Bright Academic Experts

Category: Computer Science

  • Homework 11

    Below, we have provided a suggested format to help you organize your work. Youre welcome to use it or adapt it to your own style just be sure your final submission meets all the requirements in the rubric.

    For each assignment, we suggest submitting two files:

    1. Your code file – contains all your work.
    2. Your answers file walks graders through what you did and what you found. For each coding question, include:
      • Methodology functions used, models implemented, and how the model was evaluated.
      • Results equations, graphs, tables, and performance metrics
      • Discussion of Results discuss what your results mean for this dataset. Mention anything surprising, possible reasons for weak performance, ways to improve the model, other analyses worth exploring, and any ethical or legal considerations

  • Communications Technology

    Question: (50 marks) One of the emerging topics in information coding theory is the low-density-parity-check (LDPC) code. This code, created in the early 1960s, has made a remarkable resurgence in the past few years. The LDPC codes are comparable to other linear block codes but differ in the sparse parity check matrix and decoding techniques. One related advantage of these codes is that they function well even in noisy environments. In this regard, write a report of 10001200 words providing, explaining and discussing the following issues:

    a) Provide an introduction about the LDPC concept.

    b) Discuss how these codes differentiate from other codes.

    c) Explain how the LDPC codes can be represented.

    d)Discuss the five related design approaches.

    You should adhere to the following guidelines in your answers:

    o The report MUST be written in YOUR OWN WORDS. You should grasp the idea and show your understanding. Any act of plagiarism WILL NOT BE tolerated.

    o The word count of your report should be between (1000 1200) words. You should adhere to this limit.

    o Use cross-referencing and citation. All references must be cited in a proper format.

    o The report must be written in a proper technical writing format and style.

  • R Programming Model Validation L4

    R Programming Model Validation

    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.
    • 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 3 assignment. The difference is that this assignment will now incorporate model validation by using training and testing data sets.

    Step 1: Read in the Data

    • Read the data into R
    • List the structure of the data (str)
    • Execute a summary of the data
    • Print the first six records

    Step 2: Classification Decision Tree

    • Using the code discussed in the lecture, split the data into training and testing data sets.
    • Use the rpart library to predict the variable TARGET_BAD_FLAG
    • Develop two decision trees, one using Gini and the other using Entropy using the training and testing data
    • All other parameters such as tree depth are up to you.
    • Do not use TARGET_LOSS_AMT to predict TARGET_BAD_FLAG.
    • Plot both decision trees
    • List the important variables for both trees
    • Using the training data set, create a ROC curve for both trees
    • Using the testing data set, create a ROC curve for both trees
    • Write a brief summary of the decision trees discussing whether or not the trees are are optimal, overfit, or underfit.
    • Rerun with different training and testing data at least three times.
    • Determine which of the two models performed better and why you believe this

    Step 3: Regression Decision Tree

    • Using the code discussed in the lecture, split the data into training and testing data sets.
    • Use the rpart library to predict the variable TARGET_LOSS_AMT
    • Do not use TARGET_BAD_FLAG to predict TARGET_LOSS_AMT.
    • Develop two decision trees, one using anova and the other using poisson
    • All other parameters such as tree depth are up to you.
    • Plot both decision trees
    • List the important variables for both trees
    • Using the training data set, calculate the Root Mean Square Error (RMSE) for both trees
    • Using the testing data set, calculate the Root Mean Square Error (RMSE) for both trees
    • Write a brief summary of the decision trees discussing whether or not the trees are are optimal, overfit, or underfit.
    • Rerun with different training and testing data at least three times.
    • Determine which of the two models performed better and why you believe this

    Step 4: Probability / Severity Model Decision Tree (Push Yourself!)

    • Using the code discussed in the lecture, split the data into training and testing data sets.
    • Use the rpart library to predict the variable TARGET_BAD_FLAG
    • Use the rpart library to predict the variable TARGET_LOSS_AMT using only records where TARGET_BAD_FLAG is 1.
    • Plot both decision trees
    • List the important variables for both trees
    • Using your models, predict the probability of default and the loss given default.
    • Multiply the two values together for each record.
    • Calculate the RMSE value for the Probability / Severity model.
    • Rerun at least three times to be assured that the model is optimal and not over fit or under fit.
    • Comment on how this model compares to using the model from Step 3. Which one would your recommend using?

