Volume 14 | Issue 6 |

 Abstract

Fruit quality plays a crucial role in consumer satisfaction, food safety, market value, and supply chain efficiency. Traditional fruit quality assessment methods rely heavily on manual inspection, which is time-consuming, subjective, and prone to inconsistencies. This study presents a deep learning and image processing integrated model for real-time prediction of fruit quality in agricultural supply chains. The proposed framework combines InceptionResNetV2-based image classification with YOLOv11 real-time object detection to accurately identify and categorize fruits according to their quality conditions. The system processes fruit images through preprocessing techniques, feature extraction, and automated classification to detect good, bad, and mixed quality categories across multiple fruit types, including apples, bananas, guavas, limes, oranges, and pomegranates. A Flask-based web application enables image upload, live camera detection, prediction visualization, and database-driven result management. The integration of deep learning algorithms with computer vision techniques enhances prediction accuracy, operational efficiency, and decision-making capabilities. Experimental evaluation demonstrates reliable performance under diverse conditions, supporting automated quality monitoring, reducing human intervention, minimizing post-harvest losses, and improving overall agricultural supply chain productivity and sustainability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fruit Quality Assessment, Deep Learning, Image Processing, YOLOv11, InceptionResNetV2, Computer Vision, Real-Time Detection, Agricultural Supply Chain, Flask, Fruit Classification.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Virtual Reality (VR) is an advanced technology that creates interactive three-dimensional environments, enabling users to experience and engage with digital content in a realistic manner. This study presents RuraVerse, an immersive virtual reality framework designed to enhance educational opportunities in rural communities. Limited access to quality learning resources, laboratory facilities, and experienced educators often affects academic development in remote regions. The proposed system utilizes VR technology to deliver engaging lessons, virtual laboratories, historical explorations, and skill-based training through interactive simulations. The framework aims to improve student participation, conceptual understanding, and knowledge retention by providing experiential learning experiences. Cloud connectivity and digital learning resources support remote access to educational content while reducing geographical barriers. The architecture integrates VR devices, educational modules, multimedia content, and network communication to create an effective learning ecosystem. Expected outcomes include improved academic performance, increased digital literacy, and greater educational inclusiveness. The study demonstrates how immersive technologies can support sustainable rural development by making modern, high-quality education accessible, engaging, effective, and available to learners regardless of location.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Virtual Reality (VR), Rural Education, Immersive Learning, Interactive Learning, Digital Education, Virtual Laboratories, Smart Learning System, Educational Technology, Remote Learning, RuraVerse.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The current research study focuses on relationship between Mental Forms Of The Brain In Influencing Emotional Intelligence Study On Hemisphercity a normative survey method was student sampling to select a sample size of 460 Teachers students . The primary objective of the study is to evaluate the reading Habits of future class room Teachers. The data for the study was collected from a sample of (130 male and 230 female) future class room teachers all over tamilnadu. Sample distribution. Statistical Techniques Used mean t-test.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Brain On Hemisphercity Emotional Intelligence.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The growth of online shopping is rapid as people buy goods over the internet by selecting items and paying for them on the internet. This has helped make shopping easier and quicker. With the popularisation of online shopping, a large amount of data about users' online activities is available on the web. Such data could be used for understanding user behaviour and classifying online customers.The proposed system aims to extract the patterns of the online shopping behaviour of the users and to display them in graphical form. These graphical representations help the administrators in the decision making process by identifying the products that are frequently purchased and the products that are in demand at the moment. It combines multiple information sources and simulates decision-making by using the statistical features of graphical hidden state model.The proposed model has achieved a performance improvement of about 30% on million-click datasets. The application is a web platform on the internet where users can view and buy products. User behaviour is tracked in sequence and then analysed and graphically displayed. This allows administrators to make decisions more quickly and efficiently. The graphical approach simplifies the visualisation of data, so administrators can quickly gain useful insights by watching patterns in customer behaviour.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Online Shopping, User Behaviour Analysis, Data Mining, Graphical Pattern Analysis, Recommendation System.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: Project: Smart ID Card System (Location Based Access Control). The project aims to develop a secure and efficient access control system based on smart ID cards and location verification. The system will be able to improve the security of the organization by allowing only the users who are in the authorised geographical areas. It combines employee identity management, real-time location tracking and access validation to prevent unauthorised entry and improve monitoring capabilities. The proposed system consists of modules such as Employee Management, Location Management, Access Verification and Access Logging. Administrators can track employee records and designate authorised locations with latitude, longitude, and access radius parameters. The system checks the user's current location against the predefined zones and allow or deny access when attempting to access. Each access attempt is logged with timestamp and location information for audit and security purposes. Application is developed using Java technologies with connectivity to database using JDBC, ODBC concept. The system is designed to be secure, portable, scalable and easy to use. Testing methods such as unit testing, integration testing, functional testing and system testing were performed for reliability and performance assurance. The project shows how smart ID systems combined with location-based services can enhance security in the workplace, automate monitoring processes and reduce risks of unauthorised access.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart ID Card System, Location-Based Access Control, Access Verification, Employee Management, Real-Time Security Monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Malvai Viah Vich Dharam Ate Lok Dharam Ik Vishleshanatmak Adhyayan

