DMPedia Lecture Notes in Multidisciplinary Research
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 more than one discipline and encourages work that addresses real-world challenges through collaborative and 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.
Prediction of VOIP Traffic in Real Mobile Networks
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.
VoIP, Quality of Service(QoS), LTE-Advanced mobile network, Multivariate time series, Forecasting, Machine learning, Network performance prediction
Copyright (c) 2026 DMPedia Lecture Notes in Multidisciplinary Research https://creativecommons.org/licenses/by/4.0
Carbon Prompting: An Empirical Analysis of LLM-Based Requirement Classification
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.
Carbon emissions, Chain-of-Thought (CoT), CodeCarbon, Large Language Models (LLMs), prompt engineering, PROMISE dataset
A Study of Autonomous Driving Object Detection Models Using Computer Vision and Deep Learning
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.
Autonomous Driving, CNN, Deep Learning, Efficiency, Neural Network, Object Detection
SanskritBERT: Language-Specific Transformer Modelling for Classical Sanskrit Texts
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.
Sanskrit NLP, BERT, Transformer Models, Morphologically Rich Languages, SentencePiece, Masked Language Modelling.
Structural Health Monitoring of Aging Buildings: A Review
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.
Structural Health Monitoring, aging buildings, cracks, corrosion, vibration-based SHM, wireless sensor networks, image processing, machine learning.
Using Supplementary Cementitious Materials for Sustainable Concrete
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.
Concrete, Supplementary Cementitious Materials (SCMs), Silica Fume (SF), MetaKaolin (MK), Fly Ash (FA), Sustainable Materials
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.
Raspberry Pi, OMV (Open Media Vault), NAS, Python Web Server
Precision Livestock Farming: Essential for Sustainability in Climate Change
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.
precision livestock technologies, climate change, stress
Front Page: International Conference on Multidisciplinary Perspectives in Advanced Computing and Technology-2026
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.
A Review on Non-Invasive Blood Group Prediction Using Fingerprint and Image Processing
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.
Blood group determination, fingerprint pattern, Convolutional neural networks (CNN), image processing, pattern recognition, Blood Groups
Blockchain-Secured Telemedicine and Remote Patient Monitoring System
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.
Blockchain, Telemedicine, Remote Patient Monitoring, IoT, Data Security, Smart Contracts, Healthcare 4.0
Transforming Education in Uttarakhand through Cloud Computing Model: An Explorative Study
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.
Education, Model, Cloud Computing, Learners, MOOCs.
Remote Sensing and GIS Approaches for Agricultural Drought Severity Assessment: A Comprehensive Review
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.
Drought, Remote sensing, Drought monitoring, GIS, Satellite data.
An Integrated Policy Framework for Urban Traffic Congestion Mitigation: A Comparative Simulation Insights from Amsterdam and Delhi
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.
Traffic Congestion, policymakers, urbanization, intelligent transportation system, traffic demand management
EV Battery Technologies and Battery Management in India: Evolution and the Way to a Solid-State Future
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.
Electric Vehicles, Lithium-ion Battery, Solid-state Battery, Battery Management System, Energy Storage, India, Sustainable Mobility, Policy Framework.
Inclusive Transit Navigation: A Real-Time Accessibility-Aware Mobile Application
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.
Accessible Transit, HCI, UX Design, Mobility, Smart Transportation
Detecting AI-Generated Phishing Websites with ML
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.
Phishing Detection, Machine Learning, Deep Learning, LSTM, Random Forest, Cyber Security, Ensemble Models
A Comprehensive Study of Web Application Security, Vulnerabilities and Modern Defence Mechanisms
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.
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
Heat and Mass Transfer Study of Multiphase Flow of Viscous and Viscoelastic Fluids
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.
Viscous Fluid, Viscoelastic Fluid, Multiphase fluid, Mathematical modelling, Governing equations.
