International Research Journal on Advanced Science Hub
International Research Journal on Advanced Science Hub (IRJASH) (eISSN 2582-4376) is an open access journal that was launched by RSP Science Hub in 2019. It aims to publish high quality peer reviewed articles to disseminate the progress in the field of science and technology therby bridging the gap between researchers, Academicians and industrial expert to remain up-to-date with the latest developments. Upon acceptance all the articles are free to read and download.
Humanization in the AI Era: Why it Should be the Focus of EdTech 4.0?
Artificial Intelligence (AI) is redefining education through EdTech 4.0, introducing adaptive platforms, intelligent tutoring systems, and data-driven personalization. While these innovations promise efficiency and scale, they risk sidelining the very essence of learning—human connection, empathy, and ethical engagement. This paper emphasizes that humanization must be the guiding principle of EdTech 4.0, ensuring technology serves as an enabler of meaningful relationships rather than a substitute for them. Drawing on global policy frameworks (UNESCO, OECD, SDG 4), critical pedagogy, and transformative learning theory, the study proposes a layered framework that embeds empathy, ethics, agency, and emotional intelligence into AI-driven education. Through thematic analysis of scholarly literature and real-world case studies, the paper demonstrates that human-centered design is not optional—it is essential for equity, inclusion, and learner well-being. The findings call for a paradigm shift from technology-centered innovation to human-centered transformation, creating learning ecosystems that are not only smart but profoundly human.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Real-Time Data Engineering for Smart Applications
The digital transformation era is characterized by intelligent applications that increasingly rely on rapid processing and analysis of large, non-homogeneous data streams. These smart systems are built on real-time data engineering, enabling seamless data ingestion, transformation, and decision-making across industries such as healthcare, transportation, manufacturing, and finance. The paper will look into the architectural principles, enabling technologies, practical uses, and the future trend of real-time data engineering. It underscores that frameworks of stream processing, edge computing, and cloud-native processing and AI integration are critical to the development of scalable and responsive intelligent systems. The paper also lists popular traps like latency, scalability, data quality, and security, and also provides a projection of the future trend, which has been brought about by federated learning, data meshes, and quantum computing. The provided insights make a detailed picture of the development of the operational and strategic landscape of smart technologies in real-time by applying data engineering.
Ecological Significance and Aquatic Diversity of Daya River in Odisha: Conservation Prospectiveness
The study aims to evaluate the ethiofaunal richness of the Daya River in Odisha, highlighting its ecological value and function in maintaining regional diversity. Being a distributary of the Kuakhai River, the Daya passes through Khordha and Puri districts before joining Chilika Lake, creating a crucial freshwater ecosystem that hosts a wide range of aquatic organisms. The research examines the diversity, distribution, and abundance of aquatic organisms including fish, molluscs, crustaceans, planktons, and amphibians, and how these correlate with the physicochemical properties of the river water. Field surveys at different locations along the upstream and downstream reaches of the river were undertaken. Parameters such as temperature, pH, dissolved oxygen (DO), biological oxygen demand (BOD), nitrates, and phosphates were measured to determine their impact on faunal diversity. The findings reflect a diversified community of water life, suggesting that the Daya River is still biologically active and ecologically rich. Nevertheless, the presence of tolerant and sensitive species concurrently indicates non-uniform water quality at different sites. The study warns of anthropogenic pressures like agricultural effluent run-off, sewage outflow, overfishing, and land development degrading habitats and water quality, causing sedimentation, eutrophication, and destruction of riparian cover. It advocates integrated river management, control of pollution, riverbank restoration, sustainable aquaculture, and community engagement to preserve biodiversity and ecological integrity and livelihoods of the Daya River.