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Creative Commons Attribution 4.0 and The Open Definition

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Malvai Viah Vich Dharam Ate Lok Dharam Ik Vishleshanatmak Adhyayan

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The school system in India has changed radically with the training strategies utilized by the teachers to think of the academic improvement of the students. The school system or the showing calling is the one inside which, an individual can harvest incredible benefit from the information on capacity to appreciate people on a profound level inferable from repetitive human correspondence that exists. As our society is turning out to be more moderates, change has become thing to take care of and this change has impacted our way of life and our schooling system. This fast change has placed part of liabilities on the shoulder of the teachers. Concrete, equipped, and successful teachers' helps in national development. In this really long period, the job of the teachers is to understudy by fostering students their higher request thinking abilities, successful correspondence, coordinated effort, and different necessary abilities alongside choices about training to accomplish different results with and for students' in their classes and make decisions about how best to assist their students' with learning in the conditions in which they instruct. Teachers should likewise have the capacity to appreciate the students is on a profound level as it's the capacity to figure out the feelings of oneself and individuals around us. The capacity to understand the students' on a deeper level is a kind of mindfulness that empowers us to perceive our sentiments and deal with our feelings and which will likewise have the extraordinary effect in the expectation to learn and adapt.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mental Health, Knowledge, Teachers Efficacy

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Children at school undergo physical, psychological, social, and emotional transformations. The positive and negative experiences people have during this vulnerable time in their lives, as well as the influence of other people and events, have a lasting effect on them for the rest of their lives. The purpose of this study is to evaluate how twelve weeks of yoga and aerobic workouts affect the schoolchildren's quality of life. 60 pupils from senior secondary schools in the North 24 Parganas area (West Bengal), ages 14 to 16, are involved in the study. They were split up into three equal groups, each with twenty participants (n = 20). Group I practiced yoga, Group II engaged in cardiovascular exercises, and Group III served as the control. The testing period is restricted to 12 weeks, or around three months, three days a week, and 45 to 60 minutes each day with appropriate warm-up and cool-down routines. Standardized instruments were used to gather the primary data. Analysis of covariance (ANCOVA) will be used to determine if the pretest and posttest means of experimental groups I, II, and the control group are significant. For significance, the degree of confidence is set at 0.05. Additionally, if the F-ratio of the adjusted post-test means is significant, the LSD post-hoc test will be used to determine the paired mean difference, if any, between the groups for each variable independently. According to the study's findings, yoga and aerobic workouts significantly improved quality of life (p<0.05). Conclusions: Given the substantial impact that yoga and aerobics exercises have on schoolchildren's quality of life, policymakers should focus on implementing yoga and aerobics at the school level through standardized curricula and training instructors to encourage and inspire students to learn and practice these disciplines at a young age.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Yoga practices, Aerobics exercises, Quality of life , school children.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Water-borne diseases remain a significant public health concern, particularly in regions where access to safe drinking water and sanitation facilities is limited. Contaminated water serves as a transmission medium for diseases such as cholera, typhoid, diarrhea, hepatitis A, dysentery, and giardiasis, resulting in severe health and economic impacts. This study presents a Smart Community Health Monitoring and Early Warning System for Water-Borne Diseases using machine learning techniques to estimate disease risk from water quality parameters. The system analyzes factors including temperature, pH, dissolved oxygen, turbidity, conductivity, total dissolved solids, and Water Quality Index to assess contamination levels and predict disease susceptibility. Data preprocessing, feature extraction, and multi-output classification techniques are employed to enhance prediction accuracy and reliability. Multiple machine learning algorithms, including Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, and XGBoost, are evaluated for performance comparison. An early warning mechanism generates alerts when high-risk conditions are detected, supporting timely preventive measures. Experimental results demonstrate effective risk prediction across multiple disease categories, indicating that the proposed framework can support proactive community health monitoring, disease prevention, and informed decision-making.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Water-Borne Diseases, Machine Learning, Water Quality Analysis, Early Warning System, Multi-Output Classification, XGBoost, Random Forest, Community Health Monitoring, Risk Prediction, Water Quality Index (WQI).

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A Historical Study of the Development of Science and Technology in the Gupta Period

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

A Historical Study of the Development of Science and Technology in the Gupta Period

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Creative Commons Attribution 4.0 and The Open Definition