rewards are also a common profit method in the Chinese fan economy. Fans can provide financial support to stars by purchasing virtual gifts and rewarding stars for live broadcasts. It can be concluded that these different profit methods reflect the differences in business models between North American and Chinese fan economies. North America pays more attention to physical merchandise sales and concert tours, while China pays more attention to digital methods such as e-commerce live S: Good INTERPRETATION social media advertisements and digital rewards. The choice of these models is related to the market environment, cultural background and consumer habits, providing more opportunities for the sustainable development of the fan economy. 2.2 How do the fan economies of North America and China differ in terms of marketing size and cultural differences? 2.2.1: Population size and spending power The fan economy is larger in China than it is in the USA. A likely explanation is that 35 China's huge population base provides fans with greater market p | Al: Paraphrasing: similarity rate too| consumer groups. China has the largest population in the world, about 1.4 billion people (Fang et al., 2023), while the population of the USA is about 330 million (Buchholz, 2022). Despite the comparatively high per capita income in the United States, China's recent economic growth and consumption improvements have caused Chinese consumers' purchasing capacity to keep rising (Figure 1). What this evidence indicates is that, although the Gross domestic product (GDP) of the United States is higher than that of China before 2025, the gap between them is decreasing year by year. And China's GDP will be more than the GDP of the United States between 2025 and 2030. With the growth of China's GDP, the purchasing power of Chinese fans will also increase, thereby driving the growth of the fan economy.See Answer
Q12: adsm كلية أبوظبي للإدارة ABU DHABI SCHOOL OF MANAGEMENT Sentiment Analysis using Artificial Intelligence for governmental organization e-services AM Supervisor Dr. Neda Abdelhamid Master of Science in Business Analytics (2022-2023) The candidate confirms that the work submitted is their own and appropriate credit has been given where reference has been made to the work of others. 1 adsm كلية أبــوظــبــي للإدارة ABU DHABI SCHOOL OF MANAGEMENT Sentiment Analysis using Artificial Intelligence for governmental organization e-services Supervisor Dr. Neda Abdelhamid A project presented to ABU DHABI SCHOOL OF MANAGEMENT In partial fulfilment of the requirement for the degree of Master of Science in Business Analytics (2022-2023) 2 AI ANN API CRISP-DM ᎠᏴ HTTP Abbreviations A sample of abbreviations is given below. The student should important abbreviations according to their project Artificial Intelligence Artificial Neural Network Application Programming Interface Cross-Industry Standard Process for Data Mining Database Hypertext Transfer Protocol IoT Internet of Things JSON JavaScript Object Notation LSTM Long Short-Term Memory ML NLP NoSQL PCA REST RNN SaaS SQL SVM TF-IDF UAE TAMM ADDA NLP CRISP-DM NLTK SVM Tweepy Machine Learning Natural Language Processing Not Only SQL Principal Component Analysis Representational State Transfer Recurrent Neural Network Software as a Service Structured Query Language Support Vector Machine Term Frequency-Inverse Document Frequency United Arab Emirates AbuDhabi E-Services Government Platform Abu Dhabi Digital Authority Natural Language Processing Cross-Industry Standard Process for Data Mining Natural Language Toolkit Support Vector Machines an open source Python package that gives you a very convenient way to access the Twitter API with Python 3 699 889aa0 .7 .8 .8 .10 .10 .10 .11 .11 .12 2 .12 .12 22 .12 .13 Table of Contents Abbreviations Chapter 1 Motivation Background Related work... Problem Statement.. Approach and methodology Table of Contents 1. Business Understanding (CRISP-DM Phase 1): 2. Data Understanding (CRISP-DM Phase 2): 3. Data Preparation (CRISP-DM Phase 3):. 4. Modeling (CRISP-DM Phase 4):. 5. Evaluation (CRISP-DM Phase 5):.. WIPLE 6. Deployment (CRISP-DM Phase 6): Scope and limitations Target group Literature Review Chapter 2 (Sentiment Analysis using Artificial Intelligence for governmental organization e-services). Introduction. Introduction to Abu Dhabi E-Services Platform (TAMM) Sentiment Analysis in E-services:. Crisp-DM for sentiment analysis.... Tweepy. Machine learning for sentiment analysis. Sentiment Analysis using Arabic Language.. Literature Review Table. Chapter 3 (Methodology). Methodology 1. Business Understanding: . 2. Data Understanding:. Tweet Flash Exploratory Data Analysis (EDA). 3. Data Preparation: 4. Modeling: Term Frequency inverse document frequency TF-IDF . 4 .14 1567222222222 .19 .20 .20 .23 .23 Overfitting in machine learning. Sentiment Analysis of Twitter TAMM Tweets: An Approach Data Acquisition and Normalization .24 .25 .25 Data Preparation. Model Training and Validation. Machine Learning Model Selection .25 .25 .26 Outcome Performance Evaluation and Model Optimization. Chapter 4 (Data Analysis) Evaluation..... Apply the trained ML algorithms to our Tweets. Analyzing the output of our results. Overall general comparison. Pairwise Classifier Agreement Heatmap. Chapter 5 (Results and Findings). MLP Classifier Word Cloud Analysis Findings. Analysis in depth using the context of frequent words.. Chapter 6 (Discussion). Sentiment analysis for decision making process.. Hypothesis Validation.. H1: Sentiment H2: CRISP-DM as a Structured Methodology for Sentiment Analysis. H3: Precision of Sentiment Chapter 7 (Conclusion)... Future work References S AMPLE .26 .26 .26 .29 .29 .29 .31 .35 .35 .36 .38 .39 .40 .41 Analysis as a Tool for Enhancing E-Service User Experience .41 .42 Classifications Through Machine Learning Techniques.. .42 .43 44 .45 5/n Student Note: This is chapter 7. To be done in 1000 words. You already did chapter 1-6.See Answer