Biodiversity Patterns and Conservation Strategies in Chandaka Wildlife Sanctuary
The study examines biodiversity of Chandaka Wildlife Sanctuary, a crucial ecological region near Bhubaneswar in Odisha, India. Extending over the Khordha and Cuttack districts, the sanctuary area measures approximately 193 square Kilometers and constitutes a region of the Eastern Ghats topography. It is mostly a dry deciduous forest community with Sal (Shorea robusta) and mixed species as the dominant one, offering a natural environment to a diverse array of flora and fauna. The goal of this research work is to record and examine the species composition, diversity, its distribution in sanctuary and determine the ecological processes and also conservation issues influencing in distinctive habitat. Field surveys were carried out in various zones of the sanctuary with conventional ecological techniques like transect walks, quadrat sampling, and camera trapping. The research documented a diversified richness of plant species consisting of medicinal plants, shrubs, grasses, and climbers supporting the livelihood of various animal species. The faunal diversity comprises large mammals like the Indian elephant (Elephas maximus indicus), leopard (Panthera pardus), spotted deer (Axis axis), wild boar (Sus scrofa), and a number of species of primates and small mammals. The sanctuary also has a diverse range of birdlife, reptiles, amphibians, insects, adding to its ecological richness and productivity. The study recognizes some of the major threats to biodiversity, mostly emanating from anthropogenic influences like deforestation, poaching, encroachment, cattle grazing, and urbanization because of the close location of Bhubaneswar city. Human-wildlife conflict, particularly with elephants, and habitat fragmentation have turned out to be serious problems for the ecological integrity of the sanctuary. Water body degradation and riparian vegetation loss have also affected the habitat quality for aquatic and semi-aquatic organisms. The research also emphasizes to intensive conservation efforts to protect Chandaka Wildlife Sanctuary's biodiversity. The proposed steps are restoration of habitat through afforestation, intensification of anti-poaching patrols, establishment of wildlife corridors, and augmentation of water resources. In addition, community participation and environmental education are necessary to ensure sustainable cohabitation between people and wildlife. In conclusion, the current study offers useful baseline data regarding the status of biodiversity at Chandaka Wildlife Sanctuary, which can provide insights for future conservation research, policy, and ecosystem management. Conservation of this sanctuary is important, not only for regional biodiversity maintenance but also for ecological balance sustenance as well as environmental resilience upholding in Odisha.
Study of Walrasian Model Using Quantum Calculus
In this work, for the first time, we present the Walrasian Model in view of q-calculus as a novel perspective of economic dynamics. Though q-methods have been widely applied in mathematical physics, their use in microeconomic theory remains unexplored. In order to study this model, at first, we begin by outlining the foundational concepts of q-calculus, and then reformulate the Walrasian framework within this q-calculus set up. This approach is not only applied to solve the one-dimensional dynamic equation that illustrates the effectiveness through a classic example of microeconomics but also the numerical validation against classical datasets confirms the robustness of this method. Most importantly, we show, if the deformation parameter q tends to 1, the results converge seamlessly to those obtained via the classical calculus that bridges the new formulation with established theory. This study also highlights the potential of q-calculus as a powerful tool for advance research in economic dynamics and opens new directions for theoretical as well as applied economics.
A Study On Online Shopping Dissonance Among College Girls with Reference to HEI'S
access to a vast array of goods and services. However, this digital landscape frequently givesrise to cognitive dissonance, a psychological discomfort stemming from a mismatch between acustomer's pre-purchase expectations and their post-purchase reality. In the context of onlineshopping, this "dissonance" manifests as a feeling of regret, uncertainty, or dissatisfaction after apurchase, often referred to as "buyer's remorse." It occurs when the actual product or experience doesn’t align with the consumer's initial beliefs, values, or expectations. This study specifically investigates how dissonance affects women, a significant demographic in e-commerce who often prioritize aesthetic, fit, and tactile attributes. We identified key sources of dissatisfaction, including misleading product visuals, inconsistent sizing, perceived quality discrepancies, and inadequate product information. These issues, combined with the inherent limitations of the online medium and challenging post-purchase processes like returns, are major drivers of cognitive dissonance. Consumer behavior is always dynamic and changes with time, with each global revolution bringing about some changes in how people shop and how marketers adapt. The current major revolution is the emergence of universal global connectivity, which has had a significant impact on digital marketing and consumer choices. As a result, consumer behavior has changed unrecognizably over time and will continue to do so in the future. To address these challenges, we propose solutions such as leveraging advanced visual technologies, implementing sophisticated fit prediction tools, streamlining logistics, and fostering transparent communication. By addressing these critical areas, e-commerce platforms can significantly mitigate cognitive dissonance, thereby enhancing customer satisfaction, fostering loyalty, and ensuring sustainable growth in the dynamic online retail environment.
