Category: Machine Learning

I have all answers i need edit and change some valus
check plagiarism andAi
Note: in attched assignment questions and requirements
i will upload assignment answers later to edit on it

The presentation is Genomic Data in Disease Prediction and Prevention. The criteria for evaluation and submission requirements are detailed below.
In 60 or more slides, prepare your PowerPoint slides and presentation about your assigned topic, which is Genomic Data in Disease Prediction and Prevention. Please note that submitting less than 60 slides will result in a grade of zero for this optional assignment. As a general rule, try to keep presentations as concise as possible while covering all necessary information. For example, a 60-minute presentation might effectively utilize between 60 and 90 slides, depending on the factors mentioned below.
Required slide titles and contents.
Use elements such as questions, short videos, or interactive objects to engage the audience and make the presentation more interactive. Also, please note that each of the slide titles listed below can be presented across one or more slides.
1. Title Slide
– Include a clear, concise title that immediately gives an idea of the subject matter.
– Add your full name, your institution/college/department, and the date of the presentation.
2. Introduction to Genomic Data in Disease Prediction and Prevention
– Provide 1-3 succinct definitions to clarify what Genomic Data in Disease Prediction and Preventionis.
– Discuss the historical development of Genomic Data in Disease Prediction and Prevention, including information about its invention and key milestones.
3. Classification of Machine Learning Relevant to Genomic Data in Disease Prediction and Prevention
– Outline which categories of machine learning (supervised, unsupervised, reinforcement) Genomic Data in Disease Prediction and Preventionfalls into and why.
4. Architectural Overview of Genomic Data in Disease Prediction and Prevention
– Present a diagram showing the architecture of Genomic Data in Disease Prediction and Prevention, explaining the components and how they interact.
5. Operational Mechanics of Genomic Data in Disease Prediction and Prevention
– Describe the process of how Genomic Data in Disease Prediction and Preventionworks, using a step-by-step approach.
– Include diagrams or flowcharts to visually represent each step.
6. Parameters and Hyperparameters of Genomic Data in Disease Prediction and Prevention
– List the main parameters and hyperparameters that influence the operation of Genomic Data in Disease Prediction and Prevention.
– Explain the impact of each parameter on the performance of Genomic Data in Disease Prediction and Prevention.
7. Python Code Snippets for Configuring Genomic Data in Disease Prediction and Prevention
– Provide actual code examples showing how to set up and configure the parameters and hyperparameters of Genomic Data in Disease Prediction and Prevention.
8. Derivatives Associated with Genomic Data in Disease Prediction and Prevention
– Explain any mathematical derivatives used in the functioning or optimization of Genomic Data in Disease Prediction and Prevention.
9. Mathematical Framework of Genomic Data in Disease Prediction and Prevention
– Present key equations and formulas used in Genomic Data in Disease Prediction and Prevention, explaining each component’s role and significance.
10. Heuristic Approaches to Modeling Genomic Data in Disease Prediction and Prevention
– Discuss heuristic models used in Genomic Data in Disease Prediction and Prevention, explaining their benefits and limitations.
11. Data Handling Capabilities of Genomic Data in Disease Prediction and Prevention
– Detail the types of data Genomic Data in Disease Prediction and Preventioncan process (e.g., numerical, categorical, images).
– Mention the typical data sizes Genomic Data in Disease Prediction and Preventionis capable of handling efficiently.
12. Analyzing the Strengths and Weaknesses of Genomic Data in Disease Prediction and Prevention
– List and explain the advantages and limitations of using Genomic Data in Disease Prediction and Preventionin various scenarios.
13. Identifying and Addressing Overfitting and Underfitting in Genomic Data in Disease Prediction and Prevention
– Describe what signs indicate overfitting or underfitting in Genomic Data in Disease Prediction and Prevention.
– Suggest methods or techniques to address these issues.
14. Practical Applications of Genomic Data in Disease Prediction and Prevention
– Showcase real-world applications of Genomic Data in Disease Prediction and Prevention, illustrating its relevance and utility.
15. Challenges Encountered with Genomic Data in Disease Prediction and Prevention
