Crowd Counting And Density Hotspot Detection Using YOLOv11 And K-Means Clustering
Authors: Riya Abhyankar, Arundhati Melinkeri, Trupti Mahajan, Dr. Sinu Nambiar
Abstract: Urban populations are rapidly growing with large scale public events , hence monitoring the crowd behaviours and count has become a necessity for modern surveillance systems. An accu-rate crowd count helps to estimate number of people in each area while anomaly detection helps identifying situations such as overcrowding, abnormal patterns. The rpaper will present the YOLOv11 object detection algorithm and apply it with K-means clustering to count crowds and detect anomalies. The proposed system aims to provide a simple yet effective mechanism for real-time crowd analysis by leveraging detection, clustering, and visualization techniques. The experi-ment results demonstrate the ability of the system to measure crowd size and identify crowded zones, making it a useful tool for surveillance purposes in urban environments.
Artificial Intelligence And Machine Learning In Bioethanol Production: Advancing Efficiency, Sustainability, And Process Optimization
Authors: Shubhangi Baghel, Om Prakash Sondhiya
Abstract: Bioethanol has emerged as one of the most promising renewable energy sources for reduc-ing greenhouse gas emissions and decreasing dependence on fossil fuels. However, con-ventional bioethanol production systems face significant challenges, including low conver-sion efficiency, process instability, high operational costs, and limitations in feedstock utili-zation. Recent developments in artificial intelligence (AI) and machine learning (ML) have introduced advanced computational approaches capable of transforming industrial bioetha-nol production through predictive analytics, process automation, and intelligent optimiza-tion. This paper examines the role of AI and ML technologies in enhancing fermentation efficiency, optimizing biomass pretreatment, predicting ethanol yield, and improving overall sustainability in bioethanol production systems. The study also discusses key machine learning algorithms, including artificial neural networks, support vector machines, random forests, and deep learning frameworks, alongside their industrial applications. Furthermore, the paper evaluates challenges associated with data quality, computational complexity, scalability, and ethical considerations. The findings indicate that AI-driven systems signifi-cantly improve process accuracy, reduce waste generation, and enhance economic feasibil-ity. Future research directions involving digital twins, autonomous biorefineries, and ex-plainable AI are also explored.
DOI: http://doi.org/10.5281/zenodo.20225773
Comparative Process Design and Modeled Performance of a Small-Scale Bioethanol Pro-duction System Using Agricultural Residues
Authors: Samriddha Sharma, Om Prakash Sondhiya
Abstract: The increasing environmental and economic concerns associated with fossil-fuel dependen-cy have intensified global interest in renewable transportation fuels. Among alternative biofuels, bioethanol has emerged as one of the most commercially viable and widely adopt-ed options because it can be produced from renewable biomass resources and integrated into existing fuel infrastructures. This study presents a comparative process-design assess-ment of a compact bioethanol production system utilizing three abundant lignocellulosic agricultural residues: rice straw, sugarcane bagasse, and corn stover. A literature-informed process model was developed for a small-scale educational bioethanol unit comprising feedstock preparation, dilute-acid pretreatment, enzymatic hydrolysis, yeast fermentation, and reflux-assisted distillation. The investigation evaluates the influence of biomass compo-sition on fermentable sugar recovery, ethanol yield, process efficiency, and energy demand. The modeled analysis indicates that sugarcane bagasse demonstrates the most favorable conversion performance under the selected operating assumptions, yielding approximately 74 g/L fermentable sugars and 34.5 g/L ethanol prior to separation. Corn stover exhibited intermediate performance, whereas rice straw produced comparatively lower ethanol con-centrations because of its elevated ash and silica content, which reduce carbohydrate acces-sibility during pretreatment. The results further reveal that pretreatment and distillation ac-count for the majority of the process energy requirement, highlighting the importance of heat integration, solids management, and process optimization in improving system effi-ciency. The study concludes that a modular small-scale bioethanol system can serve as an effective educational and research platform for demonstrating biomass-to-fuel conversion technologies. Furthermore, transparent presentation of modeled assumptions and calculation procedures strengthens the academic reliability of design-stage biofuel studies intended for instructional and comparative analysis.
DOI: http://doi.org/10.5281/zenodo.20225753
Predicting Employee Attrition And Engagement Using Multimodal Workforce Analytics
Authors: Alka G. Saraf, J Rathnamala
Abstract: Attrition and employee engagement remain among the most pressing concerns related to human capital, directly impacting organizational effectiveness, but current techniques for predicting such outcomes rely solely on limited survey data. In this paper, we propose an end-to-end multimodal workforce analytics solution that utilizes structured HR information (employee demographics, performance evaluation metrics, remuneration), semi-structured textual information (exit interviews, management feedback), and behavioral time-series data (usage statistics of internal communication platforms and badge access logs). The proposed predictive model uses multimodal transformer with cross-modal attention techniques to jointly forecast the likelihood of employee attrition (binary classification task, AUROC = 0.89) and their overall engagement (regression task, MAE = 0.31). Tested on data collected over 18 months for 8,472 employees at a multinational IT company, our method discovers distinctive behavioral indicators, with the decrease in collaboration entropy and higher activity outside regular hours predicting attrition 12 weeks in advance. By combining NLP techniques for parsing exit interviews, we discovered that "career development opportunities" and "management competency" were the top textual predictors of leaving the job.
DOI: https://doi.org/10.5281/zenodo.20233561
Privacy Preserving Federated Or Post-Quantum Authentication Scheme
Authors: Farzeen Basith, A R Deepti
Abstract: The interplay between the advancements in quantum computing techniques and the adoption of the distributed learning approach pose an enormous challenge to conventional cryptographic authentication protocols. Traditional public key systems and federated learning (FL) authentication methods based on the hardness of solving the integer factorization problem or discrete logarithms become inefficient due to the existence of Shor’s algorithm. This paper gives a detailed review of the latest research efforts toward the development of efficient and secure privacy-preserving FL authentication methods based on Post-Quantum Cryptography (PQC). In particular, we present the state-of-the-art of three schemes, namely, PQBFL (Post-Quantum Blockchain-based Federated Learning), ZKFL-PQ (Zero-Knowledge Federated Learning with Lattice-Based Encryption), and Enhanced EAADE for vehicular networks. It is shown that lattice-based authentication is both computationally efficient (signing times of around 0.65 ms) and robust against quantum attacks. Our proposed hybrid scheme is comprised of ML-KEM for key encapsulation, ML-DSA-65 for digital signatures, and Zero-knowledge proof for gradient integrity verification. The empirical evaluation shows a reduction of 44.96% in the computation cost and 22.16% in the communication cost relative to the class.
DOI: https://doi.org/10.5281/zenodo.20233619
A Theoretical Study of Range for Energy ( 1MeV/amu to 12MeV/amu) Protons In Aluminum, Gold , Copper and Germanium Solid Materials
Authors: Wafaa N. Jasim, Faten N. Jasim, Rana K. Albonwas
Abstract: To evaluate the effects of radiation, the range of protons in the target material is an important variable for this purpose. For this study, the range of protons with energy from 1MeV/amu to 12MeV/amu which represent Within the low energy range of protons, which are of particular importance in surface applications, some are medically and technically simple. that interacts with some elements (Al,Au,Cu,and Ge) was calculated using a semi-empirical equation and compare it with SRIM2012 data ,PASTR data which they are advanced simulation tools then we use two methods of fitting : used MATLAB’s polyfit function to carry out a polynomial regression and fitting the data with a 7th-degree polynomial. The results of both methods were well agreed. Our proton range values show good agreement with SRIM2012 data and PASTR data.
DOI: https://doi.org/10.5281/zenodo.20233980
Cyber Security Threat Detection Using Machine Learning Techniques
Authors: Shah Md. Tanzimul Kabir, Md. Saiduzzaman
Abstract: This paper provides a comprehensive analysis of machine learning in cyber security threat detection, tracing the history of its development from traditional signature-based systems towards intelligent and adaptive systems that can identify new and sophisticated threats. The study systematically examines recent research articles from 2021 to 2026 to explore the use of supervised, unsupervised, and deep learning in various domains of network intrusion detection systems, malware classification systems, and anomaly detection systems. The study proposes a new Integrated Threat Detection Framework (ITDF) that includes data preprocessing, feature engineering, model selection, and real-time detection. The study indicates that machine learning algorithms such as ensemble methods using Random Forest and XGBoost provide the best results with 95-99% accuracy on various benchmark datasets such as NSL-KDD, CIC-IDS2017, and UNSW-NB15. Deep learning methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform exceptionally well in identifying patterns in network traffic with 98-99% accuracy for network intrusion detection systems. Emerging trends in machine learning for cyber security include federated learning for privacy in distributed environments and Generative Adversarial Networks (GAN) for generating training data for rare types of threats. The key challenges that still need to be addressed relate to the problem of concept drift, adversarial attacks on ML models, and the need for interpretability in security operations. The comparative evaluation of the proposed approach with respect to four analytical dimensions—detection accuracy, false positive rate, real-time capability, and adversarial robustness—shows that the hybrid approach provides the best robustness against cyber attacks.