Intelligent Automation Adoption in Manufacturing Supply Chains: A Systematic Literature Review of RPA and AI Integration Frameworks, Performance Impacts, and Implementation Strategies
Manufacturing supply chains are becoming more tangled and unpredictable, with mounting pressure to adapt quickly, revealing just how inadequate old, step-by-step planning can be when shipments change overnight. By blending Robotic Process Automation with Artificial Intelligence, intelligent automation has become a game changer—sharpening predictions, speeding up decisions, and giving supply chains clearer visibility, like spotting delays before a truck even leaves the warehouse. This review pulls together studies on how RPA and AI are being adopted in manufacturing supply chains, zeroing in on integration frameworks, performance benchmarks, key enablers and obstacles, and how they shape forecasting and day-to-day logistics, from warehouse scheduling to delivery routes. Following PRISMA guidelines, we systematically screened peer reviewed studies from 2017 to 2025 across Web of Science, Scopus, IEEE Xplore, and ScienceDirect, combing through titles like flipping through well-worn index cards. Out of 381 records reviewed, just 50 studies made the cut. We pulled data on implementation strategies, outcomes, and contextual factors, checked quality with the Mixed Methods Appraisal Tool, and uncovered themes through careful, line by line coding. Research shows that using structured adoption frameworks that blend RPA with AI makes supply chains quicker to adapt and easier to see end-to-end, delivering clear gains—costs drop by 24.8%, lead times shorten by 21.3%, forecasting improves by 23.1%, and errors fall by more than half, down 52.6%. Start with RPA, then bring in AI, and you’ll hit about 85% success—far better than the 70% you get when you try both at the same time. Sharp leadership, talented teams, and savvy change management are needed, but spreadsheets, system linkages, upfront fees, and ethical issues remain. Intelligent automation, based on evidence-based, human-centered, scalable frameworks, may improve operations, strategy, and performance. Businesses should launch plans in stages, support them with solid data governance, and lead staff through each step of a new system until every click and screen is familiar to make significant progress.
Integrated AI Model for Traffic Density Management and Emergency Vehicle Prioritization
This paper is concerned with a Smart Traffic Management System based on AI that operates traffic lights dynamically via real-time multi-modal data. The system combines computer vision, audio recognition, and RFID detection to give precedence to traffic movement, particularly for emergency responders. Camera inputs are analyzed with the YOLO object detection model to estimate traffic density in multiple lanes, while an audio classifier processes siren audio features using Fast Fourier Transform (FFT) features. Furthermore, RFID tags on emergency vehicles allow correct detection even in adverse conditions. A decision module implements hierarchical priority rules, in which the passage of emergency vehicles is given priority over calendar-based event handling, lane congestion control, and regular signal cycling. The ultimate decision is implemented by a Raspberry Pi-based signal controller, which controls traffic lights using GPIO or relay connections. The system also updates a dashboard interface to offer real-time monitoring for authorities. The invention greatly decreases traffic jams, provides quicker response in emergencies, and dynamically adjusts to urban traffic patterns by integrating several sensing modalities with smart decision -making. The inventive concept of the invention is in its combination of image, audio, and RFID data for real-time prioritization and rule-based decision hierarchy that makes it a more robust and adaptive traffic management solution than traditional timer-based systems.
Heritage Ornamentation of Garli–Pragpur Translating Tradition into Modern Design
Garli and Pragpur, India’s first recognized heritage villages, embody a unique decorative language where arches, jaalis, murals and woodwork tell stories of cultural exchange and craftsmanship. Their façades, alive with Gothic, Colonial and Himachali influences, form an architectural identity that is both practical and poetic. However, modernization threatens the integrity and visibility of these traditional motifs; this research seeks to bridge the gap between heritage conservation and contemporary design by exploring how these patterns can be meaningfully adapted in modern architecture. This study employs a mixed-method approach combining visual surveys, documentation, and spatial analysis of heritage buildings with interviews of local residents, artisans, and architects. The findings reveal that the ornamentation of Garli and Pragpur is not a static relic but a hybrid vocabulary where Western stylistic cues are seamlessly blended with vernacular traditions. However, this research also highlights urgent conservation challenges like many buildings lie vacant or dilapidated due to changing demographics, economic migration and complex ownership issues, raising the stakes for documenting and adapting traditional patterns before they disappear. By analyzing and translating these motifs, the research demonstrates their potential for meaningful adaptation in modern architecture. The study argues that these decorative traditions can inspire design solutions that are culturally rooted, aesthetically rich and environmentally responsive. In doing so, Garli and Pragpur are positioned not only as living museums of heritage but also as active sources of design innovation for contemporary practice.
Interior Choreography in Design Practice
A shaft of sunlight pierces through a carved jaali. A devotee walks barefoot across the cold stone floor. Each step echoes like a drumbeat in the sanctum. The temple does not stand still, it performs. The idea of interior choreography is examined in this study, which frames interior design as a dynamic performance influenced by movement, light, narrative, and historical memory. Interiors are often seen as fixed, designed objects—containers for life. Yet the truth is more fluid: they perform. They guide, they pause, and they crescendo. By suggesting interiors as choreographed spaces where narrative, sensory perception, and cultural memory converge, this study fills that gap. A qualitative interpretative methodology was adopted, combining case studies of temples, heritage buildings, schools, and museums with literature on spatial sequencing and environmental psychology. Fieldwork included site observations, photographic documentation, and analysis of spatial components to trace how interiors guide behavior and construct narratives. The results show how interior spaces serve as experiential scripts, with materials maintaining continuity with the past, movement defining rhythm, and light directing atmosphere. Reinterpreting these ideas in contemporary interior design results in improved cultural relevance, emotional resonance, and memory retention. The study concludes that a transformative design methodology can be achieved by approaching interiors as staged performances. For architects and interior designers, this approach provides a framework to integrate cultural memory, sensory experience, and narrative into built spaces—fostering environments that move beyond static form into living, performative encounters.
