Bright Academic Experts

Category: Computer Science

  • Computer Science Question

    Please help me complete this reflection paper: Week 8 Reflections Paper

    Instructions

    Reflections Paper: Due by Sunday of Week 8, 11:59 PM EST.

    .

    Submit a reflections paper on your ISS642 readings, exercises and weekly discussion posts. The paper should be at least 2 pages long. The paper should include:

    A brief summary of your course experience.

    Identify and explain relevant conceptual material (theories, concepts) from the course.

    How the course concept/idea/theory may or will change your future actions/activities.

    This assignment is a summative assessment.

    Assignment Criteria ( 100 Points)Synthesis of Concepts60Writing Standards – APA format20Timeliness20.. Here are some of the concepts we did for the course: Visual Table of Contents Widget

    Course Overview & Introduction

    Course Overview & Introduction

    88

    Topics Completed

    Module Complete

    Show Description

    Week 1: Introduction to Intrusion Detection & Incident Handling

    Week 1: Introduction to Intrusion Detection & Incident Handling

    55

    Topics Completed

    Module Complete

    Show Description

    Week 2: Deployment considerations, Reference Intrusion Model, Full content data

    Week 2: Deployment considerations, Reference Intrusion Model, Full content data

    1818

    Topics Completed

    Module Complete

    Show Description

    Week 3: Data analysis, Session data, Statistical data

    Week 3: Data analysis, Session data, Statistical data

    55

    Topics Completed

    Module Complete

    Show Description

    Week 4: Alert Data

    Week 4: Alert Data

    99

    Topics Completed

    Module Complete

    Show Description

    Week 5: Best practices, Case studies for Managers, Analyst training

    Week 5: Best practices, Case studies for Managers, Analyst training

    1717

    Topics Completed

    Module Complete

    Show Description

    Week 6: Discovering DNS, Session data, Examining Packets

    Week 6: Discovering DNS, Session data, Examining Packets

    66

    Topics Completed

    Module Complete

    Show Description

    Week 7: Tools & Tactics

    Week 7: Tools & Tactics

    55

    Topics Completed

    Module Complete

    Show Description

    Please write in paragraphs and and be simple yet proffessional

  • Computer Science Question

    Summary:

    Create an Entity-Relationship (ER) diagram for the information system of your business. The ER diagram should illustrate key entities, relationships, and attributes relevant to the system’s data requirements. Additionally, provide a brief explanation of each component in the diagram, explaining the role and importance of the entities, the nature of the relationships between them, and the significance of their attributes. Make sure to research other use cases

    Below are the steps to complete this assignment:
    1. Identify Key Entities: Start by determining the major objects (entities) your business will need to track. These are typically tangible objects like Customers, Products, or Orders, but can also include abstract entities like Invoices or Transactions.
    2. Establish Relationships: Next, identify the relationships between these entities. For example, a Customer might place multiple Orders, and each Order might contain multiple Products. Consider how these entities will interact in your system.
    3. Determine Attributes: For each entity, specify key attributes (or fields). For example, a Customer might have attributes like Customer ID, Name, and Email, while a Product might have attributes like Product ID, Product Name, and Price.
    4. Create the ER Diagram: Use a diagramming tool like Lucidchart, Draw.io, or any other ER diagram software to visually map out your entities, their attributes, and relationships. Be sure to indicate cardinality (e.g., one-to-many, many-to-many) between the entities.
    5. Provide Explanations: Along with your diagram, write a brief explanation of each entity, attribute, and relationship. Explain how each entity functions in your business model and why the relationships are structured the way they are.
    Notes:

    Use the attached document as an example or reference (doesn’t have to be the same exact thing and please use different data not the same example i provided also this is an extended example you can have less tables)

    assignment_helper.docx

  • R Programming L7

    R Programming PCA and TSNE

    For this assignment you will write an R program to complete the tasks given below. You will hand in two files for this assignment.

    • A File with your R program. This file should contain only the code (no output) and must have the typical r extension. No other file extensions will be accepted. The reason is that the assignment be graded based on your R code and not the output file. The output file will be used to verify the code commands. Also, please make sure that all comments, discussion, and conclusions regarding results are also annotated as part of your code.
    • A PDF/DOC file with your output code. We are giving you more flexibility regarding how you want to present your output (tables, plots, etc.). You can either use RMD files that combine code, narrative txt, and plots or you can use word document with copy and paste from the R platform you are using. However, please remember that all output (tables, plots, comments, conclusions, etc.) shown in this file has to be generated by the same R code that you submit. This is important! Output shown that is generated using a separate code or output shown that is not supported by the submitted code will not be graded. Screenshots will not be accepted.

