DMPedia Lecture Notes in Multidisciplinary Research
type: channel
description: DMP-LNMR (DMPedia Lecture Notes in Multidisciplinary Research) is a peer-reviewed conference proceedings series published by Digital Manuscriptpedia. The series provides a publication platform for conferences that bring together research from multiple academic fields, including management, social sciences, education, humanities, commerce, applied sciences, and other interdisciplinary areas. Each volume in the series includes original contributions selected through peer review and checked for academic quality, ethical compliance, and plagiarism. DMP-LNMR supports conferences whose themes span multiple disciplines and encourages work that addresses real-world challenges through collaborative, cross-domain perspectives. The series offers ISBN assignment, DOI allocation (when requested), structured editorial processes, and online hosting through Digital Manuscriptpedia’s digital library. DMP-LNMR aims to make multidisciplinary conference research accessible, citable, and discoverable, while maintaining clear publication standards and a consistent editorial workflow.
publisher: Digital Manuscriptpedia
language: en
publication name: DMPedia Lecture Notes in Multidisciplinary Research
issn: 3139-468X
items: इक्कीसवीं सदी के समकालीन युग में अनामिका और कात्यायिनी की हिंदी कविताओं में ग्रामीण बोध एवं लोक संस्कृति
items: A PRISMA-Based Systematic Review of Generative AI in STEM Education: Taxonomy, Challenges, and Future Research Direction
items: A Hybrid Framework for IoMT Threat Detection and Mitigation
items: High-Speed Descent and Low-Speed Ascent: A Novel Gravity–Buoyancy Hybrid System for Sustainable Power Generation
items: AI-Based Dream Pattern Decoder Using Jung and Freud Theory
items: Low-Cost Automation Framework for Sustainable Hydroponic Farming in Urban Poly Houses
items: Prediction of VOIP Traffic in Real Mobile Networks
items: Carbon Prompting: An Empirical Analysis of LLM-Based Requirement Classification
items: A Study of Autonomous Driving Object Detection Models Using Computer Vision and Deep Learning
items: SanskritBERT: Language-Specific Transformer Modelling for Classical Sanskrit Texts
items: Structural Health Monitoring of Aging Buildings: A Review
items: Using Supplementary Cementitious Materials for Sustainable Concrete
items: Pi-NAS: Personal Home Cloud Network
items: Precision Livestock Farming: Essential for Sustainability in Climate Change
items: Front Page: International Conference on Multidisciplinary Perspectives in Advanced Computing and Technology-2026
items: A Review on Non-Invasive Blood Group Prediction Using Fingerprint and Image Processing
items: Blockchain-Secured Telemedicine and Remote Patient Monitoring System
items: Transforming Education in Uttarakhand through Cloud Computing Model: An Explorative Study
items: Remote Sensing and GIS Approaches for Agricultural Drought Severity Assessment: A Comprehensive Review
items: An Integrated Policy Framework for Urban Traffic Congestion Mitigation: A Comparative Simulation Insights from Amsterdam and Delhi
items: EV Battery Technologies and Battery Management in India: Evolution and the Way to a Solid-State Future
items: Inclusive Transit Navigation: A Real-Time Accessibility-Aware Mobile Application
items: Detecting AI-Generated Phishing Websites with ML
items: A Comprehensive Study of Web Application Security, Vulnerabilities and Modern Defence Mechanisms
items: Heat and Mass Transfer Study of Multiphase Flow of Viscous and Viscoelastic Fluids
items: Towards Data-Driven Software Engineering: Integrating Machine Learning for Smarter Development Lifecycle Management
items: Recovery of Deleted Data and Associated Metadata from XFS and Btrfs Filesystems
items: Realistic 3D Cloth Wearing Using AR/VR for Immersive Virtual Try-On in E-Commerce
items: A Review of Prioritization Methods for Rural Road Network Planning: From Traditional Approaches to Modern MCDM and GIS-Based Frameworks
items: Privacy in Quantum Computing: A Comprehensive Review

इक्कीसवीं सदी के समकालीन युग में अनामिका और कात्यायिनी की हिंदी कविताओं में ग्रामीण बोध एवं लोक संस्कृति
type: item
description: आधुनिक युग में स्त्री विमर्श, स्त्री मुक्ति, नारीवाद एवं नारी सशक्तिकरण की चर्चा चारो ओर है। वर्षो से होते आये स्त्री शोषण ने ही स्त्री विमर्श की ओर ध्यान आकर्षित किया है। विभिन्न लेखकों ने स्त्री चेतना को अपनी कविताओं का विषय बनाया परंतु जिस प्रकार नारी संवेदना को महिला लेखिकाओ ने नारी मन की भावना को उकेरना शुरू किया वह स्त्री विमर्श नारी के आत्मागौरव और सम्मान का उल्लेखनीय उदारण प्रस्तुत करता है। स्त्री, दलित, आदिवासी, वृद्ध विमर्श आज भी साहित्य के शोध केंद्र में नहीं है। विमर्श का अर्थ है किसी विषय के बारे में सोचना और विचार करना। इस दृष्टि से देखा जाए तो स्त्री विमर्श नारी से संबंधित सभी मुद्दों को सोचने समझने और विचार करने वाला चिंतन है। नौवीं शताब्दी में आते आते स्त्री विमर्श ने विद्रोहात्मक रूप धारण कर लिया और इक्कीसवी सदी तक आते आते स्त्री विमर्श साहित्य, संगोष्ठी और शोध के केंद्र में भी छा गया। आधुनिक युग में नारी विमर्श, स्त्री मुक्ति, नारी वाद और नारी शशक्ति करण का शोर चारो तरफ है। इस शोध पत्र की प्रस्तुति समाज को स्त्री की स्थिति और अस्तित्व के प्रति जागरूक करना और उसकी चिंता के लिए समाज को जागरूक करना, पुरुषो के समान इंसान को समझना और उसके दुख दर्द के कारणों को समाज को समझाना है।
subject: Articles
  topic: section
subject: स्त्री विमर्श, अस्तित्व, आत्मसम्मान, विद्रोहात्मक रूप, नारी सशक्तिकरण
  topic: keywords
creator: Vandana Nautiyal
creator: Nidhi Upreti
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-07-02
publication date: 2026-07-02
starting page: 1441
ending page: 1446
doi: 10.65890/dmp.lnmr.IMPACT26.179

