Volume 14 | Issue 4

 Abstract

The study was to identification of posture defects among Basic schools children and their remedial programme in Kanpur and also to make some suggestions to parents and related Government Authority regarding corrective program of postural defects. For this study one thousand students studying in 2nd, 3rd, 4th and 5th standard in hundred basic schools in Kanpur were selected as subjects for the investigation. The age level of the students was 7 to 10 years. Five postural defects Kyphosis, Lordosis, Scoliosis, Knock knee and Flat foot were selected for the study. The evaluation of posture were made by lateral, medial view of posture and by observing the cervical, thoracic, lumbar spine region for spinal deformities, like kyphosis and scoliosis deformity was detected by the forward bending test in which a rib hump is observed on either side of the spine. Whereas as the bow legs and knock knee deformities were diagnosed by measuring the inter condylar distance and inter malleolar distance in erect standing position, flatfoot deformity was diagnosed by the Simple wetted stand by observing the foot impression the conclusion was derived whether the participants has a normal or a flat foot arch. To analyze data, Mean and standard deviation in respect of each of the posture defects were computed in order to identify the normal children and those suffering from above defects. In order to determine the number of subjects suffering from posture defects in relation to the population, a percentage analysis was be used. To test the hypothesis the level of significance was set at 0.05. The research conducted by the scholar did reveal that the numbers of deformed students were quite high compared to the general population in schools.

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 Keywords

Posture, Posture Defects, Kyphosis, Lordosis, Scoliosis, Knock Knee, Flat Foot

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This study examines the constitutional protection of digital privacy in India with a focus on Article 21 of the Constitution, which guarantees the right to life and personal liberty. It traces the evolution of privacy as a fundamental right, particularly after the landmark Justice K.S. Puttaswamy v. Union of India (2017) judgment, which firmly recognized privacy as intrinsic to Article 21. The research further analyzes the legislative framework introduced by the Digital Personal Data Protection Act, 2023 (DPDP Act), highlighting its key provisions, objectives, and alignment with constitutional principles. The paper critically evaluates the compliance challenges and implementation gaps associated with the DPDP Act, including issues of enforcement, data fiduciary obligations, consent mechanisms, and state exemptions. It also explores the judicial response in balancing individual privacy rights with state interests such as national security and governance. By integrating constitutional analysis with statutory developments, this study aims to assess whether the current legal framework adequately safeguards digital privacy in India. The research concludes with suggestions to strengthen data protection mechanisms, improve regulatory clarity, and ensure effective enforcement to uphold the fundamental right to privacy in the digital age.

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 Keywords

Digital Privacy ,Article 21 of the Indian Constitution Right to Privacy Fundamental Rights, Justice K.S. Puttaswamy v. Union of India ,Digital Personal Data Protection Act, 2023 (DPDP Act) ,Data Protection ,Data Fiduciary, Data Principal, Consent Mechanism , Compliance Challenges, Implementation Gaps,Information Technology Law.

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This paper presents ChurnShield, an enterprise-grade, full-stack machine learning platform designed for real-time customer churn prediction in subscription-based and marketing-driven business environments. ChurnShield integrates a Random Forest classifier, augmented by SMOTE-based class balancing and GridSearchCV hyperparameter optimisation, to achieve 95.25% accuracy and 98.86% ROC AUC on a real-world telecom customer dataset of 7,043 records. The system eliminates reactive churn management through a five-stage ML pipeline encompassing data ingestion, feature engineering, model training, SHAP-based explainability, and automated customer segmentation into four actionable risk tiers. A Flask-based RESTful backend with SQLAlchemy ORM and a Bootstrap 5 administrative dashboard deliver sub-second individual predictions and batch processing of 10,000 records in under 3 seconds. Security design implements defence-in-depth across authentication (bcrypt + JWT), application (CSRF protection), and data layers (parameterised SQL). Benchmarking results, system architecture, database design, and a future roadmap including deep learning and federated learning integration are presented.Index Terms--churn prediction, machine learning, random forest, SHAP explainability, customer retention, Flask, scikit-learn, class imbalance, SMOTE, web application.

