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Design and Evaluation of a Mobile-Friendly College Information
College websites are important sources of information about courses, examinations, notices, contact details, and student services. However, a website that is difficult to navigate on a small screen or that does not follow accessibility principles can create barriers for students, including people who use assistive technologies. This study proposes the design and evaluation of a mobile-friendly college information portal using usability testing and accessibility checks. The objective is to determine whether a redesigned interface helps users complete common information-seeking tasks more successfully and with fewer errors, while also improving conformance with selected accessibility practices. A lightweight prototype will be developed using HTML, CSS, and JavaScript, with responsive layouts and accessible navigation, headings, labels, contrast, and form controls. The evaluation will compare an initial interface with an improved version using predefined tasks such as locating course information, finding an examination notice, and submitting an enquiry. Measures will include task completion rate, task time, user errors, automated accessibility findings, and perceived usability measured with the System Usability Scale. At the time of this draft, implementation and user testing have not been completed; therefore, no empirical results are claimed. The final paper will report observed measurements, discuss limitations, and identify practical design recommendations based on the evidence collected. The intended contribution is a reproducible, low-cost evaluation procedure for improving the usability and accessibility of college information portals.
Predictive Mechine maintenace using AI&ML
Unexpected machine failures can interrupt production, increase repair expenses, and create safety risks. Predictive machine maintenance uses sensor data, Artificial Intelligence (AI), and Machine Learning (ML) to estimate when equipment may develop a fault. Instead of servicing every machine at fixed intervals or waiting for a breakdown, the system continuously monitors parameters such as vibration, temperature, pressure, current, and operating hours. Data is cleaned and analysed, and a trained ML model identifies abnormal behaviour or predicts the remaining useful life of a component. The system can then generate an alert so that maintenance is planned before failure occurs. This paper explains the concept, working process, major technologies, model design, applications, benefits, limitations, and a practical implementation framework. It also discusses data quality, model evaluation, cybersecurity, and human verification. Predictive maintenance can improve equipment availability and maintenance planning, but its results depend on representative data, suitable sensors, and regular model monitoring.
User experience and trust in AI powered virtual assistant
AI-powered virtual assistants are increasingly used for information retrieval, task support, customer service, and everyday digital interaction. Their usefulness depends not only on technical performance but also on the experience they provide and the trust users place in their responses. This paper presents a conceptual study of user experience and trust in AI-powered virtual assistants. It examines factors such as perceived usefulness, ease of use, response quality, transparency, privacy, reliability, and the handling of errors. A proposed research framework connects these factors with user satisfaction, trust, and intention to continue using an assistant. The paper outlines a survey-based methodology using a structured questionnaire and proposes descriptive and relationship-based analysis. As this is a proposed study, no participant data or statistical findings are claimed. The framework highlights the importance of clear communication, privacy protection, consistent performance, and appropriate disclosure of AI limitations for developing trustworthy virtual assistants.
Explainable Machine Learning-Based Student Academic Performance Prediction
Student academic performance prediction can help educational institutions identify learners who may benefit from timely, targeted support. Machine-learning models can combine carefully selected indicators such as prior assessment results, attendance, assignment completion and learning-platform engagement to estimate likely performance. However, a prediction that cannot be explained may be difficult for teachers and students to trust, and may conceal data quality problems or unfair patterns. This paper presents a conceptual framework for explainable machine-learning-based student academic performance prediction. The proposed workflow includes data governance, preprocessing, feature engineering, baseline and advanced model comparison, interpretable explanations, fairness checks, and human-led intervention. Candidate models include logistic regression, decision trees, random forests and gradient-boosted trees, with SHAP- or permutation-based explanations where appropriate. Evaluation should use held-out data, class-sensitive metrics, calibration and subgroup analysis rather than accuracy alone. The framework is designed to support—not replace—professional educational judgement. No original dataset or experimental result is claimed; model performance must be established through a documented local study.
AI-Powered Phishing Detection
Phishing is a social-engineering threat in which attackers impersonate trusted organisations or individuals to persuade users to disclose information, open harmful attachments, or visit deceptive websites. Conventional filters can be challenged by newly created domains, changing message patterns, and carefully crafted text. This paper proposes an AI-Powered Phishing Detection system that analyses selected email, URL, and message features to estimate whether an item may be suspicious. The workflow includes input validation, feature extraction, machine-learning classification, risk scoring, explanation of influential indicators, and a user-facing warning or review action. Candidate features include URL structure, domain signals, message wording, sender metadata, and selected email-header indicators where available. The system is a defensive screening aid, not a guarantee that a message is safe or malicious. The paper describes architecture, workflow, implementation, evaluation, applications, and limitations. Diagrams and chart values are illustrative, not experimental results.