DOI: https://doi.org/10.5281/zenodo.20270619
Satellite-Based Analysis Of River Surface Changes During Mahakumbh 2025 In The Prayagraj Sangam Region
Authors: Saurabh Singh
Abstract: Large-scale mass gathering events can substantially influence riverine environments through intense anthropogenic activities, sediment disturbance, and rapid alterations in surface water char-acteristics. The present study investigates the spatial variability of Sentinel-2 spectral reflectance and water surface characteristics in the Prayagraj Sangam region during Mahakumbh 2025 using multispectral satellite imagery and cloud-based geospatial analysis. Sentinel-2 MultiSpectral In-strument (MSI) imagery acquired through the Google Earth Engine (GEE) platform was utilized to evaluate variations in spectral reflectance associated with major bathing activities and river surface disturbances during the event period. The study primarily focused on the analysis of green (B3), red (B4), and near-infrared (B8) spectral bands along with the Normalized Difference Water Index (NDWI) for assessing river surface characteristics and spatial environmental varia-bility. Spatial analysis was performed to identify reflectance hotspots and disturbed river sections influenced by intense human activities, ritual bathing, temporary settlement expansion, and riverbank interactions during Mahakumbh 2025. The generated spectral reflectance maps revealed considerable spatial heterogeneity across the study area, particularly near the Sangam confluence and major bathing ghats. Elevated reflectance values and noticeable variations in NDWI distribu-tion were observed in regions experiencing high anthropogenic pressure and continuous river surface disturbances. The analysis further demonstrated distinct differences in spectral behavior between relatively stable river sections and highly occupied bathing zones. The study highlights the capability of Sentinel-2 imagery and Google Earth Engine for rapid and large-scale monitoring of dynamic river environments during mass gathering events. The integra-tion of multispectral satellite observations with geospatial analysis provides a cost-effective and efficient framework for identifying spatial environmental disturbances and evaluating river sur-face variability in highly populated riverine systems. The findings of the study may contribute toward improved satellite-based environmental monitoring, river management strategies, and sustainable assessment of anthropogenic impacts during large religious gatherings such as Maha-kumbh.
DOI: http://doi.org/10.5281/zenodo.20270657
Seasonal Variability Of Groundwater Quality Using Entropy Weighted Water Quality Index (EWQI) In Uttar Pradesh, India
Authors: Nitin Mishra
Abstract: Groundwater serves as a major source of freshwater for domestic consumption, irrigation, and industrial utilization in India, especially in highly populated regions such as Uttar Pradesh. In recent decades, groundwater quality has been increasingly threatened by rapid urban growth, intensive agricultural activities, industrial discharge, and natural hydrogeochemical interactions. Moreover, seasonal changes associated with monsoon rainfall significantly affect groundwater composition and contaminant distribution. In this context, the present study evaluates seasonal variations in groundwater quality using the Entropy Weighted Water Quality Index (EWQI) and hydrogeochemical interpretation techniques. Groundwater quality data for premonsoon and post-monsoon periods were collected from the Central Ground Water Board (CGWB), Uttar Pradesh. To maintain the accuracy and reliability of the dataset, quality assessment was performed using the Ion Balance Error (IBE) method. EWQI was calculated independently for both seasons to determine the suitability of groundwater for drinking purposes. The analysis revealed noticeable seasonal fluctuations in important physicochemical parameters such as Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Hardness (TH), nitrate, and fluoride. Higher concen-trations of dissolved constituents were generally observed during the premonsoon season due to limited recharge and increased evaporation, whereas postmonsoon groundwater exhibited com-paratively improved quality because of rainfall-induced dilution and aquifer recharge. Seasonal groundwater quality was evaluated using EWQI classification and hydrogeochemical analysis. The results indicated substantial seasonal variation in groundwater quality across the study area. The outcomes of the study indicate that the integration of EWQI and hydrogeochemical analysis provides an effective framework for groundwater quality assessment under varying seasonal conditions.The developed methodology can assist policymakers and water resource authorities in groundwater monitoring, pollution assessment, and sustainable groundwater management practic-es.
DOI: http://doi.org/10.5281/zenodo.20280351
Enhancing Project Success In Building Construction Projects Through Effective Leadership Behaviour Strategies Of Project Managers In The Federal Capital Territory, Abuja
Authors: Adebiyi Adeniyi Mayowa
Abstract: Improving project success in building construction remains a major concern in the Federal Capital Territory, Abuja, due to persistent challenges such as delays, cost overruns, weak coordination, and poor project delivery outcomes. This study examined strategies that could be applied to improve the level of project success by enhancing the effectiveness of leadership behaviour of project managers on building construction projects in FCT, Abuja. A quantitative research design was adopted, and data were collected through a structured questionnaire administered to construction professionals including architects, quantity surveyors, builders, facility managers, and estate surveyors. From a sampling frame of 523 professionals, a sample size of 227 was derived using Yamane’s formula, while 185 valid responses were retrieved and analyzed using mean score ranking, Kaiser-Meyer-Olkin and Bartlett’s tests, and factor analysis.The findings showed that clearly defined project mission (Mean = 4.5421), proper project schedule and plan (Mean = 4.5157), and top management support (Mean = 4.3750) were the most significant strategies for improving project success. Other important strategies included effective communication (Mean = 3.9813), monitoring and feedback (Mean = 3.8637), and assigning technical tasks to competent hands (Mean = 3.8419). The average total mean score of 3.7323 confirmed that the identified strategies were generally effective. Further analysis revealed that these strategies clustered around leadership dimensions such as planning, communication, team coordination, stakeholder engagement, competence, and decision making. The study concludes that project success in building construction projects in FCT, Abuja can be significantly enhanced when project managers adopt effective leadership behaviour strategies supported by strong organizational structures and stakeholder collaboration. It is recommended that construction firms should strengthen leadership development through training, clear project planning, improved communication systems, competent supervision, and continuous monitoring of project activities. These measures will improve project delivery and overall construction performance in the study area.
DOI: https://doi.org/10.5281/zenodo.20324569
Existence, Uniqueness, And Ulam–Hyers–Rassias Stability Of A Nonlinear ψ-Hilfer Variable-Order Fractional Integrodifferential System With Nonlocal Integral Boundary Conditions
Authors: Dr. M. K. Vediappan, Dr. K. Srinivasan
Abstract: This paper establishes a comprehensive well-posedness and stability theory for a class of nonlinear ψ-Hilfer variable-order fractional integrodifferential equations (VO-FIDEs) of the form ᴙ^{α(⋅),β}_{ψ} x(t) = f(t, x(t), ∫₀ᵗ κ(t,s,x(s))ds) subject to nonlocal integral boundary conditions on a finite interval [a, b]. The fractional derivative is taken in the ψ-Hilfer sense with a continuous variable order α : [a,b] → (0,1] and type β ∈ [0,1], which simultaneously unifies the Riemann–Liouville, Caputo, Hilfer, and Hadamard operators as special cases. Three principal results are established: (i) existence of at least one solution via the Schauder fixed-point theorem in a suitably weighted Banach space; (ii) uniqueness of the solution via the Banach contraction principle under a generalized Lipschitz condition; and (iii) Ulam–Hyers–Rassias (UHR) stability, providing quantitative bounds on the deviation of approxi-mate solutions from exact ones. The variable-order framework captures systems whose memory depth evolves dynamically, a feature relevant to viscoelastic materials, anomalous diffusion with space-dependent porosity, and variable-memory epidemic models. New inte-gral inequalities for ψ-Hilfer variable-order operators are derived as auxiliary results. Two illustrative examples confirm the theoretical findings, and a comparison with constant-order results reveals the strictly broader applicability of the variable-order framework.
