Volume 14 | Issue 8 |

 Abstract

The increasing digitisation of criminal investigations and judicial proceedings has fundamentally transformed the nature of evidence presented before Indian courts. Electronic records such as emails, digital photographs, CCTV footage, call-detail records, mobile-phone data, social-media communications, cloud-based documents, metadata and computer-generated records have become increasingly important in establishing facts during criminal trials. However, the admissibility, authenticity, integrity, preservation and management of digital evidence present complex legal and technological challenges. This paper critically examines the role of digital evidence and electronic record management in criminal trials in India, with particular reference to the transition from the Indian Evidence Act, 1872 to the Bharatiya Sakshya Adhiniyam, 2023 (BSA). The study adopts a doctrinal and analytical legal methodology, examining statutory provisions, judicial decisions, procedural safeguards, forensic standards and institutional mechanisms governing electronic evidence. Particular attention is given to the requirements of authentication, certification, chain of custody, preservation, metadata, hash values and the reliability of electronic records. Landmark judicial decisions including State (NCT of Delhi) v. Navjot Sandhu, Anvar P.V. v. P.K. Basheer and Arjun Panditrao Khotkar v. Kailash Kushanrao Gorantyal are examined to understand the evolution of Indian jurisprudence on electronic evidence. The study argues that although the BSA modernises the evidentiary framework, effective implementation requires standardised digital-forensic procedures, secure electronic-record management, trained investigators, reliable technological infrastructure and clear chain-of-custody protocols. The paper concludes that technological reliability, evidentiary integrity and procedural fairness must remain central to the successful integration of digital evidence into criminal trials.

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 Keywords

Digital Evidence; Electronic Records; Criminal Trials; Bharatiya Sakshya Adhiniyam, 2023; Chain of Custody

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?????? ????: ???? ????, ???? ??????, ???? ????, ?????? ??????, ?????, ???????, ????????, ????? ???????

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Saransh: 21vi sadi ke shaikshanik kshetra mein digital takneek ki teevra pragati ne adhyapan-adhigam prakriya ko prabhavi, sahbhagi tatha chhatra-kendrit banaya hai. Is badalte paridrishya mein shikshakon ki bhoomika keval gyaan pradata tak seemit na hokar digital yogyata, navachar tatha satat vyavsayik vikas se bhi jud gayi hai. Rashtriya Shiksha Neeti 2020 ke anusaar vikasit Rashtriya Vyavsayik Shikshan Manak (NPST) shikshakon ke liye aise vyavsayik manakon ko tay karta hai jo unhe utkrsht, samaveshi tatha takneek-aadharit shikshan karne ke liye prerit karte hain. Yah shodh-patra 21vi sadi mein digital shikshan vatavaran ke nirmaan mein NPST ki bhoomika ka vishleshan prastut karta hai, jismein NPST ke pramukh manak--moolbhoot moolya tatha peshevar vikas--ka digital shikshan ke sandarbh mein adhyayan kiya gaya hai. Iske saath hi, Soochna evam Sanchar Praudyogiki (ICT), digital dakshata, Kritrim Buddhimatta (AI), smart kaksha, e-learning tatha satat vyavsayik vikas jaise aayamon ka bhi vishleshan kiya gaya hai. Shodh se yah bhi spasht hota hai ki NPST shikshakon ko takneeki roop se daksh, navachari evam shiksharthi-kendrit shikshan apnane ke liye ek prabhavi khaka taiyar karta hai. Lekin digital avsanrachna, prashikshan evam sansadhanon ki prachurta aur sulabhata jaise chunautiyan bhi maujood hain. Tathapi, tarkasangath prayason evam satat kshamata-nirmaan ke madhyam se NPST Bharat mein digital shikshan parivesh ko sudridh banane mein mahatvapurn bhoomika nibha sakta hai.

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Keywords: Digital Shikshan, NPST, Rashtriya Shiksha Neeti 2020, Digital Dakshata, ICT, Kritrim Buddhimatta (AI), Smart Kaksha, E-Learning, Satat Vyavsayik Vikas, Shiksharthi-Kendrit Shikshan.

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Generative Artificial Intelligence (GenAI) presents significant challenges to the traditional functioning of copyright societies by transforming how copyrighted works are accessed, processed and exploited. The use of vast and often opaque datasets for AI training makes it increasingly difficult for copyright societies to identify the use of protected works, detect infringement, determine licensing requirements and distribute royalties accurately.

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Generative Artificial Intelligence; Copyright Societies; Copyright Monitoring; Infringement Detection; Collective Licensing; Collective Rights Management; AI Training; Text and Data Mining; Royalty Distribution; Copyright Act, 1957.