A PRISMA-Based Systematic Review of Generative AI in STEM Education: Taxonomy, Challenges, and Future Research Direction
type: item
description: Generative Artificial Intelligence (GAI) has emerged as a transformative force in Science, Technology, Engineering, and Mathematics (STEM) education by enabling intelligent tutoring, automated content generation, real-time coding assistance, simulation-based learning, and adaptive assessment. Despite its rapid adoption, a structured understanding of how GAI is systematically applied across diverse STEM domains remains limited. To address this gap, this paper presents a PRISMA-based systematic review of generative AI applications in STEM education. The selected studies are classified into five major categories: concept explanation and intelligent tutoring, simulation and virtual laboratories, programming and coding support, design thinking and creativity, and assessment and feedback automation. A comparative analysis of widely used GAI tools, including ChatGPT, GitHub Copilot, and Bard, is conducted to highlight their educational roles and limitations. The findings indicate that GAI significantly improves learner engagement, conceptual understanding, and coding productivity. However, challenges such as limited personalization depth, weak multimodal integration, insufficient cognitive feedback, hallucination, and ethical reliability remain unresolved. Finally, key research gaps are identified and a future research agenda is outlined for building trustworthy and adaptive GAI-driven STEM learning systems.
subject: Articles
  topic: section
subject: Generative Artificial Intelligence, STEM Education, Systematic Re view, PRISMA, Intelligent Tutoring Systems, Adaptive Learning, Educational Automation
  topic: keywords
creator: Sanjeev Giri
creator: Vineet Vishnoi
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-04-25
publication date: 2026-04-25
starting page: 1428
ending page: 1440
doi: 10.65890/dmp.lnmr.IMPACT26.178

A Hybrid Framework for IoMT Threat Detection and Mitigation
type: item
description: The Internet of Medical Things (IoMT) is transforming healthcare through continuous monitoring, remote diagnostics, and data-driven care. Still, pervasive connectivity expands the attack surface and elevates risks of data breaches, ransomware, and service disruption with direct patient-safety implications. This paper proposes a comprehensive, multi-layered IoMT security framework that unifies edge/fog intrusion detection with privacy-preserving and integrity-assurance mechanisms across sensor, network, and cloud tiers. The detection plane combines a hybrid deep learning architecture, Convolutional Neural Networks with Long Short-Term Memory (CNN-LSTM) for real-time traffic analysis with high recall at the edge/fog, and ensemble machine learning models (XGBoost, LightGBM, and Deep Neural Networks) for static and dynamic malware/ransomware analysis at aggregation layers. To strengthen integrity, non-repudiation, and secure pr ovenance, the framework integrates a lightweight private blockchain; for privacy and scalability, it supports federated learning to enable cross-institutional model updates without centralising protected health information. A priori STRIDE threat modelling guides design choices and control placement, while the response plane uses adaptive policies to isolate compromised devices and sustain clinical workflows automatically. On the CICIoMT2024 benchmark and SDN-integrated simulations, the framework achieves over 99% accuracy, recall, and F1-score for intrusion and malware detection, with 99.60% accuracy and an F1-score of 0.9966 using XGBoost, and maintains 99.82% service availability during automated containment. The approach aligns with risk and safety practices in ISO 14971 and IEC 81001-5-1, and is consistent with FDA/IMDRF expectations, while anticipating future extensions in quantum-resistant cryptography and energy-aware deployment. By fusing AI-driven detection, distributed trust, and proactive threat modelling, the framework delivers a res…
subject: Articles
  topic: section
creator: Baliram Kumar
creator: Vishal Kumar
creator: Sarthak Sanghai
creator: Himanshu Sharma
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-04-11
publication date: 2026-04-11
starting page: 1409
ending page: 1427
doi: 10.65890/dmp.lnmr.IMPACT26.177

High-Speed Descent and Low-Speed Ascent: A Novel Gravity–Buoyancy Hybrid System for Sustainable Power Generation
type: item
description: Modern renewable power systems often face challenges in achieving continuous output and low cost per kWh. This study introduces a gravity–buoyancy hybrid energy system that leverages hydro-mechanical interactions to provide baseload-capable, self-sustain electricity generation. By combining high-velocity gravitational descent of dense payloads with low-velocity buoyant ascent in a closed-loop architecture, the system achieves continuous mechanical-to-electrical conversion. Unlike perpetual motion fallacies, it adheres to thermodynamic laws, deriving output from gravitational potential replenished via efficient hydrostatic mechanisms. The design emphasises modularity, economic scalability, and environmental compatibility, aligning with United Nations Sustainable Development Goal 7 (Affordable and Clean Energy). Theoretical analysis shows a net mechanical-to-electrical conversion efficiency approaching 90%, with per-column output of ~0.5 MW. Cost assessment yields ₹0.652/kWh with a payback period of 3.67 years for a 100 MW plant, outperforming conventional renewables in cost and continuity.
subject: Articles
  topic: section
subject: gravitational kinetic energy; buoyancy power; hydro-mechanical system; renewable baseload; energy conversion; sustainable power; green energy.
  topic: keywords
creator: Sanjay Kumar
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-04-07
publication date: 2026-04-07
starting page: 1373
ending page: 1383
doi: 10.65890/dmp.lnmr.IMPACT26.176

AI-Based Dream Pattern Decoder Using Jung and Freud Theory
type: item
description: Dreams have always offered a mysterious look into the human mind, inspiring both curiosity and research. This study presents a computational approach to interpreting dream narratives by combining artificial intelligence with classical psychoanalytic concepts proposed by Sigmund Freud and Carl Jung. Using natural language processing techniques, the system analyses written dream reports to identify recurring themes, emotions, and symbolic patterns that reflect personal and collective psychological meanings. Freud’s idea of manifest and latent content helps uncover hidden desires and suppressed emotions, while Jung’s archetype framework supports the recognition of universal symbols such as Shadow, Anima, or Self. The model was trained and evaluated on dream-related datasets to examine its ability to extract psychological insights similar to those derived from expert interpretations. The findings suggest that AI-based interpretation can complement traditional psychoanalysis by offering a consistent, data-driven, and scalable tool for research and mental health applications. This work highlights how technology can enhance our understanding of the subconscious mind and expand the possibilities of psychological exploration in modern contexts.
subject: Articles
  topic: section
subject: AI, Dream Analysis, Psychoanalysis, NLP, Symbolic Pattern Recognition, Emotion Recognition
  topic: keywords
creator: Jatin Kumar Singh
creator: Anirudh Sharma
creator: Teena Verma
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-04-07
publication date: 2026-04-07
starting page: 1399
ending page: 1408
doi: 10.65890/dmp.lnmr.IMPACT26.175