DOI: http://doi.org/10.5281/zenodo.20347750
Design and Fabrication of Rocker Bogie Mechanism Using Fire Fighting Robot
Authors: B.P Hithesh Kumar, Balaraju V S, Bharatesh V V, Chandan V, Pavan Krishna K
Abstract: This paper presents the design and fabrication of a rocker bogie mechanism based fire fighting robot developed for rescue and firefighting operations in hazardous environments. The proposed system is capable of traversing uneven terrains, climbing obstacles, and suppressing fire using a remotely operated water spraying mechanism. The rocker bogie suspension system provides enhanced stability and mobility over rough surfaces where conventional wheeled robots face operational difficulties. The robot is powered using direct current geared motors controlled through a wireless communication system. A water pump and nozzle arrangement are integrated to extinguish fire effectively in industrial, residential, and disaster affected areas. The chassis is fabricated using mild steel and lightweight materials to achieve sufficient strength and maneuver-ability. The performance of the system is evaluated based on terrain adaptability, obstacle climb-ing capability, motor torque, and firefighting efficiency. The results indicate that the developed robot reduces human risk during firefighting operations and provides reliable movement in diffi-cult environments.
A Study On The Role Of Government Initiatives In Promoting Digital Payment System With Special Reference To Coimbatore District
Authors: Ms. Nandhini. R, Mr Vishnu Kanth K
Abstract: The rapid growth of digital payment systems has transformed the financial landscape in India, driven largely by proactive government initiatives. This study examines the role of government measures in promoting digital payment adoption with special reference to Coimbatore District. Key initiatives such as Digital India, demonetization, Unified Payments Interface (UPI), and incentives for cashless transactions have significantly influenced consumer behaviour and merchant acceptance. The study analyses awareness levels, usage patterns, and challenges faced by users in adopting digital payments. Data collected from respondents indicate that increased accessibility, convenience, and government support have positively impacted digital payment usage. However, issues such as security concerns, lack of digital literacy, and internet connectivity continue to hinder full adoption. The study concludes that while government initiatives have played a crucial role in accelerating the shift towards a cashless economy, continuous efforts in awareness, infrastructure development, and user education are essential for sustained growth.
DOI: http://doi.org/10.5281/zenodo.20355155
A Study On Awareness Utilization And Satisfaction Of Customers About Artificial Intelligence Chatbots For Customer Relationship Management With Special Reference To Coimbatore District
Authors: Ms. Vineetha V, Ms. Vaishnavi V
Abstract: The emergence of Artificial Intelligence (AI) has revolutionized Customer Relationship Management (CRM), with AI-powered chatbots becoming indispensable tools for delivering efficient and responsive customer service. This study explores the awareness, utilization, and satisfaction levels of customers regarding AI chatbots in CRM, with special reference to Coimbatore District. The primary objectives are to measure customer awareness of AI chatbot technology, analyze utilization patterns across various service sectors including banking, retail, e-commerce, and telecommunications, and evaluate satisfaction levels based on chatbot performance and service quality. A descriptive research design was adopted. Primary data was collected through a structured questionnaire administered to selected respondents in Coimbatore District using convenient sampling. Statistical tools including percentage analysis, Chi-square test, weighted average method, and Likert scale were employed for data interpretation. The findings indicate moderate-to-high awareness among urban consumers, while utilization varies across age, income, and educational demographics. Key satisfaction drivers include response accuracy, 24/7 availability, ease of use, and query resolution efficiency.
DOI: http://doi.org/10.5281/zenodo.20355163
A Study On Awarness And Utiliztion Of E-Vehicle And Petrol/Diesel Vehicle With Special Reference To Coimbatore District
Authors: Ms. Vineetha V, Ms. Vaishali V
Abstract: The rapid evolution of the automotive industry has brought electric vehicles (EVs) to the forefront of sustainable transportation, challenging the long-established dominance of internal combustion engine (ICE) vehicles powered by petrol and diesel. This study presents a comparative analysis of electric vehicles and conventional petrol/diesel vehicles with special reference to Coimbatore District, Tamil Nadu, India a region increasingly recognized as an emerging hub for manufacturing, technology, and green mobility initiatives. The primary objective of this research is to examine and compare electric vehicles and conventional fuel-based vehicles across multiple dimensions, including purchase cost, operational expenses, environmental impact, maintenance requirements, performance, consumer awareness, and government policy support. The study also seeks to understand the factors influencing consumer preferences and adoption patterns among residents of Coimbatore District. A structured survey methodology was employed, utilizing both primary and secondary data sources. Primary data was collected through questionnaires distributed among vehicle owners, prospective buyers, and daily commuters across urban and semi-urban areas of Coimbatore. Secondary data was gathered from published reports, government records, automotive industry publications, and relevant academic literature. The findings reveal that while conventional petrol and diesel vehicles continue to dominate the market due to established infrastructure and consumer familiarity, awareness and acceptance of electric vehicles is steadily growing, particularly among younger, environmentally conscious demographics. Key barriers to EV adoption identified include limited charging infrastructure, higher initial acquisition cost, and range anxiety. The study concludes with actionable recommendations for policymakers, automotive manufacturers, and local government bodies to accelerate the transition toward sustainable e- mobility in Coimbatore District, thereby contributing to India's broader national electric vehicle mission and carbon emission reduction goals.
DOI: http://doi.org/10.5281/zenodo.20355180
A Study On The Impact Of Adopting Ai In Ecommerce Platform With Special Reference In Coimbatore District
Authors: Ms. R. Nandhini, Ms. P. Mownica
Abstract: AI is changing e-commerce pretty fast. Online stores are using it to run things better and connect with customers in new ways. It helps with efficiency and making things more competitive in the market. Tools like chatbots help answer questions right away. Predictive analytics figures out what people might buy next. Personalized recommendations pop up based on what you looked at before. Automation takes care of boring tasks. Platforms are picking these up to make processes smoother and fit what customers want. Studies say adopting AI boosts marketing and gets customers more involved. Sales go up too. But there are issues. Costs for setting it up can be high. Privacy with data is a big worry. Not everyone is ready for the tech side. In places like Coimbatore district, things are still catching on with digital changes. It’s a busy area for local business. This study looks at how AI fits into e-commerce there. Businesses integrate it into daily operations. It affects how customers act and how well the business does. Barriers make it hard to do right. Like maybe not enough skills or money. The research mixes numbers with talks from local firms and shoppers. Quantitative data shows patterns. Qualitative stuff adds why things happen. Aims to give real info on pluses and minuses for AI in this spot. I think findings could help add to what we know in books. For small and medium shops in Coimbatore, it might give ideas on digital stuff. Recommendations for using AI to grow steady. Stakeholders might find ways to handle it. Some parts get messy with implementation. Not totally sure on every barrier yet.
DOI: http://doi.org/10.5281/zenodo.20355197
A Study On The Impact Of Quick Commerce In Traditional And E-Commerce Businesses With Special Reference To Coimbatore District
Authors: Ms Mithuna R, Bharath Vignesh L
Abstract: Quick Commerce (Q-Commerce), defined as the ultra-fast delivery of goods within 10 to 30 minutes of order placement, has emerged as a disruptive force reshaping the global retail landscape. In India, platforms such as Blinkit, Zepto, and Swiggy Instamart have expanded rapidly in urban and semi-urban markets, fundamentally altering consumer expectations around speed, convenience, and accessibility. This study investigates the impact of Q-Commerce on traditional brick-and-mortar retailers and conventional e-commerce platforms, with special reference to Coimbatore District, Tamil Nadu. Adopting a descriptive research design and a mixed-method approach, primary data were collected from 150 consumers and 50 retailers using structured questionnaires and retailer interview schedules. Cluster sampling was employed to ensure geographical representation across urban, semi-urban, and rural segments. Statistical tools including Percentage Analysis, Weighted Average Mean, Chi-Square Test, ANOVA, and Pearson's Correlation Coefficient were applied for data analysis. The findings reveal that Q-Commerce has achieved deep consumer adoption driven primarily by delivery speed and convenience, causing significant declines in customer footfall, daily sales volumes, and profit margins among traditional kirana stores. A strong positive correlation (r = +0.864) was established between footfall decline and revenue erosion. While Q-Commerce and conventional e-commerce exhibit a largely complementary relationship through consumer behavioural segmentation, measurable competitive pressure on conventional platforms is intensifying. The study concludes with evidence-based recommendations for retailers, e-commerce operators, policymakers, and regulators to navigate the transformative and enduring impact of Q-Commerce on the Indian retail ecosystem.
