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Category: Computer Science

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  • What is meaning of accounting

    Sounds like you’re diving into accounting jargon . An accounting description detail typically refers to a brief explanation or breakdown of a transaction or entry in financial records. It’s like a summary of what’s happening with the numbers, helping you understand the ‘why’ behind the figures. Is this related to a specific transaction or financial statement you’re dealing with?

  • help review

    please help me review.

  • ISWA direct science article writing


    topic: Deep Learning-Based Automatic Target Recognition (ATR) from UAV Imagery

    • Description: Develop CNN/Transformer models to detect and classify vehicles, personnel, or threats in high-resolution aerial imagery for defense operations.
    • Why it fits ISWA: Strong in pattern recognition, intelligent control, and AI-driven decision support, which are central to the journals topics on intelligent systems applied to real-world problems.

    Brief Deep Learning-Based ATR from UAV Imagery

    Develop a CNN/Transformer model to detect and classify military targets (vehicles, personnel, threats) from UAV imagery. The model must handle small targets at high altitude, occlusion, and cluttered backgrounds. It should be robust to day/night variations, weather changes, and seasonal differences, and leverage multi-modal data (RGB, IR, thermal) to detect camouflaged or adversarial targets. Use semi-supervised or transfer learning to address limited labeled datasets. Optimize the model for real-time UAV deployment using lightweight architectures, pruning, or quantization. Include precision, recall, F1-score, and inference speed metrics, along with visualizations for operator interpretability. Document all methods, datasets, and configurations clearly for reproducibility and potential journal submission.

    Expected Outcomes & Deliverables:

    1. Fully trained and optimized ATR model capable of real-time UAV inference.
    2. Evaluation report with performance metrics (precision, recall, F1-score, inference speed) under varied environmental conditions.
    3. Sample detection visualizations and example scenarios demonstrating small, occluded, and camouflaged target recognition.
    4. Complete documentation of datasets, model architecture, training procedures, and code for reproducibility.

  • ISWA direct science article writing


    topic: Deep Learning-Based Automatic Target Recognition (ATR) from UAV Imagery

    • Description: Develop CNN/Transformer models to detect and classify vehicles, personnel, or threats in high-resolution aerial imagery for defense operations.
    • Why it fits ISWA: Strong in pattern recognition, intelligent control, and AI-driven decision support, which are central to the journals topics on intelligent systems applied to real-world problems.

    Brief Deep Learning-Based ATR from UAV Imagery

    Develop a CNN/Transformer model to detect and classify military targets (vehicles, personnel, threats) from UAV imagery. The model must handle small targets at high altitude, occlusion, and cluttered backgrounds. It should be robust to day/night variations, weather changes, and seasonal differences, and leverage multi-modal data (RGB, IR, thermal) to detect camouflaged or adversarial targets. Use semi-supervised or transfer learning to address limited labeled datasets. Optimize the model for real-time UAV deployment using lightweight architectures, pruning, or quantization. Include precision, recall, F1-score, and inference speed metrics, along with visualizations for operator interpretability. Document all methods, datasets, and configurations clearly for reproducibility and potential journal submission.

    Expected Outcomes & Deliverables:

    1. Fully trained and optimized ATR model capable of real-time UAV inference.
    2. Evaluation report with performance metrics (precision, recall, F1-score, inference speed) under varied environmental conditions.
    3. Sample detection visualizations and example scenarios demonstrating small, occluded, and camouflaged target recognition.
    4. Complete documentation of datasets, model architecture, training procedures, and code for reproducibilit

  • ESTI article writing

    AI-Based Cross-Layer Optimization Framework for UAV Tactical Communication Networks

    AI-Based Cross-Layer Optimization Framework for UAV Tactical Communication Networks

    Scientific angle:
    Cross-layer optimization across:

    • PHY layer
    • MAC layer
    • Network layer
    • Application layer

    Novel contribution:
    Unified AI architecture improving:

    • reliability
    • throughput
    • resilience

    Technical Scope & Focus

    • Topic: AI-Based Cross-Layer Optimization Framework for UAV Tactical Communication Networks.
    • Core Content: The paper must focus on how AI (e.g., Deep Reinforcement Learning or Federated Learning) optimizes multiple layers (Physical, MAC, and Network) simultaneously to improve latency, throughput, or energy efficiency in a military/tactical UAV context.
    • Novelty: Instruct them to clearly state the “Research Gap.” Why are existing cross-layer methods insufficient for tactical (high-mobility, jam-prone) environments?

    2. Journal-Specific Formatting (Elsevier/JESTECH)

    • Template: Use the standard Elsevier single-column or double-column manuscript format.
    • Structure: Follow the standard IMRAD format: Introduction, Related Work, System Model & Problem Formulation (crucial for this topic), Proposed AI Framework, Results and Discussion, and Conclusion.
    • Abstract: Limit to 250 words. It must include: Problem, Method, Key Results, and Significance.
    • Keywords: Provide 57 keywords (e.g., UAV Communications, Cross-layer Optimization, Artificial Intelligence, Tactical Networks, Resource Allocation).