Explainable AI for Early Prediction of Student Academic Risk Using Machine Learning
Student academic risk prediction aims to identify learners who may need timely academic support before final outcomes are known. Machine learning can analyse historical indicators such as attendance, assessment performance, assignment completion, and learning-platform engagement to estimate the likelihood of academic difficulty. However, a prediction is more useful to educators when its basis can be understood. This paper proposes an explainable machine-learning framework that combines early risk estimation with interpretable explanations for students, faculty, and academic advisors. The workflow includes data governance, preprocessing, model training, validation, risk scoring, local and global explanations, and human review. Candidate models include logistic regression and decision trees as interpretable baselines, alongside more complex models where justified. Explanation methods such as feature importance and SHAP-style contribution analysis can help communicate which recorded indicators influenced a prediction. The system is designed to support—not replace—professional judgement, and it should not automatically penalise students. Fairness, privacy, data quality, and clear communication are treated as core design requirements. The proposed architecture and evaluation plan are presented for an academic prototype; no experimental performance results are claimed.
AI Based Email security System
Email remains a major communication channel for individuals and organizations, but it is frequently abused for phishing, spam, malware delivery, impersonation, and social engineering. Traditional filtering methods often rely on fixed rules, known signatures, or sender reputation and may struggle with new or carefully crafted attacks. This paper presents a conceptual AI-Based Email Security System that analyzes email content and available message metadata to identify suspicious messages and support safer handling. The proposed approach combines text preprocessing, machine-learning classification, phishing indicators, URL and attachment checks, and a risk-based decision layer. Emails can be categorized as legitimate, spam, phishing, or requiring further review. The design also includes user feedback, explainable warnings, privacy-conscious processing, and periodic model evaluation. The system is intended as a defensive support tool rather than a guarantee that every malicious email will be detected. Its effectiveness would depend on representative training data, secure implementation, and continuous testing.
“AI-Based Code Generation and Self-Testing System”
Software development often requires programmers to write repetitive code, understand unfamiliar requirements, and spend considerable time identifying and correcting errors. Recent advances in artificial intelligence have enabled systems to generate source code from natural-language instructions. However, generated code may contain syntax errors, incorrect logic, insecure patterns, or incomplete handling of edge cases. This paper proposes an AI-Based Code Generation and Self-Testing System that accepts a programming task in natural language, generates a candidate solution, prepares or selects test cases, executes the code in a controlled environment, and analyses the results. The proposed workflow combines prompt interpretation, code generation, static validation, test-case generation, sandboxed execution, and feedback-based refinement. The system is intended to support developers and learners by reducing repetitive effort and providing an initial validation cycle. It does not replace code review or guarantee correctness; execution safety, test coverage, model limitations, and human verification remain important. This paper presents a proposed methodology and evaluation plan. Quantitative performance claims should be added only after implementation and testing.
AI Based Phishing Website Detection Using Machine Learning
Phishing websites are a common cybersecurity threat that trick users by pretending to be legitimate websites and attempting to obtain sensitive information. Traditional methods, such as blocklists and rule-based techniques, may not always detect newly created or modified phishing websites. Machine learning provides an effective approach to phishing detection by learning patterns from examples of legitimate and phishing websites. This paper presents a conceptual framework for AI-based phishing website detection using machine learning. The framework includes collecting phishing and legitimate website data, extracting important URL and website features, preprocessing the data, selecting relevant features, applying machine learning classification, and evaluating the detection performance. Recent research on feature engineering, feature selection, machine learning, deep learning, XGBoost-based detection, and phishing campaign detection is also reviewed. The proposed framework aims to provide a simple and structured approach for distinguishing phishing websites from legitimate websites while considering the changing nature of phishing attacks. Since this study is research and visualization oriented, the proposed system is presented as a conceptual framework rather than a developed application or trained software model.
AI Based Password Strength Analyzer
Passwords remain one of the most common methods of user authentication, but weak and reused passwords continue to expose accounts to guessing, credential stuffing, and brute-force attacks. This paper proposes an AI Based Password Strength Analyzer that evaluates password quality and provides understandable recommendations for improvement. The system combines conventional password-strength indicators—such as length, character variety, predictable patterns, and common-password matches—with a machine-learning component that can learn patterns associated with weak passwords from appropriately prepared datasets. Rather than treating a password as simply valid or invalid, the analyzer assigns a strength category and explains which characteristics influenced the assessment. Privacy is a central design requirement: password text should be processed locally whenever possible, should not be stored or logged, and should never be transmitted to a server without a clear security justification. The paper describes the proposed architecture, analysis workflow, implementation approach, evaluation plan, and limitations. The system is intended as a decision-support tool that encourages safer password choices; it does not replace multi-factor authentication, password managers, secure storage, or established authentication controls.