Volume-11, Issue-6

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Volume-11, Issue-6

November-December, 2025

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1. Locating the Role of Naga Women in the Naga Political Process since the Nineties

The 1990s were a significant period in Naga politics. The period witnessed the intensification of inter-factional violence between warring Naga insurgent groups. The signing of the ceasefire agreement between the Indian government and the Nationalist Socialist Council of Nagaland (Isak-Muivah) in 1997, and the subsequent peace talks that followed decades of violence, opened democratic space for Naga civil society to actively engage in the political process. Within this new dispensation, Naga women’s entry into the public sphere as ‘mothers’, rooted in private values and attributes such as nurturing, caring, and pacificatio, assumes significance. Naga women, traditionally confined to the private sphere, carved out a space for themselves in the public sphere by mobilizing for peace through a motherhood strategy. Based on primary and secondary sources, the paper examines the unique role that Naga women play in the public sphere, within the constraints of a traditional patriarchal ethos and Naga tribal customary practices that define gendered roles and perceptions for women. The paper concludes that the peace activism of Naga women in the public sphere has, in a way, empowered their political agency to assert for gender justice and greater participation and representation in formal politics. The 1990s were a significant period in Naga politics. The period witnessed the intensification of inter-factional violence between warring Naga insurgent groups. The signing of the ceasefire agreement between the Indian government and the Nationalist Socialist Council of Nagaland (Isak-Muivah) in 1997, and the subsequent peace talks that followed decades of violence, opened democratic space for Naga civil society to actively engage in the political process. Within this new dispensation, Naga women’s entry into the public sphere as ‘mothers’, rooted in private values and attributes such as nurturing, caring, and pacification, assumes significance. Naga women, traditionally confined to the private sphere, carved out a space for themselves in the public sphere by mobilizing for peace through a motherhood strategy. Based on primary and secondary sources, the paper examines the unique role that Naga women play in the public sphere, within the constraints of a traditional patriarchal ethos and Naga tribal customary practices that define gendered roles and perceptions for women.

Published by: Narotila ImchenResearch Area: Political Science

Organisation: Dimapur Government College, Dimapur, NagalandKeywords: Political Process, Peace Process, Civil Society, Patriarchy, Naga Women, Naga Mothers Association

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Research Paper

2. Performance Appraisal- A source of employee motivation in a Small Organisation

In today’s competitive and dynamic business environment, small organisations play a crucial role in economic development and job creation. However, their success largely depends on the motivation and commitment of their employees. Performance appraisal, when implemented effectively, serves as a key human resource tool to recognise employee contributions, provide constructive feedback, and foster motivation. This paper explores how performance appraisal systems influence employee motivation in small organisations. It analyses the relationship between fair evaluation, feedback mechanisms, goal alignment, and intrinsic as well as extrinsic motivation. The study is based on a mixed-method approach involving surveys and interviews conducted among employees of small enterprises. Findings reveal that transparent and participatory appraisal systems enhance motivation and job satisfaction, while a lack of clarity and bias in evaluation tend to demotivate employees. The paper concludes with recommendations for designing simple yet effective performance appraisal systems tailored to the needs of small organisations.

Published by: Rakshita BhattResearch Area: Management

Organisation: Graphic Era Deemed to be University, UttarakhandKeywords: Performance Appraisal, Employee Motivation, Small Organisations, Human Resource Management, Fairness, Feedback, Transparency, Job Satisfaction

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3. Hospital Appointment and Patient Management System

The CareSync Hospital and Patient Management System is a web-based platform designed to streamline hospital operations and improve patient care efficiency. The system provides separate login access for patients, doctors, nursing staff, administrative staff, and transport teams. Each user role has distinct functionalities tailored to their responsibilities. Patients can register, book appointments, view doctor schedules, access their medical history, and receive billing details online. Doctors can manage patient records, update diagnosis reports, and track appointments. The administrative staff can oversee hospital operations such as room and bed availability, billing, and staff scheduling. Nursing staff can track patient vitals, medication schedules, and treatment updates, while the transport team manages ambulance and in-hospital patient transfers efficiently. CareSync enhances hospital management by integrating modules like Appointment Scheduling, Room and Bed Booking, Billing Management, and Transport Coordination, all in a secure and user-friendly interface. The system minimizes manual paperwork, reduces human error, and ensures better coordination between departments. Ultimately, CareSync improves healthcare service delivery and patient satisfaction through automation, accessibility, and data centralization.

Published by: Deepika, Poorvi S Jadimath, Sneha C Yaligar, Preeti S PuranikResearch Area: Web Development

Organisation: Basaveshwar Engineering College, BagalkotKeywords: Hospital Management System, Patient Management, Appointment Scheduling, Healthcare Automation, Payment and Billing System, Web Application, CareSync.

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4. The Biochemical Basis of Schizophrenia: The Dopamine Hypothesis and Beyond

Schizophrenia is a complex neuropsychiatric disorder marked by a wide spectrum of symptoms, yet its biochemical basis remains unclear. While the classical dopamine hypothesis links excess dopaminergic activity to hallucinations and delusions, recent research implicates glutamatergic, GABAergic, serotonergic, and cholinergic systems, along with inflammatory and oxidative stress pathways. This paper argues that schizophrenia results from interactions among multiple neurotransmitter and receptor systems. By reviewing neuroimaging, pharmacological, and clinical trial evidence, it demonstrates that understanding receptor-specific contributions is crucial for developing targeted therapies addressing both cognitive deficits and affective symptoms.

Published by: Bhumika TeckchandanyResearch Area: Neuroscience

Organisation: Suncity School, GurgaonKeywords: Schizophrenia, Dopamine Hypothesis, Glutamate Dysfunction, NMDA Receptor Hypofunction, GABAergic Dysfunction, Serotonin Modulation, Neuroinflammation, Personalized Psychiatry

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5. Early Diabetic Retinopathy Detection using Federated Learning

Diabetic Retinopathy (DR) is a leading cause of vision loss worldwide, primarily affecting individuals with prolonged diabetes. Early detection is crucial to prevent irreversible blindness. This project proposes a secure and intelligent framework for the early detection of Diabetic Retinopathy using Federated Learning (FL), ensuring both data privacy and efficient model training across multiple healthcare institutions. The system utilizes Optical Coherence Tomography (OCT) images for accurate retinal analysis and employs Convolutional Neural Networks (CNNs) such as ResNet-50 and VGG-16 for disease classification. To enhance data security, Multi-Factor Authentication (MFA) is integrated, allowing only authorized medical professionals to access sensitive information. Unlike traditional centralized AI models, the proposed system prevents raw data sharing, thus maintaining patient confidentiality while improving diagnostic performance. Future enhancements, including Homomorphic Encryption (HE) and Explainable AI (XAI), will further strengthen data protection and interpretability of results. Overall, this system contributes to Sustainable Development Goal (SDG 3): Good Health and Well-being, by promoting accessible, secure, and intelligent healthcare solutions for early eye disease diagnosis.

Published by: Dharish Prasath D, Kalai arasi. M, Faziya. A, Jai Ganesh. S, Kavitha. IResearch Area: Information Technology

Organisation: SRM Valliammai Engineering College, Kattankulathur, Kanchipuram, Tamil NaduKeywords: Diabetic Retinopathy, Federated Learning, Optical Coherence Tomography (OCT), Convolutional Neural Network (CNN), Multi-Factor Authentication (MFA), Data Privacy, Secure Healthcare, Early Disease Detection, Homomorphic Encryption (HE), and Explainable AI (XAI).

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6. Financial Literacy among Youth

Financial literacy has emerged as a critical life skill in the 21st century, particularly for young people navigating an increasingly digital and complex financial environment. This paper examines the growing financial literacy gap among youth, emphasizing its implications for personal well-being, national productivity, and sustainable economic growth. It analyzes barriers such as limited access to structured financial education, overreliance on social media for financial advice, and the rising incidence of digital scams. Drawing on global comparisons, it highlights how countries like Singapore, Canada, and Australia have successfully integrated financial literacy into school curricula, while India continues to lag. The study underscores the urgent need to embed financial education into formal schooling, enhance teacher training, and strengthen digital financial awareness. Government initiatives such as the National Strategy for Financial Education (NSFE 2020–2025) demonstrate progress, yet a stronger post-2025 roadmap is essential. The paper concludes that equipping youth with financial skills not only improves individual financial resilience but also drives entrepreneurship, savings, and inclusive national development.