Low-Cost Automation Framework for Sustainable Hydroponic Farming in Urban Poly Houses
type: item
description: Urbanisation and shrinking arable land demand cost-effective, sustainable food production methods. This paper presents an open-source automation framework for hydroponic farming in urban polyhouses that achieves full functionality for under US $100. The system employs multi-parameter sensing (pH, EC/TDS, temperature, humidity, water level) and a dual-loop control strategy that sequentially regulates pH and nutrient dosing to avoid chemical antagonism. An ESP-NOW–based fail-safe maintains operation during internet outages, while a secure bilingual mobile app (Flutter, AES-256-GCM encryption) enables real-time monitoring and control. Three replicated 42-day nutrient film technique (NFT) trials with Coriandrum sativum yielded a 27.5% ± 3.2% increase in fresh weight and 82% water savings compared with manual control (p < 0.05). Economic analysis showed a 6.1-month break-even and 96 % ROI in year one. Results demonstrate that the proposed framework is a scalable, resilient, and economically viable solution for resource-limited urban agriculture.
subject: Articles
  topic: section
subject: Hydroponics, Automation framework, Urban agriculture, Dual-loop control, ESP-NOW, Internet of Things (IoT), Nutrient film technique (NFT), Precision farming
  topic: keywords
creator: Mehul Kumar
creator: Aryan Raj
creator: Sivam Visnu
creator: Nishant Upadhyay
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-04-07
publication date: 2026-04-07
starting page: 1384
ending page: 1398
doi: 10.65890/dmp.lnmr.IMPACT26.174

Prediction of VOIP Traffic in Real Mobile Networks
type: item
description: Voice Over Internet Protocol(VOIP) live analysis is also very viable where it concerns the maximisation of what the network resources can provide as far as its utilisation and the service that the mobile communication systems can provide through the most efficient avenue are concerned. The given paper is dedicated to Quality of Service parameters prediction of real measurements in the LTE-Advanced mobile network. Parameters used are modelled as a multivariate time series that not only examines how the measures evolved over time but also the nature of interaction of the measures with each other. To determine the accuracy of the prediction, the study uses three prediction algorithms: Vector Auto Regressive (VAR), Random Forest (RF), and Extra Gradient Boost (XGBoost). The raw data is first transformed into a form suitable for supervised learning, enabling the learning models to learn and make predictions about future traffic occurrences. In addition, the statistical tests are copied, including the correlations between the Quality of Service parameters and how to define call performance using the parameters. It illustrates that the suggested methodology would yield credible predictions of network behaviour, which may be used to optimise the network in the future and also to control VoIP traffic.
subject: Articles
  topic: section
subject: VoIP, Quality of Service(QoS), LTE-Advanced mobile network, Multivariate time series, Forecasting, Machine learning, Network performance prediction
  topic: keywords
creator: S. Rajesh
creator: J. Aravind
creator: M. Mathesh
creator: S. Raja Mayil Pandian
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-15
publication date: 2026-03-15
starting page: 1348
ending page: 1372
doi: 10.65890/dmp.lnmr.IMPACT26.173

Carbon Prompting: An Empirical Analysis of LLM-Based Requirement Classification
type: item
description: The growing use of Large Language Models (LLMs) in real-world applications has raised new questions about their environmental impact, especially during inference. While much research has focused on the energy demands of model training, this study highlights the often-overlooked emissions generated when these models are used at scale. We introduce the concept of carbon prompting, which examines how different prompt designs affect the energy use and carbon emissions of LLM inference. Using the PROMISE dataset for classifying functional and non-functional requirements, we tested nine prompting strategies with the LLaMA 3.2 model, including zero-shot, few-shot, Chain-of-Thought, detailed, JSON-based, self-critique, expert-engineer, instruction-few-shot, and no-explanation formats. Energy consumption and CO₂ emissions were tracked using CodeCarbon, allowing a detailed comparison of performance and environmental cost. The results show that simpler prompts, particularly no-explanation and zero-shot, achieved the best balance between accuracy and energy efficiency. More elaborate prompts, although designed to elicit deeper reasoning, produced higher emissions without meaningful accuracy gains due to unnecessary token generation. These findings emphasise that prompt design should consider not only model performance but also sustainability, positioning environmental impact as a key metric in future AI development and evaluation.
subject: Articles
  topic: section
subject: Carbon emissions, Chain-of-Thought (CoT), CodeCarbon, Large Language Models (LLMs), prompt engineering, PROMISE dataset
  topic: keywords
creator: V Sathiyasuntharam
creator: Kumkum Mishra
creator: Devesh Kumar Gola
creator: Lakshya Sharma
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-15
publication date: 2026-03-15
starting page: 1332
ending page: 1347
doi: 10.65890/dmp.lnmr.IMPACT26.172

A Study of Autonomous Driving Object Detection Models Using Computer Vision and Deep Learning
type: item
description: Object detection (OD) is a computer technique that enables localisation in computer vision, allowing the location and classification of objects in images/videos. With the help of deep learning (DL), OD has seen significant advancements, delivering higher accuracy with greater efficiency outcomes than traditional methods. This study provides an overview of work on object detection models. This study focuses especially on work in the field of autonomous driving over the last few years. The various models utilised in autonomous driving applications include LM-CNN-SVM, YOLOv4, and Bayesian Neural Networks (BNNs). In the discussed models, VGG16 yielded better results than the other models in this study. This study compares different models and algorithms in tabular form.
subject: Articles
  topic: section
subject: Autonomous Driving, CNN, Deep Learning, Efficiency, Neural Network, Object Detection
  topic: keywords
creator: Mumukshu Bhatt
creator: Subodh Prasad
creator: Rajesh Singh
creator: Pooja Tamta
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-15
publication date: 2026-03-15
starting page: 1324
ending page: 1331
doi: 10.65890/dmp.lnmr.IMPACT26.171