Adaptive Reuse for Community Development
Garli and Pragpur, a heritage village cluster in Himachal Pradesh, are known for their fading havelis, narrow lanes, and a distinctive blend of colonial, Sikh, and vernacular Himachali architecture. Once flourishing under the enterprise of the Sood community, these settlements today face neglect as many historic structures remain abandoned or underutilized, leading to a gradual erosion of cultural identity. This research explores adaptive reuse as a sustainable strategy to revitalize void heritage buildings for social welfare and community development. Through site observations, architectural documentation, and analysis of reuse approaches, the study examines how abandoned structures can be assigned new functions that respond to present societal needs. The findings reveal that heritage conservation must extend beyond physical restoration to address the social and functional dimensions of space. Buildings that lose their original utility risk falling into disrepair; however, adaptive reuse bridges the past and present by providing renewed purpose while respecting original character. The study concludes that integrating modern, community-oriented functions within heritage buildings can foster inclusive development, preserve cultural continuity, and revitalize neglected environments, positioning adaptive reuse as a key strategy for ensuring the sustainable survival of cultural assets and enhancing the quality of life for local communities.
Modernizing PeopleSoft Financial Systems: Automation, Cloud Integration, and Workflow Optimization
Enterprise Resource Planning (ERP) systems have been a part of the financial and operational processes of contemporary institutions. Although a strong and mature platform, Oracle PeopleSoft Financials was developed based on the old architectures that are becoming less and less fit to meet the modern business requirements of agility, automation, and cloud-native integration. This literature review indicates the need and ways of modernising PeopleSoft Financial systems with three key pillars such as automation, cloud integration, and workflow optimisation. The paper presents a theoretical framework backed by literature in the industry and the scholarly world to inform the strategies of modernisation. The main issues of customisations of the past, data integrity, and the readiness of the organisation are addressed. Further research directions and recommendations include a focus on establishing a consistent modernisation framework, transitioning to the use of hybrid clouds, and the use of advanced analytics. The presented insights will help to inform research and practical implementation to implement sustainable ERP transformation.
Zero Trust Architectures for Responsible AI in Enterprise Applications
The current-day business decisions have been centered on artificial intelligence (AI). Nonetheless, obtaining them without compromising transparency and ethics is becoming a problem. The paper has explored how the application of Zero Trust Architecture (ZTA) and the principles of Responsible AI (RAI) can be used to build AI systems that are both secure and ethically right. We research existing frameworks, field testing, and design techniques that deploy ZTA to the AI lifecycle - collecting data to training, deployment, use, monitoring, and auditing. We have found that ZTA does not only make security to be more secure through hard access control and privacy, but it also increases the level of fairness, transparency, and resistance to adversarial attacks. We finish by a couple of suggestions to future research on ZTA-powered Responsible AI and conclude with the argument that one model of governance can unite both cybersecurity and AI ethics.
DineWise RMS: A QR Code-Based Restaurant Management System for Table Booking and Queue Management
The restaurant industry faces challenges such as long waiting times, inefficient table management, and high operational costs due to manual processes. In this study, developed mobile and Windows-based a QR code-based restaurant management system which provides the facility to streamline table booking along with automated queue management and order processing, named “DineWise RMS”. By leveraging QR code technology, customers can scan codes to access digital menus, reserve tables, join virtual queues, and place orders directly from their smartphones. The system integrates with a central database to provide real-time updates on table availability, queue status, and order progress. This work highlights the potential of QR code technology to enhance operational efficiency and customer satisfaction in the dining sector.