Published by: Ridhaan JainResearch Area: Financial Literacy

Organisation: Shri Ram School, AravaliKeywords: Financial Literacy, Youth, Financial Education, Digital Finance, Economic Development, Financial Inclusion, Entrepreneurship, India, National Strategy for Financial Education (NSFE), Global Comparison

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7. Harnessing the Wind: Evaluating Technological Advancements, Policy Frameworks, and Environmental Strategies for Large-Scale Wind Energy Adoption in India

India’s transition to clean energy is crucial for achieving its goal of 500 GW of non-fossil fuel capacity by 2030 and mitigating its dependence on coal. This research examines how advancements in wind energy technology, supportive policy frameworks, and sustainable environmental practices can collectively drive large-scale wind energy adoption in India. It explores the evolution of turbine design and efficiency, the promise of offshore wind energy, and the integration of digitalization and energy storage systems that enhance reliability and cost-effectiveness. The study further analyzes India’s policy landscape—including Power Purchase Agreements (PPAs), Renewable Purchase Obligations (RPOs), and incentives under the National Action Plan on Climate Change—highlighting their role in promoting investor confidence and infrastructure development. Moreover, it discusses environmental sustainability through Environmental Impact Assessments (EIAs), community engagement, and wildlife protection strategies. By comparing global best practices from Denmark, Germany, and China, the research identifies pathways for India to overcome challenges such as grid integration, regulatory inconsistencies, and land acquisition barriers. Ultimately, this paper concludes that a synergistic approach—combining technological innovation, policy support, and environmental stewardship—can position India as a global leader in sustainable wind energy development.

Published by: Vivaan DumirResearch Area: Renewable Energy

Organisation: Oberoi International School, Mumbai, IndiaKeywords: Wind energy, Offshore Wind, Renewable Energy Policy, Turbine Technology, Grid Integration, Environmental Impact Assessment (EIA), Sustainable Development, Climate Change Mitigation

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8. The Role of Mathematics in Sports Statistics: Analyzing Player Performance

Mathematics forms the backbone of modern sports analytics, enabling the transformation of raw performance data into structured, quantifiable insights that enhance both individual and team evaluation. This study examines how mathematical and statistical tools—including weighted averages, regression analysis, principal component analysis (PCA), z-score standardization, clustering methods, Monte Carlo simulations, and Expected Goals (xG/xA) models—collectively contribute to a sophisticated understanding of athlete performance. By applying these techniques, the research demonstrates how multidimensional player data can be normalized, compared, and interpreted to produce objective ratings and identify key performance indicators. Furthermore, the study highlights the predictive power of mathematical modelling in sports. Regression-based forecasting and probabilistic simulations allow analysts to estimate goal-scoring likelihood, evaluate tactical strategies, and anticipate match outcomes with greater accuracy. Dimensionality-reduction methods, such as PCA and factor analysis, streamline complex datasets into meaningful components, revealing hidden patterns and correlations within player behavior. Beyond individual assessments, mathematics also enhances broader strategic decision-making in areas such as player recruitment, opponent analysis, load management, and game planning. The findings emphasize that the integration of quantitative models not only improves analytical precision but also reduces subjective bias, resulting in a more transparent and reliable framework for performance evaluation. Overall, this research illustrates the essential role mathematics plays in shaping the contemporary sports analytics landscape, demonstrating how rigorous quantitative analysis contributes to improved tactical understanding, more informed coaching decisions, and data-driven competitive advantage.

Published by: Kashyap JalaliResearch Area: Statistics

Organisation: Edify School, Karnataka, BengaluruKeywords: Sports Analytics, Player Performance Evaluation, Statistical Analysis, Quantitative Analysis, Regression Analysis, Monte Carlo Simulations

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9. An Intelligent Matchmaking System for Peer-Assisted Learning in Educational Communities

This study presents an AI-driven intelligent matchmaking system that facilitates peer-assisted learning in educational communities by dynamically connecting students based on academic profiles, shared interests, and availability. The system addresses critical challenges in modern digital learning environments, including inefficient peer matching, a lack of personalization, and low student engagement. By leveraging machine learning algorithms, particularly TF-IDF vectorization and cosine similarity, the platform intelligently forms collaborative study groups that promote peer-to-peer knowledge exchange. The system integrates gamified learning features, including badges, points, and leaderboards, to sustain motivation, and incorporates real-time progress tracking through comprehensive analytics dashboards. Built using a microservice architecture with React.js frontend and FastAPI backend, the platform demonstrates superior scalability and modularity compared with traditional learning management systems. The testing results indicate the successful implementation of all core functionalities with 100% test case pass rates across the user management, matchmaking, resource sharing, and gamification modules. The system represents a significant advancement in creating student-centered, adaptive digital learning ecosystems that foster meaningful academic collaboration beyond conventional classroom boundaries.

Published by: Vinay Kumar Siddha, Poojitha Karuturi, Palli Kalyan Babu, Kona Karthik, Sneha PradhanResearch Area: Education Technology

Organisation: Sri Vasavi Engineering College, Andhra PradeshKeywords: Peer-Assisted Learning, Intelligent Matchmaking System, Educational Technology, Machine Learning Algorithms, Gamification in Education, Collaborative Learning Platforms, Microservices Architecture

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10. Chess and Business: Strategic Thinking, Risk, and Cognitive Ability

This paper explores how the strategic plays and discipline that chess cultivates translate into strategic thinking, cognitive and leadership abilities, and risk perception in the real business world. Studies cited in this paper show that people who interact with this strategic gameplay regularly generally display higher cognitive and problem-solving abilities and are more adept at dealing with high-stakes situations in the business world. This paper also looks at differences in risk-taking tendencies between different genders. This paper analyses the overlap between chess strategies and abilities that develop from regular interaction with the game, and the critical thinking, and business strategies which likely develop from the game as well, to the point where even the elite credit their success to the game.

Published by: Jaisal KohliResearch Area: Business Strategy

Organisation: Shiv Nadar School, GurgaonKeywords: Chess strategies, Business Strategies, Strategic Thinking, Problem Solving, Cognitive Abilities.

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11. Interpreting Opaque Scheduling Heuristics in Timetabling via Surrogate GNN

Opaque timetabling heuristics often deliver strong schedules yet remain difficult to interpret, limiting trust, diagnosis, and controlled adaptation in educational scenarios. This work presents a practical pipeline that learns a constraint-aware graph neural network (GNN) surrogate from input-output pairs of a black-box timetabling solver and then applies global explainability to characterize the surrogate’s decision-making logic across entities and relations in the context of timetabling. Using a synthetic dataset with hard constraints, the study evaluates both the assignment fidelity to the solver outputs and the feasibility under hard constraints, complementing these with global explanations and counterfactual sensitivity analysis. The results highlight which entities and relations most influence the predicted assignments. The pipeline is intended as a methods-oriented contribution that standardizes data generation, surrogate training, and explanation estimators for timetabling, enabling reproducible assessment of interpretability alongside fidelity and feasibility without claiming domain deployment readiness.

Published by: Kashish Ukey, Nakul Badwaik, Labdhi Soni, Ojas Kamde, Smita NirkhiResearch Area: Explainable AI

Organisation: GH Raisoni College of Engineering and Management, Nagpur, MaharashtraKeywords: Scheduling Algorithms, Surrogate Models, Explainable Artificial Intelligence, Black-Box Optimization, Model Interpretability, Decision Support Systems

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12. Visual Storytelling on Instagram: Redefining Digital Narratives in Indian Contexts

In contemporary India, Instagram has evolved into a powerful site of visual storytelling where images, reels, and aesthetic choices continually reshape how narratives are created, shared, and consumed. This paper explores how Indian creators—from rural artisans and independent journalists to lifestyle influencers and grassroots activists—use Instagram to express identity, culture, and socio-political consciousness. Moving beyond the platform’s commercialised image culture, the study highlights how Indian users adapt Instagram’s global features into localised storytelling traditions such as “darshana,” “kahani,” “dastangoi,” and “lok-drishti.” Through qualitative digital ethnography, content analysis of 200 Instagram accounts, and semi-structured interviews with 25 creators, this paper argues that Instagram in India is not merely a social media platform but a transformative narrative ecosystem. It enables micro-communities, democratizes content creation, and crafts new hybrid forms of cultural expression that blend imagery, captions, hashtags, and audio-visual cues. Findings reveal that Indian Instagram storytelling is shaped by regional languages, socio-economic aspirations, caste-class markers, digital literacy gaps, and platform algorithms that reward emotion-driven, relatable, and visually immersive content. The paper concludes with implications for digital culture, media literacy, and storytelling futures in India.