SanskritBERT: Language-Specific Transformer Modelling for Classical Sanskrit Texts
type: item
description: Recent breakthroughs in transformer-based architectures, such as BERT, have revolutionised natural language processing (NLP) across many languages. However, low-resource and morphologically complex languages like Sanskrit remain poorly represented in large-scale pretrained models due to scarce digital corpora, orthographic variation, and compounding. This paper presents a fully custom Sanskrit BERT model trained from scratch on a corpus of 21 million+ curated Sanskrit sentences written in pure Devanagari script. To represent the morphological richness of the language, a Sentence-Piece Unigram tokeniser with a 64k subword vocabulary was built, and a light 6-layer BERT architecture with 256-dimensional hidden states was used to balance performance and compute efficiency. Experimental results show that the model significantly outperforms multilingual baselines such as mBERT, IndicBERT, and MuRIL on masked language modelling test sets, achieving Top-1 accuracy of 0.35, Top-5 accuracy of 0.50, and a perplexity score of 69.0. These outcomes confirm the merits of corpus-specific tokenisation and pretraining in monolingual style for morphologically rich classical languages. Future work will investigate scaling the model to larger architectures, incorporating more complex subword representations, and fine-tuning for Sanskrit NLP downstream tasks such as word segmentation, translation, and semantic role labelling.
subject: Articles
  topic: section
subject: Sanskrit NLP, BERT, Transformer Models, Morphologically Rich Languages, SentencePiece, Masked Language Modelling.
  topic: keywords
creator: Soumya Sharma
creator: Tanuj Saxena
creator: Kusum Lata
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-15
publication date: 2026-03-15
starting page: 1313
ending page: 1323
doi: 10.65890/dmp.lnmr.IMPACT26.170

Structural Health Monitoring of Aging Buildings: A Review
type: item
description: Progressive aging of building structures is an unavoidable process that can be influenced by environmental exposure, material degradation, and cumulative loading over time. The transition to ageing brings about various defects, including microcracking, reinforcement corrosion, delamination, and reduced stiffness, which undermine the safety, functionality, and service life of buildings. Structural Health Monitoring (SHM) is a scientific and systematic method for diagnosing and assessing degradation processes by combining sensors, data-collection devices, and analytical models. Recent developments in non-destructive testing (NDT), wireless sensor networks, and machine learning algorithms have changed SHM from a non-periodic inspection design to a real-time assessment framework. The paper reviews the relationship between building ageing and SHM, the typical defects at different stages of a building's life, suitable diagnostic methods, and maintenance treatments. It focuses on developing predictive maintenance schemes and green practices for sustainable rehabilitation that prolong structural service life and enhance structural resilience across various environmental conditions.
subject: Articles
  topic: section
subject: Structural Health Monitoring, aging buildings, cracks, corrosion, vibration-based SHM, wireless sensor networks, image processing, machine learning.
  topic: keywords
creator: Maitri Kathayat
creator: VK Verma
creator: Bhaskar Bhatt
creator: Deepak Kulyal
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1303
ending page: 1312
doi: 10.65890/dmp.lnmr.IMPACT26.169

Using Supplementary Cementitious Materials for Sustainable Concrete
type: item
description: The need for infrastructure expansion and the need to preserve the environment, driven by the use of concrete as a building material, are growing daily. Concrete is the second-most-consumed material after water. It is necessary to use alternative, sustainable materials, particularly recycled ones. The construction industry must urgently utilise SCMs (Supplementary Cementitious Materials) or industrial waste as alternatives to cement in concrete manufacturing, as this exacerbates environmental problems and increases cement costs. The development of sustainable, high-performance concrete involves the use of silica fume (SF), metakaolin (MK), and fly ash (FA). The mixture with SF (5–10%), MK (5–15%), and FA (10–30%) maintains workability through optimised water-to-binder ratios and admixtures. Durability assessments, including chloride penetration, sulfate resistance, and water absorption, as well as compressive, flexural, and split tensile strengths at multiple curing ages, are evaluated in this analysis. This research demonstrates how to produce low-carbon, durable, and sustainable concrete suitable for next-generation construction applications. Silica fume (5–10%), metakaolin (5–15%), and fly ash (10–30%) are integrated as partial replacements for cement in a single optimised mix design for a ternary SCM concrete system. The complementary reactivity and particle-packing characteristics of the three SCMs are used simultaneously in this proposed mix to improve the targets' strength, durability, and sustainability. The chloride penetration, sulfate resistance and water absorption for compressive, flexural and split tensile strengths were evaluated at multiple curing ages. The compressive strength and up to 40% reduction in chloride permeability, sulfate-induced expansion compared with the control mix, and the optimised ternary blend achieved about 16% higher 28-day strength, demonstrating low-carbon, durable, and sustainable concrete.
subject: Articles
  topic: section
subject: Concrete, Supplementary Cementitious Materials (SCMs), Silica Fume (SF), MetaKaolin (MK), Fly Ash (FA), Sustainable Materials
  topic: keywords
creator: Gunturu Sai Pragna
creator: Lakshmi Prasanna
creator: Nayab Mahaboob Subhani
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1292
ending page: 1302
doi: 10.65890/dmp.lnmr.IMPACT26.168

Pi-NAS: Personal Home Cloud Network
type: item
description: This Research proposes an in-depth study of the implementation of a cost-effective home solution comprising a private server (Raspberry Pi + OpenMediaVault (OMV)) to enable Network Attached Storage (NAS) capabilities, and a Python Streamlit-based media server manager web application. The research first examines the increasing demand for personal cloud storage, then illustrates the practical application of an entire media server ecosystem using modern Python frameworks. Its research methodology includes the design and implementation of experimental systems and the measurement of their performance using specific Python libraries, such as Streamlit 1.28. 0, Pandas 2.0.3, Pillow 10.0.0, psutil 5.9.5, requests 2.31.0, and OpenCV 4.8.0. 76. Compared with the results, it can be noted that the integrated solution creates a viable alternative to commercial NAS systems and extends customisation options via the Streamlit web interface.
subject: Articles
  topic: section
subject: Raspberry Pi, OMV (Open Media Vault), NAS, Python Web Server
  topic: keywords
creator: Sumit Kumar
creator: Bharat Nagar
creator: Kamini
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1283
ending page: 1291
doi: 10.65890/dmp.lnmr.IMPACT26.167