– Discuss common challenges and obstacles faced when implementing or operating Genomic Data in Disease Prediction and Prevention.
16. Evaluation Metrics for Assessing Genomic Data in Disease Prediction and Prevention
– List the metrics used to evaluate the effectiveness of Genomic Data in Disease Prediction and Prevention.
– Provide a brief explanation of each metric.
17. Cost Function Analysis for Genomic Data in Disease Prediction and Prevention
– Describe the cost functions commonly used with Genomic Data in Disease Prediction and Prevention, highlighting how they influence outcomes.
18. Optimization Algorithms for Genomic Data in Disease Prediction and Prevention
– Detail the optimization algorithms that work best with Genomic Data in Disease Prediction and Prevention, discussing their benefits.
19. Fine-Tuning Strategies for Genomic Data in Disease Prediction and Prevention
– Offer strategies for fine-tuning Genomic Data in Disease Prediction and Preventionto enhance performance, including practical tips or adjustments.
20. Essential Python Libraries for Genomic Data in Disease Prediction and Prevention
– List key Python libraries used with Genomic Data in Disease Prediction and Prevention.
– Provide brief installation and usage instructions for each library.
21. Learning Genomic Data in Disease Prediction and Prevention: Recommended Tutorials
– Include links to friendly tutorials and resources that offer a good introduction to Genomic Data in Disease Prediction and Prevention.
22. Advanced Resources for Genomic Data in Disease Prediction and Prevention
– Recommend top articles, textbooks, and online materials for deeper insights into Genomic Data in Disease Prediction and Prevention.
23. Accessing Full Python Source Code for Genomic Data in Disease Prediction and PreventionImplementations
– Provide a link or QR code that attendees can use to access the full source code of Genomic Data in Disease Prediction and Preventionimplementations.
24. Open Research Questions in Genomic Data in Disease Prediction and Prevention
– Highlight current research gaps or unanswered questions in the field of Genomic Data in Disease Prediction and Prevention.
25. Summary and Conclusion
– Recap the key points covered in the presentation.
– Summarize the potential future developments and outlook for Genomic Data in Disease Prediction and Prevention.
26. Q&A Session
– Invite questions from the audience to clarify topics covered or explore related areas further.
27. References
– Cite all sources used in the preparation of the presentation in an appropriate format.
Required slide formatting.
A good PowerPoint slideshow complements your presentation by highlighting your key message, providing structure, and illustrating important details. While it is not difficult to create a good PowerPoint presentation, it is very easy to create a bad one. Bad PowerPoint presentations may have one or more of the following characteristics: too much specialized detail, too many slides, too many colors, unnecessary images or effects, small text, unreadable figures, and/or unclear slide order.
The strategies below can help you to create effective presentations and to save your audience from “death by PowerPoint.”
Creating Slides
The classic PowerPoint error is to write sentences on a slide and read them. Rather than treating your slides as a script for your presentation, let the content on your slides support your message. Remember: LESS IS MORE.
Keep It Simple and Clear
Text
ØWhere possible, include a heading for each slide.
ØAim for no more than 6-8 lines of text per slide.
ØLimit bullet points to 4-6 per block of text and avoid long sentences.
ØFont size: 30 – 48 point for titles, 24 – 28 for text
ØAvoid all capital letters.
ØMaintain the same font size and style for all slide headers.
ØUse a small font size (e.g., 12pt) at the bottom of the slide for references.
ØProofread carefully for spelling and grammar.
Figures and Images
ØEnsure images are clear and relevant.
ØLabel all figures and tables.
ØPut units beside numbers on graphs and charts.
General Design Principles
ØEmbrace empty space.
ØUse vertical and horizontal guide markers to consistently align elements.
ØAvoid too many colors, clutter or fancy visual effects.
ØUse high contrast to ensure visibility: e.g. Black text on white background or black on light blue.
ØMaintain consistency of the same elements on a slide (colors, fonts, styles, placement etc.), as well as, between slides in the slide deck
ØUse animation sparingly, if at all. If you use transitions, use the same kind each time.
ØEdit entire slide deck to ensure organization is logical and design is consistent.
ØUse a small font size (e.g., 12pt) at the bottom of the slide for references.
ØMaintain consistency in the citation style throughout the presentation.
ØDouble-check for typos and grammatical errors.

