Category: Artificial Intelligence

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

1.We ask you to solve the assignment in more detail.
2.Take into account all the points mentioned and do what is required.
3.Please take care to avoid plagiarism, rephrase sentences if necessary.
4.Take the necessary time, to achieve our goal of creating the perfect assignment solutionI
As you can see, we have two files. In the assignment file, I want the model solution in detail, which I will attach to the university, but in the presentation file, I want an explanation and clarification of what we did in the assignment solution to present in the class for the students.
Explain each task for project in an external file

Title: Assessing the Implications and Barriers of Computer Vision Technologies in E-Commerce Domain
Research Domain: Knowledge Systems, Computer Vision, E-Commerce Platforms, User Experience.
Research Questions:
1. How are computer vision technologies being employed in the e-commerce sector, and what hindrances are encountered by businesses in its implementation?
2. What is the impact of specific computer vision techniques such as image classification, object detection, semantic segmentation, panoptic segmentation, etc., on the efficacy, performance, and user experience of e-commerce platforms?
3. What are the evolving trends of computer vision in the e-commerce sector, and how can they address the existing barriers?
Research Objectives:
1. Technological Assessment:
• Conduct a thorough assessment of computer vision technologies used in the e-commerce sector, identifying the challenges that hinder its implementation by businesses.
2. Impact Analysis:
• Analyze the effects of distinct computer vision techniques on the efficacy, performance, and user experience of e-commerce platforms, focusing on image classification, object detection, semantic segmentation, and panoptic segmentation among others.
3. Trend Evaluation:
• Evaluate the emerging trends of computer vision in the e-commerce sector, proposing solutions to overcome the identified obstacles and enhance the application of computer vision technologies.
Literature Review
Methodology
References

This is a research specification for an MSC level project which represent ‘the blue print’ for an MSC Project.

I will provide you with a dataset that you would need to preprocess correctly and build a neural network whatever kind works with the task RNN or CNN, or LSTM whatever architecture you deem fit, NOT A MACHINE LEARNING ALGORITHM; to extract keywords from this task. You need expertise in deep learning and NLP. I want a full explanation of the solution for every step what it does and why is it necessary, as well as for the layers and optimizer choice. I will have the full right to ask you if I don’t understand even after submission

I need an expert that can add 6000 words to my dissertation according to the comments of the prof, with references and citations. no plagiarism and can deliver before the due date.
Will send details after discussion

I will provide you with a dataset that you would need to preprocess correctly and build a neural network whatever kind works with the task RNN or CNN, to extract keywords from this task. You need expertise in deep learning and NLP. I want a full explanation of the solution for every step what it does and why is it necessary, as well as for the layers and optimizer choice. I will have the full right to ask you if I don’t understand even after submission
Requirements: sufficient | .doc file | Python