Category: Artificial Intelligence

Assignment Requirements
APA Format
Please note that the word limit for the assignment is 600 words.
Assignment topic: Ethical Implications of AI Technology
Provide a description and justification of the research design used. The research design can be described at two levels:
a. Descriptive/Predictive/Prescriptive
b. Experimental/Quasi-experimental/Non-experimental
Elaborate on the choice/design of the measurement scale. (Explain all the points with appropriate justifications.)
Create or replicate a questionnaire using an online survey tool (Qualtrics/Google Forms/Survey Monkey). Download the questionnaire in word format and attach it with the submission.
Generate the data for the pilot study. Download the Qualtrics data as an Excel file. Share the PDF version.
Components (Marks)Meets ExpectationsDoes Not Meet ExpectationsCreating or Replicating a Questionnaire for a Pilot Study (15)
Contains all elements:
Description of the research design (1)
Components of the questionnaire and their alignment with the constructs of the study are described with proper justification (2)
Measurement scale is explained with proper justification (2)
Questionnaire is created or replicated in accordance with the pilot study (5)
Adheres to academic writing style (Grammar rules, clarity of sentences and American English) (3)
Questionnaire is submitted in a properly-formatted MS Word file (2)
Generating Data from the Questionnaire (5)
The data is generated for at least 50 – 100 respondents (2)
Qualtrics data is generated and downloaded in Microsoft Excel format. (1)
The context of the dataset is clearly mentioned. Note on the different constructs and variables of the dataset is added. The column headers in the generated data, or any codes used to describe data are clearly explained (2)

Assignment Requirements
APA Format
Please note that the word limit for the assignment is 600 words.
Assignment topic: Ethical Implications of AI Technology
Provide a description and justification of the research design used. The research design can be described at two levels:
a. Descriptive/Predictive/Prescriptive
b. Experimental/Quasi-experimental/Non-experimental
Elaborate on the choice/design of the measurement scale. (Explain all the points with appropriate justifications.)
Create or replicate a questionnaire using an online survey tool (Qualtrics/Google Forms/Survey Monkey). Download the questionnaire in word format and attach it with the submission.
Generate the data for the pilot study. Download the Qualtrics data as an Excel file. Share the PDF version.
Components (Marks)Meets ExpectationsDoes Not Meet ExpectationsCreating or Replicating a Questionnaire for a Pilot Study (15)
Contains all elements:
Description of the research design (1)
Components of the questionnaire and their alignment with the constructs of the study are described with proper justification (2)
Measurement scale is explained with proper justification (2)
Questionnaire is created or replicated in accordance with the pilot study (5)
Adheres to academic writing style (Grammar rules, clarity of sentences and American English) (3)
Questionnaire is submitted in a properly-formatted MS Word file (2)
Generating Data from the Questionnaire (5)
The data is generated for at least 50 – 100 respondents (2)
Qualtrics data is generated and downloaded in Microsoft Excel format. (1)
The context of the dataset is clearly mentioned. Note on the different constructs and variables of the dataset is added. The column headers in the generated data, or any codes used to describe data are clearly explained (2)

The work should be your own, copying from AI or other resources will result in ZERO mark. 😎
Why is it important to match the data to the
needs of a specific predictive algorithm or software tool?
Suppose a teacher asks you to design a dashboard to
monitor the students’ grades for the following courses IT-351: Computer
Networks,IT-353: System Analysis and Design , IT-475: Decision Support Systems,
And IT-479: Senior Project I . Design a dashboard by identifying the basic
elements (at least 7 KPIs ) that should be displayed on the dashboard using any
drawing programming to give an idea of how the dashboard should look like. You
should use some graphs and analysis on the dashboard..
What are the components of Decision Support Mathematical
Models? Choose any area and give examples.
AI technologies affect our lives in different
domains such as education, healthcare, business, etc. Choose any one domain and
discuss one AI application that has been used in that domain. You should be
specific about how the technology has been implemented and which AI method has
been used?