Published by: Vidhi Sikka, Mayank AroraResearch Area: Media And Communication

Organisation: Tecnia Institute of Advanced Studies, DelhiKeywords: Instagram, Visual Storytelling, Indian Digital Culture, Narrative Studies, Influencers, Digital Identity.

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13. Tackling Rural Healthcare Gaps: Intelligent Doctor Recommendation by Distil BERT with Coordinated Medicine Delivery

In rural healthcare, patients often struggle to navigate complex medical systems, leading to misdirected consultations and treatment delays. Traditional paper-based clinics further exacerbate inefficiencies with crowded queues and poor emergency handling. Current digital solutions typically offer only basic appointment booking or generic disease prediction, failing to provide personalized guidance or integrate the complete patient journey. Our AI-powered platform addresses these gaps using a fine-tuned Distil BERT model that analyses patient-described symptoms and recommends appropriate medical specialities with 83.8% accuracy. The system seamlessly integrates intelligent doctor matching with a token-based queue management system, real-time emergency SOS alerts, and digital prescription generation. This creates a comprehensive, patient-centric workflow from initial symptom assessment through treatment completion. Future enhancements will incorporate multi-lingual support, voice-input capabilities, pharmacy integration, and telemedicine modules to further expand accessibility and create a complete healthcare ecosystem for underserved communities.

Published by: Mansi Yadav, Mohammad Faizan, Nitin Singh Thakur, Neha KumariResearch Area: Artificial Intelligence In Healthcare , Natural Language Processing , Human-Computer Interaction

Organisation: Oriental Institute of Science and Technology, Madhya PradeshKeywords: Healthcare Access, Rural Healthcare, Doctor Recommendation System, Distil BERT, Natural Language Processing, Symptom Classification, Real-time Alert System, Digital Prescription.

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14. Real-Time Detection of Obfuscated Abusive Multilingual Comments using Prompt-Tuned and LoRA Fine-Tuned LLMs

The rapid expansion of multilingual communication on social media has led to a surge in abusive, toxic, and offensive user-generated content. Traditional rule-based filters and monolingual detection systems fail to address modern linguistic challenges such as code-mixing (English–Hindi– Hinglish), transliteration variance, and obfuscation (e.g., “id!ot”, “b!tch”). To address these limitations, this work proposes a real-time multilingual abusive comment detection framework built using a LoRA-based parameter-efficient fine-tuning approach on Multi-lingual BERT (mBERT), augmented with a custom preprocessing pipeline and prompt-based linguistic normalization. The proposed system integrates leetspeak decoding, repeated-character normalization, slang expansion, context-driven abusive-pattern recognition, and Hindi transliteration handling, significantly improving classification robustness. The fine-tuning utilizes Low-Rank Adaptation (LoRA) to enable efficient domain adaptation without full-model training costs. A Flask-based REST API and Web Interface provide real-time detection capabilities with confidence scoring and content-restriction logic. Experiments show improved F1 scores on code-mixed and obfuscated datasets, demonstrating substantial gains over baseline mBERT and rule-based systems. Future work aims to extend the system to multimodal toxicity detection, emoji-semantic embeddings, and adversarial robustness.

Published by: Ayushi Gupta, Arjita Prajapati, Anushka Agrawal, Ayushi Uikey, Anamika JoshiResearch Area: Computer Science Engineering

Organisation: Oriental Institute of Science and Technology, Madhya PradeshKeywords: Abusive Speech Detection, Multilingual NLP, mBERT, LoRA Fine-Tuning, Text Normalization, Obfuscation Handling, Real-Time Moderation.

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15. Smart Home System Automation Based on Zigbee Protocol with Internal Cloud Server

The standard protocols used in educational, business, residential, and industrial environments in Indonesia still rely on standard protocols such as Wi-Fi and Bluetooth. This applied research is related to a standard protocol called Zigbee, which is IEEE 802.15.4 standard that has begun to be widely used in networks for IoT-based control purposes. In smart home technology, connectivity is very important. To build a smart home system, users must consider the connectivity or communication between one smart home device and another. The most familiar and widely used communication protocol network for smart home devices is currently Wi-Fi. However, alternative wireless connectivity protocols are now emerging, one of which is Zigbee. Zigbee is a global standard for low-power, short-range networks, offering a complete and operable Internet of Things (IoT) solution for homes and buildings. Zigbee has features that enable it to manage its own network and data exchange on the network, and it can support hundreds of devices and has reliable security features. In this applied research, monitoring uses the Zigbee protocol with a cluster topology consisting of a Zigbee coordinator and several Zigbee end devices that will be installed in locations with or without wall obstructions.

Published by: Muhammad Sulkhan Arif, Wawan Heryawan, bangbang HermantoResearch Area: Electrical Engineering

Organisation: Politeknik Negeri, IndonesiaKeywords: Zigbee, IoT, Smart Home.

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16. The Impact of English language on different languages

This research paper examines the impact of English on different languages, German, Hindi and French. It looks at how English has evolved from being a regional language confined to a small area to a lingua franca through colonial expansion and globalisation. This research shows that the impact of English is neither uniform nor superficial. This paper highlights how the English language influences each of these three languages differently based on different historical, political, and socio-cultural contexts. This paper demonstrates how English simultaneously opens a path for global opportunities, but at the same time presents a challenge to linguistic integrity and heritage.

Published by: Aanya ChahalResearch Area: Sociocultural Linguistics

Organisation: Heritage Xperiential Learning School, HaryanaKeywords: Language Erosion, Language Imperialism, Language Hybridization, Loanwords and Borrowing, Globalisation.

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17. The Triple Barrier: Pricing, Distribution, and Policy Dynamics Shaping Organic Food Sustainability in India

This study employs the rigorous frameworks of the Triple Bottom Line (TBL)—assessing people, planet, and profit and Value Chain Analysis (VCA) to investigate the structural imperatives of pricing, distribution, and policy in determining the long-term sustainability of India’s burgeoning organic food sector. The market demonstrates robust economic potential, with growth projections estimated up to a Compound Annual Growth Rate (CAGR) of 20.13% through 2033, driven largely by burgeoning urban health consciousness and a strong global export orientation. However, the analysis indicates that true sustainability remains structurally fragile. The sector faces a critical "triple barrier" that restricts value capture and systemic resilience. The report concludes that achieving a sustainable organic ecosystem by 2030 requires integrated policy intervention, specifically focusing on certification reform, public-private investment in cold-chain logistics, and implementing dual incentive models to ensure fair pricing and broader market access.

Published by: Naina Singh KhatkarResearch Area: Organic Food Sustainability

Organisation: Delhi Public School, HaryanaKeywords: Organic Food Sustainability, Organic Food Sector in India, Policy Intervention Models, Social Sustainability, Policy Regulation.

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18. How Can Global Brands Balance Cultural Authenticity and Universal Appeal in an Era of Glocalization?

In today’s globalized yet culturally diverse marketplace, multinational brands face the complex challenge of balancing universal brand identity with localized cultural relevance. This research explores the strategic concept of glocalization, which integrates global brand consistency with authentic local adaptation to enhance consumer resonance. Through qualitative methodology, secondary data analysis, and case studies of McDonald’s, Coca-Cola, Nike, and Starbucks, the study demonstrates that cultural authenticity significantly strengthens consumer trust, emotional engagement, and brand loyalty. Findings reveal that successful glocalization requires maintaining universal brand values while adapting products, messaging, and customer experiences to align with cultural beliefs, traditions, and socio-emotional expectations. The study also analyzes branding failures such as Dolce & Gabbana and Pepsi to highlight risks of cultural insensitivity. As digital transformation accelerates hyper-local targeting and consumer co-creation, glocalization emerges as a strategic necessity for competitive advantage. The research concludes that brands that develop cultural intelligence, empower local insight, and adopt flexible global frameworks can achieve sustainable global-local equilibrium.

