Volume 14 | Issue 5

 Abstract

"Health Care Card - Swasthya Protection Prevention Mission (SPPM)" is a technological solution created with the purpose of enhancing the process of managing patient medical records while increasing their protection from any possible data loss, duplication, and late availability of necessary medical information especially in cases of emergency situations. The suggested system is based on the use of smart technology involving a health care card, a QR-code, and password protection. The QR-code contains a unique number which identifies patients and ensures quick and safe access to medical records. This technology allows for achieving better quality of healthcare services by making the process efficient, error-proof and safe as well as faster and easier to operate. SPPM card will provide a secure and convenient way of managing medical information in a healthcare environment.

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Health Care Card - Swasthya Protection Prevention Mission

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The purpose of this study was to examine the effects of moderate intensity neuromuscular training on speed and agility among university soccer players. Thirty subjects aged 18-25 years were randomly assigned into two groups: Moderate Intensity Training Group (MITG), and Control Group (CG). The experimental groups underwent neuromuscular training for 8 weeks, while the control group maintained their routine. Variables such as speed and agility, were measured pre- and post-intervention. Analysis of Covariance (ANCOVA) revealed significant improvements in speed and agility among experimental groups, with the moderate intensity group showing greater improvements. The study concludes that moderate intensity neuromuscular training is effective in enhancing physical performance and body composition.

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Neuromuscular Training, Motor Fitness, Body Composition, Training Intensity, Physical Performance

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The rapid integration of artificial intelligence into enterprise technology ecosystems is fundamentally transforming the roles, responsibilities, and required competencies of IT professionals across industries. This research investigates the multifaceted evolution of IT professional roles driven by AI adoption, examining how traditional technical functions are being augmented, redefined, or in some cases displaced by intelligent automation. Through systematic analysis of industry reports, workforce surveys, and case studies across diverse sectors, this study identifies five critical dimensions of role transformation: task automation and augmentation where AI assumes routine technical tasks enabling professionals to focus on strategic activities, skill set evolution requiring proficiency in machine learning operations and AI governance, organizational repositioning as IT professionals transition from technology implementers to strategic business enablers, ethical and governance responsibilities surrounding algorithmic accountability and bias mitigation, and continuous learning imperatives necessitating perpetual skills updating amid rapid technological change. The research introduces a comprehensive framework mapping traditional IT roles including software developers, systems administrators, data analysts, cybersecurity professionals, and IT support specialists to their AI-enhanced counterparts, documenting displaced tasks, augmented capabilities, and emergent responsibilities. Empirical analysis of workforce data from 500 organizations across technology, finance, healthcare, and manufacturing sectors reveals that 68% of IT tasks considered routine in 2015 have been partially or fully automated by 2025, while demand for AI-adjacent skills including machine learning engineering, data pipeline development, and algorithmic auditing has increased 340%. The paper concludes with strategic recommendations for IT professionals navigating this transition, organizational policies supporting workforce adaptation, and educational reforms aligning computer science curricula with AI-augmented professional requirements.

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IT Professionals, AI Adoption, Workforce Transformation, Automation, Skills Evolution, MLOps, AI Governance, Career Development, Technology Employment, Human-AI Collaboration, Reskilling, IT Roles.

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Abstract The rapid advancement of Artificial Intelligence (AI) has significantly transformed information management and knowledge services across various sectors, including academic libraries. Academic libraries, traditionally responsible for organizing, preserving, and disseminating knowledge, are increasingly adopting AI-driven technologies to enhance service delivery, improve user experience, and streamline internal operations. This article examines the applications of artificial intelligence in academic libraries, focusing on areas such as information retrieval, automated cataloguing, user assistance, collection development, and digital resource management. The study integrates theoretical insights with empirical analysis using quantitative techniques. Data were analyzed using statistical tools including Cronbach's Alpha, descriptive statistics, one-sample t-test, and correlation analysis. The results indicate that AI significantly improves library service efficiency, information retrieval, and personalized user services. The findings also highlight challenges such as technological infrastructure limitations, skill gaps among librarians, and ethical considerations in AI deployment. The study concludes that strategic adoption of artificial intelligence can significantly enhance the efficiency and service quality of academic libraries while supporting the evolving digital knowledge environment.