Towards Data-Driven Software Engineering: Integrating Machine Learning for Smarter Development Lifecycle Management
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.
Data-Driven Development, Software Lifecycle Management, Predictive Analytics, Software Quality, Automation, Explainable AI
Recovery of Deleted Data and Associated Metadata from XFS and Btrfs Filesystems
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.
XFS, Btrfs, Data Recovery, Metadata, Digital Forensics, File System Analysis, Deleted Files, Copy-on-Write, Checksum Verification
Realistic 3D Cloth Wearing Using AR/VR for Immersive Virtual Try-On in E-Commerce
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.
Augmented Reality, Virtual Reality, 3D Clothing, Cloth Simulation, Virtual Try-On, Blender, Unity3D, Marvelous Designer
A Review of Prioritization Methods for Rural Road Network Planning: From Traditional Approaches to Modern MCDM and GIS-Based Frameworks
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.
Rural Roads; Prioritization; Maintenance; Road Planning; Prioritization Strategies
Privacy in Quantum Computing: A Comprehensive Review
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.
Quantum Privacy, Differential Privacy, Post-Quantum Cryptography, Quantum Federated Learning, Privacy Amplification
Baseline Modeling and Comparative Evaluation of Control Strategies for Utility-Tied and Islanded Microgrid with Bidirectional Electric Vehicle Integration
As electric vehicle (EV) integration into modern microgrids accelerates, ensuring stable voltage regulation and safe bidirectional power exchange has become increasingly critical. This research introduces a modified Proportional Integral (PI) controller enhanced with feedforward and anti-windup mechanisms for a utility-connected DC microgrid incorporating Vehicle-to-Grid (V2G)-enabled EVs and a Battery Energy Storage System (BESS). The proposed model is realized in Simulink using an averaged DC microgrid approach to ensure the voltage remains stable, deviates less than ±1%, and limits transient voltage dips to below 1.3 V during islanding. It offers higher precision and stability than Droop and classical PI controls, while avoiding the computational complexity of Model Predictive Control (MPC), thereby establishing the proposed PI scheme as an efficient, reliable, and real-time, scalable solution for next-generation EV-integrated smart microgrids.
IPv6 LANMARKER-PLUS : Enhancing Geolocation Accuracy and Efficiency via Staged Probing and Density-Based Clustering
The accurate estimation of the street-level geolocation of IPv6 addresses poses a significant challenge due to the vast address space and the rotation of prefixes driven by privacy concerns. Methods like IPv6Landmarker achieve substantial improvement compared to prior IPv4 techniques; however, these rely on brute-force querying and heuristic-based clustering, which are computationally inefficient and vulnerable, respectively. In this paper, we present IPv6Landmarker-Plus-an enhanced extension that significantly improves IPv6 landmark mining in terms of both accuracy and resource efficiency. Our system utilizes Java with a MongoDB architecture to filter EUI-64 addresses out of active IPv6 hitlists and extract associated WAN MACs for cross reference with the Wigle.net database. Our contributions include two major algorithmic enhancements: 1) Staged Offset Probing (SOP): a probabilistic query model that, for high probability offsets such as -1, +5, and 0, reduces the number of external API calls by more than 80%, significantly reducing latency and resource consumption. 2) DBSCAN-Centric Filtering: this replaces heuristic clustering with Density-Based Spatial Clustering to accurately identify the true coordinate cluster while robustly rejecting noise from adjacent network devices, leading to direct improvements in the mean geolocation error. Real-world validation using RIPE Atlas ground truth probes confirms that the DBSCAN-based selection of landmarks achieves a lower mean error and geolocation success rate compared to the original technique in IPv6Landmarker.
