Volume 13 | Issue 5 |

 Abstract

CAPTCHAs are widely used to differentiate between human users and automated bots, ensuring security in online interactions. However, traditional CAPTCHA systems often suffer from usability issues and vulnerabilities to evolving machine learning-based attacks. This research presents a novel machine learning-driven approach to refining CAPTCHA mechanisms, enhancing both security and user experience. Our proposed solution leverages deep learning models to analyse and improve CAPTCHA complexity, making it more resistant to automated solvers while maintaining accessibility for genuine users. By utilizing advanced image processing and adaptive challenge generation techniques, the system dynamically adjusts CAPTCHA difficulty based on real-time threat analysis, reducing friction for legitimate users while thwarting bots. Experimental results demonstrate that our approach significantly improves CAPTCHA robustness against automated attacks while ensuring a seamless experience for human users. This research contributes to the ongoing development of secure and user-friendly authentication mechanisms, bridging the gap between security and usability in modern web applications

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CAPTCHA Security, Machine Learning, Deep Learning, Automated Bot Detection, Adversarial attacks.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Built with Next.js, the AI-Driven Examination System is a web application that employs machine learning to give adaptive tests and feedback to enhance student performance. The program looks at factors including time-per-question, subject-wise accuracy, and difficulty-level performance to identify individual learning gaps. A Flask-based machine learning model processes this data to provide personalized test recommendations, which are subsequently stored in MongoDB and shown on a dynamic dashboard. The disadvantages of traditional "one-size-fits-all" exams are removed with this approach, which offers personalized practice and real-time progress tracking. The system demonstrates a scalable approach to personalized learning by fusing modern web technologies (Next.js, Tailwind CSS) with artificial intelligence (scikit-learn, TensorFlow).

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI in Education, Adaptive Assessments, Next.js, MongoDB, Machine Learning, Flask

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents an advanced Optimal Power Flow (OPF) strategy that integrates the Line Utilization Factor (LUF) index with the Harris Hawks Optimization (HHO) algorithm for optimal reallocation of generator outputs in the presence of a Thyristor Controlled Series Capacitor (TCSC). The objective is to minimize real power losses and voltage deviation while enhancing system stability. The proposed method is implemented on the IEEE 57-bus system under normal operating conditions. The LUF index is utilized to identify the most critical transmission line, with the weakest line (8-9) compensated by installing a TCSC. The HHO algorithm is employed to solve the multi-objective OPF problem, ensuring efficient generator dispatch and improved voltage profiles. Simulation results demonstrate that the inclusion of TCSC, guided by LUF and optimized via HHO, significantly improves power system performance by reducing losses, stabilizing voltages, and lowering generation costs. This study confirms the effectiveness of combining LUF-based line assessment with HHO for robust and intelligent power system optimization.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Harris Hawks Optimization (HHO), Thyristor-Controlled Series Capacitor (TCSC), Optimal Power Flow (OPF), Power Management, Voltage Stability, Real Power Losses, Line Utilization Factor (LUF), Generator Reallocation, Transmission Line Optimization, Multi-Objective Optimization, Power System Performance, IEEE 57-Bus System.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

English serves as a global language. It plays a significant role in various developed and developing fields. Mastering English has become essential for everyday life. However, there are challenges in acquiring English, particularly for students studying in Tamil medium. Grammar forms the foundation of any language. Specifically, verb tenses are crucial for constructing sentences. This study investigates the effectiveness of Grammar Games in enhancing English teaching among Tamil Medium students at high school level. Grammar Games, encompassing planning, monitoring, and evaluating cognitive processes are increasingly recognized as powerful tools for fostering and deep understanding and academic success. The research employs quantitative analysis of post-test scores of control and experimental groups. High School students are exposed to Grammar Games interventions tailored to English learning tasks, with a focus on self-regulation, reflection, and goal-setting. Results indicate significant improvements in students' English language knowledge, critical thinking skills, and self-efficacy following the implementation of grammar games.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Grammar Games, critical thinking and goal-setting

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The emergence of digital hiring platforms has significantly reshaped recruitment practices across diverse industries, offering highly scalable and efficient methods for identifying, attracting, and acquiring talent, thereby improving the overall effectiveness and speed of the hiring process. This study investigates the role of recruitment metrics in evaluating the effectiveness of digital hiring platforms, aiming to identify and analyse key performance indicators that measure their efficiency, cost-effectiveness, and overall quality in organizational recruitment processes.Structured questionnaires were used to gather primary data from the employees and secondary data is collected from research papers, journals, research scholars. A descriptive research design was adopted, with a 110-sample size determined via the Morgan table, employing Mann-Whitney and Annova.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Recruitment Metrics, Recruitment Strategies, Business Goals, Digital Hiring Platforms, Candidate Quality, Recruitment Tools, Data-Driven Decision