Precision Livestock Farming: Essential for Sustainability in Climate Change
type: item
description: With rising environmental temperatures, livestock production efficiency is drastically affected, affecting animal growth, production (milk, meat, etc.) and reproductive capacity. The interaction between genotype and environment cannot be ignored. The effect is more pronounced in non-adapted livestock species. Temperature fluctuations have led to changes in species composition and breed due to variations in feed resource availability. When resources are inadequate and infrastructure is lacking, animals become more prone to the effects of extreme climate. Stress in animals is causing a loss in production, leading to malnutrition in the human population. To achieve sustainable development goals, innovative technical interventions are being applied to animal resource management.
subject: Articles
  topic: section
subject: precision livestock technologies, climate change, stress
  topic: keywords
creator: Anshu Rahal
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1279
ending page: 1282
doi: 10.65890/dmp.lnmr.IMPACT26.166

Front Page: International Conference on Multidisciplinary Perspectives in Advanced Computing and Technology-2026
type: item
description: The International Conference on Multidisciplinary Perspectives in Advanced Computing and Technology (IMPACT-2026) is scheduled to be held on 10th and 11th January, 2026. The conference is organized by the Department of Information Technology, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India. IMPACT-2026 serves as a vital academic forum, bringing together researchers, academicians, industry professionals and practitioners from diverse disciplines to share knowledge and explore recent developments in advanced computing and technology. The conference aims to provide a multidisciplinary platform for presenting and discussing research advancements in areas related to computing, information technology and emerging technological domains. By encouraging interaction across disciplines, IMPACT-2026 promotes innovative thinking and the integration of theoretical and applied research perspectives that address contemporary scientific and technological challenges. Advanced computing and related technologies continue to influence multiple sectors by enabling intelligent systems, data-driven decision-making, and efficient technological solutions. IMPACT-2026 aspires to contribute to this progress by facilitating the exchange of ideas, methodologies and practical experiences that support sustainable and scalable technological development.
subject: Articles
  topic: section
creator: Ashok Kumar
creator: Avinash Reddy Pothu
creator: Harish Apuri
creator: Sita Rama Praveen Madugukla
creator: Mia Md. Tofayel Gonee Manik
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
doi: 10.65890/dmp.lnmr.2026.165

A Review on Non-Invasive Blood Group Prediction Using Fingerprint and Image Processing
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description: The blood group identification is critical for blood transfusions, organ transplantation, and emergency medical care. Conventional methods rely on invasive blood sampling and laboratory testing, which are time-consuming and resource-dependent. Recent advances in biometrics and artificial intelligence have enabled non-invasive alternatives, particularly fingerprint-based blood group detection. Fingerprint ridge patterns, influenced by genetic factors, exhibit correlations with ABO and Rh classifications. This review systematically analyzes existing approaches ranging from image processing and statistical models to deep learning architectures such as CNN, ResNet, and hybrid multimodal frameworks. Comparative evaluation shows that deep learning models achieve accuracies exceeding 95%, significantly outperforming traditional methods. The paper highlights current trends, challenges in dataset standardization, and future opportunities for explainable AI, multimodal biometrics, and portable healthcare solutions. The findings indicate that fingerprint-driven, AI-powered systems can provide rapid, accurate, and scalable blood group identification, offering strong potential for clinical and forensic applications.
subject: Articles
  topic: section
subject: Blood group determination, fingerprint pattern, Convolutional neural networks (CNN), image processing, pattern recognition, Blood Groups
  topic: keywords
creator: Divyani Patel
creator: Raj Laxmi
creator: Amita Sharma
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1244
ending page: 1254
doi: 10.65890/dmp.lnmr.IMPACT26.123

Blockchain-Secured Telemedicine and Remote Patient Monitoring System
type: item
description: In this paper, the author introduced a blockchain-protected telemedicine system to improve data privacy, integrity, and access control in remote patient monitoring settings. Combining IoT-based health data with blockchain and smart contracts provides security, transparency, and the impossibility of changing medical records. The experimental validation results using an IoT healthcare dataset demonstrated the effectiveness and practicality of the proposed solution. The next step in work will be the integration of AI, Blockchain, remote patient monitoring (RPM) and telemedicine, which has revolutionized health care delivery. Still, data integrity and privacy are among the significant issues because of centralized architectures. The paper suggests using IoT, AES encryption, SHA-256 hashing, and smart contracts as components of the Blockchain-Secured Telemedicine and RPM System to guarantee the security of decentralized data management. It is based on a four-layer architecture, including an IoT layer, vital signs collection, an Edge layer, encryption and hashing, a Blockchain layer, immutable storage and access control, and an application layer for secure telemedicine interaction. The system was coded in Python, Ethereum (Solidity), IPFS, and Flask/Streamlit and tested on a real IoT Healthcare Patient Monitoring dataset. Experimental results show low latency, high throughput, and strong access control, demonstrating that it should be used in real-time telemedicine. Confidentiality, integrity, and tamper resistance were checked through security analysis. In the future, AI, Federated Learning, Layer-2 scaling (Polygon), and Edge Computing will be integrated to improve performance and intelligence, providing intelligent, scalable, and real-time solutions in healthcare.
subject: Articles
  topic: section
subject: Blockchain, Telemedicine, Remote Patient Monitoring, IoT, Data Security, Smart Contracts, Healthcare 4.0
  topic: keywords
creator: Bazif Ahmed Syed
creator: Arunendra Mani Tripathi
creator: Yuvraj Kumar
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1231
ending page: 1243
doi: 10.65890/dmp.lnmr.IMPACT26.122

Transforming Education in Uttarakhand through Cloud Computing Model: An Explorative Study
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description: The education system in Uttarakhand, India, faces significant challenges, including inadequate infrastructure, limited teacher availability, and difficulties in delivering quality education to remote areas. This study explores the potential of cloud computing to transform the education system in Uttarakhand, improving access, reducing costs, and enhancing collaboration. The study's implications suggest a ‘TESU’ model that cloud computing can play a critical role in bridging the digital divide and providing quality education to all in Uttarakhand. The findings of this study can inform education policy and practice in Uttarakhand, highlighting the need for additional investment in cloud computing infrastructure and educator training.
subject: Articles
  topic: section
subject: Education, Model, Cloud Computing, Learners, MOOCs.
  topic: keywords
creator: Harsh Vardhan Pant
creator: Jeetendra Pande
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1218
ending page: 1230
doi: 10.65890/dmp.lnmr.IMPACT26.121