IPv6 LANMARKER-PLUS : ENHANCING GEOLOCATION ACCURACY AND EFFICIENCY VIA STAGED PROBING AND DENSITY-BASED CLUSTERING
Food Diet Recommendation System Using AI
This collection of scholarly papers reviews diverse interventions for dietary and health management, employing methodologies ranging from technology-driven systems to clinical and policy-based research. Several studies focus on integrating artificial intelligence (AI) and machine learning (ML) to deliver personalized dietary recommendations. One notable study described the “Diet Engine” system, which achieved 86% accuracy in food identification using image recognition and a deep learning network with 295 layers. It also reported that the Extra Tree Classifier reached 99% accuracy in predicting diet plans. Another study applied the K-means clustering algorithm to group users for personalized dietary planning, obtaining 96% accuracy. Clinical studies explored dietary interventions in specific populations. For example, one investigation tested the MIND diet on individuals with multiple sclerosis and found increased antioxidant status and weight loss in females. Another clinical trial targeting participants with cardiovascular disease reported modest improvements in diet quality after intervention-based education, although lifestyle modifications did not significantly change cardiovascular risk factors. A related study confirmed the feasibility of low-energy diets for individuals with obesity and chronic kidney disease, showing notable reductions in both weight and blood pressure. Beyond individual health, broader socioeconomic and environmental concerns were also addressed. One policy-focused paper examined the “affordability gap” of nutritionally adequate diets across countries such as the Dominican Republic, Ethiopia, and Indonesia, recommending this metric to guide social assistance and nutrition programs. Another study raised concerns about the health risks associated with plastic additives in food, emphasizing that infants are particularly vulnerable due to exposure levels exceeding safety thresholds.Finally, an analysis of diet and lifestyle mobile applications found that many incorporated behavior change techniques correlated with app quality. However, the review found limited scientific validation and a lack of safety features in most applications, underscoring the need for more evidence-based, user-protective digital health tools.
Artificial Intelligence (AI); Machine Learning (ML); K-means Clustering; Extra Tree Classifier; Social Assistance Programs; Plastic Additives; Food Safety; Behavior Change Techniques.
Palm Vein Recognition Using Near-Infrared Imaging and CNN-Based Feature Extraction for Secure Biometric Authentication
Biometric authentication systems have evolved significantly to meet the growing demand for secure, reliable, and scalable identity verification systems. Among the available modalities, palm vein recognition has emerged as a promising candidate due to its intrinsically secure nature-vein patterns are subcutaneous, unique to individuals, and resistant to forgery. This research investigates the design, development, and evaluation of a complete palm vein recognition system based on near-infrared imaging and convolutional neural network-driven feature learning. It integrates hardware-level acquisition, image preprocessing, feature extraction, and CNN-based matching into a unified pipeline, therefore presenting an academically rigorous investigation of the feasibility of subcutaneous biometrics assisted by deep learning. This work is initiated with a comprehensive literature review of the state of the art in biometrics, NIR imaging physics, and computer methods for vascular pattern recognition. Traditional feature extraction methods-which include Gabor filters, Local Binary Patterns (LBP), Maximum Curvature, and Repeated Line Tracking-are reviewed to establish a relative theoretical framework. The limitations of handcrafted descriptors motivate the use of CNN-based architectures for superior hierarchical extraction of vascular features without human intervention. A customized imaging device was designed with 850-950 nm near-infrared LEDs, an NIR-sensitive camera system, and an optical bandpass filter that can capture raw palm vein images in both controlled and semi-controlled settings. Particular attention has been paid to the optical absorption characteristics of deoxygenated hemoglobin, the scattering properties of biological tissues, and the required geometry of illumination to optimize the appearance of veins. Multiple subjects are recorded under a standardized acquisition protocol to form a structured dataset for experimentation. Preprocessing techniques, such as histogram equalization, Gaussian smoothing, extraction of ROIs, contrast stretching, and reduction of specular noise, were used to enhance vascular clarity. A CNN classifier was then trained on this preprocessed dataset through supervised learning, thereby allowing identification by feature embeddings and classification probabilities. Even with a small database, the model demonstrated an 83% matching accuracy, highlighting the strength of deep-learning-based feature representation even under hardware constraints. A close analysis of the system's performance, variability factors, and error distribution points to both strengths and remaining limitations of the approach. In conclusion, palm vein recognition, combined with CNN-based learning and custom NIR acquisition hardware, represents a highly promising biometric modality for applications involving high security, such as financial authentication systems, secure facility access, or integration into platforms like BioPay. This paper provides a comprehensive, hardware-to-software contribution to the academic community related to palm vein recognition, drawing on empirical results, theoretical foundations, and practical implementation considerations. It identifies future directions, such as dataset expansion, augmentation strategies, multi-modal fusion, mobile deployment, and improved anti-spoofing mechanisms, which are meaningful pathways for continued research in subcutaneous biometrics.