Published by: Siya SarojResearch Area: Global Branding Strategy

Organisation: SP Jain School of Global Management, Dubai, Mumbai, Singapore and SydneyKeywords: Glocalization, Cultural Authenticity, Global Branding, Localization Strategy, Consumer Perception, Brand Identity, Cultural Intelligence, Digital Hyper-Localization, Cross-Cultural Marketing, Global-Local Balance, Brand Loyalty, Case Studies.

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19. Smart Basket

The Smart Basket is an automated, RFID-enabled shopping system designed to enhance the retail shopping experience by eliminating manual billing and reducing customer waiting time at checkout counters. The proposed system integrates an RFID reader, RFID tags, a microcontroller, and an LCD display into a shopping trolley, enabling automatic identification and pricing of products as they are placed inside or removed from the cart. Each product is equipped with a passive RFID tag, which is detected instantly by the RFID reader, and the corresponding information—such as product name, price, and updated total bill—is displayed to the user in real time. The system also incorporates an RFID card-based authentication mechanism to ensure secure access and user identity verification during purchase. The Smart Basket minimizes human intervention in billing, reduces errors associated with manual scanning, and increases overall operational efficiency in shopping malls and supermarkets. By providing a transparent, user-friendly, and time-saving shopping environment, the system contributes to improved customer satisfaction and smoother store management. This project demonstrates that RFID technology can serve as a cost-effective, scalable, and reliable solution for modern retail automation and lays the foundation for future integration of IoT, mobile payments, and AI-based analytics.

Published by: Rajwardhan Ashok Pawar, Rupesh Natha Pawar, Paresh Rajendra Jagtap, Shahid Nazim Mulani, S. P. SuryawanshiResearch Area: Iot And Ai

Organisation: Adarsh Institute of Technology and Research Centre, MaharashtraKeywords: Smart Shopping Basket, Internet of Things (IoT), Automatic Billing System, RFID Technology, Load Cell Sensor, Embedded Systems.

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Research Paper

20. Data-Driven Crop Recommendation for Rajasthan Using Linear and Ensemble Models

The agricultural sector is a vital part of the Indian economy, comprising 18.2% of India’s GDP and representing approximately 44% of the total labour force. However, one of the biggest problems faced is the loss of crop yield, especially among farms using traditional methods of farming that lack the technological means to predict and maximise their potential yield. The problem is further compounded by farmers often being unaware of which crops are suitable, given conditions that are specific to individual farmers or parcels of land. This research paper focuses on maximising crop yield by helping farmers choose a suitable crop in Rajasthan, one of the largest Indian states by land mass and population, where over 54% of citizens depend on agriculture as a primary source of income. The data used throughout this paper are publicly accessible and are taken from multiple official Indian government sources. Using these data, the paper incorporates exploratory data analysis to identify key variables such as soil nutrient levels, rainfall, and temperature that influence crop performance. Furthermore, the paper aims to lay out the groundwork for building a crop yield prediction and, primarily, a crop recommendation model that is easily accessible and simple to understand. This is implemented using a transparent linear regression baseline and a decision-tree-based ensemble approach, specifically Random Forest.

Published by: Aryaveer JainResearch Area: Data Science, Statistics, Agriculture, Technology

Organisation: Hill Spring International School, MaharashtraKeywords: Crop Yield Modelling, Random Forest, Multiple Linear Regression, Soil Nutrients, Rainfall Variability, Agricultural Decision Support, Rajasthan Agriculture.

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21. A Study of Clustering Analysis in Identification of Butterfly Species

This study investigates the use of clustering analysis techniques for identifying butterfly species based on their morphological characteristics. Butterflies exhibit substantial variation in wing patterns, colors and body size which makes traditional taxonomic identification both time-consuming and error-prone. Clustering analysis provides a data-driven strategy to group individuals into putative species based on similarities in measurable features. By applying multiple clustering algorithms together with appropriate validation methods, this work evaluates the effectiveness of clustering analysis for butterfly species identification and highlights its potential applications in biodiversity research and conservation. Accurate identification of butterfly species is fundamental to biodiversity conservation, ecological monitoring, and environmental impact assessment. This study examines the efficacy of clustering methods for species identification using butterfly image data. Several algorithms, including K-means, hierarchical clustering, spectral clustering, Gaussian mixture models, and DBSCAN, are employed to partition images into species clusters. To represent discriminative visual information, feature extraction techniques such as Histogram of Oriented Gradients (HOG), Gray Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns (LBP) are used to encode wing textures and shape characteristics. The quality of the resulting clusters is assessed by comparing them with known species labels, enabling a systematic evaluation of each method. The results indicate that clustering analysis offers a scalable and promising approach for automated butterfly species identification and biodiversity monitoring, while also clarifying the strengths and limitations of different clustering techniques for image-based species classification.

Published by: Ajaykumar RResearch Area: Machine Learning

Organisation: Christ College Mysore, KarnatakaKeywords: Butterfly, Identification, Species.

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22. Ergonomic Mechanical Tools for Reducing Repetitive Strain Injuries among Workers in Small-Scale Manufacturing Units

Repetitive strain injuries (RSIs) have emerged as a major challenge within small-scale manufacturing environments, damaging worker health and productivity. Ergonomically designed mechanical tools are increasingly recognized as a pivotal solution to this chronic problem. This paper undertakes a thorough examination of RSI etiology, analyzes ergonomic design methodologies relevant for mechanical tools, and synthesizes practical recommendations for designing interventions targeted at small enterprises. Emphasizing both technical and organizational dimensions, the discussion highlights evidence-based strategies and implementation barriers, providing a comprehensive roadmap for sustained ergonomic improvement.

Published by: Nevaan AggarwalResearch Area: Mechanical Engineering

Organisation: Shiv Nadar School, HaryanaKeywords: Ergonomic Tool Design, Repetitive Strain Injuries, Small-Scale Manufacturing, Anthropometric Data, Vibration Reduction, Grip Force Optimization, Participatory Ergonomics, Occupational Health.

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23. AI Bias in Data Training

This research paper talks about AI bias in data training and how it creates age, gender and cultural discrimination. This paper also talks about how spreading awareness about AI bias can help mitigate the issue. It examines how biased training data distorts decision-making in various fields like hiring, healthcare and law enforcement. This paper shows us the need for transparency, accountability and awareness in AI systems and how mitigating data bias is essential for creating an AI system that is fair, responsible, and that can be held accountable in case of any biased decisions and output.

Published by: Sarjas Gauhar SinghResearch Area: Artificial Intelligence

Organisation: Heritage Xperiential Learning School, HaryanaKeywords: AI Bias, Biased Training Data, Algorithmic Discrimination, Awareness and Bias Mitigation, Transparency and Accountability.

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24. Parkinson’s Disease – History, detection, and cure

This paper talks about the neurodegenerative disease commonly known as Parkinson's disease, about the history of this disease and its causes, various subtypes, as well as how the diagnosis of this disease takes place. It has mainly 3 causes those being environment, genetics, or interactions. It usually happens to people above the age of 60, but there are younger cases as well. This disease causes loss of dopaminergic neurons in the brain; these neurons help in motor activities of the body, thus their loss causes loss of motor activities of the body. The dopamine-producing neurons Substantia Nigra are directly affected. The neurons degenerate due to the accumulation of Alpha’s nuclein in the brain. The paper discusses how motor, non-motor and psychological aspects should be taken into account during the identification of this disease.

Published by: Prisha TeotiaResearch Area: Neurodegenerative Disorder

Organisation: Suncity World School, HaryanaKeywords: Parkinson's Disease, Neurodegenerative Disease, Brain Disorder, Carbidopa-Levodopa, Dopaminergic Neurons, Disease Treatment.