Remote Sensing and GIS Approaches for Agricultural Drought Severity Assessment: A Comprehensive Review
type: item
description: A natural hazard caused by prolonged drought, drought has a significant effect on ecosystems, agriculture, and socioeconomic systems. Drought develops gradually, unlike abrupt natural disasters, making it challenging to identify, track, and measure. It poses major risks to crop productivity and livelihoods by negatively affecting vegetation growth, soil moisture, and water availability. Many satellite-based indices have been developed at the regional and national levels to monitor and assess drought and address these issues. Both meteorological and satellite-derived indices are frequently used to identify various types of droughts, including hydrological, agricultural, and meteorological. Global sensors such as NOAA-AVHRR and MODIS are widely used for vegetation analysis and drought assessment. Key meteorological parameters, including precipitation, temperature, humidity, and evapotranspiration, are essential for determining drought severity. These factors are used to create drought indices that account for precipitation shortages, climatic dryness, and delayed hydrological responses such as decreased soil moisture, declining reservoir levels, and weakened crop health. In order to analyze drought patterns, comprehend spatial-temporal variability, and pinpoint vulnerable areas, long-term historical records of satellite imagery and climate data are essential. Remote sensing is a useful tool for agricultural drought assessment and for well-informed decision-making, as the agricultural sector is particularly vulnerable to drought stress. Governmental organizations and local authorities can create efficient drought management and mitigation plans by integrating satellite-derived drought data with GIS. The importance of remotely sensed data in agricultural drought assessment is emphasized in this review, along with its potential to boost resilience, increase drought preparedness, and promote sustainable agricultural management.
subject: Articles
  topic: section
subject: Drought, Remote sensing, Drought monitoring, GIS, Satellite data.
  topic: keywords
creator: Kusum Rawat
creator: P. V. Singh
creator: Manisha Dhapola
creator: Himanshu Bharti
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1207
ending page: 1217
doi: 10.65890/dmp.lnmr.IMPACT26.120

An Integrated Policy Framework for Urban Traffic Congestion Mitigation: A Comparative Simulation Insights from Amsterdam and Delhi
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description: Urban traffic congestion is a global concern due to rapid urbanization, increased vehicle ownership, and limited transport infrastructure. This paper proposes a comprehensive framework for evaluating and mitigating traffic congestion using both conventional approaches and emerging smart mobility technologies. Through literature synthesis and simulation-based analysis, we compare strategies such as infrastructure expansion, traffic demand management (TDM), and intelligent transportation systems (ITS) under multiple urban contexts. Using case studies from Amsterdam and Delhi, the study employs traffic simulation tools (SUMO/VISSIM) and AI models (LSTM/GRU) to evaluate scenarios against key performance indicators, including travel time, emissions, and delay indices. Findings demonstrate that hybrid strategies that incorporate AI-driven control and policy interventions yield superior, sustainable results. The paper concludes with a set of policy recommendations and future research directions aimed at urban planners and policymakers.
subject: Articles
  topic: section
subject: Traffic Congestion, policymakers, urbanization, intelligent transportation system, traffic demand management
  topic: keywords
creator: Manisha Dhapola
creator: Himanshu Bharti
creator: Sanjeev Suman
creator: Kusum Rawat
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1193
ending page: 1206
doi: 10.65890/dmp.lnmr.IMPACT26.119

EV Battery Technologies and Battery Management in India: Evolution and the Way to a Solid-State Future
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description: The technological development of electric vehicle batteries has deeply influenced the course of sustainable mobility. LIBs have traditionally been the main energy source within the EV industry due to their favourable energy density, reliability, and manufacturing maturity. However, limitations such as thermal instability, resource dependency, and safety concerns have driven the development of SSBs. SSBs, which use solid electrolytes rather than liquid ones, offer better safety, longer lifespans, and higher energy densities. This review provides an overall analysis of EV battery evolution from LIBs to SSBs, highlighting the key factors for consideration: electrochemical characteristics, materials development, interface engineering, and control integration via advanced Battery Management Systems. Further, the paper discusses India's strategic role in the global transition to electric mobility, with an emphasis on national policies and industrial development, as well as localised research initiatives. Such a synthesis of global and Indian perspectives identifies how India can emerge as a major stakeholder in solid-state innovation through industrial localization, policy incentives, and research-driven manufacturing.
subject: Articles
  topic: section
subject: Electric Vehicles, Lithium-ion Battery, Solid-state Battery, Battery Management System, Energy Storage, India, Sustainable Mobility, Policy Framework.
  topic: keywords
creator: Sandeep Singh
creator: Sunil Singh
creator: Anshul Kumar Bharalia
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1182
ending page: 1192
doi: 10.65890/dmp.lnmr.IMPACT26.118

Inclusive Transit Navigation: A Real-Time Accessibility-Aware Mobile Application
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description: Transportation is required for social participation, equality of opportunity and independence. Nevertheless, a billion-plus disabled individuals continue to face challenges using the transportation systems. Ordinary issues such as cars with steps, broken ramps or elevators, confusing signs, absence of tactile trails, and audio announcements make travelling hard and sometimes impossible. Although there are laws in place, such as the Americans with Disabilities Act (ADA), the UK Equality Act, and the UN Convention on the Rights of Persons with Disabilities (CRPD), they are still not equally applied. Most cities have not yet implemented accessible trip-planning tools or barrier-free infrastructure. Most transit apps already in operation make all routes look the same and fail to indicate whether a route is reachable. In this paper, a mobile application that provides real-time information on train, bus, and station accessibility is discussed. It combines GTFS transit data, OpenStreetMap, and user feedback to suggest the most accessible routes based on a user's mobility, visual, auditory, or cognitive requirements. The system consists of a scoring model, a multi-criteria routing algorithm and an Accessibility Knowledge Graph. Based on the initial tests, the user is more confident and has fewer travel failures. The proposed solution aligns with smart city goals and closes gaps in current policies by fostering independence and inclusivity.
subject: Articles
  topic: section
subject: Accessible Transit, HCI, UX Design, Mobility, Smart Transportation
  topic: keywords
creator: Dhritisree Biswas
creator: Riya Ganguly
creator: Kalidindi Sowmya
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1170
ending page: 1181
doi: 10.65890/dmp.lnmr.IMPACT26.117