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Primary Keywords: Customer Churn Prediction Machine Learning Random Forest Classifier SMOTE (Synthetic Minority Oversampling Technique) Hyperparameter Optimization GridSearchCV ROC AUC System & Architecture Keywords: Full-Stack Machine Learning System Real-Time Prediction System Flask Web Application RESTful API SQLAlchemy ORM Bootstrap Dashboard Advanced ML & Analytics: SHAP Explainability Feature Engineering Predictive Analytics Customer Segmentation Risk Classification Performance &am

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Accurate Detection Of Small Foreign Objects Is a Critical Requirement In Industrial Inspection Systems, Where Minor Defects Can Significantly Impact Product Quality, Safety, And Operational Efficiency. Conventional Inspection Methods Often Fail To Identify Small, Low Contrast, Or Partially Occluded Foreign Objects In Complex Industrial Environments. To Address These Limitations, This Work Proposes a Deep Learning Based Detection Framework That Integrates Multi Scale Feature Extraction With Region Based Object Detection. The Proposed Approach Employs a Residual Network As The Backbone For Hierarchical Feature Extraction, Enhanced By a Feature Pyramid Network To Capture Discriminative Features Across Multiple Scales. Faster R-Cnn Is Utilized For Precise Localization And Classification Of Foreign Objects. Experimental Evaluation Using Precision, Recall, F1 Score, And Mean Average Precision Demonstrates That The Proposed Framework Provides a Robust And Reliable Solution For Real Time Industrial Quality Inspection Applications.

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Foreign object detection, small object detection, ResNet, Feature Pyramid Network, Faster R-CNN, industrial inspection

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ABSTRACT Functional ankle instability (FAI) is a common issue among female athletes, leading to recurrent ankle sprain, impaired balance, and compromised postural control. It results from deficits in proprioception and neuromuscular co-ordination, strength, increasing the risk of further injuries. Addressing these deficits through targeted training programs is essential for improving athletic performance and reducing injury recurrence. Balance training and the plyometric training has been widely studied as effective intervention for enhancing stability, proprioception, and neuromuscular control athletes with FAI. NEED OF THE STUDY Female athletes are at a higher risk of ankle sprains due to anatomical, hormonal, and neuromuscular factor. Recurrent ankle sprains can lead to chronic instability, affecting performance and long-term joint health. Understanding the best training approach can help prevent recurrent injuries. AIM This study aims to evaluate the effect of balance training and plyometric training on balance and postural control in female badminton athletes with functional ankle instability. METHODS A systematic review of literature from the past five years was conducted, focusing on randomized controlled trails (RCTs) and observational studies analysing the impact of balance and plyometric training on FAI studies were retrieved from database such as PubMed, Scopus, and Google Scholar. out come measure included improvements in static and dynamic balance, postural control and neuromuscular function. RESULT The result of the study proves that each group shows p value (0.0001) however (group A) Balance training program shows more significant result compare to (group B). CONCLUSION This study is concluded that the effect of balance training and plyometric training on balance and postural control in female badminton athletes with functional ankle instability

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Functional ankle instability, balance training, plyometric training, postural control, proprioception, neuromuscular training, female badminton athletes.

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Early disease risk prediction is essential for effective preventive healthcare. This paper presents an explainable multimodal machine learning framework that integrates clinical and lifestyle data for improved prediction accuracy. Models such as Logistic Regression, Random Forest, and Support Vector Machine are evaluated using metrics like accuracy and ROC-AUC. To enhance interpretability, SHAP is used to explain feature contributions. The results show that combining multimodal data with explainable AI improves both prediction performance and model transparency.

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Machine Learning, Multimodal Learning, Explainable AI, Healthcare Analytics, Disease Risk Prediction

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This study explores the intricate intersections of trauma, psychology, and poetic expression in the works of Christina Georgina Rossetti, with particular emphasis on selected poems that reflect emotional fragmentation, repression, and the negotiation of inner conflict. By employing a psychoanalytic and trauma-informed framework, the analysis foregrounds how poetic language becomes a site of both concealment and revelation, where desire, guilt, fear, and resilience are subtly encoded. The poems examined reveal a persistent tension between innocence and experience, bodily constraint and imaginative escape, as well as between spiritual aspiration and psychological unrest. Special attention is given to symbolic structures, recurring motifs, and the representation of female subjectivity, which together illuminate the poet's engagement with Victorian anxieties surrounding gender, morality, and emotional suppression. The study further interrogates how trauma manifests not only as thematic content but also as a shaping force within poetic form, rhythm, and imagery. Through close textual reading, the research demonstrates that Rossetti's poetry offers a nuanced portrayal of psychological struggle while simultaneously suggesting possibilities of healing and transcendence. Ultimately, this analysis contributes to a deeper understanding of the poet's oeuvre by situating her work within contemporary critical discourses on trauma and psychology, thereby opening new avenues for interpretation and scholarly engagement.