DOI: http://doi.org/10.5281/zenodo.20355239
Design And Estimation Of Bucket Elevator Tower Using Tekla Structure
Authors: M.P.Iniya, P.Gowtham, A.Jeriyafrankline, T.Kamalesh
Abstract: The bucket elevator tower is a necessary supporting structure in silo structures that allows for the vertical transportation of bulk commodities like grains, cement, and other aggre-gates. The current research involves the design and estimation of a bucket elevator tower with the use of Tekla Structures software. The design process aims to ensure stability, dura-bility, and safety against static and dynamic loads and also satisfaction of functional re-quirements. Tekla Structures supports precise 3D modeling, detailing, and clash detection to ensure accuracy of structural elements like columns, bracings, and connections. The soft-ware also supports material optimization, minimizing wastage and project cost. Load factors like wind, seismic, and operating loads are included to improve structural performance. The estimation process offers a detailed bill of materials (BOM), cost estimation, and fabrication information, allowing effective project planning and execution. This methodology provides reliability in construction, enhances productivity, and reduces errors over traditional meth-ods. The result is a cost-efficient and structurally sound bucket elevator tower appropriate for contemporary silo use.
DOI: https://doi.org/10.5281/zenodo.20384205
Bank ATM Simulation System Using Java With Enhanced Security And User Management
Authors: M. Shiva Nageshwarrao, M. Anjil Reddy, P. Shiva Ganesh
Abstract: Traditional ATM systems support basic financial operations but suffer from critical security limitations, including unencrypted data storage, weak authentication, and inadequate administrative controls. This paper presents an enhanced Bank ATM Simulation System developed using Java, JavaServer Pages (JSP), Servlets, Java Database Connectivity (JDBC), and MySQL on Apache Tomcat. The system operates in two modes—Admin Mode and User Mode. Advanced Encryption Standard (AES) secures sensitive card details and PINs in the database, while Multi-Factor Authentication (MFA) governs system access. The Admin module automates credential generation and supports transaction monitoring and application approval workflows. The User module handles deposits, withdrawals, balance checks, and profile management. All transactions are logged for full auditability. Comparative evaluation across seven system dimensions confirms that the proposed system improves upon existing approaches in security, usability, and administrative control.
DOI: https://doi.org/10.5281/zenodo.20423206
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Data-Driven Agricultural Decision Support System Using Historical Crop Production Data
Authors: Ambuj Kumar Misra
Abstract: Contemporary agriculture operates under the compounding pressures of climate variability, resource scarcity, and an expanding global population. This paper introduces a comprehensive Data-Driven Agricultural Decision Support System (DADSS) that harnesses decades of historical crop production records, real-time sensor telemetry, and satellite-derived remote sensing imagery to generate actionable, site-specific recommendations for farm management. Four machine learning architectures — Random Forest (RF), Long Short-Term Memory (LSTM) networks, Gradient Boosting Machines (GBM), and Support Vector Machines (SVM) — were trained and benchmarked against a baseline linear regression model across six major U.S. cropping regions spanning the years 2000 to 2022. The hybrid DADSS framework, which fuses LSTM temporal modeling with gradient-boosted ensemble predictions, achieved an overall forecasting accuracy of 92.5%, outperforming all individual baselines. Results confirm that data-driven advisory tools can meaningfully reduce input costs, improve yield stability, and bolster farmers' adaptive capacity in the face of climate uncertainty. The system architecture, feature engineering pipeline, validation results, and policy implications are discussed in detail.
DOI: http://doi.org/10.5281/zenodo.20426302
Impact of goods and services tax compliance on working capital management of MSME’s
Authors: Assistant Professor Dr.S.Mahalakshmi, Assistant Professor Ameer Ulla
Abstract: The introduction of the Goods and Services Tax (GST) in India marked a paradigm shift in the system of indirect taxation, replacing it with a uniform framework for the entire country. GST compliance, for Micro, Small and Medium Enterprises (MSMEs), which make up a significant share of India's economy, has been an important factor affecting their working capital manage-ment. This paper provides an empirical study of the influence of GST compliance on different elements of the working capital of 500 MSMEs operating in the manufacturing, trading, and services industries using a mixed-method approach of financial statement analysis (GST compli-ance before 2015-17 vs. after GST 2018-2025) and surveys. It is established that, on average, due to GST compliance, there was an increase in the working capital of 22%, mainly due to the delay in receiving ITC refunds and blocked input taxes. Meanwhile, companies complying with GST have demonstrated increased efficiency in managing inventories (decrease in days by 14%) and decreased logistics costs by 18%. A two-stage least squares regression model has shown the effect of the moderating variables such as firm size, digitization, and professional assistance in tax matters.
AI Powered Anomaly Detection System for Smart Cyber Defense
Authors: G. Veera Shekar, Associate Professor N.S.C. Mohana Rao
Abstract: Due to the growing complexity and frequency of cyber attacks, it is important to shift the para-digm towards predictive security solutions. This paper presents a new anomaly-based framework for smart cyber security using Artificial Intelligence techniques. It utilizes a hybrid Deep Learning (DL) model with the ability to combine CNNs(CNNs) for spatial anomaly detection and Long Short-Term Memory (LSTM) networks for temporal anomaly detection. The proposed frame-work is tested using the CIC-IDS2017 and CSE-CIC-IDS2018 data sets. It shows improved accuracy and low false positive rates compared to machine learning-based security solutions. It is capable of achieving 99.82% accuracy and 0.15% false positive rates, making
DeepVision-XAI: Explainable Deep Learning Framework for Real-Time Medical Image Diagnosis
Authors: Research Scholar Roshan Rukshana Sulaima Lebbe, Dr.V.Priyalakshmi
Abstract: While the incorporation of deep learning in medical imaging has significantly boosted diagnostic accuracy, the "black box" problem of deep learning models continues to be an important obstacle towards their use in clinical practice. Not only are accurate predictions required by clinicians, but also clear explanations that are medically plausible. In this paper, we propose DeepVision-XAI, an explainable deep learning framework for real-time medical image diagnosis. Specifically, the framework incorporates an efficient EfficientNet-B4 model for feature extraction, MHSA self-attention for spatial information capture, and a hybrid explainability module that combines Grad-CAM, SHAP values, and Bayesian uncertainty estimates. When tested on three publicly available benchmark datasets (ChestX-ray2017 for pneumonia, ISIC 2019 for skin lesions, and APTOS 2019 for diabetic retinopathy), DeepVision-XAI attains diagnostic accuracies of 96.2%, 91.8%, and 94.5%, with an inference latency of less than 150 ms per image.
A Comprehensive Critical Review Of Emerging Paradigms In Cloud Computing: Synergizing Edge-Cloud Architectures, AI-Driven Orchestration, And Sustainable Frameworks
Authors: Dr. Nidhi Mishra, Ashee Parihar, Sneh Patel, Shubham Singh Parihar, Varun Shrivastava
Abstract: Cloud computing has changed significantly in recent years because modern applications now require faster processing, lower delay, and better energy management. Traditional centralized cloud systems are often unable to support real-time services such as autonomous vehicles, smart healthcare systems, industrial automation, and large-scale IoT networks. As a result, researchers and industries are increasingly moving toward edge cloud architectures where data processing is distributed across edge devices and cloud servers. This paper reviews recent developments in edge-cloud collaborative computing, AI-driven orchestration, and sustainable cloud infrastructure. It discusses how technologies such as Deep Reinforcement Learning (DRL), Federated Learning (FL), and workload optimization techniques help improve resource management and reduce laten-cy. The paper also examines sustainable frameworks such as MAIZX and GEECO that focus on lowering carbon emissions and improving energy efficiency. Based on the reviewed studies, edge-cloud systems provide better response time, lower bandwidth consumption, and improved operational efficiency compared to traditional cloud only systems. However, challenges related to hardware heterogeneity, privacy, infrastructure cost, and AI explainability still remain important research issues.
DOI: http://doi.org/10.5281/zenodo.20568693
Understanding The Future of Digital Currency
Authors: Mr. Manikandan K, Dr Mr.Abishek S, Mrs. Jeya Padma Deepa I
Abstract: Digital currencies are transforming the global financial landscape through the rapid growth of cryptocurrencies such as Bitcoin and Ethereum, along with advancements in blockchain technology and the emergence of Central Bank Digital Currencies (CBDCs). Increasing adoption, technological innovation, and evolving regulatory frameworks are creating new opportunities for financial transactions, investments, and economic growth. At the same time, challenges related to security, regulation, market volatility, and public trust continue to influence the development of digital currencies. The evolving digital finance ecosystem highlights the need for effective policies, improved security measures, and balanced innova-tion to ensure sustainable growth and wider acceptance in the future.