    Use the following file

  • R Data Set: HMEQ_Scrubbed.csv (in the zip file attached).
  • The Data Dictionary in the zip file.

    • Note: The HMEQ_Scrubbed.csv file is a simple scrubbed file from the previous week homework. If you did more advanced scrubbing of data for last week, you may use your own data file instead. You might get better accuracy! If you decide to use your own version of HMEQ_Scrubbed.csv, please hand it in along with the other deliverables.

    This assignment is an extension of the Week 6 assignment. The difference is that this assignment will now incorporate PCA and tSNE analysis.

    Step 1: Use the Decision Tree / Random Forest / Decision Tree / Regression code from Week 6 as a Starting Point

    In this assignment, we will not be doing all the analysis as before. But much of the code from week 6 can be used as a starting point for this assignment. For this assignment, do not be concerned with splitting data into training and test sets. In the real world, you would do that. But for this exercise, it would only be an unnecessary complication.

    Step 2: PCA Analysis

    • Use only the input variables. Do not use either of the target variables.
    • Use only the continuous variables. Do not use any of the flag variables.
    • Do a Principal Component Analysis (PCA) on the continuous variables.
    • Display the Scree Plot of the PCA analysis.
    • Using the Scree Plot, determine how many Principal Components you wish to use. Note, you must use at least two. You may decide to use more. Justify your decision. Note that there is no wrong answer. You will be graded on your reasoning, not your decision.
    • Print the weights of the Principal Components. Use the weights to tell a story on what the Principal Components represent.
    • Perform a scatter plot using the first two Principal Components. Color the scatter plot dots using the Target Flag. One color will represent “defaults” and the other color will represent “non defaults”. Comment on whether you consider the first two Principal Components to be predictive. If you believe the graph is too cluttered, you are free to do a random sample of the data to make it more readable. That is up to you.

    Step 3: tSNE Analysis

    • Use only the input variables. Do not use either of the target variables.
    • Use only the continuous variables. Do not use any of the flag variables.
    • Do a tSNE analysis on the data. Set the dimensions to 2.
    • Run two tSNE analysis for Perplexity=30. Color the scatter plot dots using the Target Flag. One color will represent “defaults” and the other color will represent “non defaults”. Comment on whether you consider the tSNE values to be predictive.
    • Repeat the previous step with a Perplexity greater than 30 (try to get a value much higher than 30).
    • Repeat the previous step with a Perplexity less than 30 (try to get a value much lower than 30).
    • Decide on which value of Perplexity best predicts the Target Flag.
    • Train two Random Forest Models to predict each of the tSNE values.

    Step 4: Tree and Regression Analysis on the Original Data

    • Create a Decision Tree to predict Loan Default (Target Flag=1). Comment on the variables that were included in the model.
    • Create a Logistic Regression model to predict Loan Default (Target Flag=1). Use either Forward, Backward, or Stepwise variable selection. Comment on the variables that were included in the model.
    • Create a ROC curve showing the accuracy of the model.
    • Calculate and display the Area Under the ROC Curve (AUC).

    Step 5: Tree and Regression Analysis on the PCA/tSNE Data

    • Append the Principal Component values from Step 2 to your data set.
    • Using the Random Forest models from Step 3, append the two tSNE values to the data set.
    • Remove all of the continuous variables from the data set (set them to NULL). Keep the flag variables in the data set.
    • Create a Decision Tree to predict Loan Default (Target Flag=1). Comment on the variables that were included in the model. Did any of the Principal Components or tSNE values make it into the model? Discuss why or why not.
    • Create a Logistic Regression model to predict Loan Default (Target Flag=1). Use either Forward, Backward, or Stepwise variable selection. Comment on the variables that were included in the model. Did any of the Principal Components or tSNE values make it into the model? Discuss why or why not.
    • Create a ROC curve showing the accuracy of the model.
    • Calculate and display the Area Under the ROC Curve (AUC).

    Step 6: Comment

    • Discuss how the PCA / tSNE values performed when compared to the original data set.

    Essential Activities:

    1. Watch all the training videos
    2. Execute the example code while watching the training videos.

    Notes:

    1. This assignment is due Sunday at 11:59 PM EST

    HMEQ_Scrubbed.zip

  • Homework 14

    Describe analytics models that could be used to help the company monetize their data: How could the

    company use these data sets to generate value, and what analytics models might they need to do it?

