Volume 14 | Issue 4 |

 Abstract

The present study aimed to compare the Work Ability Index (WAI) among academic female staff across different age groups--young, middle-aged, and senior in in polytechnic colleges Bhopal. Work ability is a crucial determinant of productivity and occupational well-being, especially in academic settings where mental and physical demands coexist. A total of ninety (N=90) academic female staff members were selected using purposive sampling, with 30 participants in each age group. The Work Ability Index questionnaire was used to assess work ability. Statistical techniques such as mean, standard deviation, and one-way ANOVA were employed to analyze the data. The findings revealed significant differences in WAI among the three age groups, indicating a decline in work ability with increasing age. The study highlights the need for age-specific interventions to maintain and improve work ability among academic female professionals.

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 Keywords

Work Ability Index, Academic Female Staff, Age Groups, Occupational Health, Physical Activity.

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This paper proposes a secure federated learning framework enhanced with blockchain technology for use in edge computing environments. The system introduces decentralized trust management, encrypted aggregation of model updates, smart contract-based validation, a reputation-based client scoring mechanism, and tamper-resistant auditing to ensure transparency and security. It effectively addresses major threats such as model poisoning, Sybil attacks, and data integrity issues while maintaining scalability and low latency. Experimental results show that the proposed framework achieves better accuracy and stronger attack resistance compared to traditional federated learning approaches.

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 Keywords

Block-chain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing

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 Abstract

EMOTIONAL INVALIDATION IN CORPORATE CRISIS COMMUNICATION

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EMOTIONAL INVALIDATION IN CORPORATE CRISIS COMMUNICATION

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This study examines the growing complexity of modern telecommunication networks and the increasing risks associated with cyber threats. With the expansion of technologies such as 5G, cloud computing, and IoT, network infrastructures have become more vulnerable to attacks like intrusions and service disruptions. Traditional intrusion detection approaches often fail to identify new and evolving threats due to their reliance on predefined rules Traditional intrusion detection systems (IDS) rely on static rule-based and signature-based mechanisms that struggle to cope with modern cyber threats. These systems are limited in their ability to detect unknown attacks, adapt to dynamic traffic patterns, and scale with increasing data volumes. Furthermore, high false alarm rates significantly reduce operational efficiency and increase the workload of network administrators. This research proposes a Artificial Intelligence driven anomaly detection framework tailored for telecom network environments. The proposed system integrates advanced preprocessing techniques, hybrid feature selection using Weighted Adaptive Feature Selection (WAFS), Synthetic Minority Oversampling Technique (SMOTE) for data balancing, and a hybrid ensemble model combining a Deep Neural Network (DeepAnomNet) and Random Forest classifier. The UNSW-NB15 dataset is employed for training and evaluation as it represents modern network traffic and diverse attack scenarios. The model is evaluated using multiple performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Results demonstrate significant improvements in detection capability and reduction in false positives compared to conventional machine learning models. A real-time Telecom Security Information and Event Management (SIEM) dashboard is also developed to visualize anomaly probability and threat severity. The proposed system offers a scalable, adaptive, and intelligent solution for securing next-generation telecom infrastructures.

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 Keywords

Telecom Networks, Anomaly Detection, Artificial Intelligence, Deep Learning, LSTM, Autoencoder, Network Security, Intrusion Detection, Time Series Analysis, Predictive Maintenance, 5G Networks, Machine Learning, Big Data Analytics, Network Monitoring.

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Cloudbursts are extreme localized rainfall events that occur within a short duration and often result in severe consequences such as flash floods, landslides,and significant damage to life and infrastructure. Traditional cloudburst prediction methods based on numerical weather prediction models face limitations in accurately forecasting such sudden and small-scale events due to their complex and non-linear nature. With the increasing availability of meteorological high-resolution data from satellites, Doppler radars, and ground-based weather stations, there is a growing need for advanced data-driven approaches that can effectively analyze these datasets for improved cloudburst prediction. This study focuses on integrating meteorological data with machine learning techniques to enhance cloudburst prediction accuracy. Various atmospheric parameters such as rainfall intensity, temperature, humidity, pressure, wind speed, and cloud characteristics are analyzed using machine learning and deep learning models including Random Forest, Support Vector Machines, and Long Short- Term Memory networks. The proposed approach aims to identify hidden patterns and early indicators of cloudburst events, providing timely and reliable predictions. The outcomes of this work contribute to the development of efficient early warning systems, supporting disaster risk reduction

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 Keywords

Cloudburst Data, Prediction, Machine Learning, Extreme Rainfall Events, Weather Forecasting, Early Warning System, Disaster Management, Real-Time Data Analysis

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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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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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 Keywords

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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 Keywords

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

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