DOI: http://doi.org/10.5281/zenodo.20616663
A Study On The Role Of Corporate Social Responsibility In Consumer Perception
Authors: Mr. Nithish R, Mr. Karthik Sabari S, Mrs. Jeya Padma Deepa I
Abstract: Corporate Social Responsibility (CSR) has evolved into a critical aspect of contemporary business strategy, extending beyond philanthropy to encompass ethical governance, sus-tainable practices, and meaningful social engagement. This study explores the role of CSR in shaping consumer perception, emphasizing how initiatives in environmental stewardship, ethical labour practices, and community development influence consumer attitudes and decision-making. As markets become increasingly competitive and consumers more social-ly conscious, CSR has emerged as a differentiating factor that strengthens brand equity and fosters long-term loyalty. The findings reveal that CSR initiatives significantly enhance brand image by aligning corporate values with societal expectations. Ethical practices, such as fair trade and transparency, cultivate trust and credibility, while environmental efforts, including carbon reduction and sustainable sourcing, resonate strongly with eco-conscious consumers. Social contributions, such as community welfare programs and charitable part-nerships, further reinforce positive associations with the brand.
DOI: http://doi.org/10.5281/zenodo.20616748
A Study on Ethical Commerce: Corporate Social Responsibility in a Digital Age
Authors: Mr. Prabhakaran M, Mr. Karuna Murthi J, Dr. Prabhakaran K
Abstract: In the Ethical commerce has become a defining feature of responsible business practice in the digital era. Technological advancements such as artificial intelligence, big data analytics, e- commerce platforms, and social media have expanded corporate influence while increas-ing ethical accountability. Corporate Social Responsibility (CSR) now encompasses data protection, algorithmic fairness, cybersecurity, sustainability, and transparent governance. This study investigates the relationship between CSR practices and consumer perception in digital commerce. Using a quantitative descriptive research design, the research evaluates how responsible digital behavior influences trust, loyalty, and purchase intention. Findings suggest that transparent data governance and ethical digital strategies significantly enhance stakeholder confidence and long- term sustainability. The study concludes that ethical commerce is both a strategic necessity and a moral obligation in the modern digital econo-my.
DOI: http://doi.org/10.5281/zenodo.20616859
A Study on Strategies for Building Brand Loyalty In the Digital Age
Authors: Mr. Praveen Kumar. M, Mr. Sanjay. S, Mrs.AR Sri Ranjani. AR
Abstract: This study developing a conceptual framework for building brand loyalty in the digital age, focusing on key strategies that modern brands can employ through digital channels. The digital age has transformed the brand–consumer relationship, making customer engage-ment, personalization, and experience central to loyalty instead of mere transactional repeti-tion. This conceptual research integrates existing literature on digital marketing, social me-dia engagement, content marketing, and customer relationship management to propose a framework of eight core strategies: omnichannel presence, personalized experiences, com-munity building, transparent communication, value driven content, experiential marketing, data-driven relationship management, and ethical digital practices. The study highlights how these strategies interact with changing consumer behaviours, such as preference for authen-ticity, real time interaction, and peer influenced decision making. The conceptual analysis concludes with practical suggestions for marketers seeking to cultivate deep, sustainable brand loyalty in the contemporary digital environment.
DOI: http://doi.org/10.5281/zenodo.20616982
The Study on Impact of Mobile Marketing on Consumer Behaviour
Authors: Ms.Nashria Seerin.A, Ms. Amirtha.R. N, Ms.Sri Ranjani.A
Abstract: Mobile marketing has become a pivotal component of modern marketing strategies due to the rapid proliferation of mobile devices and their deep integration into everyday life. This study examines how mobile marketing influences consumer behaviour, including decision-making processes, purchasing patterns, and engagement with brands through mobile chan-nels. By analysing various mobile marketing techniques — such as SMS promotions, mo-bile app notifications, and location-based advertisements — the research highlights how personalized, timely, and accessible mobile communications shape consumers’ attitudes and actions at different stages of the buying journey. Findings indicate that mobile marketing significantly impacts convenience, product awareness, impulse buying, and in-store as well as online purchase decisions, ultimately altering how consumers interact with brands and make purchasing choices. The study also discusses challenges marketers face in creating relevant and non-intrusive mobile content that resonates with diverse consumer segments. The results offer valuable insights for businesses seeking to refine mobile marketing strate-gies to better align with evolving consumer preferences and behaviours in a mobile-centric marketplace.
DOI: http://doi.org/10.5281/zenodo.20631241
The Rise of Online Trading: Opportunities and Risks
Authors: Mr. Kavin.P, Mr. Sanjay.G, Ms.Sri Ranjani.A. R
Abstract: The rapid development of digital technology has transformed traditional financial markets, leading to the emergence of online trading platforms that allow individuals to buy and sell financial assets through the internet. Online trading has increased market accessibility, re-duced transaction costs, and enabled real-time participation in financial markets. It has also encouraged greater participation from retail investors who were previously excluded from formal trading systems. However, despite these advantages, online trading involves signifi-cant risks such as high market volatility, cyber fraud, emotional decision-making, and lack of financial knowledge. This study aims to analyze the rise of online trading, identify the major opportunities created by it, and examine the risks associated with online trading activ-ities. The study is based on secondary data collected from academic journals, books, and reports published by recognized financial institutions.
DOI: http://doi.org/10.5281/zenodo.20631307
Study Of Artificial Intelligence in Promoting Green Marketing and Sustainable Consumer Behavior in Financial Services in Coimbatore
Authors: Mr.Shaheen Khan.M, Mr.Buvin Sri, Ms. Arsitha.A, Mrs.Jeya Padma Deepa.I
Abstract: Artificial Intelligence (AI) in promoting green marketing practices and encouraging sustain-able consumer behavior in the financial services sector. The research focuses on how AI-driven technologies such as data analytics, machine learning, and digital platforms influence eco-friendly financial decisions among consumers in Coimbatore. The study examines cus-tomer awareness, adoption levels, and satisfaction with AI-enabled green financial services such as paperless banking, digital payments, and sustainable investment options. Both pri-mary and secondary data were used to analyze the impact of AI on consumer behavior. The findings highlight that AI significantly contributes to reducing environmental impact and enhances sustainable financial practices. The study provides recommendations for improv-ing AI-driven green marketing strategies in financial institutions.
DOI: http://doi.org/10.5281/zenodo.20631490
Features And Innovations of the Latest Iphone Models
Authors: Mr.Lalith Kumar.O, Mr. Hariharan.K, Dr. Rajedran.N
Abstract: The latest iPhone models introduced by Apple Inc. represent significant advancements in smartphone technology through improved performance, advanced artificial intelligence capabilities, enhanced camera systems, and innovative design features. Recent models such as the iPhone 16 series and iPhone 17 showcase powerful processors like the A18 and A19 chips that deliver faster performance, improved energy efficiency, and enhanced mobile gaming experiences. These devices integrate advanced AI features, including intelligent assistance, personalized user experiences, and improved privacy protection. The latest iPhones also introduce major innovations in photography and videography, including high-resolution 48MP camera systems, enhanced ultra-wide and telephoto capabilities, spatial photo and video recording, and professional-quality video features. Display technology has been significantly upgraded with Super Retina XDR displays, adaptive refresh rates, Al-ways-On display features, and improved brightness and durability. Additionally, the devices offer improved battery life, advanced connectivity technologies such as 5G and Wi-Fi 7, and durable materials like Ceramic Shield and titanium designs. Overall, the latest iPhone models demonstrate continuous innovation in hardware and software integration, focusing on user experience, performance optimization, and technological advancement. These in-novations highlight the growing role of smartphones as powerful tools for communication, productivity, and digital lifestyle enhancement.