  • Project it485

    You must submit two separate copies (one Word file and one PDF file) using the Assignment Template on Blackboard via the allocated folder. These files must not be in compressed format.

    It is your responsibility to check and make sure that you have uploaded both the correct files.

    Zero mark will be given if you try to bypass the SafeAssign (e.g. misspell words, remove spaces between words, hide characters, use different character sets, convert text into image or languages other than English or any kind of manipulation).

    Email submission will not be accepted.

    You are advised to make your work clear and well-presented. This includes filling your information on the cover page.

    You must use this template, failing which will result in zero mark.

    You MUST show all your work, and text must not be converted into an image, unless specified otherwise by the question.

    Late submission will result in ZERO mark.

    The work should be your own, copying from students or other resources will result in ZERO mark.

    Use Times New Roman font for all your answers.

  • Microsoft Access the directions are long

    use the file in the zip do not open a new microsolf access file because it will get rejected.

  • BUSE604 – Management Information System (MIS)

    Hi, I have a Masters-level MIS assignment and I require high-quality, professional work. Please read carefully before starting.

    The assignment requires:

    1. An INTERACTIVE digital mind map (shareable link ONLY NOT a screenshot or image).
    2. A 2-minute narrated video walkthrough.
    3. A 150-word AI reflection.

    Strict requirements:

    • Choose the BEST sector for high academic marks (preferably Government/Public Sector or HR with strong analytical depth).
    • The work must demonstrate CRITICAL THINKING, not just description.
    • Use REAL, verifiable case studies with working links (e.g., Unilever, Saudi e-government platforms like Absher, Tawakkalna, etc.).
    • Clearly link AI applications to BUSINESS VALUE, ROI, and decision-making impact.
    • Include a strong analytical perspective (WHY it matters, not just WHAT it is).

    Mind Map Requirements:

    • Minimum 5 main branches (Applications, Value/ROI, Data & Insights, Risks, Future).
    • Each branch must have at least 3 high-quality sub-nodes (keywords only, no long text).
    • Strong logical structure (from general specific).
    • Use colors, icons, and hierarchy to enhance clarity.
    • Must be visually clean, professional, and easy to navigate.

    Video Requirements:

    • EXACTLY around 2 minutes (no unnecessary length).
    • Clearly present:
      3 key insights
      1 surprising insight
      1 major barrier
    • Delivery must be executive-level: clear, confident, and structured.

    Reflection Requirements:

    • Around 150 words (NOT generic).
    • Must show CRITICAL EVALUATION of AI tools (benefits + limitations).
    • Clearly explain how AI influenced thinking and decision-making.

    Additional Conditions:

    • NO plagiarism, NO generic AI-generated text without refinement.
    • All content must be accurate, realistic, and verifiable.
    • The final work must be submission-ready (no major edits needed).
    • If any requirement is unclear, ASK before proceeding.

    This is a graded Masters assignment, so quality, clarity, and analytical depth are essential.

    The assignment file is attached

  • it 361 project

    • You must submit two separate copies (one Word file and one PDF file) using the Assignment Template on Blackboard via the allocated folder. These files must not be in compressed format.
    • It is your responsibility to check and make sure that you have uploaded both the correct files.
    • Zero mark will be given if you try to bypass the SafeAssign (e.g. misspell words, remove spaces between words, hide characters, use different character sets or languages other than English or any kind of manipulation).
    • Email submission will not be accepted.
    • You are advised to make your work clear and well-presented. This includes filling your information on the cover page.
    • You must use this template, failing which will result in zero mark.
    • You MUST show all your work, and text must not be converted into an image, unless specified otherwise by the question.
    • Late submission will result in ZERO mark.
    • The work should be your own, copying from students or other resources will result in ZERO mark.
    • Use Times New Roman font for all your answers.

  • Computer Science Question

    You must go to at least 1 concert (and must stay for the WHOLE concert) and write a minimum of 1 complete page
    but up to 3 pages (typed, single-spaced, 12-point font, minimum of 450 words) report about your experience and
    observations, including some elements of music discussed in class (ex. instruments/voices, elements of music,
    historical era of music, style, etc.).