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Keywords: Trauma, Psychoanalysis, Victorian Poetry, Female Subjectivity, Symbolism, Psychological Conflict, Emotional Repression

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The rapid proliferation of digital education platforms and remote learning environments has fundamentally transformed the way academic examinations are conducted worldwide. While online examinations offer unparalleled flexibility and accessibility, they simultaneously introduce a range of pressing challenges related to academic integrity, fairness, and institutional accountability. Traditional invigilation methods, which depend on physical presence and human observation, are wholly inadequate in virtual settings, necessitating the development of sophisticated, automated monitoring systems capable of operating at scale. This research paper presents a comprehensive Intelligent Examination Monitoring System (IEMS) that employs artificial intelligence, computer vision, and machine learning techniques to ensure secure, reliable, and tamper-resistant online examinations. The proposed system integrates facial recognition, multi-object detection, gaze tracking, and behavioral pattern analysis to monitor candidates in real time, identifying and flagging anomalies such as multiple faces in frame, prolonged absence from the camera view, presence of unauthorized devices or materials, and irregular movement patterns. The system architecture follows a modular, layered design comprising a responsive frontend interface, a robust backend server, and a dedicated AI processing engine. Experimental evaluations conducted across diverse testing scenarios demonstrate that the system consistently achieves accuracy rates between 93% and 96%, with low false positive rates and real-time response capabilities. These results underscore the system's viability as a scalable and practically deployable solution for modern educational institutions seeking to uphold examination integrity without disproportionate reliance on human invigilators.

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Keywords: Online Proctoring, Deep Learning, YOLOv8, Face Net, Behavior Analysis, Academic Integrity, Computer Vision.

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Son preference remains an important component to shape demographic trend and cultural reality in the country despite its socio-economic advancement. The research explores the prevalence, determinants, and consequences of son preference among residents of Nabadwip Municipality, West Bengal, through primary data collection from 100 participants. The adopted research methodology is a combination of qualitative and quantitative approaches, which include descriptive statistics, chi-square tests, odds ratios, and logistic regression analysis. The findings reveal a consistent but verities in dimension on son preference and related complex opinion of parents. While a majority (56%) favored balanced gender distribution in their family, 28% had a greater preference for male children, as also evidenced by the existing Child Sex Ratio of 909 girls to every 1000 boys. Education turns out to be the most important variable as higher education level tends to decrease gender bias. On the contrary, low socio-economic classes tend to exhibit son preference. Further, this research indicates that there is an influence of son preference on fertility practices such as delayed use of contraception and following the 'stopping rule'. Issues of lineage, old age security, and dowry continue to drive cultural influences in this regard. Although knowledge of gender equity programs is fairly high, their effects are rather minimal in terms of changing existing mindsets.

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Son Preferences, Gender equity, old age security, West Bengal.

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Consumer preferences for quick commerce services are primarily shaped by convenience and speed, with most users valuing time-saving benefits. Digital accessibility through smart phones and high-speed internet fosters seamless adoption of on-demand platforms. User-friendly interfaces and simplified ordering processes enhance satisfaction and repeat usage. Youth demographics show stronger inclination, driven by lifestyle needs and influencer marketing. Trust in reliability and delivery accuracy remains critical for sustained engagement. Overall, quick commerce thrives on the intersection of technology, convenience, and evolving consumer expectations.

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Quick commerce, Consumer preferences, Consumer behavior, Ecommerce trends, etc.,

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Timely intervention during emergencies plays a decisive role in reducing mortality and morbidity, particularly within the "golden hour." Delays in professional emergency response services necessitate the development of community-based interventions. Community First Responders (CFRs) provide an effective solution by enabling trained citizens to deliver immediate assistance. This paper reviews the role of CFR systems, examines global best practices, and proposes an implementation framework for occupational and public health settings. Evidence suggests that early intervention significantly improves survival outcomes. Integration of CFR systems is essential for building resilient communities.

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Community First Responder, Emergency Preparedness, CPR, First Aid, Occupational Health, Public Safety

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This study examines the growing role of predictive analytics powered by Artificial Intelligence (AI) in transforming employee retention strategies within modern organizations. With rising employee turnover posing significant financial and operational challenges, companies are increasingly leveraging advanced HR analytics tools such as SAP SuccessFactors and Workday to anticipate and address attrition risks proactively. The research exploress how AI-driven models analyze employee data--including performance metrics, engagement levels, compensation patterns, and behavioral indicators--to identify potential turnover trends and enable data-driven decision-making.The study adopts a mixed-method approach, combining primary data collected through employee surveys and HR professional interviews with secondary data from existing literature and industry reports. It evaluates the effectiveness, accuracy, and practical implications of predictive analytics in reducing attrition rates and enhancing employee engagement. Additionally, the research highlights key challenges such as data privacy concerns, algorithmic bias, and organizational readiness for AI adoption.