    3. Technical Requirements (The “Must-Haves”)

    • Mathematical Modeling: The writer must include a formal “System Model.” This should include equations for the UAV mobility model, the communication link budget, and the objective function for optimization.
    • Algorithm Pseudo-code: At least one high-quality pseudo-code block for the proposed AI algorithm.
    • Simulation Environment: Explicitly describe the simulation setup (e.g., MATLAB, NS-3, or Python/PyTorch). They must provide details on parameters like UAV speed, altitude, and bandwidth.
    • Comparative Analysis: The results must compare the proposed AI framework against at least two “baselines” (e.g., a traditional non-AI optimization and a single-layer AI approach).

    4. Ethical & Quality Standards

    • Originality: The paper must be 100% original. JESTECH uses iThenticate/CrossCheck; any similarity index above 15% will result in an immediate desk reject.
    • AI Disclosure: If they use AI tools to assist in writing, they must disclose it according to Elseviers AI policy (though for a technical paper, the research is AI-based, which is different from generative text).
    • References: Minimum 3040 references, with at least 50% from the last 3 years (20232025) and primarily from high-impact journals (IEEE, Elsevier, Springer).

    5. Deliverables Checklist

    Request these specific files from the writer:

    1. Main Manuscript (without author names for double-blind review).
    2. Title Page (with your name, affiliation, and “Highlights” 3 to 5 bullet points of the paper’s contribution).
    3. High-Resolution Figures: Ensure all diagrams (System Architecture, Graphs) are at least 300 DPI and provided in editable formats (e.g., .eps, .pdf, or high-res .png).

  • Homework 9

    Recommended Submission Format

    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

  • Assignement Infer Stats in Decision-Making

    Question 1

    The hourly salaries at a business follow a normal distribution with a mean of 18 and a standard deviation of 4.

    What is the probability that someone has a salary between 16 and 26? Round your answer to two decimal places.

    Don’t forget to draw the picture to help you. You can use Excel or the table in your book on page 386 to find the probabilities.

    Question 2

    The attached dataset contains measures for a set of cars and trucks. Use JASP and the attached dataset to calculate the mean, median, standard deviation, minimum and maximum of the car and truck variables.

    cars16.sav

    Question 3

    The attached dataset contains the height and weight measurements of 49 people. Use JASP and the attached dataset to calculate Pearson’s correlation along with the p-value. Write your solution in APA format and copy and paste your JASP output into the solution box.

    heightweight132.sav

    Question 4

    Please interpret the results, write you conclusion statement and the APA formatted statement.

    Is there a relationship between time spent studying and quiz scores? A student ran a correlation test and had the following results:

  • CIST1601 savannah tech Research Project 2

    Write a report on ONE of the following topics:

    • Chapter 5: “Crisis Planning“. Explain why crisis planning is as important as securing and protecting information that is within an organization.
    • Chapter 6: “CISSP & Ethical Rules“. Explain what CISSP stands for, and what are the ethical rules that CISSP holder must follow. Identify these ethical guidelines and their importance in maintaining professionalism and trust within information security.
    • Chapter 7: “Certifications“. Using the different certifications presented in the module as a starting point, what certifications are relevant to an information security professional? How impactful are the certifications relative to the job duties? Do the costs outweigh the benefits?
    • Chapter 8: “VPN Technology“. Research the three VPN technologies that the VPNC defines.

    This report should be approximately 500 – 750 words. This word count does NOT count the title page OR the references page.

    Your references can include the textbook:

    Whitman, M. E., & Mattord, H. J. (2022). Principles of Information Security (7th ed.). Cengage Learning Custom P.

  • CIST1601 savannah tech Research Project 2

    Write a report on ONE of the following topics:

    • Chapter 5: “Crisis Planning“. Explain why crisis planning is as important as securing and protecting information that is within an organization.
    • Chapter 6: “CISSP & Ethical Rules“. Explain what CISSP stands for, and what are the ethical rules that CISSP holder must follow. Identify these ethical guidelines and their importance in maintaining professionalism and trust within information security.
    • Chapter 7: “Certifications“. Using the different certifications presented in the module as a starting point, what certifications are relevant to an information security professional? How impactful are the certifications relative to the job duties? Do the costs outweigh the benefits?
    • Chapter 8: “VPN Technology“. Research the three VPN technologies that the VPNC defines.

    This report should be approximately 500 – 750 words. This word count does NOT count the title page OR the references page.

    Your references can include the textbook:

    Whitman, M. E., & Mattord, H. J. (2022). Principles of Information Security (7th ed.). Cengage Learning Custom P.