    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

    February 4 2026, 3:13 PM

  • Computer Science Question

    Week 4: Bonus R Programming GGPLOT on HMEQ data – 25 points (LO1)(LO2)

    Completion requirementsOpened: Monday, March 30, 2026, 11:59 PM

    Due: Sunday, April 5, 2026, 11:59 PM

    The library, ggplot, is one of the most powerful and widely used data visualization tools in the R programming language. In this assignment, we will get up to speed using this library.

    For this assignment, we will expand upon the ggplot assignment by using real world data.

    HMEQ Scrubbed Data set provided below.

    You have complete freedom to decide upon the graphs you generate. The only condition is that you must use ggplot to create the graph.

    As you create each graph, either download it to your computer or take a screen shot of it.

    Hand in the following:

    • 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.
    • Under each graph, describe in a few sentences what the graph is presenting. The sentences must be useful and understandable to a manager who does not understand technical concepts. Interesting and advanced graphs with good descriptions will get more points. So push yourself!

    Essential Activities:

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

    Notes:

    1. You may only use the HMEQ data set. If you use any other data, you will not receive any points.
    2. This assignment is due Sunday at 11:59 PM EST

    HMEQ_Scrubbed.zip

    February 4 2026, 3:13 PM

  • Computer Science Question

    R Programming GGPLOT

    The library, ggplot, is one of the most powerful and widely used data visualization tools in the R programming language. In this assignment, we will get up to speed using this library.

    For this assignment, watch the simply watch the following video and follow along executing the same code presented in the video. Generate every graph created in the video.

    As you create each graph, either download it to your computer or take a screen shot of it.

    Hand in the following:

    • 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.

    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

  • R Programming Model Validation L4

    R Programming Model Validation

    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.
    • 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 3 assignment. The difference is that this assignment will now incorporate model validation by using training and testing data sets.

    Step 1: Read in the Data

    • Read the data into R
    • List the structure of the data (str)
    • Execute a summary of the data
    • Print the first six records

    Step 2: Classification Decision Tree

    • Using the code discussed in the lecture, split the data into training and testing data sets.
    • Use the rpart library to predict the variable TARGET_BAD_FLAG
    • Develop two decision trees, one using Gini and the other using Entropy using the training and testing data
    • All other parameters such as tree depth are up to you.
    • Do not use TARGET_LOSS_AMT to predict TARGET_BAD_FLAG.
    • Plot both decision trees
    • List the important variables for both trees
    • Using the training data set, create a ROC curve for both trees
    • Using the testing data set, create a ROC curve for both trees
    • Write a brief summary of the decision trees discussing whether or not the trees are are optimal, overfit, or underfit.
    • Rerun with different training and testing data at least three times.
    • Determine which of the two models performed better and why you believe this

    Step 3: Regression Decision Tree

    • Using the code discussed in the lecture, split the data into training and testing data sets.
    • Use the rpart library to predict the variable TARGET_LOSS_AMT
    • Do not use TARGET_BAD_FLAG to predict TARGET_LOSS_AMT.
    • Develop two decision trees, one using anova and the other using poisson
    • All other parameters such as tree depth are up to you.
    • Plot both decision trees
    • List the important variables for both trees
    • Using the training data set, calculate the Root Mean Square Error (RMSE) for both trees
    • Using the testing data set, calculate the Root Mean Square Error (RMSE) for both trees
    • Write a brief summary of the decision trees discussing whether or not the trees are are optimal, overfit, or underfit.
    • Rerun with different training and testing data at least three times.
    • Determine which of the two models performed better and why you believe this

    Step 4: Probability / Severity Model Decision Tree (Push Yourself!)

