Volume 14 | Issue 5 |

 Abstract

Somdevsurivirachit Neetivakyamritam main Nyaaya Vyavastha

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2320-2882

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This project aims to enhance the classification of bone fractures in X-ray images using deep learning models. Accurate identification of fractures is crucial in the medical field, and this project explores multiple approaches to improve classification performance. The models used include VGG19 combined with Random Forest, MobileNet with Support Vector Machine (SVM) and Random Forest, EfficientNet with SVM and XGboost,. Each model is trained and tested to evaluate its ability to correctly classify different types of bone fractures. By comparing these models, the project identifies strengths and limitations of both standalone and hybrid architectures. A simple frontend interface is created using HTML, CSS, and JavaScript to allow users to interact with the classification system and view predictions. The application is not intended for deployment but rather to help understand the workflow of deep learning in medical imaging. This work provides insight into how deep learning and traditional machine learning methods can be combined for better results in image classification tasks, particularly in healthcare.

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Deep Learning, Bone Fracture, X-ray, VGG19, MobileNet, EfficientNet, SVM, Random Forest, Classification.

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This study explores the potential of e-resource-based employment and livelihood diversification in the disadvantaged haor regions of Bangladesh, where traditional occupations like agriculture and fishing dominate. Despite the challenges posed by climatic vulnerabilities and limited access to modern resources, these communities exhibit adaptability through informal digital entrepreneurship and mobile-based services. The study examines the current state of digital infrastructure, skill levels, and socio-economic barriers that limit the full integration of technology in these regions. A mixed-methods approach, combining qualitative interviews and quantitative surveys, was employed to assess the role of ICT (Information and Communication Technology) in improving local livelihoods. Findings suggest that while there is a growing interest in digital solutions, key barriers such as poor internet connectivity, low digital literacy, and gender disparities need to be addressed. The study proposes targeted interventions, including digital literacy programs, improved infrastructure, and the promotion of female entrepreneurship, to facilitate a more inclusive digital economy in haor areas. Ultimately, the research highlights the significant opportunities and challenges of integrating e-resource-based employment strategies into the rural livelihoods of Bangladesh, aiming to contribute to sustainable rural development in vulnerable regions.

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E-resource, Digital Employment, Haor Regions, Rural Livelihoods, ICT for Development, Rural Entrepreneurship, Rural Development

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College students experience stress as a complex condition which involves multiple areas of their mental and emotional states together with their physical health and social interactions. Researchers currently study stress through combined stress metrics which fail to show how different stressors develop over time. The present study addresses this gap by examining the effects of Rajyoga meditation using a dimension-wise analytical framework. A quasi-experimental pre-test-post-test control group design was employed with 100 college students divided into experimental and control groups. Data were analyzed using descriptive statistics, paired samples t-tests, independent samples t-tests, and Cohen's d. The experimental group participated in an 8-week Rajyoga meditation intervention, while the control group received no structured training. The researchers used descriptive statistics together with paired samples t-tests and independent samples t-tests for data analysis, while effect size estimation was conducted using Cohen's d. The experimental group demonstrated significant reductions across all stress dimensions (p < .001). The change magnitude differed among domains because cognitive and emotional dimensions showed maximum decline while behavioral and physical dimensions achieved moderate progress and social stress experienced minimum transformation. The experimental group displayed significantly lower post-test stress scores when compared to the control group across all testing dimensions. Rajyoga meditation functions primarily through internal regulatory mechanisms which include improved thought awareness and cognitive-emotional restructuring, that ultimately affect social and behavioral development. The study establishes that multidimensional analysis functions as vital component for understanding stress reduction which occurs through different processes across various domains.

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Rajyoga Meditation; Multidimensional Stress; Cognitive Stress; Emotional Regulation; Quasi-Experimental Design

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This project presents a smart and portable mini digital tool designed for the prediction of optimum maturity of sugarcane crops using NIR (Near Infrared) sensors, image processing, deep learning, and IoT technologies. The main objective of the system is to identify the ideal harvesting stage of sugarcane accurately, helping farmers and sugar industries improve sugar yield and crop quality. Traditional methods for checking sugarcane maturity are time-consuming, laboratory dependent, and often inaccurate. To overcome these limitations, the proposed system uses a combination of NIR sensors for sugar content estimation, camera-based image processing for cane color and texture analysis, and deep learning algorithms for intelligent maturity prediction. The NIR sensor collects spectral data related to sugar concentration and moisture content, while image processing techniques extract important features such as color variations, texture, and surface patterns of the sugarcane. The collected data is processed using deep learning models such as Convolutional Neural Networks (CNN) to classify the maturity level of the crop. The entire system is controlled using ESP8266/ESP32 microcontroller technology integrated with IoT for real-time monitoring and cloud-based data storage. The predicted maturity status is displayed on a digital display and can also be viewed through a mobile application or web dashboard. The proposed mini digital tool provides a low-cost, portable, and efficient solution for smart agriculture applications. It reduces manual effort, increases prediction accuracy, and supports farmers in making better harvesting decisions. The project can further be enhanced using Artificial Intelligence and advanced Machine Learning models for fully automated crop monitoring systems.