Q13: Creating Synthetic Experts with Generative Artificial Intelligence Daniel M. Ringel 707 חמווובו וIAי עI בו ©2024 Daniel M. Ringer © 2024 Daniel M. Ringel Synthetic Experts Classification is paramount in today's data rich Environment Organizations increasingly depend on machine learning to distill intelligence from vast amounts of unstructured data Sort items into specific categories based on their characteristics • News articles • Reports and policies Social media • Internal communications Classification models can swiftly identify constructs of interest in data . Sentiment • . . Opinions and arguments Type of rhetoric Product categories Versatility of classification models extends their utility across sectors and functions © 2024 Daniel M. Ringel Image by Midjourney/n Student note : Create a well-performing Synthetic Expert (see attached pdf to get an idea on how to create a Synthetic Expert) for a specific use case. Demonstrate its's accuracy and publish it on Hugging Face. You will have to spend considerable time creating a validation sample to demonstrate your synthetic expert's accuracy on. [Note provide me the model and provide the steps to publish the model on huggingface] Tutor can choose the specific usecase In the document it just tells about the steps to do it but the to create the actual synthetic expert we need to write a python code to develop the models (This part was not mentioned). On high level we need to create a synthetic expert similar to the mentioned example but not same tutor need to get pre trained model from the huggingface and need to train the model for the specific usecase and need to write the code for above this link shows the sample synthetic expert "https://huggingface.co/dmr76/mmx classifier microblog_ENv02"See Answer
Q14: ARIN7013/2023-2024 UNIVERSITY OF HONG KONG DEPARTMENT OF MATHEMATICS Numerical Methods for Artificial Intelligence Assignment 3 Due Date: 23:59, April 11, 2024. There are 3 exercises. All assignments submitted after the due time will be given 0 mark. Please submit one group work only for each group. 1. (Compute the eigenvalue with the largest magnitude) Consider the symmetric ma- trix 2 1 1 0 1 3 1 1 A: = 11 41 01 1 3 and let v(0) = (0.5, 0.5, 0.5, 0.5) be the initial eigenvector. (a) Find the eigenvalue with the largest absolute value starting with v(0) use the power iteration. Write down your code, and display the results for the first three iteration. (b) Can you find the eigenvalue with the second absolute value using power itera- tion? If so, write down your result for the first three iteration. (c) Apply Rayleigh quotient iteration to A starting with v(0) to find the results for the first three iterations. = [x1 x2 x3 x4], where x₁ = 1 2. (PCA and kernel PCA) Given a data matrix X (1,0)ª, x2 = (0, −1)², x3 = (½‚¯✓½)³ and x₁ = ( √ √½)². √2 (a) Find the first principal component w Є R², and then compute 4 Σ Σ ||xi - (xi, w) w || i=1 (b) Let K(,) R² × R² → R given by : K(x, x') = |(x, x')|². Note that K(·) is a reproducing kernel, and thus there is a corresponding RKHS, denoted by (H, (·, ·)µ)· 1 i. Use this kernel to perform kernel PCA, and find the first principal com- ponent vЄH. ii. Recall that this first principal component v can be expressed as 4 v(x) = Σ a¿K (xi, x), Σαν satisfying (u, v) H = 1. Calculate 4 i=1 i=1 K(x) (v, K(x₁, ·))|| 3. (Implementation of PCA) Extract one subject in a particular pose from the following link: The Yale Face Database B, which include a number of 65 images. Denote them as X = [x₁, ···‚×n] with n = 65 and each image contains 50 × 50 i.e., x; Rd with d = 2500. (a) Depict two images from your extracted images. (b) Use PCA to find the first 10 principal component wk for k Calculate n Σκα i=1 10 | ||xi - (xi, Wk) Wk || 2. k=1 = 2500 pixels, == : 1, 2, . . ., 10. (c) Reconstruct your selected two images in (a) using the first 10 principal com- ponent and plot them. 2See Answer
Q15: 5COM2003 Practical Assignment: Report on a paper (Variant A - Graph measures) 5COM2003 Artificial Intelligence Semester B 2022/2023 In this assignment, you will apply some of the notions, principles, methods and algorithms we touched on in the Artificial Intelligence lectures and prac- ticals to design, program, test, explain and demonstrate an agent based on a given paper. There are 25 marks to achieve, each translating to 1% of your overall module grade. This assignment requires you to: 1. read (parts of) a paper 2. program parts of it (with some alterations) 3. explain your design choices 4. run a simulation and collate results 5. evaluate your simulation's results Submission requirements The work must be your own. You may of course collaborate but the work handed in must be distinctly yours. The following two sets of documents must be submitted on StudyNet/Canvas: (a) A .zip archive containing your commented code. 1 (b) A report in PDF format providing the explanations and figures re- quested in the tasks given below. You do not specifically get marks for comments, but where code is required and not readable marks might be deducted based on unreadability. Assume the reader of your code is one of the better programmers in your cohort. On some task you will see a word count. This is not a specific requirement, but a guidance about expectations. Do make sure you are using the words effectively to describe key aspects and choices. Reading: Empowerment and Relevant Goal Information as Alternatives to Graph-Theoretic Centrality for Navigational Decision Making Your paper can be found here: Empowerment and Relevant Goal Information as Alternatives to Graph- Theoretic Centrality for Navigational Decision Making by Marcus Clements. It is the work of a former (research) MSc student here at UH, Marcus Clements. And sits in the triangle between established graph theoretical metrics, information theoretical measures such as empowerment and rele- vant goal information(RGI), and human decision making. All of this on the example of a street network in Soho. You are asked to read these parts of it in particular: • The abstract to learn about the general goal. (page 2) • Chapter 1 until including 1.2, for a general motivation and idea. (page 6-10) • Chapter 2.5 explaining some graph measurement you will be asked to implement. (page 23) ● The conclusion 5.1 for some of the results of the research. (page 55) You may of course - read more of the paper and it will help in your under- standing, but these are the required parts the later test will be based on. You 2 will find a lot of parallels to our lectures in the module, feel free to compare :) World design [5 total marks] The basis for this world can be found on page 33 of the reading material. It is a undirected graph with seven vertices and seven edges. You have three tasks based on this: 1. Implement the graph using a node, an edge, and a graph class. [1 mark] 2. Draw an UML Class diagram containing the variables and functions of each class as well the relations between them. [2 marks] 3. Write a paragraph explaining your design choices. (20-50 words) [2 marks] World metrics [8 total marks] In chapter 2.5, three graph metrics are given. Your task is to implement these measure in your program. 