The work should be your own, copying from students or other resources will result in ZERO mark. 😎
Why is it important to match the data to the
needs of a specific predictive algorithm or software tool?
Suppose a teacher asks you to design a dashboard to
monitor the students’ grades for the following courses IT-351: Computer
Networks,IT-353: System Analysis and Design , IT-475: Decision Support Systems,
And IT-479: Senior Project I . Design a dashboard by identifying the basic
elements (at least 7 KPIs ) that should be displayed on the dashboard using any
drawing programming to give an idea of how the dashboard should look like. You
should use some graphs and analysis on the dashboard..
What are the components of Decision Support Mathematical
Models? Choose any area and give examples.
AI technologies affect our lives in different
domains such as education, healthcare, business, etc. Choose any one domain and
discuss one AI application that has been used in that domain. You should be
specific about how the technology has been implemented and which AI method has
been used?

10 slides, with examples and screenshots of service.
You need to choose 5 top services and describe their advantages

Your research paper should be structured as follows:
Introduction:Introduction to the problem domain.
Significance of the problem.
Brief description of the dataset.
Literature Review:A review of existing methods that have been used in the problem domain.
Any existing challenges or gaps in the current methodologies.
Methods:Detailed description of the AI/data science techniques you applied.
Reasons for selecting these methods.
Any preprocessing steps undertaken.
Description of the dataset (features, number of samples, any specific characteristics).
Results:Present the results obtained using your methods.
Use appropriate visualizations like graphs, charts, etc.
Provide screenshots of relevant sections of code and output.
Discussion:Interpretation of the results.
How do your results compare with existing methods (if any comparisons were made)?
Any challenges faced and how they were overcome.
Limitations of your study.
Conclusion:Summary of your findings.
Potential implications of your work.
Suggestions for future work in this domain.
References:Ensure all sources, including datasets and code libraries, are appropriately cited.
Use peer-reviewed sources wherever possible.
Submission Format:
The paper should be a minimum of 5 pages and a maximum of 15 pages, including references.
Use 12-point Times New Roman font, double-spaced.
Ensure all figures, tables, and code snippets are appropriately labeled and referenced in the text.

Objective:
The objective of this assignment is to enable students to apply data science and AI techniques on a real-world dataset. This will help in understanding the intricacies of selecting appropriate datasets, formulating research problems, selecting suitable methodologies, and interpreting results in a scientific manner. The assignment is divided into two parts: a proposal submission and a full research paper.
Part A: Proposal Submission
Dataset Selection: Select a dataset that is not commonly used in typical data science tutorials or coursework. Examples of overused datasets include Iris, MTcars, etc. Your dataset selection must be approved by the instructor.
Research Problem Definition: Clearly define the problem you wish to address with this dataset. This could be a classification problem, a clustering task, time series analysis, regression, etc.
Preliminary Methodology: Provide a brief description of the techniques or algorithms you plan to use to address your research problem.
Submission:Dataset Description: A brief description of your dataset including its source, the type of data it contains, and why it’s significant.
Research Problem: A clear definition of the problem you aim to solve.
Planned Methodology: A brief description of your planned approach.
Evaluation Criteria: – Relevance and uniqueness of dataset. – Clarity in the definition of the research problem. – Suitability of the planned methodology.
Here are some examplesto give you some ideas:
Predicting Housing PricesDataset: Historical housing sales data from a specific region/country.
Objective: Utilize regression techniques to predict future housing prices based on features like location, square footage, number of bedrooms, etc.
Methods: Linear regression, decision trees, random forests, etc.
Analyzing Customer Sentiment from ReviewsDataset: Customer reviews for products from an e-commerce website.
Objective: Classify the sentiment of the review (e.g., positive, negative, neutral) and determine key factors that contribute to customer satisfaction.
Methods: Natural language processing (NLP), sentiment analysis, Naive Bayes, SVM, etc.
Recommendation System for Movies or BooksDataset: User ratings for movies or books from platforms like IMDb, Goodreads, etc.
Objective: Develop a recommendation system that suggests movies/books to users based on their historical preferences.
Methods: Collaborative filtering, matrix factorization, deep learning techniques.
Forecasting Stock Market PricesDataset: Historical stock market data for selected companies.
Objective: Utilize time series analysis to predict future stock prices or identify patterns that could suggest buy/sell decisions.
Methods: ARIMA, Prophet, LSTM neural networks, etc.
Clustering News ArticlesDataset: A collection of news articles from various sources over a specific time period.
Objective: Group articles into clusters based on their content to identify common themes or topics being discussed.
Methods: K-means clustering, hierarchical clustering, topic modeling (e.g., Latent Dirichlet Allocation), etc.