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Predictive Analytics, Employee Retention, Data-Driven Decision Making

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This review article examines the existing state of Diversity, Equity & Inclusion (DEI) practices in Indian organizations, with particular emphasis on ground-level implementation metrics. Drawing from contemporary research and organizational practices, the study identified gap between policy frameworks and practical implementation of DEI initiatives across Indian corporate, public sector and social enterprises. The article synthesized existing literature, highlights measurement challenges and proposes a comprehensive framework for assessing DEI effectiveness through quantifiable ground-level metrics. Findings revealed that while Indian organizations adopt DEI policies, significant disparities existed in implementation, particularly concerning caste, gender, regional diversity and disability inclusion. The study recommends evidence-based metrics and accountability mechanisms to strengthen DEI outcomes in the Indian organizational context.

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Keywords: Diversity, Equity, Inclusion, Indian Organizations, DEI Metrics, Workplace Diversity & Organizational Justice.

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This paper offers a comparative analysis of Art Spiegelman's Maus and Marjane Satrapi's Persepolis, examining how both graphic narratives employ the conventions of the medium to document personal and familial histories situated within major twentieth-century upheavals, the Holocaust and the Iranian Revolution respectively. Drawing on Marianne Hirsch's concept of postmemory and Viktor Shklovsky's notion of defamiliarisation, the paper argues that both authors deploy the graphic novel not as a merely unconventional choice of form, but as a deliberate aesthetic and epistemological strategy. The visual grammar of the medium, anthropomorphic characterisation, minimalist line work, symbolic visual motifs, and the relationship between panel, gutter, and caption enables Spiegelman and Satrapi to negotiate the gap between lived experience and inherited trauma, and to establish forms of testimony and self-authorisation unavailable through conventional prose. The paper further explores how each author constructs a narrative avatar through non-photorealistic self-representation, at once personalising and universalising their accounts of oppression and loss. Dominant visual symbols such as the mouse in Maus and the veil in Persepolis are read as sustained reminders of the regimes against which both narratives bear witness. The paper concludes that the graphic novel form uniquely facilitates the recovery of suppressed or destroyed memory, and that both texts function simultaneously as acts of mourning, political resistance, and archival reconstruction.

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 Keywords

Graphic Narratives, Post Memory, Autobiography, Trauma, Graphic Novel

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The growing demand for faster and reliable deliveries has increased the importance of warehouse efficiency in third-party logistics (3PL) operations. This study analyses how Warehouse Management System (WMS) usage and manpower management influence on-time delivery performance in a multi-client warehouse environment. Operational data collected before and after process improvements, along with employee feedback, were used for the analysis. The results show that better workforce planning and improved coordination with WMS helped reduce processing delays and improve operational flow. After implementation, on-time delivery increased from 72% to 85%, while average processing time decreased from 30-40 minutes to 23-27 minutes. The study shows that warehouse efficiency depends not only on technology, but also on how effectively manpower is planned and utilized during daily operations.

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3PL Warehouse; Warehouse Management System (WMS); Operational Efficiency; On-Time Delivery (OTD); Manpower Management; Labour Productivity; Warehouse Operations; Process Optimization

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This study explores the linguistic, cultural, and socio-anthropological dimensions of the Kurukh language, primarily spoken by the Oraon community in South Asia. Language is understood not merely as a communicative tool but as a repository of collective memory, identity, and cultural continuity. Kurukh, belonging to the Northern Dravidian subgroup, has historically evolved as an oral language, though recent developments such as the introduction of the Tolong Siki script have significantly contributed to its preservation and standardization. The research is based on field survey data collected from Rajibpur village in the Dakshin Dinajpur district, employing a random sampling method to document core vocabulary and everyday linguistic usage. The study analyzes Kurukh's phonological, morphological, and syntactic structures, highlighting its agglutinative nature, SOV word order, and distinctive phonetic features such as retroflex and nasal sounds. Detailed morphemic breakdowns of sample sentences demonstrate the internal grammatical organization of the language.The paper further examines the impact of bilingualism, language contact, and socio-educational factors on Kurukh. The growing dominance of languages like Hindi, Bengali, and English, along with the widespread use of Sadri as a lingua franca, has contributed to a decline in native language usage among younger generations. Despite these challenges, initiatives such as the promotion of the Tolong Siki script, inclusion of Kurukh in primary education, and digital dissemination efforts indicate possibilities for language revitalization. The study emphasizes the urgent need for developing a standardized grammar, comprehensive dictionary, and institutional support to ensure the sustainability of the language. Ultimately, preserving Kurukh is crucial not only for the Oraon community but also for safeguarding broader linguistic diversity and understanding the historical evolution of Dravidian languages.