1. Write a class GraphMetrics and implement all three metrics as func- tions in it. [1/2/3 marks] 2. Write a text explaining your overall design choice. Mention how you computed the shortest paths(geodesics) for your implementation for the Betweenness Centrality function. (40-55 words) [2 marks] Agent design and simulation [12 total marks] Agent Design [5 total marks] Write an agent capable of walking from a given start node to another given target node through the graph. The agent should posses two modes of movement: 1. Random Walk [1 marks] 3 2. Shortest Path [1 marks] In order to help your shortest path walking. Compute the shortest paths between each pair of nodes once and store them in a suitable data structure to avoid constant recomputation. [1 mark] To enable future analyses the agent should also posses some memory for each episode (each navigation from start to target). And the ability to return this information at the end of the navigation task. Give your agent the ability to sense its current state (including start and target) and store each visited node during one episode. [1 mark] To round the task out, update the UML Class Diagram from earlier to include the agent and its capabilities. [1 mark] Simulation [2 total marks] Use both Agent and World you can now run one simulation - and episode - as given below [1 mark]: 1. Randomly select a start and (different) target node from the seven possible choices. 2. Count all nodes visited. 3. Store the results. Run 1000 simulations each Random Walk and the shortest path movement, for a total of 2000. Keep the result separated. [1 mark] Evaluation [5 total marks] Compare your results for both movement modes in a table. [0.5 marks] Write an analyses of your obtained results along the lines of these questions: [30-60 words] [2 marks] • What are the differences between the two movement modes? • Why are we seeing these results? • Do the results surprise you? 4 ст Create a second table comparing the outputs of the graph metrics with the simulation results.[0.5 marks] (You may also do this in one big table.) Compare your results to the metrics. In how far do the results align with the metrics? Compare the different numbers along the same lines as above. [30-60 words] [2 marks]See Answer
Q16:Instructions Part A: Initial Post You are to role-play an independent consultant for this assignment. Research artificial intelligence (AI) and how it can be implemented into Kibby and Strand's operational processes and products. Prepare your submission as a combination impact study and recommendation paper. This paper should be prepared as a Microsoft™ Word document, and then attached to the unit discussion thread. There is no minimum or maximum in terms of the word count; however, the response should explicitly address all required components of this discussion assignment. The document should be prepared consistent with the APA writing style and reflect higher-level cognitive processing (analysis, synthesis, and or evaluation). Need to Do in 500 Words double spaced/nDiscussion Guidance Class, This week you are writing an impact study to inform senior management of Kibby and Strand how the organization can leverage Al in its operational processes and the textile products it sells. I recommend you create the following sections: Purpose, Background, Analysis, Impacts, and Recommendations. The Background is where you look at how manufacturing and marketing uses Al, as well as how Al is being integrated into textiles products, The Background is based on research and you will need several sources to cover manufacturing, marketing, and customer support of textile products. The Analysis section is where you analyze the research in the Background, and Impacts is where you state the impacts you identified in your analysis. Number your impacts and there should be a clear linkage between analysis, impacts, and recommendations. By that I mean is every impact needs to be supported in the analysis section, and each impact needs to be addressed in recommendations. Going backwards, if I look at a recommendation it should state which impact it is addressing, and I should able to take that impact and see how you arrived at it in your analysis. There is not a simulation assigned this week in Connect for the discussion so you will use the information in the discussion scenario provided above. Let me know if you have questions. Bill Week 3 Layout of Impact Study for Artificial Intelligence on Kibby and Strand Operations.docx/nImpact Study of Artificial Intelligence on Kibby and Strand Operations Background and Purpose of Study - Summary of Kibby and Strand operations -- included mission statement, goals, and objectives (this you did in week 1) -Purpose of study is to identify operational functions of Kibby and Strand where Al can help achieve goals and objectives Research and Analysis -write down research questions - review textbook (probably will not be much help for this study) - look for 3-5 peer reviewed journal articles on using Al in marketing, production, and customer support - analyze the research and look for ways Kibby and Strand might utilize Al List Impacts - using the analysis list possible impacts to Kibby and Strand operations if they ignore or implement Al. These can be positive and negative impacts. List Recommendations - Every impact should be addressed in Recommendations, even if the recommendation is to do nothing. -Any negative impacts should be addressed in recommendations to include the risk if Kibby and Strand does not use Al in an operational task, e.g. what happens if Al is not integrated into marketing or manufacturing? When you finish, every recommendation needs to address a specific impact and each impact is supported in the Research and Analysis section.See Answer
Q17:Q5. (8 points, 2 points each) Which of the following statements about alpha-beta pruning are true or false? Justify your answer. a. Alpha-beta pruning may find an approximately optimal strategy, rather than the mini- max optimal strategy. b. Alpha-beta prunes the same number of subtrees regardless of the order of child nodes. c. Alpha-beta generally requires more run-time than minimax on the same game tree. d. the minimax search is bredth-first, so at any point we have to consider the nodes at a level. Answer:See Answer