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Keywords: Artificial Intelligence, Academic Libraries, Library Automation, Digital Libraries, Information Retrieval, Knowledge Management

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Artificial Intelligence (AI) has emerged as a significant innovation in the field of sports and physical education, rapidly transforming its overall landscape. It plays a vital role in areas such as performance tracking, injury prevention, tactical decision-making, and personalized training. AI provides real-time, data-driven insights that are reshaping the roles of athletes, coaches, and sports institutions. This paper examines the application of AI across various sports contexts, including school and college-level programs, professional training environments, and rural sports development. It also highlights how AI is redefining the way sports are experienced and consumed globally. However, these advancements are accompanied by notable challenges, such as high implementation costs and concerns regarding data privacy. The study emphasizes the need for a balanced approach, where innovation is aligned with inclusivity and integrity, ensuring that technology enhances rather than diminishes the essence of human performance in sports.

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Artificial Intelligence, Sports development, AI Opportunities, future improvement, Innovation, Trending etc.

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Abstract It is clear that AI has transformed the way we live in today's world. Artificial intelligence is transforming how legal system, professional roles and educational systems works. AI is making easy for lawyers to complete their work by making research faster and legal services more approachable . So that future lawyers can have both legal knowledge with strong technological and critical thinking skills. This research paper looks at joining of AI, society and legal education by focusing on opportunities, challenges and the need for favourable reforms. It agrues that adaptation of AI is necessary for legal education to shape lawyers who can confidently use technology with strong ethical values and be sensitive to their role in society.

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AI, Society and future of legal education

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Floods are among the most frequent and destructive natural disasters, causing significant damage to infrastructure, economy, and human life. Accurate flood prediction is challenging due to uncertainties in rainfall patterns, river discharge, and environmental conditions. This study presents the development of a flood prediction model using Fuzzy Logic, which effectively handles imprecise and uncertain data. The proposed model considers key input parameters such as Rainfall, slope, elevation and infiltration to classify flood situations into categories like Low, Medium and High. A fuzzy inference system is designed using membership functions and rule-based logic to simulate real-world decision-making processes. The implementation of the model is carried out using MATLAB, where MATLAB coding is used to define fuzzy sets, develop rule bases, and perform simulation and testing of flood scenarios. The results demonstrate that the fuzzy model provides reliable and accurate flood risk prediction under varying conditions. The use of MATLAB enhances computational efficiency and visualization of the model through rule surfaces and membership functions. This approach offers a practical and cost-effective solution for flood forecasting and disaster management, supporting early warning systems and improving decision-making in flood-prone regions.

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Flood Prediction Fuzzy Logic Flood Risk Assessment MATLAB Mamdani Fuzzy Inference System (FIS)

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Self-concept is all the information and beliefs individuals have about their own characteristics and themselves. Self-concept refers to domain-specific evaluations of the self. Children can make self-evaluations in many domains of their life. These include academics, athletics, physical appearance, social interactions, moral, temperamental, intellectual and so on. Students begin to construct a self-concept, the set of attributes abilities, attitudes and values that an individual believes defines who he or she is. The study aims at finding the relationship between Self-concept and Academic achievement. The investigator has adopted descriptive research design for the present study. In order to study the impact of self concept on academic achievement the statistical techniques used in the present study were Mean, Standard Deviation. 't' test and Co-efficient of Correlation. The results of the study indicate that individuals with a high self-concept have higher academic achievement and with a low self-concept tend to have lower academic performance . High self-concept students will perform better which will in turn increase their general academic achievement significantly.

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Self-concept , Academic Achievement.

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Two major policies that have influenced elementary education in India are the Right to Education (RTE) Act of 2009 and the National Education Policy (NEP) 2020. The paper provides a comparative evaluation of the two policies, focusing on their key provisions, implementation strategies, and efforts to achieve universal access and enrolment in elementary education. The major objective of the study is To Compare the key Provisions of NEP 2020 and RTE Act, 2009 for elementary education in Odisha. Using The secondary data sources such as UDISE+, RTE Act 2009 and NEP 2020 official Documents, Peer-reviewed journals, policy reviews, and articles. The evaluation also explores implementation issues, policy flaws, and ways to make improvements so that every child receives a fair and excellent education. The findings of the study Reveals that RTE Act laid the foundation for access and equity, the NEP 2020 builds a more holistic and future-ready education framework, addressing both quality and inclusion more deeply. This study also includes infrastructure deficits, teacher shortages, administrative constraints, digital divides, and inadequate funding.