Solar Energy System Simulation with Respect to Cybersecurity Using Decoy Device
The rapid expansion of renewable energy deployment has placed solar photovoltaic (PV) at the forefront of the clean power supply for the residential, commercial and industrial users. With their insertion into smart Grids, though these systems are facing increasing exposure to cyber physical threats to stability simulation and protection system that embedded with solar panels, inverters and battery management units along with smart sensing using LDR, PIR, IR, temperature sensors, and actuator responses through LED and buzzer notifications. A multi-layered cybersecurity framework is designed, including AI-powered anomaly detection for timely threat detection, blockchain-enabled decentralized logging to provide tamper-proof audit records, honeypot-derived decoy devices to divert attackers, and a Moving Target Defence (MTD) function for adaptive system reconfiguration against dynamic threats. The system is implemented on Raspberry Pi and ESP32 boards to facilitate real-time data acquisition, secure processing, and visualization of solar performance data, environmental parameters, and intrusion attempts. Experimental validation confirms the system's accurate estimation of solar irradiance, effective inverter–battery interaction, and strong intrusion resilience based on coordinated responses. Furthermore, the decoy-based deception method proposed in this work effectively misdirects adversaries from important resources while ensuring operational continuity. The findings identify a new paradigm for incorporating sustainable energy management with sophisticated cybersecurity measures and provide a resilient, scalable, and smart model for future decentralized smart grid infrastructures.
Adaptive Traffic Signal Control System Using Machine Learning and Neural Evolution: Multi-Case Study for Intersection Optimization and Emergency Vehicle Prioritization
Urban traffic congestion poses persistent challenges, aggravated by insufficient integration of adaptive control and emergency vehicle prioritization. Existing AI models largely focus on either traffic flow optimization or emergency response, rarely combining both. This research fills this gap by developing a unified framework that predicts optimal green light durations using real-time traffic density, vehicle heterogeneity, and emergency prioritization. A four-way intersection traffic simulation was developed using the pygame library to closely mimic real traffic dynamics, lane configurations, vehicle types, and turning movements. Five case studies were conducted: (1) a mathematical green-time prediction model incorporating weighted vehicle classes, clearance times, arrival rates, and queue spillover controls; (2) reinforcement learning (RL) trained on varied traffic conditions; (3) RL enhanced with emergency vehicle priority; (4) application of the Neuroevolutionary of Augmenting Topologies (NEAT) algorithm for model architecture optimization; and (5) comparative analysis of model performance. The mathematical model reduced mean vehicle delay by 23% (p < 0.05), standard RL achieved 31% improvement (p < 0.01), emergency-aware RL maintained a 28% reduction while ensuring emergency vehicle clearance within 18.7 seconds on average, and the NEAT-based system improved throughput by 34% with superior adaptability to traffic fluctuations. The integrated framework significantly enhances traffic efficiency compared to fixed-time signals while guaranteeing rapid emergency vehicle response. Its extensibility supports integration into smart city traffic management platforms, offering scalable and adaptive solutions for urban mobility challenges.
Adaptive Mask Optimization-Driven Hybrid Gan with Swarm-Based Tuning for High-Fidelity Image Inpainting: Results and Performance Analysis
Image inpainting is a computer vision technique that aims to fill in missing or damaged areas of an image with plausible content. This paper presents the results and analysis of an Adaptive Mask Optimization-Driven Hybrid GAN designed to enhance image inpainting quality. The model integrates dynamic mask refinement with swarm-based hyperparameter tuning and a multi-component loss formulation to improve structural accuracy, texture realism, and computational efficiency. Evaluated on the Paris Street View dataset with cross-validation on three additional datasets, the proposed model achieved a PSNR of 34.72 dB, SSIM of 0.942, and EPI of 0.087, surpassing GAN-Inpaint, Painter Net, Hybrid Swarming Algorithm, and Swarm-Optimized Transformer GAN. Cross-dataset evaluation confirmed generalization across urban, artistic, and natural scenes. Ablation studies revealed that adaptive mask optimization and swarm-based tuning significantly improve perceptual quality, while computational analysis showed a balanced trade-off between accuracy and inference time. These results establish the model as a robust, efficient, and generalizable solution for applications in digital restoration, medical imaging, and remote sensing.
Assessment of Ethylene-Vinyl Acetate Material Properties and Compliance Testing for Complete EVA Footwear
Ethylene-vinyl acetate (EVA) is widely used in the footwear industry due to its excellent cushioning, flexibility, and lightweight characteristics. This study investigates the performance properties of EVA material for complete footwear applications, focusing on critical tests including sole abrasion, tear strength, compression resistance, full shoe flexing, and chemical safety parameters such as cadmium, lead, phthalates, and pH value. The analysis combines empirical testing with a review of regulatory compliance standards and existing literature. The results demonstrate (white shoe of density 0.19 g/cc and black shoe of density 0.17g/cc) versatility and potential to meet functional and environmental safety requirements for footwear manufacturing.