Q18:Q2. (20 Points, 3 points a to e, 5 points for f) Futoshiki is a Sudoku-like Japanese logic puzzle that is very simple, but can be quite challenging. You are given an n x n grid, and must place the numbers 1,...., n in the grid such that every row and column has exactly one of each. Additionally, the assignment must satisfy the inequalities placed between some adjacent squares. The inequalities apply only to the two adjacent squares, and do not directly constrain other squares in the row or column. Below is an instance of this problem, for size n = 4. Some of the squares have known values./n1.1 1.2 1,3 1,4 2.1 2.2 2,3 Λ Λ 2,4 3.1 3.2 3.3 3,4 4,1 4.2 Λ 2 3 Let's formulate this puzzle as a CSP. We will use 42 variables, one for each cell, with Xij as the variable for the cell in the ith row and jth column. The only unary constraints will be those assigning the known initial values to their respective squares (e.g. X34 = 3). a. Complete the formulation of the CSP. Describe the domains of the variables, the two unary constraints, and all binary constraints you think are necessary. You can describe the constraints using concise mathematical notation. Do not use general n-ary con- straints (such as alldiff). b. After enforcing unary constraints, consider the binary constraints relating X14 and X24- Enforce arc consistency on just these constraints and state the resulting domains for the two variables. c. Suppose we enforced unary constraints and ran are consistency on this CSP, pruning the domains of all variables as much as possible. After this, what is the maximum possible domain size for any variable? Hint: consider the least constrained variable(s); you should not have to run every step of arc consistency to answer this. d. Suppose we enforced unary constraints and ran are consistency on the initial CSP in the figure above. What is the maximum possible domain size for a variable adjacent to an inequality? e. By inspection of column 2, we find it is necessary that X32 = 1, despite not having found an assignment to any of the other cells in that column. Would running are con- sistency find this requirement? Explain why or why not. f. Provide a solution to this puzzle by assigning values to the variables. Answer:See Answer
Q19:75/ Arad C 118 71 Oradea Zerind 140 Timisoara 111 70 75 Dobreta 151 Lugoj Sibiu Me hadia 120 80 99 Rimnicu Vilcea 97 Fagaras 146 138 Cralova Pitesti 211 101 Neamt Q 85 87 90 Giurgiu Bucharest lasi 92 142 Urziceni 98 Vaslui Hirsova 86 Eforie Straight-line distance b Bucharest Arad Bucharest Craiova Dobreta Eforie Fagaras Giurgiu Hirsova Iasi Lugoj Mehadia Neamt Oradea Pitesti Rimnicu Vilcea Sibiu Timisoara Urziceni Vaslui Zerind 366 160 242 161 176 77 151 226 244 241 234 3.80 10 193 253 329 80 199 374 1. (100 points, undergraduate student) Please use the uniform cost search method to find a path from Oradea to Bucharest in Romania Map shown in the following figure. Major tree expansion steps must be shown clearly in detail. (10 bonus points, undergraduate student) Please make your new search method to find a path from Oradea to Bucharest in Romania Map shown in the following figure. Major steps must be shown clearly in detail.See Answer
Q20: 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) | 978-1-6654-1450-0/20/$31.00 ©2021 IEEE | DOI: 10.1109/ICIEM51511.2021.9445303 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Supervised Sentiment Analysis on Amazon Product Reviews: A survey Monir Yahya Ali Salmony Department of Computer Science Aligarh Muslim University of Aligarh, India myali 107@myamu.ac.in Abstract- Sentiment Analysis (SA), which is also known as Opinion Mining, is a hot-fastest growing research area, making it challenging to follow all the activities in such areas. It intends to study people's thoughts, feelings, and attitudes about topics, events, issues, entities, individuals, and their attributes in social media (e.g., social networking sites, forums, blogs, etc.) expressed by either text reviews or comments. Amazon is an example of the world's largest online retailer that allows its customers to rate its products and freely write reviews. Analyzing these reviews into positive or negative; will assist customers' decision making, which varies from purchasing a product like a camera, mobile phone, etc., to writing a review about movies and making investments - all of these decisions will have a significant impact on the daily life. Sentiment analysis draws the attention of both scientific and market research in Natural Language Processing and Machine Learning fields. In general, the machine learning approach consists of supervised and unsupervised algorithms. In this research study, a detailed typical workflow process often adopted by the researchers is presented. Moreover, traditional supervised machine learning classification techniques have been investigated on various categories of Amazon product reviews to find the best method that provides a reliable result of sentiment analysis and assists future research in this newly emerging area. Keywords Sentiment analysis, Sentiment classification, opinion mining, machine learning, polarity classification, supervised algorithms, Amazon classification. I. INTRODUCTION In modern times, social media and online shopping play a vital role in connecting people around the world and help them express their feeling and opinion about any topic on the globe. Amazon.com, as an example, is one of the reputed online retailers nowadays. It allows its users to post and share their opinions about its products freely. As a result, a massive amount of structured and unstructured data generated. Sentiment analysis is utilized to study and analyse these data and finds valuable insights beyond people's thinking and feeling about anything that can help in decision making. Sentiment analysis or SA as a shortcut is considered as a text classification task, which is a subfield of natural language processing (NLP). NLP is used by machine learning techniques to understand, analyse, and gain in-depth meaning from a human language with intelligence [1]. Recent supervised classification approaches that have been used to find sentiment analysis in Amazon product reviews are surveyed in this paper to discover the best one that can provide reliable and accurate results. This approach can then be used as a baseline for Amazon reviews, classification tasks, recommendation systems [2], etc. This research study has been structured as follows: Section 2 presents an overview of Amazon. Section 3 introduces sentiment analysis, its levels, and approaches. 