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RTE 2009, NEP 2020, Elementary Education

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Burnout is a chronic psychological condition experienced by athletes who are exposed to prolonged physical, emotional, and social stressors. In collegiate athletics, burnout may become especially pronounced during the competitive season, when athletes are expected to maintain high levels of performance while also managing academic responsibilities, travel schedules, team obligations, and recovery demands. Unlike ordinary fatigue, burnout develops over time and is often characterized by emotional and physical exhaustion, a reduced sense of athletic accomplishment, and a growing devaluation of sport participation. This descriptive study investigates burnout symptoms among collegiate athletes participating in six sports: basketball, football, track and field, swimming, volleyball, and tennis. The purpose of the study was to describe and compare burnout levels across sport types during the midpoint of the competitive season, a period when athletes are likely to experience increased training intensity, pressure to perform, and cumulative fatigue. A total of 180 athletes participated in the study, with 30 athletes selected from each sport. Participants were drawn from two regional universities and were actively competing at the intercollegiate level. A descriptive survey design was used. Burnout symptoms were measured using the Athlete Burnout Questionnaire (ABQ), which assesses three major dimensions of athlete burnout: emotional and physical exhaustion, reduced sense of accomplishment, and sport devaluation. Scores were converted to a 0-100 scale to allow for easier comparison across sports. Results showed that football athletes reported the highest average burnout score at 75, followed by basketball athletes at 72 and volleyball athletes at 70. Swimming athletes reported a moderate burnout score of 68, while track and field athletes reported an average score of 65. Tennis players reported the lowest average burnout score at 60. The findings suggest that sport-specific factors may influence the severity of burnout symptoms among collegiate athletes. Sports involving frequent physical contact, intense team expectations, extensive travel, and high public visibility may place athletes at greater risk for burnout. Conversely, sports with more individualized pacing, greater perceived autonomy, or more flexible competitive structures may be associated with lower burnout levels. This study provides useful descriptive insight for coaches, athletic departments, trainers, and sports psychology professionals seeking to develop sport-specific mental health interventions, recovery plans, and workload management strategies. The results also highlight the importance of monitoring athletes' psychological well-being throughout the competitive season rather than focusing only on physical performance outcomes.

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Athlete burnout, collegiate athletes, emotional exhaustion, Athlete Burnout Questionnaire, sports psychology, competitive season, overtraining, sport devaluation, student-athlete well-being, mental health interventions, training load, athletic performance

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The present study focuses on the perception of B.Ed. trainees towards the school internship program with special reference to the development of teaching skills and confidence. The school internship program forms a crucial part of teacher education because it exposes trainees and enables them to bridge their theoretical learning with actual schooling experience. The study primarily aimed to analyze the extent of teaching skills gained and confidence level gained among B.Ed. trainees during the internship program A survey method was applied for the study using descriptive approach. The sample was made of 100 B.Ed. Trainees. The questionnaire on the Likert scale was utilized for data collection. The results of the study showed that most of trainees generally viewed internship program as good. With regard to teaching skills development, 84.4% of the trainees agreed that internship program enhanced their teaching skills. In like manner, with respect to certainty building, 88.5% of trainees answer that the program added to increment in their trust in themselves. Fewer than 15% of nurse executives had neutral or negative responses. The findings suggest that the school internship program contributes to the core teaching competencies and skills needed by future teachers along with preparation in classroom experience and dialogue supporting self-efficacy. It exposes you to practical learning, getting a feel of how to manage classrooms and interact with students which are vital for professional growth. It can be concluded from the study that the school internship program in teacher education is an effective one. Therefore, it should be reinforced further by offering adequate support, supervision and proper guidance for its better impact on the B.Ed. trainees.

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Pupil teacher, school internship program, skill development, policy, teacher training, teacher education

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The strength of any banking system is fundamentally reliant on sound and effective practices for managing credit risk; strong governance and sound management practices, followed by transparency of financial practices. Banks have recently experienced major challenges as a result of the increasing incidence of Financial Fraud as well as also Non-Performing Assets (NPAs), particularly in the area of Co-operative banking. This research examines Governance Failures and systemic weaknesses leading to the failure of cooperative banks, with a focus on one case study, the New India Co-operative bank financial fraud discovered in 2025 when more than INR122 crore was discovered to be misappropriated and NPAs of over INR400 crore existed. The investigation by the Reserve Bank of India revealed there was much scrutiny to prevent these failures from occurring; however, through lack of due diligence on the part of borrowers, continued processes to approve irregular loans, and due to collusion within institutions there were gaps in Internal Controls/Processes; Supervisory Gaps. The researcher adopted a case study-based qualitative exploratory method; and primarily utilized secondary data sources including regulatory reports, financial statements, and previous published literature. The case study indicates that weak internal controls, poor audit processes, and lack of on-line access contributed to the accumulation of NPAs and delayed detection of fraud by banking institutions. As a result, the researcher presented a new conceptual framework (based on Technology with Artificial Intelligence (AI) and Machine Learning (ML)) to be implemented by Banks to Improve Credit Risk Assessment, Enhance Early Detection of Fraud, and Improve Overall Governance/Control of Banking Institutions.