    Yes, you can do research but primarily I am not interested in what Google has to say about the music heard at the concert, I am interested in your observations and opinions. If you choose to use any outside resources for research or choose to watch a virtual concert, please make sure to cite your sources (I dont care what bibliographic style
    you use but please at least make sure to include the links to the sources used). Your paper will be submitted with
    a plagiarism detector, plus I can always just tell if you are copying and pasting. The use of AI should be kept to a
    bare MINIMUM (less than approximately 15% of your paper). If you do use any AI, please cite at the end of your
    paper in this format: I used [insert AI system(s) and link] to [specific use of generative artificial intelligence and
    describe content used in task]. The output from this tool was modified by [explain use]. Your paper will be
    scanned by an AI detector, although it is also usually just as easily noticeable like copying and pasting. You may
    use AI for proofreading or editing services (Grammarly, for instance), and if you do please include a notice to the
    instructor if you used AI proofreading services, something like (note: Grammarly was used to edit this submission).

    You may go to a concert in person, or you may choose a virtual option (see options below). Make sure to cite
    your video in your paper if you choose an online concert.

    Online Concerts
    Live Streamed:
    Hilton Head Symphony Orchestra Soundwaves live streams every Monday night at 7:30pm
    .
    The Metropolitan Opera
    The Vienna
    Opera
    d9pa7bE
    The Berlin Philharmonic.
    Opera Vision
    YouTube Videos:
    Tchaikovsky-Swan Lake     ,

    or Sleeping
    Beauty
    Bizet-Carmen
    Gilbert and Sullivan-The Pirates of Penzance
    Verdi-La Traviata
    All of these options should have subtitles (for those with singing), make sure you turn them on (CC) if they don’t
    automatically.

  • Two University Projects: WSN Design & Cyber Forensic…

    Project 1 Wireless Sensor Networks (WSN)

    Overview

    This project focuses on designing a Wireless Sensor Network (WSN) solution for a real-world problem selected from a given list (e.g., smart cities, healthcare, traffic, agriculture, etc.).

    Required Work

    1. Introduction (Problem & Proposal)

    • Select one problem
    • Explain its importance
    • Propose a WSN-based solution

    2. Literature Review & Research Questions

    • Review existing solutions and technologies
    • Include at least 5 academic references
    • Define research questions based on analysis

    3. Methodology (System Design)

    • Sensors used
    • Communication protocols
    • Network architecture
    • Role of each component
    • Step-by-step implementation plan
    • Task distribution
    • Optional simulation (if applicable)

    4. Results / Findings

    • Expected outcomes
    • Ideal solution explanation
    • Limitations

    5. Conclusion

    • Full summary of project (problem, design, feasibility, challenges)

    6. PowerPoint Presentation

    • Prepare slides for a 5-minute presentation covering:
      • Problem
      • Solution
      • Design
      • Implementation
      • Feasibility

    Project 2 Cyber Forensics (Mobile/Computer Forensics)

    Overview

    This project is a digital forensic investigation based on a case where an employee is suspected of leaking confidential data using a mobile or computer device.

    Required Work

    1. Planning & Tools

    • Define objectives and investigation plan
    • Explain forensic tools (e.g., Autopsy, ExifTool, Maltego, etc.)
    • Include legal and ethical considerations

    2. Investigation

    a. Document / PDF Analysis (3 files)

    • Extract:
      • File details
      • Timestamps
      • Author
      • Metadata
    • Include screenshots

    b. Image Analysis (3 files)

    • Extract:
      • Image details
      • Capture time
      • Device info
      • GPS (if available)
      • Editing traces
    • Include screenshots

    c. Browser Artifacts (2 browsers)

    • Analyze:
      • History
      • Downloads
      • Cookies
      • Cache
      • Credentials (metadata only)
      • Suspicious activity
      • Upload traces
    • Include screenshots

    3. Final Report

    • Objectives and plan
    • Case description
    • Tools used + justification
    • Evidence and analysis
    • Findings and conclusion

    Important Requirements (For Both Projects)

    • Must follow the official assignment template (Project 1)
    • Submit Word + PDF files (no ZIP)
    • All work must be clearly shown (no shortcuts)
    • Strict plagiarism/SafeAssign rules apply
    • Screenshots are mandatory (Project 2)
    • Must use real tools for analysis (Project 2)
    • Writing must be detailed, structured, and academic
    • Font: Times New Roman
    • Late or incorrect submission format will result in zero

    This is not simple work both projects require:

    • Technical understanding
    • Proper research
    • Clear explanation
    • Organized reports