132 Section 4, 'literature review' summarizes some related work on sentiment analysis, followed by section 5, the typical sentiment analysis methodology. The last parts 6 and 7 cover the discussion and the conclusion of our study as well as address some future directions for research, respectively. Arman Rasool Faridi Department of Computer Science Aligarh Muslim University Aligarh, India ar.faridi.cs@amu.ac.in II. AMAZON BACKGROUND 2 Amazon is one of the world's largest online retailers. It had overgrown since 1994 when it launched as an online platform. Currently, It provides over 12 million different products and had 150.6 million active mobile users (Statista, 2019), so it is considered a microcosm for excellent user- supplied reviews. Amazon sells various products like books, phone apps, movies, clothes, electronics, toys, etc. and makes use of the universal rating system -from 1 to 5 stars; lowest to highest score, respectively, and writing product review text. This scoring system has no user guide on how customers should use it; besides, the product reviews are subjective and personal. Therefore a user can give a good product a score of "1", but a bad buying experience, such as late delivery, or high price, and vice versa. The absence of guidelines makes it difficult to identify the user's sentiments about different product aspects and parts of a shopping experience [3]. Another challenge is the J-shaped distribution, with an overwhelming majority being positive reviews of the sampled reviews crawled by Amazon's standard identification number (the ASIN). III. SENTIMENT ANALYSIS Sentiment analysis or subjectivity analysis is the top research field under NLP. It utilizes textual data available almost on social media like Amazon to analyse people's opinions and concentrates on the subject part of the text (phrase or sentence) that leads to a good or bad direction [4][5]. SA plays an essential role in the business domain by providing the businesses with in-depth insight into the attitude of buyers' feedback about their product; therefore, they can improve their strategy to meet the customer's expectations & needs and avoid loss. On the other hand, it is helpful for potential customers to decide about the products they are going to buy. Star Level Fig. 1 Product rating & Review example Example I hate it. I don't like it. It's ok. I like it. I love it. 978-1-6654-1450-0/21/$31.00 ©2021 IEEE Authorized licensed use limited to: New York University. Downloaded on November 10,2023 at 12:55:57 UTC from IEEE Xplore. Restrictions apply. 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) The typical sentiment analysis task involves taking a piece of text, whether it's a word, sentence, or an entire document; classify it into either binary classification or multi-classification using a Machine Learning classification model that results in a score that measures the text polarity. That is a big challenge zone, due to the structure of the text that may include negation, comparative, slangs, domain, multi-language [6], etc. A. Sentiment analysis levels In general, sentiment analysis is mainly explored into three different levels based on the text scope, namely, A) document, B) sentence, and C) feature level [5][7]. (A) The task at the document level is to determine whether the overall opinion of the document expresses a positive or negative sentiment about a single entity. In contrast, the Sentence level of analysing (B) is concerned with determining whether each sentence in the document(s) holds a positive, negative, or neutral opinion. Whereas the previous analysis levels cannot perform, (C) Entity and Aspect level analysis since they are focusing directly on identifying the people's opinion about specific aspects either preferred or not. It is also called phrase-level sentiment analysis and feature level analysis [5] [8]. It is employed when conducting sentiment analysis on reviews like mobile phones and restaurants. B. Approaches of sentiment analysis In general, two main conventional approaches are employed in solving sentiment analysis challenges; (A) Machine Learning (ML) methods based, (B) Lexicon based [9], Fig. 2. All these approaches applied to a piece of text- a product review or comment, to distinguish between positive and negative opinions appear in that text. 1. Machine learning Approaches These approaches tackle the problem of how the computer program itself can learn to identify complicated patterns and create intelligent-decisions based on data text review. It mainly comprises supervised learning and unsupervised learning approaches Fig. 2. The supervised learning employs ML classification techniques, whereas the unsupervised methods employ clusters that imply Lexicon approaches. A. Supervised machine learning In the supervised learning approach, they mainly focus on the classification of data. It often requires an extensive labelled training dataset to teach an algorithm- how each word (sequence) in a text corresponds to the overall sentence's outcome is negative or positive in a supervised manner. Examples of benchmark supervised techniques are Decision Tree DT, Naïve Bayes NB, Maximum Entropy ME, Support Vector Machine SVM, etc. This approach requires manually labelled data, which is not always possible, and often time-consuming [10]. B. Unsupervised machine learning In contrast, in the unsupervised approach, they concentrate on the grouping of unsorted data according to the similarities or differences without any prior knowledge about the data that is not labelled- no training of data given to the machine. 