An IoT Based Smart Home System for Fault Detection
This paper includes an IoT-based smart house system to detect faults in real time with the help of embedded platforms including. It incorporates the environmental, electrical, and operation sensors to detect abnormalities and failures of the home facilities and equipment. NODE to NODE communication is facilitated with BLE, Zigbee, Z-Wave and Wi-Fi that is optimized to support different power and bandwidth demands. Firmware is run on microcontrollers, which have a capability to interface low-level sensors and do edge processing in C/C++, or Python. Advanced diagnostics are done on complex components based on embedded Linux and either Python or JavaScript. The cloud services such as AWS IoT offer access at a distance, data recordings, and fault classification based on machine learning. Scalable and modular system architecture ensures powerful and effective fault detection in a smart home setting. Isolation Forest and LSTM neural networks for sensor problem diagnosis and predictive maintenance. Network adaptability is increased by local fallback strategies, watchdog timers, and secure firmware updates. This research supports the development of smart home systems which are scalable, safe, and intelligent.
Stress Detection in It Workers Using Image Processing and AI
In today’s fast-paced IT industry, employees frequently experience stress due to prolonged computer usage, tight deadlines, and constant mental pressure. Often, individuals may not realize they are under stress until it negatively impacts their health, focus, or performance. Chronic stress can lead to serious consequences such as burnout, increased absenteeism, and reduced productivity. This project proposes a non-intrusive, real-time stress detection system using a widely available tool the computer’s built-in webcam. The system passively observes facial expressions and behaviours during regular work activities without requiring the user to wear any sensors or interrupt their workflow. Through image processing and artificial intelligence techniques, it analyses live webcam footage to detect subtle cues like a furrowed brow, clenched jaw, or eye fatigue, which are indicative of stress. Trained AI models assess these cues in real time and provide immediate feedback, such as suggesting short breaks or relaxation exercises, when elevated stress levels are detected. Unlike traditional methods involving surveys or medical equipment, this approach is contactless, effortless, and integrated seamlessly into the user’s routine. Its passive operation ensures minimal disruption while still offering meaningful support. By identifying stress at an early stage, the system enables timely intervention, helping individuals manage stress before it escalates. This real- time monitoring promotes workplace wellness by encouraging healthier habits and self- awareness. Over time, it can contribute to reduced burnout, fewer stress-related health issues, and improved employee performance. Ultimately, this AI-powered webcam-based system offers a smart, effective solution for supporting mental well-being in high-pressure IT environments without adding extra burden on users.
Jagrukta: SVM-Based Disaster Forecasting System
This paper introduces Jagrukta, an intelligent disaster management and early warning system that leverages a Support Vector Machine (SVM) model trained on historical weather and geophysical data to predict and classify natural disasters such as floods, tsunamis, and earthquakes. Designed with the goal of enhancing disaster preparedness and minimizing the impact of calamities, Jagrukta processes decades of meteorological and seismic records—including rainfall intensity, oceanic temperature variations, tide levels, atmospheric pressure shifts, and seismic wave patterns—sourced from national and international datasets. These features are preprocessed and used to train an SVM classifier capable of categorizing input conditions into normal, alert, or critical states. The model achieves an average accuracy of 89%, showing high precision in forecasting flood and tsunami events, and delivering promising results for earthquake detection through temporal seismic pattern recognition. The system includes a real-time visualization interface that maps predictions geographically, enabling early alerts and faster response by disaster management authorities. Unlike resource-intensive deep learning models, Jagrukta’s SVM-based approach is interpretable, efficient, and optimized for deployment in regions with limited computational infrastructure. This project demonstrates the practical application of machine learning in disaster mitigation and aims to empower communities with timely, reliable, and data-driven insights. Future developments include incorporating dynamic forecasting through deep learning models, expanding the regional dataset, supporting mobile-based alerts, and integrating multilingual support to reach broader populations across India.
Real-World Strategies for Secure, Scalable, and Cost-Optimized Cloud Migrations
As organisations scale their operations both on-premise and in the cloud, they face increasingly complex challenges in securely and efficiently migrating their processes at scale and within budget. Migrating to the cloud is no longer merely a technological choice, but a strategic business transformation. Successful implementation requires careful consideration of risk management, performance evaluation, compliance requirements, and budgetary constraints. Yet, despite the wide availability of migration tools and techniques, a disconnect remains between the chosen migration strategies and the desired outcomes in terms of performance, security, and cost-efficiency. This review has presented a combined theoretical framework that can be applied to guide the movement of the cloud across a broad spectrum of industries and within various organisations. It summarizes the current research findings, introduces the most prominent pitfalls, and proposes the feasible options that the new frontiers in the evolution of automation, orchestration, and cloud-native architecture open. Other post-migration optimization practices and other governance systems, which are central to the long-term sustainability, are also discussed. A perspective study of future trends and enablers of cloud migration in the future is the final section of the paper.