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Research Paper

25. AI-Driven Smart Air Quality Monitoring and Predictive Pollution Control System Using IoT and Edge Computing

Air pollution has become a major environmental threat, yet traditional monitoring systems rely on expensive fixed stations with limited coverage and delayed reporting. This project introduces an AI-driven Smart Air Quality Monitoring and Predictive Pollution Control System that integrates IoT sensors, edge computing, machine learning, and cloud analytics for real-time, scalable monitoring. A network of low-cost sensors measures pollutants such as PM2.5, PM10, CO₂, CO, and NO₂, sending data to an edge device (ESP32/Raspberry Pi) for cleaning, filtering, and anomaly detection. Edge processing minimizes latency, saves bandwidth, and enables rapid local decision-making. Cleaned data is then uploaded to the cloud, where models like Random Forest, XGBoost, and LSTM generate short- and long-term pollution forecasts. An interactive dashboard visualizes real-time AQI, spatial patterns, and predictive insights to support timely interventions. Overall, this cost-effective system demonstrates key CS engineering skills and offers a practical framework for smarter, healthier, and more resilient cities.

Published by: Daksh JainResearch Area: Environmental Engineering

Organisation: Jayshree Periwal International School, RajasthanKeywords: IoT, Air Quality Monitoring, Predictive Analytics, Machine Learning, AQI Forecasting, Edge Computing, Smart Environmental System, Pollution Control, Real-time Sensors, LSTM.

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Research Paper

26. Greenscan: An AI-Powered, Cross-Platform System for Instant Plant Identification and Care Guidance

This paper presents GreenScan, an intelligent and interactive web platform developed to enable fast, accurate, and user-friendly plant species recognition through uploaded images. Addressing the persistent challenges of manual plant identification, such as inefficiency, limited accessibility, and a lack of centralized information, GreenScan leverages the power of Artificial Intelligence (AI) and Deep Learning to deliver real-time classification of more than 100 distinct plant species. The system employs a Convolutional Neural Network (CNN) model trained on a large and diverse dataset of plant images to ensure high recognition accuracy, even under varying lighting and background conditions. The platform integrates a responsive and intuitive web interface, allowing users to seamlessly upload images, view classification results, and explore detailed plant profiles. Each identified species is linked to a comprehensive backend database containing essential details such as taxonomy, physical characteristics, ideal growing conditions, and care guidelines. Furthermore, GreenScan provides external purchase links and educational resources, making it an invaluable tool for students, researchers, horticulturists, and nature enthusiasts. A key feature of GreenScan is its feedback-driven learning mechanism, which enables continuous model retraining based on user input to progressively enhance prediction precision over time. The platform’s implementation achieved high confidence scores, including a 91% accuracy rate for identifying species such as the Snake Plant. Beyond its technical merits, GreenScan contributes significantly to promoting environmental education, sustainable living, and ecological awareness by bridging the gap between modern technology and biodiversity knowledge. This work demonstrates the potential of AI-powered solutions to transform traditional plant identification into a more engaging, efficient, and educational digital experience.

Published by: Vaishnavi Duratkar, Twinkal Sapate, Snehal Ninawe, Sanket Barapatre, Ashwary Dhakate, Sharwari Mohadikar, Prajakta Singham, Mamta BalbudheResearch Area: Computer Science And Engineering

Organisation: Karmaveer Dadasaheb Kannamwar Engineering College, MaharashtraKeywords: GreenScan, Plant Recognition, Artificial Intelligence, Image Classification, Web Application.

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Research Paper

27. Geospatial Threat Assessment: Safety Analytics using RNN, XGBoost and Isolation Forest

Personal safety applications today mostly respond after an incident occurs, which limits their ability to prevent harm. In this work, we develop a proactive safety-risk prediction system that estimates how dangerous a location may become in the near future. The system combines sequential deep-learning models with boosted decision-tree techniques to understand how local crime risk evolves over time and space. Historical crime records, temporal patterns, nearby points of interest, and environmental context are merged into structured spatio-temporal data grids. The proposed approach uses an LSTM network to learn short-term temporal changes in risk at the grid-cell level, while an XGBoost model evaluates spatial and contextual factors to produce interpretable risk scores. An Isolation Forest module is used alongside these models to detect sudden, unusual conditions that may indicate unsafe situations. The outputs of all three models are merged into a unified risk score that updates continuously and highlights emerging danger zones. When the score crosses certain thresholds, the system can issue early warnings, suggest safer travel routes, or escalate alerts if needed. The system is evaluated on real crime datasets using spatio-temporal cross-validation, and performance is measured using metrics suited for imbalanced data such as AUC, Precision@K, and F1-score. Results demonstrate that the system can provide meaningful early-risk signals while maintaining transparency and privacy-aware processing.

Published by: Mukul Malviya, Mohak Pandagre, Kushagra Singh Chouhan, Mayank DhakarResearch Area: Deep Learning

Organisation: Oriental Institute of Science and Technology, Madhya PradeshKeywords: Spatio-Temporal Modeling, Behavioural Modeling, Anomaly Detection, Deep Learning, LSTM, RNN, XGBoost, Crime Prediction, Safety Analytics, Hyperparameter Optimization, Multimodal Data Integration, Data Preprocessing Automation, Predictive Analytics.

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Research Paper

28. Hydrogen Yield Efficiency Based on Current Density in AWE

Hydrogen production through alkaline water electrolysis (AWE) remains one of the most reliable and economically feasible pathways for generating clean hydrogen. However, the efficiency of AWE is strongly influenced by the operating current density, particularly at higher loads, where bubble accumulation, increased overpotential, and mass-transport limitations reduce the practical hydrogen yield. This study examines the effect of varying current density on hydrogen yield efficiency by comparing the experimentally collected hydrogen volume with theoretical values derived from Faraday’s law. Electrolysis was performed using stainless steel electrodes in a 0.50 M NaOH electrolyte over current inputs ranging from 0.10 A to 0.50 A. For each current setting, the corresponding hydrogen volume was measured via the water displacement method, converted to moles using the ideal gas law, and evaluated against the predicted stoichiometric output. The results show a near-linear increase in hydrogen production at lower current densities but a noticeable deviation from ideal Faradaic behaviour at higher currents. Faradaic efficiency decreased from approximately 94% at 0.10 A to around 85% at 0.50 A, confirming that bubble blockage, resistive heating, and kinetic limitations become more pronounced as current density increases. The study provides a clear, empirical relationship between current density and hydrogen yield efficiency in a simple AWE system, offering useful insights for small-scale electrolysis applications and highlighting the practical limitations encountered when transitioning to higher operational loads. Beyond quantifying efficiency trends, this study also demonstrates the importance of understanding electrochemical behaviour when scaling up hydrogen production systems. Since many educational and laboratory AWE setups operate without advanced engineering features—such as forced electrolyte circulation, porous electrodes, or catalytic coatings—the findings provide a realistic baseline for performance expectations in simple electrolyzers. The observations reinforce that while increasing current density boosts hydrogen output, it simultaneously introduces non-idealities that lower conversion efficiency. These insights can support future optimisations in electrode design, electrolyte composition, and cell configuration for improved hydrogen yield in low-cost AWE systems. Overall, the study highlights the value of Faradaic efficiency as a diagnostic tool for evaluating real-world electrolyzer performance. By directly comparing theoretical and experimental hydrogen yields, the method used here provides a simple yet powerful way to identify operational losses without requiring advanced instrumentation. This approach can be applied in future work to assess the influence of factors such as electrode spacing, electrode surface treatment, electrolyte concentration, and temperature on hydrogen output. The findings, therefore, not only document the behaviour of AWE under varying current densities but also establish a practical framework for improving system efficiency in academic, laboratory, and introductory research settings.

Published by: Harmaya ThukralResearch Area: Electrochemical Engineering

Organisation: Oberoi International School, MaharashtraKeywords: Hydrogen Production, Alkaline Electrolysis, Current Density, Faradaic Efficiency, Electrochemical Performance.