My idea in my research is to develop a browser extension that examines the entire content of emails to detect potential phishing emails and alert users in real-time about suspicious or malicious emails, then evaluate the efficacy of the extension using machine learning model metrics such as True Positive, True Negative, Accuracy, and Rate Score to assess its effectiveness in optimizing phishing detection features and ensuring user-friendliness.
So I would like to ask for your assistance in preparing the ‘backgrounds’ section for the research paper “IEEE” as the attachment file.
“backgrounds” can be used to refer to the fundamental and historical information that the reader needs to understand before delving into the reading or study of previous research (related work). This information may include basic concepts, an overview of the field, the history of research in the field, and any significant developments that have occurred.
I need from you to add this things in the background:
+details about machine learning techniques, including deep learning and other types.
Explain also the process like select the model then dataset and trainings and testing methodology that people use in machine learning +details about email and what the vulnerabilities of emails and how analysis the header such as header has important information and what the vulnerability.
Also use academic word,and but reverence about research papers that use it.

My problem statement is In the face of ever-evolving malware, cybersecurity demands robust solutions that can effectively detect and predict malicious activities. While traditional methods have limitations, deep learning offers promising advancements. However, there is a need for a comparative study on the efficacy of various deep learning architectures, including Multi-Layer Perceptron (MLP) classifiers, in conjunction with powerful frameworks like TensorFlow and Keras, for malware detection and prediction. The study aims to implement and evaluate the performance of these models against benchmarks, focusing on the ability to handle complex patterns and adapt to changing malware characteristics. Utilizing a combination of static and dynamic analysis, the project will explore feature extraction and representation learning to optimize model performance. The ultimate goal is to develop a versatile and scalable system that can dynamically update its detection capabilities to counteract emerging malware threats.
I need complete project while involves providing dataset files,csv files, methodology, dataset preparation,preprocessing, features extraction,ppts, discussions, and conclusions. Provide programming code for the problem statement
I will provide sample files and links please follow it and complete the project

Implement a basic neural network using TensorFlow/Keras to classify images from the MNIST dataset. The network should have one hidden layer with 128 neurons and use ReLU activation function. Use softmax activation function for the output layer. Train the network for 10 epochs with a batch size of 32 and evaluate its performance on the test set.

1. Your Python code: def BaumWelch(N,A,B,Pi,T) :

return [A,B,Pi] # Trained Model
Where:
N: Number of model states A: Transition probability matrix
B: Output probability matrix Pi: Initial state probability matrix
T: Training data Return re-estimated A, B and Pi matrices
Notes:
1. Your code must be original work – That is: it must not be a copy of code found online.
2. Submit your python code in a separate file named “viterbi_.py”
3. As Outlook filters out executable files, submit your work in a .zip file
2. A 2 page write-up describing the Baum-Welch algorithm and your implementation of it
Note: Be sure to list all references
3. Your hand calculations for the first observation vector
4. A sample run for each of the three observation vectors
Note: Your sample run for the 1st two observation vectors must match your hand calculations.
Please Note:
1. Your code must be original work – That is: it must not be a copy of code found online.
2. All references must be cited.
3. No scans of hand-written calculations for any part of the assignment will be accepted.
4.The sample runs may be screen-grabbed.