A Comparative Study Analysis and Implementation of Nano-Enabled Power Generation from Recycled E-Wastes in India
The paper addresses a critical environmental concern: the increasing accumulation of electronic waste (e-waste) in rural India. It explores the dual aspects of this issue - the environmental impact of e-waste and it’s potential as a sustainable energy source. Key points of the study include: Environmental Impact: Analysis of e-waste's effects on rural Indian ecosystems. Energy Potential: Investigation of e-waste as a viable source of sustainable energy. Nano-generator Technology: Exploration of how nanotechnology can be applied to convert e-waste into energy. Comparative Analysis: Study of different e-waste components and their respective energy-generation capacities. Implementation Model: Presentation of a case-based model for practical application of the proposed solution. The research emphasizes three main aspects: Green Energy: Focusing on environmentally friendly energy production methods and Rural Sustainability: Addressing the specific needs and challenges of rural areas in India. Innovative Nanotechnology: Utilizing cutting-edge technology to solve environmental issues. This study contributes to the fields of environmental science, renewable energy, and nanotechnology, offering a potential solution to two pressing issues: e-waste management and sustainable energy production in rural areas. The case-based implementation model provides a practical framework for applying these concepts in real-world scenarios.
Comparative Analysis of Object Detection Models for Peacock Detection: Evaluating Their Performance and Key Points
Peacocks being a major contributor in the damage caused in the agriculture sector, an imminent requirement of efficient detection system for timely intervention. Object Detection being an important application of Computer Vision aims in precisely finding the location and identifying the object in images and videos. With increased advancements in the deep learning field and with various models, choosing the best model that not only performs accurate object detection but is also evaluated based on its inference time. This research aims in conducting an analysis on various models in market on the application of peacock detection evaluating them based on accuracy, precision, recall and F1-score. The Yolo11 yields the highest result with the accuracy of 84.9%. The detailed comparison with various evaluation metrics gives the efficient solution in mitigating this problem in-hand saving the agriculture from further damage and incurring future losses.
Balancing the Load in a Multi-Paradigm Era: A Comprehensive Survey of Algorithms from Cloud to Quantum
Load balancing is a pivotal facet of distributed computing systems that significantly influences the performance of the system by achieving optimal resource utilization, reduced response time, and enhanced system reliability through the even distribution of workloads to computing nodes. The rate at which the computing paradigms are changing has resulted in the increased complexity of the load balancing problem, which is evident in the cloud, fog, edge, grid, and quantum computing since they are particularly characterized by the issues of latency sensitivity, energy efficiency, heterogeneity, scalability, and quantum decoherence. This paper surveys load balancing algorithms implemented in the five computing paradigms, highlighting the main operating principles, architectural differences, scheduling strategies, and performance evaluation criteria applicable to each context. The cloud computing section is devoted to the classification of static and dynamic algorithms, and we also touch on the weighted round-robin, honeybee foraging, and VM migration strategies. The paper also surveys the load balancing of the fog and edge computing wherein we pinpoint the latency-aware and mobility-aware load balancing strategies that are best for the resource-limited, geographically distributed infrastructures. In the grid computing section, peer-to-peer, and decentralized scheduling methods are discussed, which are best suited to loosely coupled networks. Quantum computing is also discussed in the paper as an early stage of load balancing in hybrid classical-quantum systems, the partitioning of quantum jobs, and the coherence limitation of qubit. In a unified taxonomy of algorithms, the paper describes how various algorithms can be mapped depending on their design philosophy, decision criteria, and applicability to different paradigms. Further, the paper lists evaluation charters that are widely utilized in the literature and underscores open issues like cross-paradigm interoperability, scheduling with context awareness, and the trade-offs between energy and performance. By bringing together understanding from these different places, this survey acts as a base for researchers and practitioners who want to create load balancing strategies that are not only adaptable but also paradigm-specific, and cross-platform, and that fit into the changing face of distributed computing.
Deep Learning for Cotton Disease Detection Lightweight, Explainable and Field-Ready Solutions
Cotton is a crucial crop for the economy that is globally recognized as white gold and a major contributor to the Indian economy. However, cotton production is threatened due to various diseases affecting the leaves, like bacterial blight, leaf curl virus, fungal infections and pest attacks impacting the crop yield and quality that affect economic losses. The traditional disease detection methods, which depend on manual inspection, are inefficient, time-consuming, laborious, inaccurate and lead to misdiagnosis and often unreliable under field conditions. The need for early and accurate diagnosis is critical for timely intervention. In recent years Machine Learning (ML) and Deep Learning (DL) have been used for automated disease detection through leaf images. The study highlighted lightweight convolutional neural networks (CNNs), transformer-based models and object detection frameworks such as YOLO and RT-DETR, which have performed accurate results. Transfer learning with advanced backbones (EfficientNet, Xception, ResNet), integrating with attention mechanisms (e.g., CBAM, DFSA) for feature enrichment. The Explainable AI (XAI) for improving the explainability, while synthetic data generation using GANs reduces dataset imbalance. The review consolidated the current state of DL models for cotton disease detection, focusing on optimized approaches for mobile and edge deployment. Finally, it identifies existing research gaps and future directions for accurate, efficient, and field‑ready solutions.