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SQL Injection (SQLi) remains a significant security concern for web applications that interact with relational databases through dynamically generated queries. The exploitation of SQL injection vulnerabilities can enable unauthorized access to application data and compromise the confidentiality and integrity of database resources. This paper presents a structured SQL Injection Detection and Mitigation Framework designed to identify and reduce SQL injection vulnerabilities in web applications. The proposed framework follows a multi-stage approach comprising vulnerability detection, analysis of identified weaknesses, categorization of SQL injection attacks, and filtering of inputs associated with SQL injection attempts before they reach the database layer. The framework considers characteristics of potentially malicious inputs, including operators, special characters, logical constructs, and abnormal query patterns, to support the identification and filtering process. A web-based application was developed to validate the framework against SQL injection and malformed query inputs. The validation demonstrated that the protected application rejected the tested malicious inputs while permitting legitimate authentication requests. In addition, the framework was evaluated by researchers and industry professionals as part of the validation process. The study demonstrates the applicability of a structured, multi-stage approach for strengthening web application protection against SQL injection vulnerabilities. The proposed framework can serve as a systematic basis for integrating SQL injection detection and filtering mechanisms into the security lifecycle of database-driven web applications.

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SQL Injection, SQL Injection Detection, Web Application Security, Database Security, Vulnerability Detection, Attack Classification, Input Filtering

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTRACT: GST also known as the Goods and Services Tax is defined as the giant indirect tax structure designed to support and enhance the economic growth of a country. More than 150 countries have implemented GST so far. However, the idea of GST in India was mooted by Vajpayee government in 2000 and the constitutional amendment for the same was passed by the Loksabha on 6th May 2015 but is yet to be ratified by the Rajyasabha. However, there is a huge hue and cry against its implementation. It would be interesting to understand why this proposed GST regime may hamper the growth and development of the country. GST is one of the most crucial tax reforms in India which has been long pending. The Goods and Services Tax (GST), introduced on 1st July 2017, is India's most significant indirect tax reform since Independence. By bringing together multiple central and state taxes into a single, unified system, GST created a common national market, reduced the cascading of taxes, simplified compliance, and improved transparency. Over eight years, GST has steadily evolved through rate rationalization and digitalization, becoming the backbone of India's indirect tax framework. The 56th meeting of the GST Council, chaired by Union Finance Minister Smt. Nirmala Sitharaman has now approved Next-Gen GST reforms, with focus on improving the lives of the common man and ensuring ease of doing business for all, including small traders and businessmen. In his Independence Day address, Prime Minister Narendra Modi had announced-"The government will bring Next-Generation GST reforms, which will bring down tax burden on the common man. In line with the PM's vision the GST Council has recommended a comprehensive reform package that includes rate rationalization with a simplified two-slab structure (5% and 18%), sweeping rate reductions across sectors, with focus on common-man, labour-intensive Industries, farmers and agriculture, health, key drivers of the economy. These recommendations are based on consensus among all members of the GST Council to make GST simpler, fairer, and more growth oriented. The revised rates and exemptions will come into effect from 22nd September 2025, ensuring timely relief for the common man, households, farmers, and businesses. This paper presents an impact of GST Reforms, explains its features along with its timeline of implementation in India. The paper is more focused on advantages of GST Reforms and challenges faced by India in execution.

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KEYWORDS: GST Reforms, Goods and service tax, models of GST, Indirect tax, Indian Economy.

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Digital marketing has emerged as a powerful tool for enhancing the growth and competitiveness of small businesses in today's digital economy. This study examines the impact of digital marketing on the growth of small businesses with special reference to Coimbatore District. The research focuses on how digital marketing platforms such as social media, search engine optimization (SEO), email marketing, online advertising, and e-commerce contribute to business performance, customer engagement, brand awareness, and sales growth. Small businesses in Coimbatore are increasingly adopting digital marketing strategies to expand their market reach, attract new customers, and improve profitability while reducing promotional costs. The study also investigates the challenges faced by entrepreneurs, including limited digital skills, financial constraints, and intense online competition. Primary data will be collected from selected small business owners through structured questionnaires, while secondary data will be gathered from books, journals, government reports, and online sources. Statistical tools such as percentage analysis, Chi-square test, correlation, and ANOVA will be used to analyze the collected data. The findings are expected to reveal the effectiveness of digital marketing in improving business growth and customer satisfaction. The study will provide valuable insights for entrepreneurs, policymakers, and marketing professionals in promoting digital transformation among small businesses in Coimbatore District.

