Volume 14 | Issue 4

 Abstract

The rapid advancement of digital technologies has significantly transformed creative industries, including textile design. Virtual Reality (VR) and Augmented Reality (AR) are emerging as powerful tools that enhance visualization, creativity, and experiential learning in textile design education and professional practice. These immersive technologies enable designers and students to simulate fabrics, patterns, textures, and garments in virtual environments, thereby reducing dependency on physical samples and traditional trial-and-error methods. The present study examines the role, applications, and impact of VR and AR in textile design education and industry practice. The research adopts a descriptive approach using primary and secondary data to analyze awareness, usability, and perceived benefits of VR and AR among textile students and professionals. The findings reveal that immersive technologies improve learning efficiency, creativity, cost-effectiveness, and sustainability in textile design processes. Despite challenges such as high initial investment and skill requirements, VR and AR have immense potential to redefine the future of textile design education and practice in the digital era.

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 Keywords

Virtual Reality, Augmented Reality, Textile Design, Digital Education, Immersive Technology

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Social Realism, Vikram Seth, The Golden Gate, Hypermodernity, Alienation, Yuppie Culture, Indian Writing in English, Silicon Valley, Identity, Postmodernism, Loneliness.

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This paper examines India's evolving role in climate diplomacy within major international environmental agreements, including the UNFCCC, Kyoto Protocol, and Paris Agreement. It explores how India negotiates the tension between developmental priorities and increasing global expectations for climate mitigation. The study adopts a qualitative research approach based on secondary data, policy analysis, and academic literature. It argues that India's climate diplomacy has transitioned from a defensive, equity-based position to a more strategic and implementation-oriented approach. However, domestic political economy constraints--particularly coal dependence, energy demand, and financial limitations--continue to shape India's negotiation stance. The study further highlights that India's emphasis on climate justice and equity remains central to its diplomatic identity and contributes significantly to global climate governance debates. The paper concludes that India's effectiveness in future climate diplomacy will depend on its ability to align domestic implementation with international commitments while strengthening its leadership through institutional and coalition-based initiatives.

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 Keywords

Climate Diplomacy, India, UNFCCC, Paris Agreement, Climate Justice, CBDR, Energy Transition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the linguistically diverse areas, access to basic healthcare information is scarce as it is affected by language barriers, delayed medical care, and unavailable digital support tools. This paper presents a multilingual healthcare assistant for preliminary symptom guidance, emergency severity assessment, and nearby hospital discovery across Indian languages. The system combines a web-based interface with machine learning models and rule-based reasoning to provide reliable and safety-aware answers. It has various Indian languages that allow interaction with users of different backgrounds. The architecture is a React based frontend with Fast API based backend, which guarantees scalable and real-time performance. Experimental evaluation demonstrated 90% symptom classification accuracy, 0.88 macro F1-score, and end-to-end response latency below 320 ms. It is a non-diagnostic design system and is focused on emergency escalation during the high-risk situations. Findings suggest that with the use of multilingual AI-based assistants in healthcare, preliminary healthcare support can be substantially more accessible and responsive.

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 Keywords

Multilingual Healthcare, Symptom Analysis, Emergency Severity Classification, Machine Learning, Conversational Systems, Healthcare Accessibility

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 Abstract

MilkRoute is a browser-based Progressive Web Application designed to digitize milk collection and payment workflows for rural dairy operations. It enables field agents to record milk entries with quality parameters (FAT, SNF, volume), automate grading, and process UPI-based payments in real time. The system supports multi-route collection, village tracking, and Bluetooth-enabled milk analyzer integration for accurate data capture. It also provides analytics dashboards for monitoring collection trends, payments, and quality distribution. Built using HTML, CSS, and JavaScript, MilkRoute operates fully on the client side, making it suitable for low-connectivity environments. The solution offers a scalable, cost-effective approach to modernizing last-mile dairy management.

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Keywords: MilkRoute, Milk Collection Management, Rural Dairy Cooperative, Progressive Web App, UPI Payment, Bluetooth Milk Analyzer, FAT/SNF Quality Analysis, Route Management, Village Visit Workflow, Digital Payment, Collection Analytics, Offline-First Web Application.