An Eﬃcient Bitcoin Network Topology Discovery Algorithm for Dynamic Display
The Bitcoin network is made up of a huge number of nodes, which makes it difficult and time-consuming for users to figure out how everything is connected. To make this easier, we’ve come up with a new algorithm that uses lightweight “probe” nodes to quickly gather information about the network. On top of that, we’ve built a clustering system that groups stable nodes together, so we can map out the network faster and more efficiently. We’ve also created a flexible, real-time visualization tool that shows how the network is structured in layers. Our tests show that this approach cuts down communication overhead by about 72%, while still maintaining 95% accuracy in mapping the network. Even better, the same method can be used for other blockchain networks with similar setups.
Network topology discovery Bitcoin Stability coeﬃcient Dynamic updates
An Analytical Review of India's Startup Ecosystem (2019-2023)
India’s startup ecosystem has witnessed exponential growth over the past decade, positioning the country as the third-largest startup hub globally. As of December 2023, over 1.17 lakh startups have been recognized by the Department for Promotion of Industry and Internal Trade (DPIIT), generating more than 12.4 lakh direct jobs and expanding across 80% of India’s districts. This paper analyses DPIIT data from 2019 to 2023 to examine the geographical distribution of startups ecosystem, employment generation and Gender inclusivity initiatives in India. The study explores the policy frameworks of top performing states contribution to the ecosystem maturity in these regions. By drawing comparisons with emerging states, the paper aims to offer actionable insights and policy recommendations to foster inclusive and balanced startup growth across India, aligning with the national vision of Viksit Bharat @2047.
Invisible Influence How Algorithms Are Redefining Advertising in The Age of Personalization
The advertising landscape has undergone a fundamental transformation with the integration of algorithmic systems into content delivery and consumer targeting on various social media platforms. Social media platforms that were once channels for mere social interactions now operate as algorithmically curated ecosystems that now critically shape what the user sees, when they see it and how often will they engage with a particular type of content. On this account, Algorithmic advertising has become a strategic imperative for brands seeking to connect with audiences across diverse social media platforms. This research conducts a cross-platform analysis of algorithmic structures on Facebook, Instagram, LinkedIn, YouTube, and X (formerly Twitter), assessing their effect on content visibility, targeting precision, and advertising performance. Incorporating current literature and industry practices, the paper highlights distinct trends in personalization, user engagement metrics, ethical dimensions and algorithmic bias. In addition to determining these patterns, it also provides practical insights into how platform-specific strategies by brands can improve campaign effectiveness and audience resonance. It additionally expands discussions on optimizing algorithm design for emerging platforms by revealing structural deficiencies and design inconsistencies in existing algorithm frameworks. Practical guidance is offered for brands to adapt content formats and strategies in alignment with each platform’s algorithmic logic. This research bridges academic theories with practical applications, enhancing both scholarly discussion and the preparedness of practitioners in the dynamic area of AI-driven brand communication.
A Comprehensive Review of CNN-Based Thermal Signature Analysis for Advancements in Personalized Health Monitoring
This review systematically examines the application of CNNs in thermal signature analysis, with a focus on their transformative role in advancing personalized health monitoring systems. The study commences with an overview of the theoretical underpinnings of CNN architectures and their efficacy in biomedical image processing, particularly in extracting discriminative features from thermographic data. A detailed analysis of current methodologies is presented, encompassing pre-processing techniques, model architectures, training paradigms, and performance metrics relevant to thermal imaging applications. The review further categorizes and evaluates recent research efforts that employ CNN-based approaches for clinical diagnostics, physiological monitoring, and anomaly detection using thermal imagery. Particular attention is given to the integration of thermal imaging with wearable technologies, emphasizing its potential to enable continuous, contactless, and real-time health monitoring in diverse environments. Additionally, the paper addresses key technical challenges—such as data heterogeneity, limited annotated datasets, and thermal noise—as well as emerging trends including multimodal fusion, transfer learning, and edge computing. The review concludes by outlining prospective research directions aimed at enhancing model generalization, interpretability, and deployment in real-world healthcare scenarios. This work highlights the critical role of CNN-driven thermal signature analysis in shaping the future of precision medicine and underscores the necessity for interdisciplinary research to accelerate clinical adoption.