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Digital Marketing, Small Businesses, Business Growth, Customer Engagement, Brand Awareness, Online Advertising, E-commerce

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: A Tumor that develops in the breast tissue is Breast Cancer. One of the main reasons women are dying more frequently around the world is Breast Cancer. A sickness known as cancer manifests in the human body due to genetic changes. Cells become Malignant and Proliferate rapidly, harming healthy cells, due to several factors. Many cases of Breast Cancer that are not detected early might result in death. To increase the survival rate, early identification of Breast Cancer is crucial. These approaches can significantly aid Breast Cancer prediction and early diagnosis. However, each method has variable accuracy depending on the circumstances, instruments, and datasets used. In this review, we examine the potential role of Machine Learning in the prompt and precise identification of Breast Cancer. For this reason, the most popular datasets, including Wisconsin Prognostic Breast Cancer (WPBC), Wisconsin Original Breast Cancer (WOBC), and the SEER Breast Cancer dataset (SEEDBCD), were analysed. For academics and medical professionals seeking to develop dependable, comprehensible, and practically applicable Machine Learning models for Breast Cancer Classification, this review offers insights by summarising recent developments and highlighting current research gaps.

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Keywords: Breast Cancer, Machine Learning, Prediction, Diagnosis, Accuracy, Machine Learning Classifiers.

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Creative Commons Attribution 4.0 and The Open Definition

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Current study mein Gautam Buddh Nagar district mein Uttar Pradesh Board ke under padhne wale Class 9 students ki memory capacity aur academic achievement par Memory Model-based Teaching Strategy ke effect ki jaanch ki gayi. Iske liye quasi-experimental pre-test post-test control group design ka use kiya gaya. Chaar secondary schools se purposive sampling ke through total 100 students select kiye gaye. Experimental group (n = 50) ko selected Science topics Memory Model strategies jaise concept mapping, visual representation aur active recall techniques ke through padhaya gaya, jabki Control group (n = 50) ko traditional lecture method se teaching di gayi. Data collection ke liye Science Achievement Test (SAT) aur Memory Capacity Test ka use kiya gaya, dono ko validate aur pilot test kiya gaya. Results mein dono groups ke pre-test scores mein koi significant difference nahi mila, jo unki baseline equivalence ko confirm karta hai. However, experimental group ke post-test aur memory capacity test scores control group ki comparison mein significantly higher (p < .05) paye gaye. Ye findings indicate karti hain ki Memory Model-based Teaching secondary-level students mein Science ke immediate learning outcomes ke saath-saath long-term retention ko bhi improve karti hai. Study recommend karti hai ki conceptual clarity aur sustained academic performance improve karne ke liye Science teaching methods aur teacher training programmes mein Memory Model strategies ko include kiya jana chahiye.

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Mukhya Shabd (Keywords): Smriti Model, Avdharna, Shaikshanik Uplabdhi, Sankalpana Mapping, Madhyamik Shiksha, Vigyan Shikshan, Uttar Pradesh Board.

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Gestational diabetes mellitus is a form of glucose intolerance that is first recognized during pregnancy and poses significant short and long term risks to both the mother and the fetus. Over the past several decades, diagnostic criteria for GDM have evolved in response to advancements in glucose testing and a growing body of evidence linking maternal hyperglycaemia to adverse perinatal outcomes. It happens when the hormones from the placenta block your ability to use or make insulin. Insulin helps your body maintain the right amount of glucose in your blood. Too much of glucose in your blood can lead to pregnancy complications. Gestational diabetes comes from hormonal changes and the way your body converts foods into energy. Early identification and intervention are essential to reduce adverse outcomes such as preeclampsia, cesarean delivery, macrosomia, and future development of type 2 diabetes mellitus. However, the prediction of GDM is often challenged by class imbalance, high- dimensional clinical data and the complexity of risk factors. The potential of hybrid machine learning approaches for reliable and early GDM prediction, supporting clinical decision making and personalized prenatal care. This review provides a comprehensive overview of class imbalance in GDM datasets and examines two widely adopted oversampling techniques, the Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), for addressing this challenge. The review discusses the underlying concepts, mechanisms, advantages, and limitations of both techniques, highlighting their ability to generate synthetic minority-class samples and improve data distribution. It also compares the characteristics of SMOTE and ADASYN, emphasizing their applicability to healthcare datasets and their role in enhancing the representation of underrepresented clinical cases. Furthermore, the review summarizes recent research trends, identifies existing challenges associated with synthetic data generation, and outlines future directions for developing more robust and clinically relevant data balancing strategies. By providing a focused analysis of SMOTE and ADASYN in the context of GDM, this review aims to support future research on balanced healthcare datasets and contribute to more reliable and equitable predictive modelling in maternal healthcare.

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 Keywords

Keywords: Gestational Diabetes Mellitus, Class Imbalance, SMOTE, ADASYN, Synthetic Oversampling, Healthcare Data Balancing

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Creative Commons Attribution 4.0 and The Open Definition