    • Using the code discussed in the lecture, split the data into training and testing data sets.
    • Use the rpart library to predict the variable TARGET_BAD_FLAG
    • Use the rpart library to predict the variable TARGET_LOSS_AMT using only records where TARGET_BAD_FLAG is 1.
    • Plot both decision trees
    • List the important variables for both trees
    • Using your models, predict the probability of default and the loss given default.
    • Multiply the two values together for each record.
    • Calculate the RMSE value for the Probability / Severity model.
    • Rerun at least three times to be assured that the model is optimal and not over fit or under fit.
    • Comment on how this model compares to using the model from Step 3. Which one would your recommend using?

    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

    February 4 2026, 3:13 PM

  • What is computer system ?

    Computer is an algorithm based system depends on the access of the CPU to provide serious conversation between user and the computer

  • Business Intelligence for Information Technology

    Complete a full report that is ready for publishing on the Power BI Service. In the report, you learn how to use conditional formatting, add logos, and apply a custom report theme.

    Once complete:

    • Be sure you are logged in using your university credentials, click your initials and take a screenshot of the popup box.
    • Expand the overview, take a screenshot of the list showing all green checks capturing your initials in the right corner and accumulated points with each screenshot.

  • Tm solve new

    i want 0 plagrasim

    and good, correct, prossing solve

  • Computer Science Question

    1

    Faculty of Science, Engineering and Computing

    Assessment Form

    Module: CI7350

    Title of Assignment: Assessment 2 Coursework

    Module weighting: 75%

    Setter: Dr Islam Choudhury

    Deadline 02/04/2026

    Submission details: Upload to Canvas by the stated deadline

    Module Learning Outcomes assessed in this piece of coursework

  • Critically analyse and explain the concepts, strengths, limitations and suitability of

    • agile development techniques;

  • Develop appropriate use of Project Management qualitative techniques within an

    • Agile framework, such as Feasibility Study, Planning, Control and Estimating,

    Risk Management and Configuration Management and quantitative techniques

    such as PERT and CPN

  • Apply management and team building techniques to appropriately managing an

    • information systems development project

  • Critically discuss social and professional issues associated with Information

    • systems Project Management.

  • Select and apply object-oriented development techniques within an agile

    • development environment;

  • Manage the development of a system by applying an agile framework.

    • Assessment 2 Brief and Assessment criteria

    Assessment 2 Report will be submitted as 1 pdf document containing 3 parts

    Part A will take the form of a PROJECT INITIATION DOCUMENT (PID) for a specific project,

    using the PRINCE 2 approach

    Part B will take the form of using AgilePM in the development of the specific project

    chosen

    Part C Will be a research paper comparing, contrasting and discussing the strengths

    and limitations of Prince 2 and AgilePM in context to the project chosen

    2

    Introduction

    Technology and information technology in particular, can now provide help in many ways that were not

    possible in the past. However, as more and more information and business and personal transactions are

    delivered or managed online, so it is increasingly important to ensure that users are not disadvantaged by

    the technology. Indeed, there is legislation that now requires organizations to provide both physical and

    virtual access so that no special needs group is disadvantaged.

    Your project should be a response to one of the issues identified on the next page and should take advantage of some

    of your design and technical skills and should help a specific group or organization, for example, by raising awareness,

    increasing membership or activity, raising funds, or providing services, developing new products or interfaces, or

    training programs for users or their supporters or some combination of these. Interfaces to computer programs,

    or other IT tools are areas where there are many opportunities for small projects. It may also be possible for you

    to consider ways of developing these or exploiting new technological developments such as mobile

    computing, wireless communication, digital imaging, new media, or electronic business processes in

    appropriate ways. You should develop an application or website prototype that will allow users to

    benefit from your project.

    You are invited to propose a project that will help a small organization, group of users, or people who

    support your users. For example, there are a large number of organizations that provide support or advice

    to people, attached to local or national health, educational or community establishments. You will need to

    do some research on your chosen organization or user group, but you are not required or expected to contact

    them directly.