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Non-Performing Assets (NPAs), Cooperative Banks, Fraud Detection, Governance, Artificial Intelligence, Machine Learning, Credit Risk Management.

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Globalization has emerged as a transformative force reshaping economic, social, and cultural landscapes across the world. In India, its effects have been particularly significant in altering the status of women by expanding access to education, employment, information, and mobility. This paper examines the role of globalization in transforming the status of women in the Asansol-Durgapur Planning Area (ADPA), a major industrial and urbanizing region in eastern India. The study investigates the progress of urbanization in ADPA over the last two decades and analyses how globalization, mediated through urban growth, transport and communication development, and welfare interventions, has influenced women's lives across urban, sub-urban, and rural areas. Drawing upon Census data, secondary sources, and primary survey findings, the study reveals substantial improvements in female literacy, awareness, and access to opportunities, particularly in urban and sub-urban areas. However, marked spatial disparities persist, with rural women continuing to face greater socio-cultural constraints and limited autonomy. The paper highlights the interplay between globalization, state-led welfare initiatives, and local socio-cultural structures in shaping women's empowerment. It argues that while globalization has created enabling conditions for gender advancement, its benefits remain unevenly distributed across space. Context-specific policy interventions, improved implementation of welfare schemes, and sustained awareness-building are essential for ensuring more inclusive and equitable outcomes for women throughout ADPA.

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Globalization, Women's Empowerment, Urbanization, Gender, Asansol-Durgapur Planning Area, West Bengal

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Mental health during adolescence is a critical concern, particularly in relation to school, home, and peer environments. This study examined the mental health of secondary school students in relation to gender and area of residence. A descriptive survey method was employed, and a sample of 200 students was selected using a multistage random sampling technique from the Venghlui Cluster, Aizawl district, Mizoram. Data were collected using the Mental Health Scale (MHS-TSBA) developed by Talesara and Bano (2017). Data were analyzed using descriptive statistics, including frequency, percentage, mean, and standard deviation, and inferential statistics through the independent samples t-tests. The findings indicated that 34.5% students exhibited a moderate level of mental health, although a substantial number also demonstrated poor mental health. No significant difference was found in the level of mental health based on gender; however, a significant difference was observed between male and female students in the school-related domain, with male students demonstrating higher levels of mental health than female students. With regard to area of residence, a significant difference was found in level of mental health, with urban students demonstrating better mental health than rural students. Domain-wise analysis revealed a significant difference in the home-related dimension, with urban students demonstrating higher levels of mental health than rural students, while no significant differences were observed in the school-related and peer group-related domains. The findings highlight the role of contextual factors, particularly family environment and living conditions, in influencing students' mental well-being and underscore the need for targeted school-based mental health interventions.

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Mental health, secondary school students, gender, area of residence

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This paper explores the interrelationship between heritage and memory through a critical re-reading of the Sabha Parva of The Mahabharata within the framework of English literary studies. It argues that the inclusion of classical Indian texts in the English curriculum is not merely recuperative but epistemologically transformative. Drawing upon Indian Knowledge Systems (IKS), the paper examines how the Sabha Parva operates as a repository of ethical deliberation, political discourse, and cultural memory. Through an analysis of the dice game episode and Draupadi's interrogation, the study foregrounds the text as a site of contested meanings and collective trauma. The paper further situates this reading within contemporary pedagogical practices, emphasizing dialogic learning, translation studies, and decolonial curricular interventions. Ultimately, it proposes that teaching the Mahabharata in English departments enables a dynamic reconfiguration of heritage as a living, interpretive process rather than a static inheritance.

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Heritage, Memory, Mahabharata, Sabha Parva, Indian Knowledge Systems, Pedagogy, Translation, Gender

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Dermatoglyphics, the study of ridge patterns on the fingers, palms, and soles, is increasingly recognized as a window into the genetic foundations of various physiological disorders [1, 3]. This study introduces a non-invasive, AI-driven decision-support tool designed to automate biometric analysis for early risk screening. Specifically, the system evaluates markers for type 2 diabetes, essential hypertension, cardiovascular disease (CAD), and perceived stress. The framework utilizes a multi-stage architecture. We first deployed a lightweight Convolutional Neural Network (CNN) to classify primary fingerprint patterns; this model was trained on a dataset of 6,000 scans, bolstered by standard data augmentation. To extract finer details, we developed a computer vision pipeline using OpenCV that applies adaptive binarization and morphological skeletonization. This allowed us to mathematically isolate specific biometric features, such as ridge density and minutiae layouts [5, 7]. For the final risk prediction, we utilized an Extreme Gradient Boosting (XGBoost) algorithm [8].When benchmarked against Support Vector Machine (SVM) and Random Forest baselines, the XGBoost model achieved an accuracy of 80.33%. Our results suggest that this system offers a fast, low-cost, and entirely non-invasive alternative for preliminary health screenings in clinical settings.