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NIR Sensor, Sugarcane Maturity Prediction, Deep Learning, Image Processing, IoT, Smart Agriculture, CNN, ESP8266, Crop Monitoring, Portable Agricultural Device

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Hair plays a vital role in human attractiveness and reflects internal health. Since ancient times, herbs have been used for cleansing and maintaining hair. Herbal shampoo powders made from ingredients like brahmi, reetha, shikakai, amla, aloe vera, fenugreek, and neem are preferred due to their safety and effectiveness. These natural formulations help remove dirt, sebum, and dandruff while promoting hair growth, strengthening, and conditioning without causing damage. Shampoo primarily cleanses the scalp and hair by removing accumulated impurities. Compared to synthetic products, herbal shampoos are gaining popularity as they nourish hair and improve its health, offering a natural and gentle alternative for hair care.

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Polyherbal shampoo, Natural ingredients, Herbal powder, Anti-dandruff, Organoleptic properties, Cleansing ability, Conditioning agent.

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Plant diseases are one of the major factors affecting crop quality and agricultural productivity. Early detection of plant diseases is important for reducing crop damage and improving farming efficiency. In this work, a Deep Learning-based plant disease detection system is developed using Convolutional Neural Networks (CNN) for identifying diseases from plant leaf images. The developed model uses the PlantVillage dataset containing healthy and diseased leaf images from different crop categories. Before training the model, preprocessing techniques such as image resizing, normalization, and data augmentation are applied to improve prediction performance. The CNN model automatically extracts important features from leaf images and classifies different plant diseases with better accuracy. During testing, the system successfully identified diseases such as bacterial spot, early blight, late blight, and leaf mold within less processing time. The developed model reduces manual effort and supports faster disease identification compared to traditional disease detection methods. The obtained results show that Deep Learning techniques can provide reliable support for smart agriculture applications. The proposed work can be further improved by integrating mobile applications, IoT-based monitoring systems, and real-time crop analysis technologies.

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Deep Learning, Plant Disease Detection, Convolutional Neural Network, Image Processing, Smart Agriculture, CNN.

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Social protection programmes play an important role in reducing poverty and promoting inclusive development, but their effectiveness depends largely on governance systems guiding their formulation and implementation. This study examines adherence to good governance principles in the implementation of social protection policy programmes in India, focusing on financial management, data governance, stakeholder engagement, and institutional collaboration. The study adopts a qualitative descriptive case study design and uses secondary data from government reports, institutional publications, and policy documents. Qualitative content analysis was applied to analyze governance practices. The findings reveal governance challenges including weak financial oversight, limited information integration, inadequate stakeholder engagement, and poor coordination among implementing institutions. These gaps contribute to inefficiencies and limit programme effectiveness. The study concludes that strengthening accountability, data management systems, stakeholder participation, and institutional collaboration is essential for improving the performance and effectiveness of social protection programmes.

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inclusive development, good governance, fiscal management, Societal Protection and stakeholder

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Rapid urbanization and population growth have intensified the global solid waste management challenge. Manual segregation is labor-intensive, error-prone, and hazardous. This paper presents an automated multi-class waste segregation system that integrates deep learning-based object detection with embedded hardware automation. The system employs a Raspberry Pi 4 as the central controller, a YOLOv6 based classification model, infrared proximity sensors, servo-motor-driven diverter arms, and a DC motor driven conveyor belt. Waste items placed on the conveyor are detected by a proximity sensor, halting the belt for image capture. The captured image is classified into one of four categories--plastic, paper, glass, or biodegradable--and the appropriate servo mechanism routes the waste to the correct collection bin. Experimental results demonstrate reliable real-time detection and sorting with minimal human intervention, offering a scalable solution for smart waste management applications.

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Waste Segregation, YOLOv6, Raspberry Pi, Object Detection, Conveyor Belt, Servo Motor, Deep Learning, Embedded Systems.

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Quinoxaline and its fused heterocyclic derivatives constitute an important class of nitrogen-containing heterocyclic compounds possessing remarkable chemical versatility and extensive biological activities. Owing to their electron-deficient aromatic framework and facile functionalization, quinoxaline derivatives have attracted considerable attention in medicinal chemistry, pharmaceutical sciences, and synthetic organic chemistry. The present review summarizes the chemistry, synthetic methodologies, structural modifications, and pharmacological significance of quinoxaline derivatives reported over the years. Various synthetic approaches including condensation of o-phenylenediamines with 1,2-dicarbonyl compounds, intramolecular cyclization reactions, ring contraction methods, and synthesis of quinoxaline-N-oxides have been discussed systematically. Particular emphasis has been placed on the diverse biological applications of quinoxaline analogues such as antibacterial, antifungal, antiviral, anticancer, anti-inflammatory, antituberculosis, anti-HIV, antidepressant, and antiglaucoma activities. Structure-activity relationship studies reveal that substitution patterns and fused heterocyclic systems significantly influence biological potency. In addition, recent developments in fused quinoxaline systems and their pharmaceutical relevance are highlighted. This review demonstrates that quinoxaline derivatives remain promising scaffolds for the development of novel therapeutic agents and functional organic materials.

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Keywords: Quinoxaline, Heterocyclic compounds, Anticancer activity, Antibacterial activity, Quinoxaline-N-oxides, Medicinal chemistry, Fused quinoxalines

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