INSTRUCTIONS
Every learner should submit his/her own homework solutions. However, you are allowed to discuss the homework with each other– but everyone must submit his/her own solution; you may not copy someone else’s solution.
The homework helps you understand and apply K Nearest Neighbor.
Follow the prompts in the attached Jupyter notebook. CAP4611-HW3-NearestNeighbors.ipynbDownload CAP4611-HW3-NearestNeighbors.ipynb
Download the data from (Modules/ Data for Homework Assignment) and place it in your working directory, or modify the path to upload it to your notebook.
Before every code cell, add markdown cells to your analysis. Include your solutions, comments, and answers on how to solve the problem. Add as many cells as you need, for easy readability comments when possible.
Hopefully this homework will help you develop skills, make you understand how K nearest neighbor works.
Submission: Save your ipynb file named with your Name_HW3 (e.g.John_Doe_HW3.ipynb).
Good luck!

Here is my Research Topic:
Predicting Bank Failures in USA
Here is my Research Question:
Which U.S. state is most likely to experience the greatest number of bank failures in the future, and what are the main factors that contribute to this likelihood?
Here is my Dataset
Dataset Name: FDIC Failed Bank List
FDIC Failed Bank List Dataset
Download the FDIC Failed Bank List (CSV)
Here is my chosen ML Method:
Classification machine learning method
Revise and Evaluate Data Analysis Model
In this milestone, you will perform an evaluation of your data analytic model and revise your decision model as needed. For the revision, you can add in additional machine learning models or do feature engineering. You can also create confusion matrices and check for accuracy, precision, recall, or F-Measures. You can do sensitivity analyses, create ROC curves, check error rates and variable selection/feature selection. Please do see the image below for some other options for revisions:
Deliverable
For milestone 4, please ensure you have the following REQUIRED sections ONLY:
Final Research Question: Please state your final research question and describe how it evolved, if it changed, from your Milestone 1 version.
Your final research question should reflect the actual analysis you conducted and the actual insight or prediction you made. For example, if you actually ended up doing a classification task to predict the value of a response variable based on some other predictor variables, that’s what you should state as your precise, quantitative research question.
Model Revision: You should discuss how you revised your model(s) and perhaps how you narrowed down the scope of the project to something coherent and managable. In addition, you should give the details of your final machine learning model and present your final results.
Concomitantly, you should assess the robustness of your model, especially noting what makes it strong or what breaks it or the distribution of uncertainty in it. As such, you’ll likely want to utilize some variant of sensitivity analysis, depending on your particular model, to demonstrate true understanding of what the model’s doing rather than just a rote implementation thereof.
Final Results and Initial Version of Final Conclusion: You should show the initial validation of metrics and also decide upon any additional machine learning algorithms you might want to try. In addition, please ensure you answer the following questions:
What did you learn about the data?
How did you answer the questions?
How can you justify your answers?