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Dermatoglyphics, Machine Learning, Computer Vision, OpenCV, Convolutional Neural Networks (CNN), XGBoost, Predictive Healthcare, Non-Invasive Diagnostics.

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This paper presents the modeling, simulation, and hardware implementation of a closed-loop speed control system for a two-phase hybrid stepper motor using an Arduino microcontroller. Stepper motors are widely used in robotics and automation systems due to their precise position control and high holding torque. However, open-loop operation may result in torque ripple, resonance, and step loss under varying load conditions. To overcome these limitations, a closed-loop control strategy is developed and evaluated. A mathematical model of the motor is implemented in MATLAB/Simulink to study voltage, current, speed, and torque characteristics under different operating conditions. The proposed control method improves dynamic performance and ensures stable operation across a broad speed range. Simulation results are validated through experimental hardware implementation using an Arduino Uno and driver interface circuitry. The results demonstrate improved speed regulation, reduced torque fluctuation, and reliable performance suitable for robotics applications.

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Controller, Power LED, PCB , Solar Panel, Resistor, Capacitor, Rectifier, PIC16F877A,

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The increasing prevalence of prolonged sitting and sedentary work culture has contributed significantly to the rise of musculoskeletal discomfort, particularly lower back pain and posture-related problems among desk workers. Inadequate seating support, static posture, and extended working hours are major contributors to spinal strain, muscle fatigue, and discomfort during prolonged sitting. Although existing ergonomic backrests provide a certain degree of lumbar support, many products primarily focus on posture correction and fail to address muscle stiffness and thermal comfort simultaneously. This highlights the need for more comprehensive ergonomic solutions that combine structural support with therapeutic relief. The present study aimed to analyse posture-related discomfort among desk workers and develop an improved ergonomic heated backrest, "Ergo-Warm," designed to enhance lumbar support and provide controlled thermal comfort during prolonged sitting. A mixed-method research design was adopted involving 160 participants engaged in desk-based occupations requiring extended sitting durations. Quantitative data were collected using structured questionnaires and the Cornell Modified Discomfort Questionnaire (CMDQ), while qualitative insights were gathered through semi-structured interviews. The study examined participants' sitting behaviour, discomfort patterns, awareness of ergonomic products, and preferences regarding lumbar support, adjustability, material, and heating features. The findings revealed that static posture, prolonged working hours, and lack of lumbar support were the most common causes of discomfort among participants. A majority of respondents reported experiencing lower back discomfort after sitting continuously for 6-8 hours daily. Participants also expressed strong preference for adjustable lumbar support, memory foam cushioning, and integrated heating features that could provide relaxation and reduce muscle stiffness. The study further highlighted growing interest in ergonomic products that combine comfort, posture correction, and therapeutic benefits. The research concludes that integrating ergonomic design with heat therapy can improve sitting comfort, reduce musculoskeletal strain, and support better posture among desk workers. The proposed Ergo-Warm design offers a user-centred approach toward enhancing occupational comfort and well-being in modern sedentary work environments.

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Keywords: Ergonomics, Heated Backrest, Lumbar Support, Thermal Comfort, Posture, Musculoskeletal Discomfort, Desk Workers.

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With the goal of enhancing effectiveness, transparency, and revenue performance, the GST is a significant change to India's indirect taxation structure. By contrasting Period before GST and Period after GST periods, the current study investigates the effects of GST on tax income and compliance behaviour. Secondary time-series data spanning 10 years--five years prior to and five years following the introduction of GST--are the basis for the analysis. Karl Pearson's correlation coefficient, an independent sample t-test, and comparative analysis are all used in this descriptive and analytical study. The t-test confirms that there was a large rise in tax collections during the Period after GST period. Additionally, correlation research shows a strong positive association between revenue and the number of registered taxpayers in both periods, with increased efficacy noted following the introduction of the GST. The results indicate that through a larger taxpayer base and better administrative procedures, GST has increased revenue generation and reinforced tax compliance. The study comes to the conclusion that GST has improved India's tax system's efficiency, but ongoing enhancements are needed to maintain long-term gains.

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GST, Tax Revenue, Tax Compliance, Indirect Taxation, Comparative Analysis, t-Test, Correlation Analysis

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