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Kurukh Language, Oraon Community, Dravidian Linguistics,Language Endangerment, Tolong Siki Script, Sociolinguistics, Bilingualism, Core Vocabulary, Language Preservation, Field Survey.

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The present study aimed to examine the relationship between social media usage, lifestyle, and academic achievement among secondary school students in Gadag district of Karnataka State, with respect to gender, locality, type of school, and socio-economic status (SES). A sample of 200 students was randomly selected for the study. Data were collected using a Lifestyle Scale constructed and standardized by S.K. Bawa and S. Kour (2017), along with a Social Media Scale and an Achievement Test developed by the investigator. The tools were administered to secondary school students. Correlation analysis was employed to determine the significance of the relationship between the independent and dependent variables. Six objectives were formulated, and corresponding hypotheses were tested. The findings of the study revealed a positive and significant relationship between social media usage, lifestyle, and academic achievement among students, irrespective of gender (boys and girls), locality (rural and urban), type of school (government and private), and socio-economic status (high, middle, and low SES).

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Social Media, Lifestyle, Academic Achievement and Secondary School Students.

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Parkinson's disease (PD) is a progressive neurological disorder that affects motor functions and is often difficult to detect at an early stage. Traditional diagnosis mainly relies on clinical observation, which may delay timely treatment. In recent years, machine learning techniques have been widely explored to assist in the early diagnosis of Parkinson's disease by analyzing measurable physiological and behavioral signals. This paper presents a literature review of recent studies on machine learning based approaches for Parkinson's disease diagnosis using multiple data modalities. The reviewed studies utilize different types of data, including speech and voice recordings, handwriting patterns, gait signals, and sensor-based measurements collected from real world environments. A number of machine learning techniques like Support Vector Machine, Random Forest, and deep learning algorithm are examined and compared. The review highlights the strengths and limitations of existing approaches and emphasizes that multimodal systems, which combine multiple sources of data, can improve diagnostic reliability and accuracy. Results of this study provide an overview of current developments and indicate the potential of multimodal Machine learning methods to aid more effective Parkinson's disease diagnosis.

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Parkinson's Disease, Machine Learning, Multimodal Data, Deep Learning, Speech Analysis, Gait Analysis, Handwriting Recognition, Data Fusion, Predictive Modeling, Neurodegenerative Disorders.

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The study examines customer satisfaction with food delivery applications among university students. The main objective is to understand the factors influencing the level of use and satisfaction. A quantitative research design was adopted, and data was collected from 100 university students using a structured questionnaire through Google Forms. The data were analyzed using percentages and descriptive statistical methods. The results show that convenience is the main reason for using food delivery apps, with high satisfaction in delivery time and app interface, while pricing and customer support show moderate satisfaction. The study concludes that food delivery apps are widely used by students, but improvements in pricing transparency and service quality are needed.

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Customer Satisfaction, Food Delivery Apps, College Students, Consumer Behavior, Service Quality, Convenience, Delivery Time.

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This study examines the awareness and perception of smart health bracelets for elderly care in urban India. With the rise of wearable technology, such devices are becoming increasingly relevant in supporting elderly individuals. The study uses a survey-based approach with 60 respondents. Findings indicate moderate awareness and generally positive perception, while highlighting barriers such as cost and lack of familiarity with technology. The present study focuses on analyzing the level of awareness and perception of smart bracelets for elderly care among consumers in an urban setting. A descriptive research design was adopted, and primary data was collected through a structured questionnaire distributed via Google Forms. A total of 60 respondents participated in the study, including students, working professionals, and individuals with elderly family members. The data collected was analyzed using simple percentage methods and tabular representation to identify patterns and trends. The findings of the study indicate that while a majority of respondents are aware of smart health bracelets, detailed knowledge regarding their functions and benefits remains limited. However, the overall perception of these devices is positive, with many respondents recognising their usefulness in ensuring the safety and well-being of elderly individuals. Factors such as affordability, ease of use, and trust in technology were found to significantly influence consumer attitudes and purchase intentions. Despite the positive perception, certain barriers such as high cost, lack of technical familiarity among elderly users, and concerns about usability were identified. The study concludes that smart bracelets have strong potential in enhancing elderly care, especially in urban environments where constant supervision may not always be possible. Increasing awareness, improving user-friendly design, and making the devices more affordable can play a crucial role in encouraging wider adoption. The research also highlights the importance of understanding consumer perception as a key factor in the successful implementation of wearable healthcare technology.

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 Keywords

Smart Health Bracelets, Elderly Care, Wearable Technology, Consumer Awareness, Perception, Health Monitoring, Technology Adoption

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