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Research Paper

29. Expenzo – The Smart Finance Tracker

The rapid growth of digital transactions and cashless payment systems has increased the complexity of personal financial management. Individuals often lack effective tools to systematically record expenses, analyze spending behavior, and maintain financial discipline. EXPENZO – The Smart Finance Tracker is a secure, web-based financial management system developed to address these challenges by providing an automated and structured approach to tracking income and expenses. The system allows users to log financial transactions in real time, classify them into predefined categories, and generate analytical summaries that reflect spending trends and saving patterns. Advanced dashboard visualizations and monthly financial reports support data-driven budgeting decisions and promote improved financial awareness. The application integrates essential security mechanisms such as user authentication and controlled data access to ensure the confidentiality and accuracy of sensitive financial information. EXPENZO is implemented using Python with the Flask framework to manage backend operations and application logic. SQLite is employed as a lightweight relational database for efficient data storage and retrieval, while HTML, CSS, and Bootstrap are used to develop a responsive and user-centric interface. The modular architecture of the system ensures scalability, maintainability, and cross-device accessibility. The proposed solution demonstrates the effectiveness of a lightweight web-based platform in simplifying personal finance management. By combining automation, visualization, and secure data handling, EXPENZO enhances users’ ability to monitor financial activities, optimize budgeting strategies, and plan for long-term financial stability. Overall, EXPENZO serves as a practical digital tool for tracking finances, improving saving habits, and developing financial discipline. The application demonstrates how a simple and user-friendly solution can help individuals gain better control over their personal finances and plan more effectively for future financial needs.

Published by: Sahil C. Madankar, Sankalp S. Pawar, Tanushree S. Patle, Vidhi P. Harode, Shivang R. Nagpure, Sakshi S. Zade, Shrawani H. Bijwar, Prof. P. A. Kuchewar, Prof. M. R. BalbudheResearch Area: Financial Technology

Organisation: Karmaveer Dadasaheb Kannamwar Engineering College, MaharashtraKeywords: Expense Tracking System, Personal Finance Management, Web-Based Application, Budget Monitoring, Financial Analytics, Secure Data Management.

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Research Paper

30. HPTLC-Based Qualitative Identification of Gallic Acid in Rauvolfia serpentina Roots Collected from Diverse Agro-Climatic Zones of India

Gallic acid is a bioactive phenolic compound widely recognized for its antioxidant and pharmacological properties. The present study qualitatively investigates the presence of gallic acid in root samples of Rauvolfia serpentina (Sarpagandha) collected from four distinct agro-climatic zones of India using High Performance Thin Layer Chromatography (HPTLC). Methanolic extracts of root samples were analyzed alongside a gallic acid standard employing a standardized solvent system. The chromatographic profiles of all samples exhibited peaks corresponding to the retention factor (Rf) range of the reference standard, confirming the presence of gallic acid across all evaluated samples. This study supports the phytochemical consistency of Rauvolfia serpentina roots and contributes to quality control and standardization efforts of this important medicinal plant.

Published by: Poornima Shrivastava, Aparna Alia, Bharty KumarResearch Area: Botany

Organisation: Rajeev Gandhi College, Madhya PradeshKeywords: Rauvolfia serpentina, Gallic acid, HPTLC, Qualitative Analysis, Medicinal Plant Standardization.

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Research Paper

31. Artificial Intelligence : JobBot

The rapid advancement of artificial intelligence has paved the way for innovative solutions in the recruitment process. This abstract introduces the AI JobBot, a cutting-edge system designed to enhance the interview experience for candidates through personalized and domain-specific interactions. Upon candidate selection of their domain, the JobBot employs natural language processing to engage in a human-like conversation, tailoring questions to the specific requirements of the chosen field. The dynamic interview process adapts to candidate responses, ensuring a comprehensive evaluation of their skills and knowledge. The AI JobBot leverages machine learning algorithms to continually refine its questioning techniques, mimicking the adaptability of human interviewers. This not only provides candidates with a realistic and engaging interview experience but also ensures that the evaluation is aligned with industry standards. Furthermore, the JobBot goes beyond the conventional interview experience by offering constructive feedback to candidates. Through real-time analysis of their responses, the AI system provides personalized insights into strengths and areas for improvement. This feedback is invaluable for candidates seeking to enhance their interview skills and refine their expertise.

Published by: Sushil Kumar B, Shiv Shobhith MResearch Area: Artificial Intelligence

Organisation: Visvesvaraya Technological University, KarnatakaKeywords: Speech Recognition, Semantic Analysis, Facial Expression Analysis, Machine Learning, Natural Language Processing, Artificial Intelligence in Recruitment, AI-Driven Interview Systems, Conversational AI, Intelligent Job Interview Bot, Natural Language Processing (NLP), Machine Learning Algorithms, Speech Recognition Systems, Semantic Analysis, Adaptive Questioning Techniques, Facial Expression Analysis, Candidate Skill Assessment, Automated Feedback and Evaluation.

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Research Paper

32. India’s Evolving Foreign Policy: Leadership and Diplomacy in the Global South (2000–2025)

This paper examines the evolution of India’s foreign policy between 2000 and 2025 and evaluates how the country has positioned itself as a leading voice of the Global South. Building on its postcolonial legacy of non-alignment and South–South cooperation, India has adopted a multi-dimensional strategy that integrates multilateral diplomacy, development finance, vaccine and health diplomacy, digital public infrastructure sharing, and strategic partnerships. Through platforms such as the G20, BRICS, and the United Nations, India has increasingly shaped global governance debates, advocating for equity, climate justice, institutional reform, and inclusive development. Initiatives like Vaccine Maitri, concessional Lines of Credit, and digital cooperation demonstrate a shift from symbolic leadership to tangible implementation. However, India’s leadership remains constrained by capacity limits, domestic pressures, and geopolitical competition. The paper concludes that India’s emerging leadership model blends normative advocacy with pragmatic partnership, offering an alternative, non-coercive framework for Global South cooperation.

Published by: Kabir BhasinResearch Area: Foreign Policy

Organisation: Scottish High International School, HaryanaKeywords: India’s Foreign Policy, Global South Leadership, Multilateral Diplomacy, South–South Cooperation, Vaccine Diplomacy, Development Finance, Digital Public Infrastructure, Strategic Autonomy.

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Research Paper

33. How Does Perceived Control in a Managerial Role Influence Physiological Stress Responses During Financial Fraud Situations?

Financial fraud events place intense psychological and physiological pressure on managerial decision-makers. Perceived control—defined as an individual’s belief in their ability to influence outcomes—plays a key role in modulating stress responses. This experimental study examined whether managers with high perceived control demonstrate lower physiological stress responses compared to those with low perceived control during a simulated financial fraud scenario. Using a laboratory-based mixed-design experiment (N = 60), participants were assigned to high-control or low-control managerial roles and exposed to both a neutral scenario and a fraud-crisis scenario. Stress responses were measured using galvanic skin response (GSR) and pulse-derived heart rate changes from baseline. Synthetic data modelled after real psychophysiological patterns were analysed using repeated-measures ANOVA and delta-based t-tests. Results showed significantly greater increases in GSR and heart rate during the fraud scenario for the low-control group compared with the high-control group. Within-group analyses confirmed that both groups exhibited elevated physiological arousal during fraud relative to neutral tasks, but the magnitude of change was consistently higher in low-control participants. These findings suggest that perceived control acts as a protective factor, attenuating physiological stress during high-stakes financial decision-making. Implications for leadership selection, crisis management protocols, and stress-mitigation training are discussed. Beyond immediate stress reactivity, the study also highlights the potential cognitive implications of autonomic arousal during fraud-related decision-making. Elevated GSR and heart rate responses in low-control managers may reflect heightened emotional load, reduced cognitive flexibility, and impaired working memory—factors known to compromise decision quality under uncertainty. Conversely, individuals in high-control positions appeared to maintain more stable physiological profiles, suggesting the presence of regulatory mechanisms that may support clearer judgment, faster threat appraisal, and more adaptive responses during organizational crises. The methodological contribution of this study lies in the integration of GSR and pulse-based heart rate monitoring within a realistic financial fraud simulation, demonstrating the feasibility of combining psychophysiological tools with organizational-behaviour paradigms. Although the dataset employed was synthetic and modelled after established physiological patterns, the experimental framework provides a robust foundation for future empirical studies involving real participants. This approach offers valuable insights into how perceived managerial control can influence biological stress pathways, ultimately shaping crisis-management performance in high-risk financial environments.

