Volume 14 | Issue 3 | March-2026

 Abstract

Purpose of the study was to investigate the Comparative Study on Agility among Different kanyashree cup women Soccer Players. For the present study a total subject (N-100) Kanyashree cup women soccer players of West Bengal, age ranging 15-18 years. The researchers selected physical fitness variables that is agility for the present study. The data was analyzed by applying F-test at 0.05 level of significance. The finding of the present study strongly reveals that there was no significant deference among different positional kanyashree cup women soccer players.

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 Keywords

physical fitness, Agility, different football position, Kanyashree cup.

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Cloud computing has significantly reshaped various aspects of software development and deployment, particularly in the area of Quality Assurance (QA). By integrating cloud technologies into QA processes, organizations gain greater flexibility, scalability, and operational efficiency, enabling them to maintain and enhance software quality more effectively. This study aims to identify and examine the most critical components of quality assurance within cloud computing environments. It focuses on analyzing essential quality parameters across different cloud service models, including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), highlighting the unique QA requirements and challenges associated with each model. The paper further explores emerging trends in cloud-based QA, such as automation, continuous testing, and multi-cloud strategies, and discusses how these advancements can strengthen quality assurance practices. Additionally, it addresses the key challenges faced by QA teams in cloud environments, including security, compliance, and performance variability, while considering how innovative technologies can help overcome these obstacles. The study concludes by proposing a set of best practices designed to streamline QA processes, reduce operational costs, and ensure the consistent delivery of reliable, high-quality cloud services.

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Cloud Computing (CC), Quality Assurance (QA), Challenges, Opportunities, SaaS, PaaS, IaaS

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Menstruation is a natural physiological process experienced by women of reproductive age, characterized by cyclic hormonal changes leading to uterine shedding. While menstruation is a normal part of female biology, it can be accompanied by various physical and emotional symptoms, including changes in nutritional behavior. This abstract aims to provide a comprehensive review of nutritional behavior during menstruation, highlighting key aspects such as dietary preferences, nutrient requirements, and the impact of hormonal fluctuations on food choices. During menstruation, many women experience cravings for specific foods, with chocolate, sweets, and salty snacks being commonly desired.

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Menstruation,Nutrient, behaviour

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Financial fraud has become a major concern with the rapid growth of digital banking and online transactions. Traditional rule-based fraud detection systems often fail to identify sophisticated fraudulent patterns, leading to significant financial losses. This paper presents a machine learning-based transaction fraud detection system that uses a Decision Tree Classifier to identify fraudulent financial transactions in real-time. The system is trained on a public payments dataset containing over 6 million transactions with features including transaction type, amount, and account balances. Feature engineering techniques such as label encoding of categorical variables are applied to improve model performance. The trained model achieves approximately 99.9% accuracy on test data. The complete system includes a training script, command-line prediction tools, a REST API for real-time inference, and a browser-based user interface for demonstration. This integrated solution provides financial institutions with an accurate, scalable, and accessible framework for automated fraud detection

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 Keywords

Fraud Detection System,Machine learning

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Retinal diseases are a major cause of vision loss, and early detection is very important. Retinal fundus imaging is a safe and non-invasive method used to examine the eye. In this study, a deep learning model using CNN and MobileNet is proposed to classify retinal images into five disease categories. The model uses preprocessing techniques and shows good accuracy, helping doctors with faster and reliable screening.

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Retinal images, deep learning, convolutional neural network, MobileNet, medical image classification, eye disease detection, image processing.

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Abstract-- This paper presents a Spoken Language Identification (LID) system designed for multilingual Indian speech. The main objective of LID is to automatically identify the language spoken in an audio segment without depending on words or any textual information. In this study, Resemblyzer is used to extract meaningful speech features, generating a compact 256-dimensional vector representation for each audio utterance. The experiments are conducted using the IndicTTS corpus, which includes speech recordings from 13 major Indian languages. To evaluate the system's performance, three deep learning models are trained and tested: a standard Deep Neural Network (DNN), an RNN-LSTM model, and a proposed BiLSTM architecture. These models are trained using speech segments of 1.5 seconds to observe the effect of speech length on language identification accuracy. The experimental results show that the BiLSTM model performs consistently better than the other models. This improvement is mainly because BiLSTM can analyze speech patterns in both forward and backward directions, allowing it to capture more contextual information from the audio signal. As a result, it provides more accurate language classification. Overall, the study demonstrates that bidirectional temporal modeling can significantly improve the performance of language identification systems for Indian languages.

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Spoken Language Identification, Indian Languages, Deep Learning, Resemblyzer Embeddings, DNN, LSTM, BiLSTM, IndicTTS,

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Yoga in pregnancy is multi dimensional physical, mental, emotional and intellectual preparation to answer the challenges faced by a pregnant woman. The challenges of pregnancy are revealed by the state of happiness and stress while yoga is a skill to calm down the mind. Pregnancy in a woman is a condition in which woman changes both from inside as well as outside. These changes create obstacles or hurdles in the normal life of a pregnant woman and yoga in pregnancy can help the women to cruise through these changes and challenges. Practicing yoga during pregnancy provides a great range of activity and benefits to unborn child and mother by numerous ways. Yoga soothes the mind, refocus the energy and prepare the woman physiologically and psychologically for labour. Different breathing techniques impart invaluable neuro-muscular control and helps in coordinated relaxation and contraction of uterus. Different type of asanas (postures),are described in Ayurveda and Yoga darshan texts which can be performed by a pregnant woman as they consume low energy and provide greater benefits. Published articles and different studies with references have been considered to support the effect of yoga in pregnancy. Yoga practicing includes physical postures and breathing techniques which minimizes the complication of pregnancy, like pregnancy induced hypertension, intrauterine growth retardation and pre-term delivery etc. Western exercises bring about what is known as phase contraction of muscles while yogic exercises create a static contraction which maintains a muscle under tension without causing repeated motions [1] . An approach to yoga in pregnancy can improve birth weight; decrease pre-term labour, decreased IUGR with least or no complications.