133 Unsupervised methods Lincar classifiers Support Vector Machine Sentiment Analysis Machine Learning approaches Supervised methods Rule based classifier Neural networks Decision tree classifier Naïve Bays Lexicon approaches Dictionary Corpus based based Probabilistic classifiers Baycsian Maximum network entropy Fig. 2 Sentiment Analysis Approaches It allows the application of traditional unsupervised clustering types like Hierarchical, K-means, K Nearest Neighbours (KNN), Principal Component Analysis (PCA), etc., to that information without human interference. This approach is beneficial once a lack of labelled data [11]. ML algorithms are used in sentiment analysis to recognize the sentiment that appears in the given text according to the word patterns, their order, and a sentiment- labelled training set in supervised learning, and similarities and differences in unsupervised learning case. These approaches aim to automate expensive manual tasks or time- consuming ones. 2. Lexicon-based This approach aims to find the lexicon containing the opinion and then analyse it by using, for example, a dictionary of antonyms and synonyms for the opinionated words and phrases with their corresponding sentiment scores. Furthermore, it is classified into the corpus and dictionary- based approaches [12]. A. Dictionary-based Generally, opinionated dictionaries such as WordNet, Senti WordNet, or online dictionaries contain a list of negative and positive sentiments. This approach intends to search the terms that hold subjective meaning if found in the text, match them with the words listed in that dictionary, and return their corresponding scores. This approach is unable to find the domain or context-specific opinion [13]. B. Corpus-based It identifies the opinionated words in the corpus and assigns the polarity to these words to find the domain or context-specific opinion that dictionary-based cannot. It needs a dictionary of all English words [13]. IV. LITERATURE REVIEW Since this research is intended to study supervised approaches to find the sentiment of Amazon reviews, the works related to analysing the sentiment using these approaches are only considered in this section. These researches are reviewed in terms of pre-processing techniques, feature extraction methods, proposed methodologies (classification methods), and evaluation metrics. Several works have focused on identifying users' opinions of different Amazon product reviews. In the Authorized licensed use limited to: New York University. Downloaded on November 10,2023 at 12:55:57 UTC from IEEE Xplore. Restrictions apply. 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) on research study by [14], a general sentiment polarity classification process is presented that implies a negation phrase identification (a phrase that conveys opposite sentiment), mathematical sentiment score computation, and a feature-vector generation method. The experiments were done the data collected from Amazon (www.amazon.com) in 2014, about online product reviews on both review and sentence levels categorization. They used a max-entropy POS tagger and three ML classification models (i.e., Random Forest, SVM, and NB). The results were promising, and the Random Forest model outperforms Naïve Bayes and SVM, table1. Also, traditional Logistic Regression, SVM, and deep learning models like Long Short Term Memory networks LSTM and convolutional neural network CNN have been applied on large-scale Amazon reviews datasets in [15]. They compared and analysed different pre-processing techniques that raise the accuracy and found that the best combination method operates stemming over lemmatization and does not include spell checking. They used various feature approaches like bag-of-words, and n-grams, and their TF-IDF. Also, paragraph vector, pre-trained word embedding like Word2Vec, and Glove to discover which combination works better. Among conventional techniques, Linear SVM classifier and bag-of-n-grams with TF-IDF features perform the best, whereas, among deep learning models, LSTM works better. Another research [3], presents a comparative study of two text sentiment classification groups to analyse Amazon reviews datasets. Supervised machine learning techniques group i.e., LR, Gradient Boosting, and SVM algorithms and the lexicon based approaches group i.e., Senti WordNet, Pattern, and VADER lexicons. They used various NLP techniques, including word lemmatization, stop words removal, and TF-IDF vectorization. They conclude that supervised classification methods with minimum hyper- parameter tuning outperform others, especially LR classifiers, and VADER works the best among all lexicon- based approaches on all accuracy, recall, precision, and F1 score measurement metrics. They added that both groups performed better in identifying positive labels than negative ones. This performance could have been due to certain stop words associated with positive emotion and the inherent class imbalance problem of having a large proportion of one class review than the other in the dataset. In this paper [16], the authors claim that the accuracy of the existing algorithm (NB and SVM) is not worthwhile. They proposed an ensemble model approach, which is combining two or more algorithms. The proposal contains three modules. They collected the dataset from the official product site using Amazon API in the first module, and unwanted data such as stop words, punctuations, and conjunctions were pre-processed in the pre-processing module. Whereas in the classification module, they combine NB and SVM and calculate the mode value based on the vote for every algorithm used. This approach provides better accuracy than could be obtained by the existing algorithms individually. Furthermore, the performance of Multinomial Naïve Bayes (MNB), Linear (SVM) and LSTM algorithms were investigated in [17], to see if it is feasible to tag the polarity of (N = 60,000) Amazon product records randomly selected from four million Kaggle benchmark dataset. Besides, they 134 scrapped some pages/ ~ 230000 real-time reviews related to Electronic devices, Toys, and furniture categories to use it along with the Kaggle rest records for the models' evaluation test. They applied data pre-processing, and the training set is fit on a 'maximum features' parameter. TF-IDF vectorizer is used for the traditional models, while the tokenization method is used for LSTM. The test shows that LSVM and MNB achieved satisfying results, but LSTM networks work the best on Amazon binary sentiment classification [17]. In another study by [18], they collected reviews from Amazon about Tablets, Mobile phones, Cameras, Laptops, and TVs. They proposed a hyper approach, dictionary-based, and supervised classification models, i.e., NB and SVM, to find the sentiment of each product reviews individually. They employed opinion lexicons that contain 4783 negative and positive words to calculate the sentiment scores of the sentences. They conclude that both NB and SVM got excellent accuracy on camera review, i.e., 98.17% and 93.54%, respectively. In