    One of the project criteria is to decide the scope of the project. You may decide to limit your project to a

    specific local organization, a specific group of users, or a national organization. You must identify a specific

    area where such a project development would be useful and feasible and justify your choice.

    You are required to submit a Project Proposal and Project Initiation Document (PID). You will get

    feedback on your initial proposals during the sessions during the taught module, and you will then be

    expected to work these up into a full Project Plan, with illustrative supporting material. In addition to

    your PID, you will be asked to submit a small look and feel prototype or walkthrough, and

    slides for boardroom presentation that describe the business case for your deliverables.

    Any proposal will need to be constrained within a small budget for startup funds and development costs of

    a nominal (virtual) ?250K. It is expected that the project, will be up and running within 6 months, and result

    in some sustainable benefit to the chosen users or organization within one year. It is expected that these

    startup funds, possibly with further funds from other identified sources, will enable any new long-term

    venture to become self-supporting after an initial period of up to 12 months. If you decide that your project

    will require additional funding beyond the ?250K, you will need to show how this money might be raised

    and spent, and howit could be recovered over twoyears. You can apply for further funding.

    Consider a project related to ONE of the following issues including how AI tools can help:

    (a) Changing UKdemographics

    The population of the UK is ageing. Over the last 25 years the percentage of the population aged 65 and over

    increased. This trend is projected to continue. By 2034, 23 per cent of the population is projected to be aged 65

    and over compared to 18 per cent aged under 16. The fastest population increase has been in the number of those

    aged 85 and over.

    As people get older, many suffer from impairment in sensory, cognitive or motor skills for example, from impaired

    hearing and vision, reduced dexterity or cognitive abilities, dementia or Alzheimer, and many other medical or

    psychological conditions that require specific support. In the current economic climate, a growing elderly

    population is also likely to experience, social isolation, and reduced access to local shops, libraries, health and

    social services.

    (b) Graduate employmentopportunities

    In 2018-2019 there were 2.38 million students studying at UK higher education institutions, following a

    steady increase over the past decade. Identify a project to help these graduates find appropriate

    employment.

    3

    (e) Student research funding and scholarships. Identify a project to create funding opportunities to

    carry out research at universities taking into consideration venture capitalists and innovative ideas.

    (f) Volunteeringandcharity work Identify a charity sector and build a system that makes their organisation

    more productive and efficient.

    (g) Look at the United Nations 17 Sustainability Goals (https://sdgs.un.org/goals) and suggest a project

    considering one of these goals.

    (h) Look at how IoT can help the council improve services, e.g. people, housing, environment, or local

    business, using location, monitoring and controlling using AI and IoT devices.

    (https://canvas.kingston.ac.uk/courses/26385/files/5011592/download?wrap=1)

    (i) Look at how drones can help in farming. https://www.travelers.com/resources/business- industries/

    agribusiness/benefits-of-drones-in-agribusiness

    Specification of Deliverables for Part A: Prince 2

    Marking scheme Criteria & Weightings Part A

    You are required to produce a Project Initiation Document (PID) .

    The PID document should contain information to be used as part of a business and project case to the

    chosen organization, plus any other potential partners who might support your venture. You should make

    a plausible and convincing case with a well-specified project plan.

    A good submission will have:

    Project Title

    Aims and Objectives of Project: This should indicate [8%]

    D Project Title and Executive Summary Mission and Vision Statement and organizational

    chart

    D Reference to any relevant research performed and criteria used to select the project.

    D Aims and objectives, and the deliverables and scope of this project. (Include Scope

    diagram)

    D Any assumptions on which the plan is based.

    D What measuresyoumight usetoenableyou todemonstratethesuccess of the

    project

    D Legal, Social and Ethical issues that might affect the project and how you would

    address these issues. (PESTLE Analysis)

    Summary of Project Justification with a Business Case and Choice of Project Management

    Methodology [8%]

    D A Business Model Canvas

    D An explanationofthechoiceof approach. (Prince 2 and Agile)

    D A brief description of the methods to be used to develop the final applications.

    Detailed analysis or system design is not required.