Published by: Yana Pranati SharmaResearch Area: Business Administration

Organisation: O.P. Jindal Global University, HaryanaKeywords: Perceived Control, Managerial Stress, Financial Fraud, Galvanic Skin Response (Gsr), Heart Rate, Physiological Arousal, Crisis Decision-Making, Autonomic Nervous System, Stress Monitoring, Experimental Psychology, Organizational Behaviour, Leadership Under Pressure.

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Research Paper

34. Comparative Physicochemical Profiling of Finger Millet (Eleusine coracana) and Pearl Millet (Pennisetum glaucum) Cultivars

Millets have re-emerged as functional grains due to their rich nutrient composition and diverse bioactive constituents. This study presents a comparative physicochemical evaluation of selected cultivars of finger millet (Eleusine coracana) and pearl millet (Pennisetum glaucum) to determine their nutritional and nutraceutical significance. Standard analytical parameters, including loss on drying (LOD), total ash, acid-insoluble ash, water-soluble ash, pH, and water-soluble extractive values were assessed following WHO and pharmacopoeial guidelines. Finger millet cultivars RAU-8 and Indaf-9 exhibited higher mineral content and extractive values, indicating superior nutraceutical potential. Pearl millet cultivars Pioneer 86M86 and ICMH-356 displayed the highest total ash and aqueous extractive yields, suggesting greater concentrations of polar phytochemicals. Comparative analysis revealed that finger millet excels in mineral richness, whereas pearl millet demonstrates higher extractive potential.

Published by: Anubha Pandey, CBS DangiResearch Area: Biotechnology

Organisation: RKDF University, Madhya PradeshKeywords: Finger Millet, Pearl Millet, Physicochemical Analysis, Extractive Values, Nutraceutical Potential.

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Research Paper

35. Clinical Evaluation Report on Aswini Hiran Strong Pain Oil

Aswini Hiran Strong Pain Oil is a topical Ayurvedic pain-relieving preparation used for knee pain, joint pain, muscular pain, shoulder pain, and backache. The product claims a rapid onset of relief within minutes of application and significant improvement within 14 days of regular use. A prospective, open-label, Phase 4 clinical study was conducted on adult subjects (n = 30) experiencing musculoskeletal pain. Pain severity was measured using a Visual Analogue Scale (VAS: 0–4) at baseline (Day 0) and after 14 days of regular application (Day 14). The study demonstrated significant improvement across all pain categories, with p < 0.001 for every parameter evaluated through a two-tailed paired t-test analysis. Mean pain reduction at Day 14 was 81.25% for knee pain, 78.13% for back pain, 89.29% for shoulder pain, and 78.57% for calf/muscle pain. Onset-of-relief assessments showed 80% of participants experienced noticeable relief within 10 minutes, with 10% reporting relief as early as 3 minutes. No adverse reactions or tolerability issues were reported. These findings substantiate the claims of Aswini Hiran Strong Pain Oil and confirm its effectiveness as a topical remedy for musculoskeletal pain.

Published by: Dr Gandhimathi, Mr.Anil KumarResearch Area: Ayurvedic Topical Preparation

Organisation: GR Ayurvedic Research Centre, Tamil NaduKeywords: Aswini Hiran Strong Pain Oil, Knee Pain, Back Pain, Shoulder Pain, Muscle Pain, Stiffness, Pain Reduction, VAS.

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Thesis

36. Effectiveness of Structured Teaching Programme on Knowledge Regarding Telemedicine among Nursing Students in Selected Nursing Colleges of District Mandi, H.P.

Good health is central to human happiness and well-being that contributes significantly to prosperity and wealth and even economic progress, as healthy populations are more productive, save more and live longer. Telemedicine is the exchange of medical information from one location to another using electronic communication, which improves patient health status. This study aimed to assess the effectiveness of structured teaching programme in improving the knowledge regarding telemedicine among nursing students. Objectives: To assess the level of knowledge regarding telemedicine among nursing students. To evaluate the effectiveness of structured teaching program on knowledge regarding telemedicine among nursing students. To find out the association between level of knowledge score regarding telemedicine among nursing students and their selected socio-demographic variables. Material and Method: Investigator adopted a quantitative research approach with the Quasi-experimental research design (non-randomized control trial design). The subject were 100 nursing students and the non-probability purposive sampling method was used for selection criteria. Data was collected using socio-demographic data profile and self-structured knowledge questionnaire regarding telemedicine. A structured teaching programme regarding telemedicine was implemented in the experimental group after the pre-test, followed by the post-test after seven days. Data were analyzed using descriptive and inferential statistics. Result: In experimental group the mean knowledge score increased significantly from 20.26 (SD= 4.818) to 25.90 (SD = 1.446). this shows that the result was highly significant (t = 8.067, p < 0.001), indicating the intervention was effective. Whereas in control group there was no significant change in the knowledge score (Pre: 19.660, Post: 19.70, t = 0.096, p = 0.924), showing no impact without intervention. Conclusion: In the present study majority of the nursing students had Average level of knowledge regarding telemedicine in pretest. After implementing structured teaching program majority of the nursing students had good level of knowledge. Which indicates that the structured teaching programme was effective in enhancing the knowledge of nursing students regarding telemedicine.

Published by: Nikita Sharma, Sunita Devi, Priyanka SharmaResearch Area: Nursing Research

Organisation: Abhilashi College of Nursing, Himachal PradeshKeywords: Assess, Structured Teaching Programme, Telemedicine, Knowledge.

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Research Paper

37. How Might Behavioural Economic Principles Be Applied to Encourage Sustainable Consumer Behaviour in Product Design?

Sustainable consumer behaviour has become a priority worldwide as unsustainable consumption patterns continue to accelerate environmental degradation. While traditional economic models often assume rational decision-making, evidence shows us that consumers tend to rely on cognitive shortcuts, their emotions, as well as social influences. These factors help shape their everyday choices in ways that are not purely reasoned. Scholars in behavioural economics highlight how defaults (Thaler and Sunstein), emotions and social norms (Cialdini, Opower studies), and simplified design approaches (eco-labelling, minimalist packaging) can significantly influence behaviour without restricting the freedom of choice, which consumers usually consider a strong determining factor of consumption. However, these insights are rarely connected systematically to product design, which is where the problem arises. Building on this foundation, this paper explores how behavioural economic principles can be applied to encourage sustainable consumer behaviour through design. This research paper is limited to secondary data collection, peer-reviewed literature, and behavioural theories rather than primary data, which could include biases in such a sensitive topic. It also includes case studies such as Alibaba’s “no cutlery” default, energy-saving appliance settings, and the EU energy label. These real-world examples help me demonstrate how defaults, emotional and social reinforcement, and simplicity can guide consumers toward sustainability by embedding these behaviours into everyday interaction with products. Therefore, I argue that by strategically integrating behavioural economic principles into product design, firms can make sustainable behaviour the most effortless, emotionally rewarding, and socially reinforced choice, thereby generating lasting environmental and economic value.

Published by: Amaira Singh ChhabraResearch Area: Behavioral Economics

Organisation: The Shri Ram School, HaryanaKeywords: Behavioural Economics, Sustainable Consumer Behaviour, Product Design, Choice Architecture, Defaults and Nudges, Social Norms, Emotional Design, Eco-Labelling.

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Research Paper

38. Women at Work: The Economics of Gender Inequality

In the last two decades, there has been a significant change in the social and economic horizon of India, with the Female Labor Force participation rate reaching 41.7% in 2023-2024. While the growing FLFPR indicates increased economic participation of women, it does not always mean that women are getting quality employment opportunities or becoming empowered. In fact, this boost in FLFPR is because most women, especially rural women, are compelled to work because of unfavorable circumstances and not because there are doors opening for them. As FLFPR is an inadequate economic metric, this study also explores other economic indicators like the global gender gap index, female entrepreneurship rate, access to financial services, women’s asset management, female unemployment rate, etc. While the participation of women is increasing exponentially across all professions, they still have to face an uphill battle to move forward in their careers and gain respect. To collect empirical evidence on workplace barriers, this study has circulated a survey filled out by 121 anonymous women belonging to all age groups from both the formal and informal sectors. The FLFPR of India has also been compared with that of emerging economies to evaluate India’s current scenario in light of global standards. Secondary data has been collected from sources such as the World Bank, the World Economic Forum, and Indian National newspapers. Thematic patterns of gender pay gap, pre-conceived notions, hostile work environment, negative and prejudiced attitude towards women, lack of mentorship programmes, and inadequate provision of childcare and maternity benefits have emerged. These become obstructions not just for women but also for organizations, India, its economy, and the Gross Domestic Product of this country. If half of the populace remains dormant in the workforce, the Indian economy will never be able to reach its maximum potential.