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The meaning of 'women empowerment' is to establish women's rights which are nothing but human rights. It is clear by the speech of Hillary Clinton, the first lady of the United States at that time, at the fourth world conference on women in year 1995.These rights are- right to live respectfully, right to choose or make choices, right to take decisions about their own life, right to speak, freedom of movement and so on, are crucial for giving rise the sense of self-worth, self- confidence and self-respect in women. In India, as a consequence of many prevailing socio-cultural norms, these rights are neglected. Slow-paced social changes although lower down the intensity of negligence nevertheless, male-child preference in most of the Indian states tells us the story of women's rights abuses.

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Women's rights, Male -child preference

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Creative Commons Attribution 4.0 and The Open Definition

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The present study titled "A Comparative Study of Consumer Preference Between Rapido and Uber Bike Services" aims to analyze and compare the preferences of consumers towards app-based bike taxi services. With the rapid growth of urbanization and increasing traffic congestion, bike taxi services have emerged as a convenient, time-saving, and cost-effective mode of transportation. This study focuses on identifying the key factors influencing consumer choice, such as pricing, safety, availability, service quality, and customer satisfaction. The research is based on both primary and secondary data. Primary data was collected through a structured questionnaire from a sample of respondents, while secondary data was gathered from various websites, articles, and journals. Statistical tools such as percentage analysis, ranking analysis, and chi-square test were used for data analysis. The findings of the study reveal that Rapido is widely preferred due to its affordability and quick availability, whereas Uber bike services are chosen for their reliability, brand image, and safety measures. Factors like convenience, time-saving, and promotional offers also play a significant role in influencing consumer preference. The study concludes that both services have their own advantages, and improving safety features, service quality, and customer support can enhance user satisfaction. The research provides valuable insights into consumer behavior and highlights the growing importance of bike taxi services in modern urban transportation.

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 Keywords

Bike taxies services , bike taxi , rapido , uber , bike taxi services

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 Abstract

What happens when a person is judged before they speak? How does it feel to be denied water? How does it feel to be addressed by something you may not like? These questions are lived experiences and realities for millions of people in India. The caste system decides who a person is, what they will do, where they will live and how other people will treat them. The caste system divides people in society. These are the situations one cannot choose. It is an accident from birth and continues for life. Caste is not just a category; it decides who is powerful and who is not. It divides people into two categories: on top, there are upper-caste people, who get access to education, land, and money. Whereas other people at the bottom, there are castes who are treated as "impure" or "untouchables" also addressed as Dalits, who have faced discrimination and oppression for their entire life.

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Caste and Oppression: Voices from the Margins in the Indian Literature

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This paper presents an industrial sorting robot based on ESP32-CAM vision processing and a pick-and-place robotic arm for automated industrial object handling. The system captures real-time video using ESP32-CAM and processes the frames using Python OpenCV for shape recognition. Objects are classified as circle, triangle, square, and rectangle using contour approximation and polygon analysis. Based on the detected shape, serial commands are transmitted to an Arduino-controlled six-servo robotic arm. The robot performs smooth pick-and-place movement using relay-based gripper control and places objects into their corresponding bins. The proposed system significantly improves sorting speed, reduces human intervention, and provides reliable industrial automation performance.

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Industrial Automation, Pick and Place Robot, ESP32-CAM, Shape Detection, OpenCV, Arduino, Smart Sorting

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 Abstract

Electricity theft continues to be one of the most per- sistent and economically damaging problems faced by power dis- tribution utilities across the world. Whether it happens through direct meter tampering, bypassing of metering equipment, or falsification of consumption records, the impact ripples across the entire grid - increasing operational costs, destabilizing energy distribution, and ultimately burdening honest consumers with inflated tariffs. Conventional detection methods, which largely depend on manual field inspections or simple threshold-based flagging, are too slow and too imprecise to keep pace with the scale of modern smart meter deployments.

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Electricity Theft Detection, Isolation Forest, Anomaly Detection, Smart Meter, Adaptive Retraining, Sliding Window, Machine Learning, Django, Time-Series Classification, Non-Technical Loss

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Cultural heritage is constituted by the dynamic relationship between tangible and intangible dimensions. Historic buildings traditionally functioned as socio-cultural systems where architectural spaces enabled ritual, performance, and collective memory. However, contemporary transformations, functional incompatibility, and deterioration have disrupted this interdependence, leading to cultural discontinuity. This research investigates the relationship between architectural spaces, cultural practices, and community interactions in historic buildings through cultural mapping. By identifying and documenting the interdependencies between space, practice, and users, the study develops an analytical understanding of how built heritage supports cultural activities. The approach shifts the focus of conservation studies from viewing historic buildings as static physical objects to understanding them as living cultural system.