Detecting AI-Generated Phishing Websites with ML
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description: In the modern digital landscape, phishing has become among the most pervasive and enduring risks. It is a technique of impersonating a trustworthy entity with the express intent of convincing victims to reveal financial or personal information, including credit card numbers, bank account details, or passwords. The reason phishing is so dangerous is that, rather than exploiting software weaknesses, it often exploits psychological weaknesses, such as urgency, anxiety, or simple curiosity. It is this psychological element that even experienced users may sometimes succumb to. Phishing comes in many forms. The most common type includes emails that pose as reputable companies or banks and ask recipients to "verify" or "update" their accounts. Spear phishing is a highly focused type that targets particular people or organizations after conducting extensive background research, making the effort much more credible. Clone phishing involves hackers replicating authentic emails and substituting harmful attachments or links. These changing tactics demonstrate that phishing is a perennial challenge for cybersecurity experts, as it evolves both technologically and socially. Phishing's overall effects are concerning. According to studies, it plays a significant role in global cybercrime, resulting in billions of dollars in losses each year from illicit transactions or stolen data. Organizations all throughout the world reported losses of more than $billion in 2023 alone due to phishing-related incidents. The issue has expanded beyond what conventional security filters can manage, with attackers now even utilising AI-generated content to produce convincingly phoney websites and emails. Because of this, it is crucial to investigate fresh, innovative solutions that can change as quickly as the threats do.
subject: Articles
  topic: section
subject: Phishing Detection, Machine Learning, Deep Learning, LSTM, Random Forest, Cyber Security, Ensemble Models
  topic: keywords
creator: Vasu Sharma
creator: Udit Sirohi
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1143
ending page: 1169
doi: 10.65890/dmp.lnmr.IMPACT26.116

A Comprehensive Study of Web Application Security, Vulnerabilities and Modern Defence Mechanisms
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description: Web applications are the backbone of the current digital ecosystem, supporting key services in e-commerce, banking, health care, education, cloud computing, and public administration. The continued rise of web-based platforms, with their shift towards distributed, API-driven, and microservices-oriented architectures, is significantly increasing the attack surface area. Traditional vulnerabilities, such as SQL Injection and Cross-Site Scripting, persist due to legacy code, improper validation, and insecure programming practices. However, in modern architectures, newer threats have come into the fore as major contributors to real-world breaches, such as Broken Access Control, Broken Object-Level Authorization, API-layer weaknesses, dependency-supply-chain vulnerabilities, cloud misconfigurations, and Cross-Channel Scripting. Academic research published in Springer, MDPI, IEEE, ACM, and arXiv shows significant advances in attack strategies and defence mechanisms. Some promising techniques include automated black-box access-control testing, deep-learning injection-attack detectors, semantic analysis engines, fuzzing-driven vulnerability discovery, and runtime protection systems (WAF, RASP). This study synthesizes the outcome of more than twenty recent scholarly works, offering a necessary holistic overview of the current stage of web application security. It examines how vulnerabilities have evolved over time, critically assesses state-of-the-art mechanisms for detection and mitigation, and identifies research challenges in cloud-native architectures, DevSecOps, and Zero Trust environments. The findings highlight that effective protection depends on layered and adaptive defences, supported by continuous automated testing, ML-powered anomaly detection, secure engineering methodologies, and robust runtime governance.
subject: Articles
  topic: section
subject:
  topic: keywords
  value: Web Application Security, SQL Injection, Cross-Site Scripting, API Security, Cloud Misconfiguration, Broken Access Control, Deep Learning, Runtime Application Self-Protection, Web Application Firewall, DevSecOps, Zero Trust Architecture, Access-Control Testing, Cross-Channel Scripting
creator: Nishchay Bansal
creator: Garvit Kanojia
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1128
ending page: 1142
doi: 10.65890/dmp.lnmr.IMPACT26.114

Heat and Mass Transfer Study of Multiphase Flow of Viscous and Viscoelastic Fluids
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description: As per this study, we investigate the flow behavior of two immiscible, electrically conducting fluids - one viscous fluid and one viscoelastic fluid - within a porous medium between two infinite horizontal plates. The research examines how these two fluids flow, given their different properties, in a confined porous space. As per the configuration, there are two fluid layers: the lower layer contains viscous fluid, and the upper layer contains viscoelastic fluid. As per the setup, these layered arrangements experience heat and mass transfer effects due to the various physical forces acting on them. The governing equations for flow, momentum, and energy transport are formulated as nonlinear coupled partial differential equations. This system itself describes the transport phenomena further through mathematical modeling. These equations are actually solved using mathematical methods with proper boundary conditions. The solutions definitely follow standard analytical approaches. We observe that the final expressions contain only coefficients, which we evaluate numerically for different parameter combinations. Also, basically, the coefficients are analyzed in detail using the same tabulated data format. Further, the changes in velocity and temperature profiles are shown in graphs to study how each fluid region itself responds to the physical parameters.
subject: Articles
  topic: section
subject: Viscous Fluid, Viscoelastic Fluid, Multiphase fluid, Mathematical modelling, Governing equations.
  topic: keywords
creator: Anuj Kumar Srivastav
creator: Neeraj Srivastava
creator: Shaily Tyagi
creator: Vimal Kumar
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1123
ending page: 1127
doi: 10.65890/dmp.lnmr.IMPACT26.115

Towards Data-Driven Software Engineering: Integrating Machine Learning for Smarter Development Lifecycle Management
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description: The rapid evolution of software systems has heightened the demand for efficiency, reliability, and adaptability across the development lifecycle. Traditional software engineering practices, while structured, often struggle to manage complex, dynamic, and large-scale projects. Machine Learning (ML) has emerged as a transformative technology that enables data-driven decision-making, predictive analytics, and automation in software engineering. This paper explores the integration of ML techniques across different phases of the Software Development Lifecycle (SDLC), including requirements analysis, design, coding, testing, maintenance, and project management. The study highlights how ML-driven models enhance defect prediction, effort estimation, test case prioritisation, and adaptive maintenance. Additionally, challenges such as data quality, interpretability, scalability, and ethical implications are examined. The paper concludes by outlining future research directions, emphasising the need for explainable ML models and hybrid approaches that combine domain expertise with data-driven intelligence to build smarter, more resilient software systems.
subject: Articles
  topic: section
subject: Data-Driven Development, Software Lifecycle Management, Predictive Analytics, Software Quality, Automation, Explainable AI
  topic: keywords
creator: Deep Chandra Andola
creator: Harendra Pratap Singh
creator: Manoj Kumar Pandey
creator: Manisha Deep Andola
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 1255
ending page: 1278
doi: 10.65890/dmp.lnmr.IMPACT26.113