Published by: Myra KhuranaResearch Area: Gender Equality

Organisation: GD Goenka Public School Model Town, DelhiKeywords: Women, Economic, Gender, Workforce, Employment.

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Research Paper

39. Evaluating the Proliferative and Inhibitory Effects of Selected Indian Spices and Herbs on Vigna Radiata Cell Growth

Scientists have always been on the hunt for a therapeutic chemical with the potential to treat deadly diseases. There is a growing interest in using natural compounds derived from plants as a natural cancer cell treatment. Herbs and spices such as turmeric, garlic, cinnamon, clove, and tulsi are rich in bioactive compounds and have long been studied for their medicinal value in humans. However, their potential role in modulating cancer cell proliferation is underutilized. By exploring the proliferative and inhibitory effects of these traditional Indian herbs on Vigna radiata, this study contributes new knowledge to medical science and phytochemistry. It also opens new avenues for applying culturally significant, easily accessible, sustainable and inexpensive natural resources in modern cancer treatments. This study focuses on finding the specific herb extracts which are potent inhibitors of cell proliferation, in turn reducing the cancerous cell growth, leading to an invaluable impact on cancer treatment worldwide. Furthermore, this research aligns with global goals for sustainable development, particularly those related to accessible, low-cost and sustainable healthcare.

Published by: Seema Bajpai, Aarav Chetan Jain, Dhairya Milin Shah, Nishka Sachin Koneri, Pahal Kayur Shah, Ms. Ranjana YadavResearch Area: Food And Technology

Organisation: Utpal Shanghvi Global School, MaharashtraKeywords: Vigna radiata, Cell Proliferation, Spices, Extracts, Eugenol, Inhibitory Effects, Seed Germination, Radicle and Plumule Growth, Qualitative Tests.

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Research Paper

40. AI-Driven Portfolio Optimisation Strategies in High-Inflation Macroeconomic Conditions

High inflation significantly affects asset prices, risk premiums, and investor behaviour, making traditional portfolio optimisation models less effective. This study explores the application of artificial intelligence (AI) techniques—specifically machine learning (ML) models and heuristic optimisation algorithms—to enhance portfolio performance during periods of high inflation. Using historical macroeconomic and financial market data, the project trains models to identify inflation-sensitive assets, predict returns, and construct optimal asset allocations. Methods such as Random Forest regression, LSTM neural networks, and Genetic Algorithms are compared with classical approaches like Modern Portfolio Theory (MPT). Performance is evaluated using metrics including Sharpe ratio, risk-adjusted returns, and inflation-adjusted returns. The findings aim to determine whether AI-driven strategies can outperform traditional models when inflation is elevated. This project contributes to the growing domain of AI-based financial modelling and offers practical insights for investors seeking resilience against inflationary volatility. In addition to evaluating performance during inflationary spikes, the study examines how AI models respond to shifting macroeconomic signals such as interest rate hikes, currency fluctuations, and commodity price volatility. By incorporating these variables into the learning framework, the models aim to provide more stable predictions and adaptive asset allocation decisions. This helps assess whether AI can truly capture inflation-driven market distortions better than conventional statistical models, which often assume linear relationships and stable correlations. Furthermore, the project highlights the practical implications of AI-driven optimisation for investors, financial planners, and policymakers operating in inflation-sensitive economies. By demonstrating how machine learning outputs can be integrated into investment decision-making, the study contributes to the growing domain of predictive financial analytics. The broader goal is to understand whether AI can create more resilient and inflation-hedged portfolios in real-world scenarios. The findings are expected to offer valuable insights into designing future-ready investment strategies that remain robust even during prolonged periods of macroeconomic uncertainty.

Published by: Aadya GoyalResearch Area: Economics

Organisation: Amity University, Uttar PradeshKeywords: AI, Machine Learning, Portfolio Optimisation, High Inflation, Asset Allocation, Genetic Algorithm, Financial Modelling, Inflation Risk.

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Review Paper

41. Autonomous Drone Navigation Using Computer Vision: Challenges and Future Directions

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as versatile platforms revolutionizing diverse fields such as precision agriculture, environmental monitoring, infrastructure inspection, and disaster management. Their growing impact is driven by advances in autonomy that enable efficient, scalable, and intelligent operations. Among the enabling technologies, computer vision plays a pivotal role by allowing drones to perceive, interpret, and interact with their environment through visual sensing. This capability has significantly improved tasks such as obstacle detection, localization, mapping, and scene understanding, even in GPS-denied or visually degraded conditions. Despite these advances, achieving full autonomy remains a major challenge. Vision-based navigation is often hindered by adverse weather, illumination changes, dynamic obstacles, and computational limitations, alongside issues of domain shift and long-tail edge cases that compromise reliability and safety. This review paper presents a comprehensive analysis of recent progress in vision-based autonomous drone navigation, spanning classical computer vision, deep learning, and emerging paradigms such as Vision Transformers, reinforcement learning, and self-supervised learning. It further highlights open challenges and outlines future research directions, including multi-sensor fusion, domain adaptation, collaborative perception, neuromorphic computing, and explainable AI—aiming to guide the development of resilient and robust UAV systems capable of dependable real-world autonomy.

Published by: Muhammad Jamil Sani, Jamal Nasser AlotaibiResearch Area: Drones

Organisation: Qassim University, Saudi ArabiaKeywords: Autonomous Drones, Computer Vision, Deep Learning, Visual Slam, Multi-Sensor Fusion, Domain Adaptation, Explainable AI, Robust Perception.

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Research Paper

42. Transforming Library Management, Leveraging Data Analytics through A Strategic Approach

In the era of digital transformation, analytics has emerged as a critical enabler across industries, including library management. Libraries, now evolving into knowledge management centres, face dynamic user needs and operational challenges. This paper explores the role of data analytics in enhancing library services, improving decision-making, and addressing contemporary challenges. It outlines the types of analytics applicable to libraries, the analytical environment, and strategies for overcoming operational constraints. Practical examples and case studies illustrate how analytics can be effectively integrated into library operations to optimise resources and strengthen user engagement.

Published by: Mr. Kishore Ramdas Ingale, Dr. Anil P Sarode, Ms. Shubhada Sachin ApteResearch Area: Data Analytics

Organisation: Tata Consultancy Services, MaharashtraKeywords: Analytics, Business Insights, Library Analytics, Library Queries, Analytical Environment.

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Research Paper

43. Determinants of School Orientation in Urban and Rural Areas of Analamanga Antananarivo, Madagascar

School orientation constitutes a decisive stage in students’ educational trajectories, influencing their motivation, academic achievement, and future professional prospects. Although this choice should ideally be guided by each student’s personal aspirations, abilities, and interests, it is often shaped by a range of external factors, notably familial, economic, cultural, and geographical influences. In many contexts, orientation decisions do not result from a free and informed process but rather from sometimes constraining influences exerted by parents, teachers, or the social environment. This study examines the dynamics of school orientation in two contrasting contexts: urban and rural settings. It aims to highlight the impact of variables such as family income, parents’ level of education, their educational attitudes and expectations, as well as students’ perceptions of their own abilities and career prospects. Through a comparative analysis, the study seeks to understand how these factors, depending on the context, contribute to either enhancing or constraining students’ freedom of choice in their educational pathways.

Published by: Radanielina Tsaranto Evatiana Rahaliva, Andrianarimanana Jean Claude Omer, Rakotoson Olivia, Andrianjary MyriamResearch Area: Educational Sciences

Organisation: University of Antananarivo, MadagascarKeywords: School Orientation, Educational Trajectories, Motivation, Family Influence, Socioeconomic Status, Career Prospects.

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