the research [19], the efficiency of applying Keyword- Based, SVM, NB, and ME classifiers were tested for classifying online Amazon reviews- reviews extracted using API, using a web model. In feature extraction, unigrams and weighted unigrams features have been used to train these classifiers. They designed a framework to consider feature extractors and classifiers as different mechanisms. Query terms consequences have been normalized, emoticons were used as rough labels in the training phase, and feature reduction is applied. The results were acceptable on weighted unigrams, specifically with SVM compared to Unigrams. It also shows that using weights can increase accuracy. In [20], NB and decision list classifiers, along with bag- of-words and bigrams features, are compared in their effectiveness in correctly tagging reviews as positive and negative attitudes towards book products. NB gave better results than the decision list classifier in the all-books dataset; and found that the system performed well on small datasets even when trained and tested on entirely different product reviews. Other researchers concern on Mobile phone reviews, in this paper [21], they proposed a framework with three modules (i.e., (1) data collection and pre-processing (2) feature selection and sentiment analysis, (3) classification, and cross-validation). They collected over 400,000 reviews of 4500 mobile phones from the e-commerce giant Amazon.com. They cleaned and handled it to be balanced. They added the score to each record using the NRC dictionary. They then employed ML classification algorithms like NB, SVM, and DT to classify these reviews as positive or negative. Out of the three classifiers, they found SVM's predictive accuracy is the best, table1. The work by [22], concentrates on mining and applies their experiments on Apple iPhone 5S, Samsung J7, and Redmi Note 3 reviews from Amazon. They proposed a system to retrieve the selected product reviews of the given URL automatically. They used ML algorithms such as NB classifier, and Logistic Regression LR, and also Senti WordNet algorithm to classify the retrieved text as negative, positive, or neutral). In the end, they have used metric parameters to measure the performance of each. The results found that NB proves to be the most efficient among all. Authorized licensed use limited to: New York University. Downloaded on November 10,2023 at 12:55:57 UTC from IEEE Xplore. Restrictions apply. 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) The research by [23], investigated three features of text representation approaches, i.e., Countvectorizer, TF-IDF, N- grams, with logistic regression classifiers to find the sentiment of mobile phone review. They found that the combination of TF-IDF with n-gram is the winner with 97 AUC. Sometimes researchers prefer to train and test their classifiers on Amazon polarity and Kaggle benchmark datasets. Also, LR, Stochastic Gradient Descent SGD, NB, and CNN performance is studied in [24], using balanced and unbalanced versions of Amazon unlocked mobile phones dataset and variety of feature extraction techniques as in [14]. They applied the Lime technique to analyse the classification results for the reviews being either negative, neutral, or positive (based on significant or most frequent words). The study found that negative reviews lengths are longer in general and reach a conclusion that the CNN model with word2vec performed the best among the other models on both versions of the data, 79.60%, and 92.72% accuracy, respectively. This paper [25] aimed to tackle traditional representation methods such as BOW and TFIDF for sentiment analysis since these non-distributed vectorization methods extract sentiment from lexical or syntactic features. These techniques do not consider word order, semantic word relations, and contextual information appear in the review. Instead, they used word2vec approaches (Continuous Bag of Words (CBOW) and skip-gram models) representation with several parameter settings to capture the deep semantic relationship between words, followed by supervised classification algorithms like Logistic Regression, SVM, Random Forest, and Naïve Bayes. The experiments show that word2vec was efficiently incorporate contextual information and semantic word relations for the sentiment classification task, and exhibit the superior accuracy obtained by using Random Forest with CBOW. Another study conducted by A. Ejaz, et.al. [26], developed a lexicon dictionary-based algorithm along with n-grams to perform sentiment evaluation of only 500 MB of Amazon product reviews database. They compared it with three ML techniques with different text representations: Random Forest RF with word vector, DT with a document vector, and Random Forest with n-gram. They used KNIME software to clean ambiguous and missing rows and other unwanted words and characters. Their experiment concludes that their approach performs better than machine learning techniques used in terms of AUC-ROC accuracy measurement. Another work to solve sentiment polarity classification is done by [27]. They developed a classification technique for a dataset of Office products and musical DVDs that crawled using python crawler. They considered five classes (Strongly Negative, Negative, Neutral, Positive, and Strongly Positive). The paper has applied RF, DT, NB, SVM, GB, LSTM classifiers along with three types of adverbs as features, i.e., Adverbs RB, Comparative adverbs RBR, Superlative adverbs RRS, and a combination of them to perform review level classification. The experiments show that a single RBR feature is suitable for most of the classifiers except LSTM and NB, and a combination of RBR-RBS features proved to be more efficient for all the classifiers. 135 Paper TABLE 1. Nguyen, Product et.al. [3] reviews X. Fang, et.al. [14] Tamara ,et.al. [15] J.Sadhasi vam, et.al. [16] R.S. Jagdale, et, al. [18] Z.Singla, et.al. [21] Kumar, et.al. [22] Aljuhani, et.al. [24] Product reviews A.Singh, Product et.al. [19] reviews B.Bansal, et.al. [25] Amazon Polarity Dataset Product reviews Product reviews M.Phone reviews M.Phone reviews Unlocke d Mobile Phones reviews Unlocke d Mobile Phones reviews SUMMARY OF SOME STUDIES SVM, GB, LR NB, SVM, RF LR, SVM, MNB & GB SVM, NB, Ensemble SVM, NB SVM, NB, ME SVM, NB, DT NB, LR, LR, SGD, NB, SVM, LR, NB, RF RF, DT, S.Kausar, Product NB, et.al.