Recovery of Deleted Data and Associated Metadata from XFS and Btrfs Filesystems
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description: Data recovery from modern file systems has become one of the critical challenges related to digital forensics, data security, and disaster management. When operating systems 'delete' files, they do not remove the actual data from storage; they remove metadata references, and thus, the underlying blocks remain intact until overwritten. Advanced file systems like XFS and Btrfs have made things worse because of features such as journaling, copy-on-write, snapshot, and extent-based allocation. Recovery tools either optimize for older file systems or use raw data carving techniques, which result in incomplete recovery with high false-positive rates. A unique end-to-end recovery framework has been developed that is exclusively tailored according to the recovery requirements of XFS and Btrfs file systems. This automated framework performs file system type detection, a scan of free blocks, the extraction of deleted file content, and recovery of associated metadata, including inodes, timestamps, and extended attributes. As such, the approach integrates file system-aware metadata analysis with constrained carving so that the accuracy and completeness of the recovery are enhanced as compared to existing methods. To ensure the integrity of the recovered data, they are checked by checksums, then categorized according to their file types, and finally presented to the user in structured reports. The proposed framework will extend support to forensic investigations and secure data restoration in enterprise and personal computing environments.
subject: Articles
  topic: section
subject: XFS, Btrfs, Data Recovery, Metadata, Digital Forensics, File System Analysis, Deleted Files, Copy-on-Write, Checksum Verification
  topic: keywords
creator: Chulbul S Pratyashrit
creator: Aayush Sharma
creator: Velayudham Sathiyasuntharam
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 347
ending page: 353
doi: 10.65890/dmp.lnmr.IMPACT26.107

Realistic 3D Cloth Wearing Using AR/VR for Immersive Virtual Try-On in E-Commerce
type: item
description: This project, “Realistic 3D Cloth Wearing Using AR/VR for Immersive Virtual Try-On in E-commerce,” aims to transform online apparel shopping by enabling users to virtually try on clothes in real time. Traditional e-commerce struggles with inaccurate fit visualization, leading to dissatisfaction and high return rates. Our system tackles this through a pipeline of 3D body scanning, motion tracking, and physics-based cloth simulation to create lifelike avatars and realistic garment draping. Using AR, users can view outfits on themselves through mobile devices, while VR provides an immersive try-on experience. Machine learning enhances the platform with body measurement estimation, fit recommendations, and pose adaptability. The solution supports real-time garment swapping, large catalogues, and customization, improving user confidence and reducing retailer return costs. Beyond e-commerce, it has potential applications in virtual fashion shows, digital wardrobes, and sustainable retail.
subject: Articles
  topic: section
subject: Augmented Reality, Virtual Reality, 3D Clothing, Cloth Simulation, Virtual Try-On, Blender, Unity3D, Marvelous Designer
  topic: keywords
creator: Nikita Kanwar
creator: Aditya Singh
creator: Amita Sharma
creator: Nishant Upadhyay
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 338
ending page: 346
doi: 10.65890/dmp.lnmr.IMPACT26.106

A Review of Prioritization Methods for Rural Road Network Planning: From Traditional Approaches to Modern MCDM and GIS-Based Frameworks
type: item
description: Rural road networks play a vital role in enabling socio-economic development by improving access to markets, healthcare, education, and other essential services. However, constrained budgets and diverse regional needs make prioritization of road projects imperative to optimize resource allocation and maximize impact. This review paper examines the various prioritization methods applied in rural road network planning, focusing on their conceptual foundations, key evaluation criteria, and methodological approaches. Traditional cost-benefit and ranking techniques are contrasted with modern multi-criteria decision-making (MCDM) frameworks, including the Analytic Hierarchy Process (AHP), fuzzy logic methods and geographic information system (GIS)-based spatial tools. The paper further compares these methods in terms of strengths, limitations, data requirements, and applicability, supported by relevant case studies. Current challenges, such as data scarcity, stakeholder engagement and integration of environmental sustainability, are identified, alongside emerging research directions incorporating digital tools and participatory approaches. By systematically reviewing existing literature, this study aims to inform planners and policymakers on effective, transparent and context-appropriate prioritization strategies to enhance rural road infrastructure development.
subject: Articles
  topic: section
subject: Rural Roads; Prioritization; Maintenance; Road Planning; Prioritization Strategies
  topic: keywords
creator: Himanshu Bharti
creator: Manisha Dhapola
creator: Sanjeev Suman
creator: Kusum Rawat
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 326
ending page: 337
doi: 10.65890/dmp.lnmr.IMPACT26.104

Privacy in Quantum Computing: A Comprehensive Review
type: item
description: Quantum technologies are beginning to reshape how large volumes of data are processed, yet they also introduce a new class of privacy challenges with no classical counterparts. These concerns stem from the availability of quantum side information, the encoding of data in non-classical states, and the fragility of conventional cryptographic systems in post-quantum environments. This work reviews the main privacy-preserving strategies proposed in peer-reviewed literature from 2020 to 2025, covering both quantum and post-quantum settings. The survey examines several key directions: (i) the integration of differential privacy into quantum machine learning pipelines, (ii) privacy guarantees derived from gentle-measurement principles, (iii) secure aggregation for quantum federated learning, and (iv) privacy amplification methods designed to remain robust under noisy quantum-channel conditions. Representative examples include the mechanisms introduced by Watkins et al. for embedding differential privacy into variational circuits, the theoretical limits established by Aaronson and Rothblum for privacy under gentle measurements, the distributed-learning protections developed by Liu and Zhang, and the noise-aware key-extraction analysis presented by Singh and colleagues. System-level considerations for future quantum infrastructures are discussed in the work of, while previous literature proposes hybrid quantum classical designs to strengthen privacy in post-quantum frameworks. Taken together, these studies reveal open challenges related to scalability, composability, and adversarial modelling, and they motivate a roadmap toward developing practical and verifiable privacy guarantees for emerging quantum platforms.
subject: Articles
  topic: section
subject: Quantum Privacy, Differential Privacy, Post-Quantum Cryptography, Quantum Federated Learning, Privacy Amplification
  topic: keywords
creator: Bagisha Roy
rights: Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-13
publication date: 2026-03-13
starting page: 644
ending page: 650
doi: 10.65890/dmp.lnmr.IMPACT26.105