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Cultural mapping, cultural practices, historic buildings, intangible heritage, tangible heritage

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Multiplication is a fundamental arithmetic operation that significantly influences the performance of modern digital systems such as processors, digital signal processing units, and embedded systems. Conventional multiplier architectures, including array and sequential multipliers, suffer from limitations such as high propagation delay, increased power consumption, and poor scalability for higher bit-width operations. This project presents the design and implementation of an efficient parallel multiplier architecture to improve multiplication speed and reduce computational delay. The proposed system generates partial products simultaneously and accumulates them using optimized adder structures. Modified Booth Encoding is employed to reduce the number of partial products, while carry save adders are used to minimize carry propagation during partial product accumulation. A high-speed final adder produces the accurate multiplication result. The proposed architecture supports both signed and unsigned multiplication using a unified design, thereby reducing hardware redundancy and improving flexibility. The design is described using hardware description language and verified through simulation and synthesis. The results demonstrate improved performance compared to conventional multiplier designs, making the proposed parallel multiplier suitable for modern VLSI and high-speed digital applications.

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 Keywords

FPGA, Xlinx ISE, Modalism, Matrix Multiplication, Parallel Block-Scheduling.

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This research study is to find correlations between gaming disorder, self esteem and academic performances among the high school students of Kannur District. Most of the research existing today is in the context of internet addiction, smart phone addiction, decline in self - esteem and academic performance. But none of the studies were conducted in Indian Territory. The behaviour and addiction status of children are crucial as it brings in lot of challenges both for them as well as to the society as it triggers even the suicidal tendency. The proposal tries to establish correlation by considering a sample of 300 high school students (age 13 - 15), with a positive trend towards gaming disorder. These students will be administered with the shortet version of Gaming Addiction Scale and Coopersmith Self - esteem inventory. Also the academic performance of the subjects will be collected. The data will be analysed using Pearson's correlation coefficient(r), t tests and ANOVA.

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Gaming disorder, Self-esteem, Academic performance, High school students, Kannur District, Gaming Addiction Scale, Coopersmith Self-esteem Inventory, Internet addiction, Adolescent behaviour, Pearson correlation, t-test, ANOVA.

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 Abstract

Vitamin and mineral deficiencies remain a widespread public health concern, particularly in developing regions where access to diagnostic healthcare is limited. Visible symptoms of such deficiencies frequently appear on external body parts including the skin, nails, eyes, lips, tongue, and hair, making image-based detection a practical and non-invasive screening approach. This paper presents a deep learning-based web application designed to detect vitamin and mineral de-ficiencies from images of human body parts. The proposed system employs InceptionV3, a convolutional neural network pretrained on the ImageNet dataset, fine-tuned through transfer learning to classify six categories of deficiencies: Vitamin A, Vitamin B complex, Vitamin C, Vitamin D, Vitamin KE, and Mineral deficiencies including zinc, iron, biotin, and protein. The dataset used for training and evaluation is publicly avail-able at https://www.kaggle.com/datasets/udaykarthik21bce9252/ vitamin-defficiency-dataset. The model achieves a classification accuracy of 85%. The system is integrated into a Django-based web application supporting user authentication, real-time image-based prediction, confidence score display, and downloadable health reports. This work establishes the feasibility of combining computer vision with accessible web technologies to support early health awareness in a user-friendly manner.

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Vitamin Deficiency Detection, Deep Learning, In-ceptionV3, Transfer Learning, Django, Medical Image Classifi-cation, Convolutional Neural Network.

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Abstract Unintentional medicine overdose and prescriptionrelated toxicity have become critical public health challenges, particularly in settings with high polypharmacy and limited real-time clinical decision support. Traditional risk assessment techniques rely on manual chart review and static rules, which struggle to capture complex, evolving prescription patterns. In this paper, a machine-learningdriven framework is presented for predicting patient-specific overdose risk using routinely collected clinical and prescription data. The proposed approach utilizes supervised learning models, including Logistic Regression, Random Forest, and Gradient Boosting, to learn patterns associated with high-risk dosage combinations, comorbidities, and prior adverse events. The system is organized as a modular architecture consisting of a data preprocessing pipeline, a model training and evaluation core, and a risk scoring service that can be integrated into clinical applications. Experimental design and evaluation metrics are described to provide a reusable blueprint for academic and project implementations. The results from a prototype implementation indicate that the proposed system can achieve competitive accuracy and recall, demonstrating its potential to support early intervention and safer prescribing practices.