[27] reviews SVM, GB, TF-IDF BOW, B-n- grams, TF- IDF, Word Embeddings POS POS with a weighted score unigrams and weighted unigrams BOW Not mentioned & Not mentioned & [89, 87, 901 Superlative adverbs & Combination [98, 90, 97] [92.88 92.90, 90.59, 82.03] [30.19 34.74, 75.95] [93.54 98.17] [81.20 77.42, 70.35] [81.77 BOW, TF- IDF,N-grams [77.47 ,Word Embeddings 76.05, 74.90] Combination Word2 Vec (CBOW, Skip Gram) Adverbs, Comparative 66.95, 81.25] [85.7, 66.1] [90.3, 90.9, 54.8, 90.6] [95.0, 95.0, 91.0, 94.0, 95.0] Bag-of- n-grams + TF- IDF Result ROC Camera product weighted unigram S F1- measure, Samsung j7 Balance d data, different word represent ation CBOW F1- measure, RBR_R BS V. SENTIMENT ANALYSIS ON PRODUCT REVIEW The following Fig. 3, along with its steps, presents the typical methodology workflow process that the researchers often adopted to conduct sentiment analysis. This methodology comprises of two phases as in the following. Phase 1: Scraping 1. A- Web scraping It is a technique used to access and extract a large amount of data from any accessible websites, e.g., Amazon, as in our case and stores it for later used analysis. B- Scraping process Fortunately, most of the famous sites like Facebook, Twitter, and Amazon offer API that facilitates access to any Authorized licensed use limited to: New York University. Downloaded on November 10,2023 at 12:55:57 UTC from IEEE Xplore. Restrictions apply. 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) Phase 1 Phase 2 (1) Product request URL Data Base Products reviews 1 amazon (2) Response status & content -(4) Retrieved reviews saved in HD or DB Data preparation 2 Fig. 3: Typical Sentiment Analysis Workflow Process kind of information, such as posts, comments, or products available on the page for the researchers with some restrictions. These APIs require having an account or becoming an associate these websites. As an alternative, Request and Beautiful Soup are python libraries that help in crawling and extracting all the structured/unstructured detailed data displayed on the product page. The request library is responsible for getting the content of the desired URLs of the inspection element. This content will contain a status code and the web page content of that particular element. At the same time, Beautiful Soup accesses this response content, uses ASIN Amazon Standard Identification Number to get all the details of it (in case of Amazon reviews). It then converts it into a proper format (CSV, JSON, XLS, etc.), and saves it on the computer for later use. Also, the Amazon dataset contains fields like ASIN, Reviewer id, Reviewer name, Review text, Helpful, Rating, and Time. 2. Phase 2: Sentiment classification A. Data Collection The previous phase serves as a data collection. However, some researchers prefer to use benchmark labelled datasets downloaded from the UCI machine learning repository or Kaggle websites to train their algorithms before they test it on real data. Loading this data, dropping columns, dealing with missing values, etc., to make data ready for further process, are the aim of this step - Pandas python library can serve a lot in this step. A proper dataset is an important step and needs to be defined for analysing and classifying the text. B. Text preparation After the text is obtained, the preparation step comes to make data ready to be used for further machine learning steps. Pre-processing employed for reducing the noise and removing the data that are irrelevant for sentiment classification, such as eliminating punctuations, numbers, accent marks, stop words, sparse terms, white spaces, and particular words. Other parts of this, convert words to lower case, tokenization, stemming, lemmatization, part of speech tagging, etc. This noisy data can affect the accuracy of the classifier [28]. Using Natural Language Processing Toolkit (NLTK) is preferable in this step. * 3 x Feature extraction & selection 136 (3) Extract content as type J! Sentiment classfication CSY XE {JSON}</ Result & Evaluation C. Feature selection & extraction Features must describe the data in a format required by a particular machine learning algorithm to be used to solve the task. Feature extraction is the process that combines and reformats these original features using a combination of (BOW, TF-IDF, N-grams, POS, Word Embedding, etc.) until it yields a new set of features to be utilized by the Machine Learning models. Then, selects the most relevant, useful, informative features and ignore the rest. It prevents redundancy or gets a limited number of features to avoid overfitting and curse of dimensionality, i.e., too many features to describe insufficient samples. Proper feature extraction and selection play a crucial role in determining classifier accuracy [29]. Hence, the appropriate technique must choose for extracting the features. Scikit-learn library offers many built-in methods that can help a lot here. D. Sentiment classification In this step, various sentiment classification mechanisms are applied to determine the polarity of the review documents- supervised learning methods commonly used for SA to assign the sentiment label for a given text. Generally, the problem of SA is of two types; one is a binary with positive and negative labels. Another is multi-class, i.e., more than two labels (very positive, positive, neutral, negative, and very negative) [30]. Scikit-learn library provides various classes that assist in this process. E. Results evaluation This final step intends to evaluate the performance of ML techniques used to determine the overall accuracy of the sentiment analysis. The result of the classification consists of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). True positives and true negatives accurately predict actual labels, while false positives and false negatives are misclassifications [3]. Accuracy (1), precision (2), recall (3), and F-measure (4) are commonly used statistical metric parameters to measure the performance of each algorithm, formed from a confusion matrix in the Scikit-learn library. After that, the final output is analysed to decide whether it should be considered or not, and then it can be displayed in a pie chart, bar/line_graph using the Matplotlib python library. Accuracy = ((TP) + (TN)) / Total of observations (1) Authorized licensed use limited to: New York University. Downloaded on November 10,2023 at 12:55:57 UTC from IEEE Xplore. Restrictions apply.See Answer

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