Dataset
Airbnb
Links to an external site.
is a company that provides an online marketplace for short-term rentals of homes and apartments. Much of the data from Airbnb’s website has been compiled and made publicly available on the website Inside Airbnb
Links to an external site.
. For this assignment, you will analyze a sample of the Airbnb listings from Washington, DC, scraped in July 2023. Each row in this dataset represents a single Airbnb listing. You can download the data dictionary here: Data Dictionary.xlsx
Download Data Dictionary.xlsx
.
Goal
Your assignment is to build a predictive model of the price of the listings included in this dataset, AirbnbListings.csv
Download AirbnbListings.csv
, deliver a report, and upload all project files as described below. In your report, be sure to support your responses. Please make sure I can transfer the codes to R, this is a team effort so I would need to be able to share my codes and make sure they open please. Please do the code first and send, before report. I need the codes in 24 hours please.
Key Requirements and Questions
Preprocess the data and prepare it for the running neural network. (30 points)
Train two different neural network models using the ‘neuralnet’ package and the ‘caret’ package (based on the ‘nnet’ method or another neural network package that caret supports). (30 points)
Compare the results of these models by their model evaluation metrics (RMSE, R-squared, and MAE). Which one is a better model, and why? (Hint: caret package has a function that calculates these three regression measures.) (15 points)
You previously ran two different regression models on the price of the listing (on the same dataset) as part of your Programming I final project. Revisit your findings and comment on the difference between your new models compared to what you ran before. Are the results comparable? What are the shortcomings and the advantages of each approach? Which approach or model is more reliable for prediction? Explain. (15)
Important notes:
Neural networks are extremely sensitive to the scale of the variables. Make sure all the variables, even the predictor, are scaled. The most common scaling for neural nets is min-max normalization.
The evaluation metrics of the test data should be calculated based on the actual scale of the target variable. So if predicted values are in a range of 0 to 1, they should be scaled back to the original scale before calculating predictive measures.
Your report should include plots of actual prices versus predicted prices for both of the two trained models.
The instructor and the TAs may not answer debugging questions regarding other neural network packages that are not discussed in class.
Deliverables
A written report answering the questions and explaining your findings (submitted as a PDF document). This report should clearly define the problem statement, data processing steps, your approach and any assumptions you make, the results of analyses you have performed, and the insights you have gained by performing these analyses. In writing it, imagine that you are a consultant submitting the report to your client. This report should not be more than 3 pages long (excluding the cover page or table of contents). If needed, you can have up to 2 pages of an appendix with supporting exhibits. Include your names on top of the first page, or add a cover page with the names.
Your fully-functional and annotated R code(s) with proper name(s). In your R file(s), highlight the different steps/questions by including annotations or section titles. Also, make sure your submitted code can be executed (with no errors) by just reading the AirbnbListings.csv file.
Rubric:
Your project will be graded based on:
Timeliness:
Submitting the complete assignment on or before the deadline. (faculty may not accept late submissions or penalize them in other ways.)
Analysis and Recommendations:
Clearly stating the scope and objectives.
Answering all study questions clearly and completely.
Demonstrating appropriate use of concepts and techniques and properly following machine learning training and testing steps.
Depth of the analysis.
Clarity and quality of the findings and recommendations.
To summarize:
Correctness of your approach, your code, answering the questions, and following the steps. (90 points)
Format of the report and your R code. (10 points)

Here is my Research Topic:
Predicting Bank Failures in USA
Here is my Research Question:
Which U.S. state is most likely to experience the greatest number of bank failures in the future, and what are the main factors that contribute to this likelihood?
Here is my Dataset
Dataset Name: FDIC Failed Bank List
FDIC Failed Bank List Dataset
Download the FDIC Failed Bank List (CSV)
Here is my chosen ML Method:
Classification machine learning method
Revise and Evaluate Data Analysis Model
In this milestone, you will perform an evaluation of your data analytic model and revise your decision model as needed. For the revision, you can add in additional machine learning models or do feature engineering. You can also create confusion matrices and check for accuracy, precision, recall, or F-Measures. You can do sensitivity analyses, create ROC curves, check error rates and variable selection/feature selection. Please do see the image below for some other options for revisions:
Deliverable
For milestone 4, please ensure you have the following REQUIRED sections ONLY:
Final Research Question: Please state your final research question and describe how it evolved, if it changed, from your Milestone 1 version.
Your final research question should reflect the actual analysis you conducted and the actual insight or prediction you made. For example, if you actually ended up doing a classification task to predict the value of a response variable based on some other predictor variables, that’s what you should state as your precise, quantitative research question.
Model Revision: You should discuss how you revised your model(s) and perhaps how you narrowed down the scope of the project to something coherent and managable. In addition, you should give the details of your final machine learning model and present your final results.
Concomitantly, you should assess the robustness of your model, especially noting what makes it strong or what breaks it or the distribution of uncertainty in it. As such, you’ll likely want to utilize some variant of sensitivity analysis, depending on your particular model, to demonstrate true understanding of what the model’s doing rather than just a rote implementation thereof.
Final Results and Initial Version of Final Conclusion: You should show the initial validation of metrics and also decide upon any additional machine learning algorithms you might want to try. In addition, please ensure you answer the following questions:
What did you learn about the data?
How did you answer the questions?
How can you justify your answers?