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Medicine Overdose Prediction, Machine Learning, Clinical Decision Support, Risk Scoring, Electronic Health Records.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Pesticides are widely used in agriculture and domestic environments, but their improper use poses significant health hazards. Housewives are particularly vulnerable due to their involvement in food handling and household pest control. This study aims to assess the knowledge regarding the effects of pesticides and the protective measures adopted by housewives in selected areas of Nashik, Maharashtra. A quantitative, non-experimental descriptive research design was used. A total of 100 housewives were selected using non-probability convenience sampling. Data were collected using a structured questionnaire. The findings revealed that the majority of participants had moderate knowledge regarding pesticide effects, while a smaller proportion demonstrated good knowledge. Although many participants practiced basic protective measures such as washing vegetables, the use of advanced protective practices was limited. A significant association was found between knowledge levels and selected demographic variables. The study concludes that there is a need for targeted educational interventions to improve awareness and promote safe practices related to pesticide use.

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Pesticides, Knowledge, Housewives, Protective Measures, Health Effects, Awareness

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Creative Commons Attribution 4.0 and The Open Definition

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bone fracture detection, deep learning, ResNet50, transfer learning, Grad-CAM, musculoskeletal radiograph, DI- COM, explainable AI, web-based clinical tool, two-stage classifi- cation.

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Creative Commons Attribution 4.0 and The Open Definition

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Digital technology is slowly changing the way of teaching and learning in the classroom. In many educational institutions, Information and Communication Technology (ICT) is already used to support the learning process. Artificial Intelligence (AI) combined with ICT tools makes the learning process more flexible and effective. This paper examines the role of AI-assisted technologies in supporting skill development within ICT integrated classrooms. Specifically, The discussion focuses on the Indian educational context. A variety of learning activities can be observed using AI-based tools. Based on this information, these systems should provide holistic advice on education and provide feedback in time. Hence, students receive support according to their individual learning needs. The paper also discusses major Indian digital learning initiatives such as DIKSHA, SWAYAM, and PM e-VIDYA. These programmes show the potential of digital platforms to enhance access to educational resources for teachers and students nationwide. At the same time, certain challenges still remain. Issues i.e. limited digital infrastructure, lack of teacher training, and the need for responsible use of AI must be handled carefully. The study indicates that artificial intelligence has the potential to enhance skill-based learning. This occurs when it is integrated with ICT by proper planning and support systems.

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Artificial Intelligence (AI), ICT Integrated Classroom, Skill Development, Digital Learning, DIKSHA, SWAYAM.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Anti-dandruff, Carpaine, papaya leaves

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid advancement of Artificial Intelligence (AI) has revolutionized modern healthcare, with particular impact on medical imaging and radiological diagnostics. This paper presents the AI Radiology Co-Pilot, a comprehensive intelligent system developed to assist radiologists in the detection of chest X-ray abnormalities and the automated generation of structured diagnostic reports. The system employs a ResNet-50-based Convolutional Neural Network for accurate image classification, achieving robust detection of pathological conditions including pneumonia, effusion, and cardiomegaly. To facilitate structured report generation, the system integrates Mistral-7B-Instruct, a state-of-the-art Generative AI language model, which converts model predictions into coherent clinical reports and patient-friendly summaries. Additional features include a Grad-CAM-based explainability module for visual interpretation, a multilingual interactive chatbot for patient assistance, and a Flask-based web interface enabling real-time deployment. Experimental evaluation demonstrates significant improvements in diagnostic efficiency, report quality, and clinical communication. The proposed system bridges the gap between image-based classification and language-based report synthesis, offering a unified, interpretable, and accessible AI-powered radiology workflow.

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Creative Commons Attribution 4.0 and The Open Definition

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Deep Learning, Medical Imaging, ResNet-50, Generative AI, Mistral-7B-Instruct, Report Generation, Grad-CAM, Chatbot, Healthcare AI, Flask

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Creative Commons Attribution 4.0 and The Open Definition