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Pregnancy, Yoga, Asanas, Pranayama, Birth weight, stress

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Background: The concept of Adhaaraneeya Vega (non-suppressible natural urges) is a vital aspect of preventive medicine in Ayurveda, forming the foundation of Swasthavritta. Suppression of these natural urges (Vega Dharana) leads to vitiation of Vata dosha, resulting in multiple psychosomatic disorders. In today's era, professionals working in organised sectors such as IT, banking, school teachers and police services often face stressful working environments, rigid schedules, and lifestyle constraints, predisposing them to frequent suppression of natural urges. Aim: To assess the impact of Adhaaraneeya Vega Dharana among employees of organised sectors and to analyse its association with occupational patterns and related health outcomes. Objectives: To identify the most commonly suppressed Adhaaraneeya Vega among employees in IT, banking, school teachers, and police sectors. To study the relationship between occupational stress, working environment, and Vega Dharana. To correlate statistical findings with Ayurvedic understanding of Dosha Prakopa and disease manifestation. Materials and Methods: A cross-sectional observational study was conducted among 400 participants from four organised sectors--IT professionals, school teachers, bank employees, and police officers (100 participants each). Data were collected using a structured questionnaire assessing suppression of 14 Adhaaraneeya Vegas and associated symptoms. Statistical analysis was performed using Pearson's Chi-square test, correlation, and multivariate linear regression to determine associations and significance levels (p < 0.05). Results: Significant associations were observed between certain Vega Dharanas and occupational groups. For instance, Kasa Vega Dharana showed statistical significance among bank employees (p = 0.04) and police officers (p = 0.02). Similar occupational trends were seen in Shukra and Nidra Vega Dharana among IT professionals, reflecting lifestyle-induced suppression. The findings indicate that Adhaaraneeya Vega Dharana varies according to professional environment and correlates with stress, irregular routines, and lack of bodily awareness. Discussion: Frequent suppression of natural urges leads to Vata-Prakopa and subsequent Doshic imbalance, manifesting as both somatic and psychological disorders. The study highlights the growing health risks in organised sectors where occupational compulsion overrides natural physiological needs, validating the classical Ayurvedic teachings of Swasthavritta and Vega Anigraha. Conclusion: Suppression of Adhaaraneeya Vega is common among organised sector employees, significantly influencing health and well-being. Awareness and implementation of Ayurvedic preventive measures such as adherence to Ayurvedic principles, proper diet, and stress management can prevent occupational disorders and promote holistic health.

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 Keywords

Keywords: Adhaaraneeya Vega, Vega Dharana, Organised Sector, Occupational Health

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No-cost Equated Monthly Installment (EMI) schemes have become an integral component of India's rapidly expanding digital retail economy. By eliminating immediate financial burdens, these credit mechanisms enable consumers to access aspirational and high-value products with ease. This paper examines the influence of no-cost EMI on consumer buying behavior by analyzing secondary data from the IIM Ahmedabad report Digital Retail Channels and Consumers: The Indian Perspective (2024), based on responses from 35,869 consumers across India. Results demonstrate that EMI and Buy Now Pay Later (BNPL) options significantly enhance affordability, reduce psychological spending barriers, and stimulate discretionary purchasing, particularly among young and low-income consumers. Integrating behavioral economics, technology acceptance theories, and the philosophical critique of consumer culture by Adorno and Horkheimer's Dialectic of Enlightenment, this study highlights how digital retail systems shape consumer rationality, desire, and identity. Managerial implications for retailers, policymakers, and financial institutions are discussed.

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No-Cost EMI, Consumer Behavior, Digital Retail, BNPL, Deferred Payment, Behavioral Economics, Dialectic of Enlightenment, India, Fintech, Online Shopping

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This study examines the influence of digital habits on mental health among college students, emphasizing the mediating role of sleep patterns. A total of 200 students (Male = 90, Female = 110) participated in the study. Standardized instruments assessed digital behavior and psychological well-being (Cronbach's ? = .773 and .872, respectively). Findings revealed a significant correlation between digital behavior and mental health (r = .789, p < .001). ANOVA results indicated significant differences in daily data usage, online gaming, and device use across gender. Regression analysis identified digital behavior as the strongest predictor of mental health (? = .707, p < .001), followed by gender, study hours, and sleep patterns. Mediation analysis further revealed that sleep patterns partially mediated this relationship (Indirect effect = 0.0317, p < .001). The study highlights that poor digital hygiene and irregular sleep can negatively impact students' mental health, emphasizing the importance of promoting balanced technology use and healthy sleep routines.

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Mental Health, Sleep Patterns, Digital Behavior, College Students, Psychological Well-Being

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