VOLUME 3, ISSUE 2, FEBRUARY 2026

Abstract: India's education system has undergone significant transformations, from the ancient gurukul tradition to the colonial Macaulay model and post-independence reforms. However, it has long been criticized for its rigid, examination-driven structure, which prioritizes rote learning over critical thinking and skill development. In recent years, innovations in teaching-learning pedagogy have emerged, driven by digital technology, policy shifts, and experiential learning methodologies. The National Education Policy (NEP) 2020 marks a crucial step towards holistic, multidisciplinary education, while EdTech platforms, gamification, personalized learning, and skill-based training are reshaping classrooms. Despite these advancements, challenges such as infrastructural deficiencies, teacher training gaps, and the digital divide persist, threatening the equitable implementation of reforms. This research critically examines the evolution of India’s education system and the impact of recent pedagogical innovations while highlighting the need for inclusive, systemic change to ensure meaningful and accessible learning for all.

Keywords: Education Reform, Teaching-Learning Pedagogy, NEP 2020, Digital Learning, Rote Learning, Historical Transformation, Gurukul System

Abstract: The emergence of generative artificial intelligence (AI) and its widespread adoption across digital platforms have fundamentally altered the nature of online communication and cybersecurity threats. AI-generated content (AIGC), encompassing synthetic text, images, audio, and multimedia, has become increasingly indistinguishable from human- created material. While these advancements provide significant benefits in productivity and accessibility, they also introduce serious security challenges. One of the most critical concerns is the heightened susceptibility of individuals to phishing and social engineering attacks, as AI-generated content enables adversaries to exploit human cognitive biases with unprecedented realism and scale.

This research paper examines the intersection of human behaviour, phishing click-through risks, and the proliferation of AI-generated content. It explores how cognitive biases, emotional triggers, and information-processing limitations contribute to user vulnerability when exposed to sophisticated AI-crafted phishing messages. The paper further analyses recent advancements in AI-generated content technologies, empirical evidence of their misuse in real-world platforms, and current defensive mechanisms such as watermarking, scalable detection frameworks, and behavioral interventions. By synthesizing contemporary research, this study emphasizes the necessity of integrating human-centric approaches with technical defences to effectively mitigate AI-driven phishing threats.

Keywords: AI-generated content, Phishing attacks, Human behaviour, Cognitive bias, Social engineering, Cybersecurity.

Abstract: This paper explores the properties of the 𝑌-function using various important integral transforms. We evaluate and derive clear expressions for the Euler, Hankel, Whittaker, K-, and 𝑃𝛿 -transforms applied to the 𝑌-function kernel. Using the Mellin-Barnes integral representation, we show that the resulting images can be clearly expressed in terms of the original 𝑌-function and the generalized Fox-H function. These findings create new functional relations and broaden the existing techniques for hypergeometric-type functions.

Keywords: Y-function;Euler Transform;Mellin Transform;Laplace Transform;Whittaker transform; Hankel Transform

Keywords: BPSK , Barker Code , DDFS, FPGA.

Keywords: forensic linguistics, legal terminology, criminal law, terminological ambiguity, legal translation, corpus analysis, ontology.

Abstract: Smartphone habits among students often signal their academic trajectory, yet capturing these patterns for reliable forecasting remains challenging. This study aimed to create an efficient deep learning system that interprets everyday mobile device activity to anticipate student grades and identify intervention points.

Anonymized usage records from 1,200 undergraduates captured key metrics, including total screen hours, app types (social, study, gaming), peak usage times, and basic demographics such as age and program year. After cleaning sparse data through simple averaging and normalizing sequences, we built a hybrid neural network: convolutional layers first detected local patterns in app switches and session lengths, followed by LSTM units to model daily rhythms across weeks, with an attention layer spotlighting critical periods like late-night scrolling. The system trained on an 80/20 split using standard optimization, benchmarked against simpler classifiers.

Results showed our model achieving 93.2% prediction accuracy for final GPA categories, with 92% F1-score—outpacing random forests (85%) and gradient boosters (87%) by wide margins. Key insights revealed that keeping recreational apps below 4 hours daily correlated with 15-20% higher outcomes, while fragmented study sessions hurt more than total time. These discoveries offer educators a practical, non-intrusive tool to spot at-risk students early through existing device data, promoting balanced tech use without overhauling routines. By turning passive logs into proactive guidance, this approach bridges digital behavior and learning success in modern campuses.

Keywords: Smartphone analytics, Academic forecasting, Hybrid neural networks, Behavioral thresholds, Educational AI.

Abstract: Mutual Funds investing in mid-cap companies serve as a significant component of India’s equity investment landscape, targeting investors interested in growth prospects from medium-sized enterprises with high return potential. This research analyzes the risk-return efficiency of ten chosen Mid Cap investment funds between April 2020 and March 2025, benchmarking them against the Nifty Midcap 150 TRI. Utilizing annual return figures, the study applies descriptive statistics, correlation, regression, along with Sharpe performance measure, Treynor performance measure, and Jensen’s performance alpha metrics to evaluate fund efficiency. Results indicate that ICICI Prudential Mid Capitalization Fund and Kotak Mid capitalization Fund achieved the highest risk-adjusted returns, while Mirae Asset Midcap Fund lagged behind the index. The findings provide practical guidance for both retail and institutional investors in optimizing mid-cap fund selections.

Abstract: This study evaluates the financial performance of ten leading automobile companies listed on the National Stock Exchange (NSE) from April 2020 to March 2025. Using historical stock prices and NIFTY 50 benchmark returns, key metrics such as annualized returns, volatility, Beta, Sharpe Ratio, Treynor Ratio, and Jensen's Alpha were calculated to assess risk-adjusted performance and market sensitivity. The results indicate that [Top Performing Stock(s)] delivered the strongest risk-adjusted returns, while [Underperforming Stock(s)] exhibited higher volatility and lower efficiency. Overall, the analysis provides a comparative framework for evaluating automobile stocks, highlighting the divergence between high-growth, high-risk players and more stable, lower-return companies within the sector.

Abstract: Rural development is a crucial factor to the way of development of any country. The women of rural area and programmes of the governments-both central and the state- play an active role in the process of development. The Indian Government took so many programmes for upliftment of rural population and development since independence. Simultaneously the West Bengal Government introduced a dozen of programmes to the aim of development of rural population and women also. Women play a greater role to rural development.

Keywords: Rural Development, Governmental Programmes, Women Development

Abstract: The introduction of the new labour codes in India marks a significant transformation in the country’s labour regulatory system. By consolidating multiple labour laws into a unified framework, these reforms aim to simplify compliance while strengthening employee welfare. This study explores the impact of these labour codes on HR compliance practices within Indian organizations. Primary data were collected through a structured questionnaire and a live interview with an HR professional. The research examines levels of awareness, organizational preparedness, policy revisions, and implementation challenges. The findings reveal that although organizations have initiated policy updates and possess basic awareness, issues such as employee adaptation, training requirements, and gaps in digital systems continue to hinder effective implementation. The study recommends focused HR training, adoption of digital compliance tools, and stronger managerial support to enhance compliance outcomes. This research contributes practical insights for HR professionals and policymakers while adding value to academic literature.

Keywords: HR Compliance, Labour Codes, India, HR Practices, Organizational Readiness.