    4

    D Total Budget requested-This should explain brieflyhowfunds wouldbeallocatedand

    scheduled over the lifetime of the project. Where other funding or resources are

    relevant, they should be mentioned in the bid.

    D Detailed Costs and Benefits You should identify the main costs and benefits, showing

    development, start-up, and operational costs for the first year, with indications of where

    extra funding (ifneeded)will come from. You should alsoshowanydirect or indirect

    benefits. You may comment on any tangible and intangible benefits of the project.

    Product breakdown structure and Product Descriptions: [8%]

    D A top-level breakdown by Product (Deliverable)

    D A full product description, including quality criteria and an outline of how quality is

    assessed.

    D AdetailedProductbreakdownthatshouldshowlower-leveldetailsofthreeofthemain

    products and their associated resources.

    Project Activities plans: These should include [8%]

    D A product flow diagram, showing the sequence in which products will be produced

    D A Gantt chart and a Critical Path Analysis to show the overall project plan, including

    Prince2 stages, as appropriate. The main activities should take place in the 6

    months from January to June after the initial planning period.

    Risk Analysis & Contingency plans: these should include [8%]

    D An evaluation (in terms of likelihood and impact) of any internal factors (projectrisks)

    and external factors (business risks) that may affect the project (at least 3 of each)

    D risk handling and mitigation plans – and an indication of the steps or procedures you

    would recommend to reduce the likelihood of each of these risks should they occur.

    D contingencyplansshowinghowyouwouldreducetheimpactofeachoftheserisksor

    recover from them should they occur.

    END of Part A

    Specification of Deliverables for Part B : Agile Development of your project

    Marking scheme Assessment Criteria & Weightings Part B

    After several facilitated workshops some useful information has been put together as described in your PID

    above. From those sections above you must submit a report that includes the following:

    A good submission will have:

    o Explanation of why Moscow technique is used. Present a list in a table format of high-level MoSCoW

    prioritised business process requirements in a table format (Prioritised Requirements List [PRL]).

    Include estimates for each requirement as number of days. The requirements should form the basis for

    four separate High Level Use Case Diagram, including the users of the system. [10 %]

    o Total of 4 users stories based on the MUST HAVE requirements. One User story for each of the 4

    subsystems for the use cases identified. Use the template: “As a

    , I want so that

    ” and the associated Acceptance Criteria for each user story. [8 %]

    o Choose 2 user stories to focus on from the 4 user stories above. Develop two paper prototypes, one

    for each of these 2 user stories. [8 %]

    o For 1 of the prototypes, present a description of the iterative development that takes place. Show the

    5

    time-box planning or sprint planning for the development of this prototype. Show some changes

    to prototypes as a result of user feedback. Include the filled-in change request form. Also, show

    the final prototype after the changes and Boardroom presentation. [10 %]

    o A first-cut class diagram showing only the class names, attributes and relationships that will form the

    basis of the database for the information system that will be developed. [4 %]

    END of Part B

    Specification of Deliverables for Part C : Research Paper

    A short academic research paper (approx. 4 pages) comparing, contrasting and discussing the

    strengths and limitations of Prince 2 and Agile and suggesting which approach is best for this

    project or is a hybrid approach Marks will be based on:

    o Appropriate content, critical evaluationofthe literatureandformingyourconclusions.

    o Academic journals, conference papers, appropriate books and Internet sites with clear

    standard referencing style and citation. [5%]

    Higher marks will be achieved for:

    o depth,detail, accuracy, succinctness, completeness, academic vigour, andwriting style

    o referring directlytoyour specific projectthroughout. [5%]

    END of Part C Brief

    Overall Quality of the whole report:

    o Language (Spelling, Grammar, Style) – Are spellings and use of grammar, correct?

    o Is the writing style clear and concise?

    o Logical Coherence and Flow (Clarity of Expression and Explanations)

    o Quality of Formatting, structure – Is the title page, headings and sub-headings font and line

    spacing consistent?

    o Quality of Diagrams Are diagrams readable, and clearly labelled?

    10%