DOI: http://doi.org/10.5281/zenodo.20631568
Impact of goods and services tax compliance on working capital management of MSME’s
Authors: Assistant Professor Dr.S.Mahalakshmi, Assistant Professor Sameer ulla
Abstract: The introduction of the Goods and Services Tax (GST) in India marked a paradigm shift in the system of indirect taxation, replacing it with a uniform framework for the entire country. GST compliance, for Micro, Small and Medium Enterprises (MSMEs), which make up a significant share of India's economy, has been an important factor affecting their working capital manage-ment. This paper provides an empirical study of the influence of GST compliance on different elements of the working capital of 500 MSMEs operating in the manufacturing, trading, and services industries using a mixed-method approach of financial statement analysis (GST compli-ance before 2015-17 vs. after GST 2018-2025) and surveys. It is established that, on average, due to GST compliance, there was an increase in the working capital of 22%, mainly due to the delay in receiving ITC refunds and blocked input taxes. Meanwhile, companies complying with GST have demonstrated increased efficiency in managing inventories (decrease in days by 14%) and decreased logistics costs by 18%. A two-stage least squares regression model has shown the effect of the moderating variables such as firm size, digitization, and professional assistance in tax matters.
Cryptocurrency Volatility Prediction Using Hybrid Time – Series and Deep Learning Models
Authors: Dr. Swathi Pothala
Abstract: The crypto currency markets are highly volatile with high levels of noise and non-stationary properties that pose challenges for classical forecasting techniques. In this paper, we intro-duce a hybrid approach which is based on time-series decomposition and deep learning to forecast the volatility of cryptocurrencies. The proposed method consists of using the Com-plete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for de-composition of the signal, Bidirectional Gated Recurrent Unit (Bi-GRU) along with multi-head self-attention for extracting temporal features, and a Particle Swarm Optimization (PSO) for tuning the hyperparameters. The performance evaluation of the introduced ap-proach was done using data for five major cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Cardano, Solana) in the period from 2019 to 2025. The obtained MAE is equal to 0.0058, RMSE – 0.0082, and MAPE is equal to 4.32%. We managed to outperform the popular GARCH family of models (GARCH – 0.0182 MAE, EGARCH – 0.0156 MAE).
DOI: http://doi.org/10.5281/zenodo.20672600
Cryptocurrency Volatility Prediction Using Hybrid Time – Series and Deep Learning Models
Authors: Dr. Swathi Pothala
Abstract: The crypto currency markets are highly volatile with high levels of noise and non-stationary properties that pose challenges for classical forecasting techniques. In this paper, we intro-duce a hybrid approach which is based on time-series decomposition and deep learning to forecast the volatility of cryptocurrencies. The proposed method consists of using the Com-plete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for de-composition of the signal, Bidirectional Gated Recurrent Unit (Bi-GRU) along with multi-head self-attention for extracting temporal features, and a Particle Swarm Optimization (PSO) for tuning the hyperparameters. The performance evaluation of the introduced ap-proach was done using data for five major cryptocurrencies (Bitcoin, Ethereum, Binance Coin, Cardano, Solana) in the period from 2019 to 2025. The obtained MAE is equal to 0.0058, RMSE – 0.0082, and MAPE is equal to 4.32%. We managed to outperform the popular GARCH family of models (GARCH – 0.0182 MAE, EGARCH – 0.0156 MAE).
DOI: http://doi.org/10.5281/zenodo.20672600
Performance Enhancement Of Spark-Ignition Engines Using An Ethanol Reforming-Based On-Board Hydrogen Generation System
Authors: Vishnu Deo Tiwari, Om Prakash Sondhiya
Abstract: The growing need for environmentally sustainable transportation has encouraged extensive re-search into alternative fuel technologies capable of reducing greenhouse gas emissions and im-proving engine efficiency. Among the various alternatives, hydrogen has attracted considerable attention because of its high flame propagation speed, broad flammability limits, and cleans com-bustion characteristics. Despite these advantages, the widespread adoption of hydrogen-fueled vehicles remains constrained by challenges associated with hydrogen storage, transportation, and infrastructure development. To overcome these limitations, the present study investigates an on-board hydrogen generation system based on ethanol steam reforming for application in spark-ignition (SI) engines. Ethanol is considered a suitable feedstock due to its renewable nature, ease of handling, established distribution network, and relatively high hydrogen content. In the pro-posed system, ethanol undergoes catalytic steam reforming to produce a hydrogen-rich gas stream, which is subsequently supplied to the engine to improve combustion characteristics and overall performance. The research focuses on evaluating the influence of key reforming parame-ters, including reforming temperature, steam-to-ethanol ratio, and catalyst loading, on hydrogen production efficiency. Furthermore, engine performance is assessed in terms of brake thermal efficiency, brake specific fuel consumption, and exhaust emissions such as carbon monoxide (CO), unburned hydrocarbons (HC), and nitrogen oxides (NOx). Advanced optimization tech-niques, namely Response Surface Methodology (RSM) and Artificial Neural Networks (ANN), are proposed to identify the optimum operating conditions for maximizing hydrogen yield and engine performance while minimizing emissions. In addition, Computational Fluid Dynamics (CFD) analysis using Fluent is considered to examine combustion behavior and flow dynamics within the engine system. The anticipated outcomes of this work include enhanced combustion efficiency, lower pollutant emissions, improved fuel utilization, and the promotion of renewable fuel-based hydrogen generation as a practical pathway toward cleaner and more sustainable trans-portation technologies.
DOI: http://doi.org/10.5281/zenodo.20675475
Explainable Deep Learning Architecture For Accurate Emphysema Detection In CT Imaging
Authors: Dr. Meghna Utmal, Ragini Gupta, Sonali Gupta, Khushboo Vishwakarma, Vandana Patel
Abstract: Emphysema, a prominent form of chronic obstructive pulmonary disease (COPD), has a substantial impact on lung function and overall patient well-being. Developing accurate and interpretable computer-aided diagnosis (CAD) systems for emphysema using computed tomography (CT) imaging remains challenging, primarily due to the inherent trade-off between predictive performance and model transparency. In this work, we introduce an interpretable deep learning framework for emphysema classification that integrates Convolutional Neural Networks (CNNs) with attention mechanisms and explainable artificial intelligence (XAI) approaches, including Grad-CAM and feature attribution mapping, to highlight critical pathological regions influencing the classification process. The proposed model was trained and validated on a carefully curated dataset of CT images with balanced emphysema severity levels. Experimental results show a classification accuracy of 97.6%, precision of 96.8%, and a root mean square error (RMSE) of 0.203, surpassing recent deep learning approaches by 5–8%. The incorporation of attention-based interpretability enhances clinical transparency by aligning the model’s salient activation regions with radiologist-identified emphysematous areas. This study presents a unified approach that combines explainable deep learning with attention-driven visualization for emphysema detection, achieving high diagnostic performance while improving interpretability and fostering greater clinical trust.
DOI: http://doi.org/10.5281/zenodo.20675710
Analyzing SkillNetAI: A Critical Study Of Intelligent Collaboration And Skill Matching
Authors: Adhithi Shetty, Gayatri Shewale, Ashutosh Kale
Abstract: In the contemporary landscape of digital education, learning ecosystems are predominantly confined to unidirectional models, wherein knowledge dissemination occurs without foster-ing authentic collaboration. Such paradigms, though effective in imparting theoretical foun-dations, are inherently inadequate in cultivating experiential learning, practical application, and interdisciplinary synergy. SkillNetAI emerges as a transformative paradigm, conceptu-alized as a peer-to-peer skill exchange and collaborative platform that redefines the dynam-ics of learning and creation. By enabling individuals to articulate both their proficiencies and aspirational competencies, the system facilitates reciprocal partnerships grounded in complementary skill sets. Leveraging an intelligent recommendation mechanism, SkillNetAI identifies optimal collaborators by evaluating multidimensional parameters such as skill congruence, exchange compatibility, availability, and credibility. Unlike traditional peda-gogical platforms, this framework transcends passive consumption by promoting co-creation, wherein learners collectively architect real-world projects that simultaneously en-hance practical expertise and expand professional portfolios. Beyond skill acquisition, SkillNetAI fosters a community-driven ecosystem anchored in trust, feedback, and continu-ous growth. In doing so, it not only democratizes access to learning but also reimagines education as a participatory, symbiotic process that seamlessly integrates knowledge ex-change with applied innovationists future research directions for building adaptive and multimodal transportation systems.