Submit project proposal here – this should be around a half-page. Discuss what you are going to do, what data you plan on using, techniques, methods, etc.
Objective:
The objective of this assignment is to enable students to apply data science and AI techniques on a real-world dataset. This will help in understanding the intricacies of selecting appropriate datasets, formulating research problems, selecting suitable methodologies, and interpreting results in a scientific manner. The assignment is divided into two parts: a proposal submission and a full research paper.
Part A: Proposal Submission
Dataset Selection: Select a dataset that is not commonly used in typical data science tutorials or coursework. Examples of overused datasets include Iris, MTcars, etc. Your dataset selection must be approved by the instructor.
Research Problem Definition: Clearly define the problem you wish to address with this dataset. This could be a classification problem, a clustering task, time series analysis, regression, etc.
Preliminary Methodology: Provide a brief description of the techniques or algorithms you plan to use to address your research problem.
Submission:Dataset Description: A brief description of your dataset including its source, the type of data it contains, and why it’s significant.
Research Problem: A clear definition of the problem you aim to solve.
Planned Methodology: A brief description of your planned approach.
Evaluation Criteria: – Relevance and uniqueness of dataset. – Clarity in the definition of the research problem. – Suitability of the planned methodology.
Here are some examples to give you some ideas:
Predicting Housing PricesDataset: Historical housing sales data from a specific region/country.
Objective: Utilize regression techniques to predict future housing prices based on features like location, square footage, number of bedrooms, etc.
Methods: Linear regression, decision trees, random forests, etc.
Analyzing Customer Sentiment from ReviewsDataset: Customer reviews for products from an e-commerce website.
Objective: Classify the sentiment of the review (e.g., positive, negative, neutral) and determine key factors that contribute to customer satisfaction.
Methods: Natural language processing (NLP), sentiment analysis, Naive Bayes, SVM, etc.
Recommendation System for Movies or BooksDataset: User ratings for movies or books from platforms like IMDb, Goodreads, etc.
Objective: Develop a recommendation system that suggests movies/books to users based on their historical preferences.
Methods: Collaborative filtering, matrix factorization, deep learning techniques.
Forecasting Stock Market PricesDataset: Historical stock market data for selected companies.
Objective: Utilize time series analysis to predict future stock prices or identify patterns that could suggest buy/sell decisions.
Methods: ARIMA, Prophet, LSTM neural networks, etc.
Clustering News ArticlesDataset: A collection of news articles from various sources over a specific time period.
Objective: Group articles into clusters based on their content to identify common themes or topics being discussed.
Methods: K-means clustering, hierarchical clustering, topic modeling (e.g., Latent Dirichlet Allocation), etc.

Learning Goal: I’m working on a artificial intelligence writing question and need the explanation and answer to help me learn.
Please answer the question without use chatgpt or any Ai tools. (human-written)
please do not plagiarize. (avoid plagiarism)
the question about comparing searching algorithms that Mentioned on two papers
please compare between them.
Please consider comparing the following:
Algorithm 1: A* Search Algorithm
Algorithm 2: heuristic search
Algorithm 3: any of your choice
The answer must be not less than 1700 words.
please add picture to illustrator the answers

Learning Goal: I’m working on a artificial intelligence writing question and need the explanation and answer to help me learn.
Please answer the question without use chatgpt or any Ai tools. (human-written)
please do not plagiarize. (avoid plagiarism)
the question about comparing searching algorithms that Mentioned on two papers
please compare between them.
Please consider comparing the following:
Algorithm 1: A* Search Algorithm
Algorithm 2: heuristic search
Algorithm 3: any of your choice
The answer must be not less than 1700 words.
please add picture to illustrator the answers