Comparative Analysis of AI-Driven Conversational Agents for Mental Health Support: Rule-Based, Transformer, and RAG Approaches
The rapid integration of AI into mental health support systems has opened up new avenues for interventions that are scalable and accessible. In this work, we conduct a comparative evaluation of three major chatbot paradigms, namely, rule-based systems, Transformer-based models, and RAG architectures, for their performance in identifying conversational cues pertaining to depression and anxiety. Each model type is representative of a different approach toward language understanding, offering unique strengths in handling user intent, contextual reasoning, and emotional interpretation. The proposed evaluation framework examines the models through a number of dimensions: linguistic coherence, emotional sensitivity, response accuracy, and quality of user engagement. We analyze real-world conversational data and simulated mental-health dialogues to highlight how each architecture reacts to complex psychological cues such as negative sentiment, distress patterns, and help-seeking behaviors. Additional assessment criteria include computational efficiency, interpretability, and safety constraints, all of which are crucial for real-world application in sensitive mental-health contexts. The results demonstrate large differences in performance among the three types of chatbots. While rule-based systems are highly reliable but inflexible, Transformer-based models demonstrate improved contextual,understa nding, while RAG models offer the best balance between contextual relevance and factual grounding. These findings highlight the role that could be played by sophisticated AI-driven conversational agents in early mental health screening and support, while also pointingto the need for ethical, secure, clinically guided deployment in real-world settings.
AI Chatbots, Mental Health Support, Depression Detection, Anxiety Screening, Transformer Models, Rule-Based Systems, Retrieval-Augmented Generation, Psychological Dialogue Analysis
Ethical AI for HR Practices: Addressing Emerging Deepfake Risks
In contemporary times, modern technologies like Artificial Intelligence (AI), Cloud Computing, Machine Learning, Industry 4.0, and the Internet of Things have played a significant role in transforming industrial practices through digitalization. It has become imperative to adapt these advancements for organizational relevance, growth, and effectiveness, and Human Resource (HR) practices are no exceptions. However, the rapid proliferation of these technologies has also led to the production, dissemination, and use of deepfake data. This paper presents a systematic literature review of the recent developments with a particular focus on addressing the use of Ethical AI in HR practices for combating the risks posed by deepfakes. It has been carried out to discern the scholarships related to HR practices and Ethical AI, to understand the level of heightened awareness regarding these issues. This systematic review employed PRISMA methodology to analyse peer-reviewed literature sourced from the SCOPUS and Google Scholar databases. Drawing on a systematic review of 49 research works, this paper makes a unique contribution by systematically synthesizing existing academic contributions to understanding the role of ethical AI in HR practices that organizations and practitioners should be aware of. The results of this research indicate a significant research gap in this field of academia, particularly at the intersection of deepfake threats and HR-specific ethical AI applications. The findings highlight how HR practices integrate ethical AI into organizations. The paper also provides suggestions on actionable strategies that can be implemented to utilize these practices as effective measures against Deepfake production and dissemination through a proposed three-level Ethical AI framework.
PRISMA, SLR, Deepfake, Misinformation, Human Resource, Ethical AI