DOI: http://doi.org/10.5281/zenodo.20766745
AI-Integrated Smart Learning Frameworks For Developing Critical Thinking, Leadership, And Managerial Competencies In Business Management Education
Authors: Dr. Ansari Pulickal Abdul Azeez, Farooq Sajjad
Abstract: Contemporary organizations need skilled individuals in addition to having relevant knowledge. Some of these include critical thinking, adaptive leadership, and skillful decision making by man-agers. Nevertheless, current business training strategies which incorporate static analysis of cases and didactic instruction are inadequate for instilling such skills in the learner. Thus, the main objective of this paper is to develop and test an innovative AISL strategy which facilitates such development effectively. The AISL strategy incorporates the following three fundamental AI-driven learning modules: AI-Driven Cognitive Apprenticeship module for developing critical thinking skills through Socratic argumentation; Generative AI-Based Dynamic Case Simulations for developing leadership skills; and finally an AI-Driven Intelligent Competency Assessment Module. As revealed from the quasi-experimental field study (N=1,200) carried out in six differ-ent business settings over 16 months, AISL is more effective than traditional business learning modules for the development of critical thinking skills (Cohen's d = 0.82), leader self-efficacy (d = 0.91), and management decision making skills (d = 0.78).
DOI: https://doi.org/10.5281/zenodo.20701570
Study to Study to Evaluate the Impact of Awreness Intervention Program On Knowledge and Attitude Regarding Mental Health and Mental Illness Among Adolescence Students High School in Se-lected Area
Authors: Mr Ashish P Rao, Dr. Sheikh Javed Ahmad
Abstract: These Study on awareness Regarding psychiatric health and Mental illness among school Candi-dates on their knowledge and attitude regarding Psychiatric illness among adolescences students high school in selected area .Frequency and percentage to be computed for describing the sample characteristics. The knowledge score of degree college students in total content area be computed. The Practice score of Coefficient of correlation to be computed to determine the relationship be-tween intelligence and Attitude Chi-square test to Identify the association of awareness with selected variables.
RFID-Enabled IoT Inventory Automation for Smart Warehouses
Authors: Assistant Professor I.Maria Anand Milani, Assistant Professor Dhanusha Mol K P
Abstract: Managing inventory in warehouses continues to pose a significant challenge to supply chain efficiency, as conventional bar code technologies involve manual efforts, line of sight issues, and batch processing problems. In this study, we introduce a novel RFID-based IoT Inventory Au-tomation System (RIAS), which consists of passive UHF RFID tags, reader portal stations, automated drones equipped with RFID readers, and a system for analyzing information on the edge-cloud. We present two new approaches: (1) multi-antenna collision resolution (MACR), which solves the problem of tag collisions and ensures 99.7% tag read rate at 1200 tags per sec-ond and (2) anomaly detection and localization (ADL) approach, based on received signal strength indication trilateration for locating misplaced items to within 0.5m. Applied to 12 ware-houses over 14 months, RIAS saves 91.4% of cycle counting time, increases inventory accuracy from 86.2% to 99.1%, and locates misplaced items within 2.3 minutes after their appearance.
High-Efficiency DC-DC Converter for Portable Electronics
Authors: Ms.C.Santhini, Assistant Professor N Sudha
Abstract: In light of increasing demand for portable devices, there is an increased requirement for efficient power conversion schemes to enhance battery capacity and reduce power loss via heat dissipa-tion. In this paper, a new hybrid DC-DC topology is presented which comprises a Switched-Capacitor (SC) block coupled with a reconfigurable interleaved buck circuit that can reach a max-imum efficiency of 94.7% under a 1.8 W load. The power supply designed for battery-operated systems, powered by Li-ion batteries (2.7V-4.2V input voltage), delivers a stable 1.2V output with low quiescent current consumption of only 18 µA. The power loss evaluation is extensively covered. When compared with three modern converters designed between 2022 and 2025, the developed converter shows higher efficiency levels at lower loads (88.2% at 10mA) and reduced settling time of 6.5 µs.
Low power CMOS Design Techniques for Portable Devices
Authors: Assistant Professor Dr.A.Sathiya, Assistant Professor Jestadi Ramesh Babu
Abstract: The growing requirement for increased battery capacity in mobile devices like smartwatches, wireless headphones, and medical patches calls for a high level of aggressiveness with respect to power saving at both circuit and architecture levels. In this paper, we introduce a comprehensive approach that incorporates Adaptive Voltage Scaling (AVS), MTCMOS-based power gating, and Clock Gating along with an efficient power management unit (PMU) fabricated using the 65 nm CMOS technology. Our design exhibits an overall dynamic power savings of 68% when operating at a frequency of 25 MHz and a leakage power savings of 94% during sleep mode against the traditional single-Vt architecture. An SoC based simulation setup of an ECG patch is presented with active mode current consumption of 320 µA and 0.6 V and sleep mode current consumption of 45 nA.
The Economic Impact of UPI Transactions on Small Business Growth
Authors: Associate Professor Dr.Punitha G, Professor Dr. Shobha B. Hangarki
Abstract: UPI can be viewed as a disruptive technology in India's digital economy, which holds the poten-tial to positively contribute to the growth of small businesses. In this context, this research paper attempts to analyze the economic effects of UPI usage on MSMEs using quantitative methods. The data used includes information regarding transaction volumes, financial performance indica-tors, and indicators of financial inclusion based on surveys conducted among 192 informal ven-dors in Bangalore and 10,000 MSMEs in the country. Using regression analysis and forecasting using ARIMA models, we explore the connection between the intensity of UPI adoption and its effects on the economic performance of small businesses. According to our results, businesses using UPI report an increase in profits by 34.2%, while high levels of UPI usage in district areas are linked to a fourfold increase in business loans.
Agentic AI Strategy As A Dynamic Capability: How Autonomous Systems Reshape Enterprise Transformation
Authors: Navya Sri Maddukuri
Abstract: As organizations transition from generative AI experimentation to agentic AI deployment, traditional frameworks for AI strategy have become structurally insufficient. This study conceptualizes agentic AI strategy as a dynamic capability through which firms systemati-cally sense automation opportunities, seize value through autonomous multi-step work-flows, and reconfigure governance, talent, and data infrastructures to sustain competitive advantage. Employing a longitudinal mixed-methods design — integrating annual-report text mining, AI investment announcements, patent data, and executive interviews from 312 large public firms across seven industry sectors between 2021 and 2026 — the study devel-ops and validates an Agentic AI Strategic Maturity Index (AAMI). Structural equation mod-eling confirms that integrated agentic AI strategies are associated with significantly higher operational performance (β = 0.35, p < .001) and revenue growth (β = 0.29, p < .001) com-pared to fragmented AI tool adoption. Qualitative analysis of 42 executive interviews re-veals five dominant strategic challenges: orchestration complexity, governance lag, talent asymmetry, value attribution difficulty, and cultural resistance to human-AI teaming. The paper advances a novel theory of autonomous digital transformation, provides empirical evidence on AI-driven competitive advantage, and offers actionable strategic guidance for executives managing enterprise-wide AI agents. Findings suggest that agentic AI maturity, not mere AI investment intensity, is the pivotal differentiator of sustained enterprise perfor-mance in the post-generative AI era.
DOI: http://doi.org/10.5281/zenodo.20837491
Explainable Multi-Modal Deep Learning Frame-work for Intelligent Healthcare Diagnosis
Authors: Assistant Professor Ramya S, Assistant Professor I. R. Suganya
Abstract: Opacity in deep learning models poses a significant challenge in adopting AI models in healthcare as decisions need to be transparent. In this study, we propose XAI-MedFusion, a deep learning explainable multi-modal framework to enable intelligence in the diagnosis of diseases. Our pro-posed XAI-MedFusion is a hierarchal framework that combines the inputs from medical imaging, electronic health records (EHR), and genomics with explainability. We use modality-specific encoders such as CNNs for images, Transformers for EHRs, and GNNs for genomics. We use cross-modal attention in our system to learn how to aggregate information across the modalities. Finally, we utilize explainability methods such as SHAP, LIME, and Grad-CAM with an aggre-gation approach. We validate our framework using Alzheimer’s and Parkinson’s disease data, achieving a classification accuracy of 94.2% compared to the unimodal approaches (12.8% high-er). Clinically relevant interpretations were obtained that match the known biological markers. Moreover, uncertainty quantification was effective in our model along with increased clinician trust (8.4/10).