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the current fast-evolving and competitive market, companies are feeling increasing pressure to follow sustainable strategies, seeking a balance between an economic, an environmental and a social performance. This study examines the concept of Green HRM and Lean Manufacturing in enhancing operational performance in a manufacturing company. The objective of this study was made and assessed. The data were collected from 152 respondents through questionnaire and analyzed with statistical tools such as Mann Whitney U test, Kruskal Wallis H test and Spearman's Rank Correlation. The results indicate a significant and positive relationship between the implementation of both Green HRM as well as lean manufacturing practices with the performance levels of the organization. The study concludes with a set of recommendations for improving existing practices and integrating them with long-term sustainability goals.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Green HRM, Lean Manufacturing, Operational performance, Sustainability.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study evaluates the effectiveness of performance appraisal systems in enhancing employee productivity and organizational performance. Using a quantitative approach, survey data was analysed through statistical tools to assess appraisal fairness, feedback quality, and motivation. Findings reveal that well-structured appraisals significantly improve job satisfaction and performance outcomes. The research highlights the importance of transparent feedback and goal alignment in appraisal systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Performance appraisal effectiveness, employee productivity, feedback mechanisms, organizational performance

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's e-commerce world, giving shoppers a personalized and interactive experience is what sets platforms like Amazon apart. This project focuses on combining two key features a Product Recommendation System and a Virtual Try-On tool to make online shopping better, more engaging, and more satisfying for customers. The Product Recommendation System uses machine learning to suggest products based on a user's behavior, preferences, browsing history, and past purchases. These personalized recommendations aim to help customers find what they need faster, keep them coming back, and boost sales. The Virtual Try-On feature lets customers "try on" clothing, accessories, or makeup virtually using augmented reality (AR). This technology allows users to see how products would look on them in real time, using body scanning and advanced image rendering. This interactive experience helps bridge the gap between shopping online and trying items in a physical store, reducing return rates and increasing customer satisfaction. The project combines these two advanced tools for an online clothing store. The recommendation system uses data like product descriptions, user reviews, and past purchases to suggest items that match the customer's style and preferences. It relies on machine learning techniques like TF-IDF (Term Frequency-Inverse Document Frequency) and cosine similarity to find the best product matches for each user.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Virtual Try-On, Augmented reality, Recommendation, TF-IDF, Best product matches, Customer satisfaction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The pharmaceutical supply chain is a critical network ensuring the delivery of safe medications, involving manufacturers, distributors, and consumers. Its complexity is compounded by vulnerabilities like counterfeit drugs and delayed payments. This research addresses persistent challenges, including a 10% counterfeit drug prevalence, opaque tracking, and centralized systems prone to tampering, which contribute to inefficiencies and risks. A novel blockchain-based framework is proposed, utilizing Ethereum smart contracts and the Inter Planetary File System (IPFS) to enhance transparency and security. The methodology integrates decentralized storage for tamper-proof certifications, automated Ethereum Request for Comment 20 (ERC-20) token payments, and role-based access control, implemented via a React.js frontend and Node.js backend. Testing reveals a 30% reduction in administrative delays and near-elimination of counterfeit drugs through cryptographic validation, with stablecoin payments mitigating currency risks. The framework ensures compliance with General Data Protection Regulation (GDPR) and Food and Drug Administration (FDA) standards and achieves scalability through Polygon's Layer-2 solutions. In conclusion, this system significantly improves supply chain integrity and efficiency, offering a robust alternative to traditional models. Future work will explore Internet of Things (IoT) integration for real-time condition monitoring and global regulatory standardization to facilitate broader adoption, positioning blockchain as a transformative solution for pharmaceutical supply chains.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Smart Contracts, Pharmaceutical Supply Chain, Ethereum, Decentralized Application, Counterfeit Detection.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores the role that ergonomics can play in improving workplace safety, preventing musculoskeletal disorders, and enhancing employee comfort, productivity, well-being, as well as safety. In recent years, emphasis on employee safety has thrust ergonomics to the forefront of contemporary industrial management. This study explores employees' perceptions of ergonomic practices in the Indian manufacturing sector. With data from a sample of 218 employees from various departments, this study established awareness gaps related to ergonomics, explored the physical discomfort associated with suboptimal ergonomics and investigated the role of ergonomic practices in absenteeism, job satisfaction, and productivity. The study concluded with the recognition of ergonomic interventions, the need for tailored ergonomic training, and the need for an occupational culture of health and safety. Recommendations were developed to improve workplace environments, practices, and expectations for ergonomics.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Ergonomics, Manufacturing Industry, Workplace Safety, Musculoskeletal Disorders, Employee Well-being, Productivity.

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Creative Commons Attribution 4.0 and The Open Definition