MID Meter PCB Soldering – Dual Heater
Authors: Atharv Nikam, Saurabh Jagtap, Priti Choudhari, Gaurav Uphade, Abhijit Lohakane
Abstract: Shunt soldering in MID (Measuring Instrument Directive) meter printed circuit boards (PCBs) is a critical manufacturing operation that directly affects electrical accuracy, reliability, and produc-tion throughput. Conventional manual soldering of multiple shunts is time-consuming and prone to inconsistency due to operator dependency. This paper presents the design and implementation of an automated dual-heater shunt soldering system for MID meter PCBs, capable of soldering four shunts per PCB with high precision and repeatability. The proposed system integrates a fixture-based automation mechanism controlled by a PLC, employing three pneumatic cylinders and a stepper motor–driven linear positioning system. One pneumatic cylinder securely clamps the PCB, while two heating cylinders equipped with soldering gun tips simultaneously heat both ends of the shunt to ensure uniform solder joints. The stepper motor indexes the fixture in four discrete steps of 17 mm, covering a total travel of 51 mm, followed by automatic homing. At each position, solder wire is applied and heated automatically. Experimental evaluation demonstrates significant improvements in cycle time, soldering precision, and process repeatability, making the system suitable for industrial MID meter production and scalable automation environments.
Semantic Intelligence For Crime Type Prediction In Smart Policing Systems
Authors: Bhagyashri Kasar, Ranjana Dahake
Abstract: Conventional predictive policing methods struggle to handle the vast and diverse data gen-erated by urban crime environments. Rule-based and traditional machine-learning models often struggle to identify contextual significance, adapt to evolving crime patterns, and maintain stable performance when faced with data imbalances. The use of large language models makes semantic intelligence a revolutionary concept, as it goes beyond the mere structured attributes and gains the reasoning that is deciphered. The current research uncov-ers a model of smart policing that is aware of language and is able to predict the types of crimes accurately with the help of the semantic understanding acquired from old incident records, spatial-temporal features, and text descriptions. The methodology investigates prompt-driven reasoning strategies like zero-shot, few-shot, and task-adaptive inference to recognize crimes without needing a lot of retraining or manual feature engineering. Com-parative analysis with traditional predictive models provided insights regarding the ad-vancements in adaptability, interpretability, and minority class recognition. The findings indicate that the use of semantics in the form of intelligence has enhanced the prediction of crime types and made support for public security operations more flexible, scalable, and context-sensitive. Linguistic-based crime analytics can significantly assist police agencies in their efforts to anticipate incidents, allocate manpower effectively, and implement data-driven policing strategies in various urban areas.
DOI: http://doi.org/10.5281/zenodo.20848323
A Review On Deep Learning-Based Multi-Disease Eye Detection And Classification Using Fundus Images.
Authors: Tukaram C. Bhoye, S. V. Gumaste
Abstract: Eye diseases such as diabetic retinopathy, glaucoma, and cataract are among the leading causes of visual impairment worldwide. Early diagnosis is crucial to prevent irreversible vision loss, particularly in areas with limited access to specialized ophthalmic care. Recent progress in deep learning (DL) has enabled automated analysis of retinal fundus images, facilitating faster and more accurate detection of eye-related disorders. This paper presents a comprehensive review of deep learning-based approaches for multi-disease eye detection and classification using fundus images. Various techniques, including convolutional neural networks (CNNs), transfer learning methods, transformer-based architectures, and hybrid models, are examined. The study also reviews commonly used retinal datasets, evaluation metrics, and comparative performance of different approaches. In addition, key challenges such as dataset imbalance, limited generalization, and lack of model interpretability are discussed. Future research directions, including the use of explainable AI, lightweight mod-els for edge deployment, and privacy-preserving techniques, are highlighted. The findings of this review provide valuable insights into current developments and support the ad-vancement of reliable and clinically applicable AI-based ophthalmic diagnostic systems.
DOI: http://doi.org/10.5281/zenodo.20848646
Deep Learning Driven Gastrointestinal Disease Diagnosis From WCE Images: A Hybrid Approach
Authors: Kaveri Rajaram Bhosle, Prashant M Yawalkar
Abstract: Wireless Capsule Endoscopy (WCE) has revolution-ized gastrointestinal (GI) diagnostics by enabling non-invasive visualization of the entire digestive tract. Despite its clinical ad-vantages, a single WCE examination produces tens of thou-sands of image frames, making manual analysis time-consuming and prone to inter-observer variability. In recent years, arti-ficial intelligence—particularly deep learning—has emerged as a powerful tool for au-tomated GI disease classification. This review presents a comprehensive analysis of existing approaches for WCE-based GI image analysis, including traditional ma-chine learning methods, convolutional neural networks (CNNs), transformer-based architectures, and hy-brid models. The paper critically examines commonly used benchmark datasets such as Kvasir, Kvasir-Capsule, HyperKvasir, and WCECCD, along with evaluation metrics and optimization strategies adopted in recent studies. Furthermore, the role of explainable artifi-cial intelligence techniques, including attention mechanisms and Grad-CAM, is discussed in enhancing model interpretability and clinical trust. Key challenges such as class imbalance, limited annotated data, cross-dataset generalization, and real-time deployment constraints are identified. Finally, emerging research directions including multimodal learning, domain adaptation, temporal video modeling, and foundation models for medical imaging are out-lined. This review aims to provide researchers and clinicians with a structured understand-ing of current advancements and future opportunities in automated GI disease diagnosis.
DOI: http://doi.org/10.5281/zenodo.20848747
AI-Based Drop-Out Prediction and Counselling System
Authors: Bharadwaj Patil, Mohammed Zaid Pathan, Om Devkar, Professor Nilesh Ahire
Abstract: Student dropout is a critical challenge in educational institutions worldwide, resulting in signifi-cant social, economic, and academic consequences. This paper presents an AI-Based Drop-Out Prediction and Counselling System that leverages machine learning algorithms to proactively identify students who are at danger and offer timely automated counselling inteventions. The suggested system combines several data sources.— including academic performance, attend-ance records, socio-economic indicators, and behavioral patterns — to build predictive models using algorithms such as Random Forest, Gradient Boosting (XGBoost), Support Vector Ma-chine (SVM), Logistic Regression, and an Artificial Neural Network (ANN). The system achieves a prediction accuracy of 94.7%, a precision of 93.2%, recall of 95.1%, and an F1-score of 94.1% on the validation dataset. An intelligent counselling module is also designed to provide personalized, AI-driven recommendations to students flagged as high risk. Experimental results on a dataset of 5,000 student records demonstrate the superiority of the suggested strategy over existing baseline methods. The system is designed as a web-based platform accessible to adminis-trators, faculty, and counsellors, enabling real-time monitoring and intervention
A Review of Epileptic Seizure Prediction Using EEG, ECG, PPG and EMG Signals
Authors: Research Scholar Praveen A. Andhale, Professor Dr. Varsha H. Patil
Abstract: Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal electri-cal activity in the brain. Predicting seizures before their onset can sig-nificantly improve patient safety and enable timely medical intervention. Recent advancements in wearable biosensors and artificial intelligence have enabled continuous monitoring of physiological signals in real-world environments. This paper presents a survey of epileptic seizure prediction approaches based on physiological signals including electroencephalography (EEG), electrocardiography (ECG), pho-toplethysmography (PPG), and electromyography (EMG). The study summarizes commonly used public datasets such as CHB-MIT, Bonn EEG, and the Temple University EEG corpus that are widely used to develop and evaluate prediction algorithms. Furthermore, traditional machine learning approaches as well as recent deep learning architectures for biomedical signal analysis are reviewed. The review also highlights recent progress in wearable monitoring systems and multimodal signal fusion strategies. Finally, major research chal-lenges including limited datasets, signal noise, patient variability, and large number of false alerts are analyzed, and future research directions such as multimodal sensing, personalized prediction models, wearable AI systems, and explainable artificial intelligence are outlined.
AVIA: A Multi-Agent Multimodal Virtual Assis-tant for Autonomous Digital Task Automation
Authors: Hitesh Vinod Dadlani, Ayaan Badshah Khan, Munaf Irfan Shaikh, Professor Jayshri Kandekar
Abstract: The rapid advancement of Large Language Models (LLMs) has significantly expanded the capa-bilities of intelligent software agents. Traditional virtual assistants are limited to reactive query-response interactions and lack persistent memory, flexible tool integration, and autonomous task execution. This paper presents AVIA (Autonomous Virtual Intelligent Assistant), a locally-hosted multi-agent multimodal assistant designed for autonomous digital task automation through an extensible skill-based architecture. AVIA integrates LLM reasoning with a persistent mark-down-based memory system, a modular skill execution framework, and proactive scheduling via heartbeat loops and cron-based task management. The system supports multimodal interaction through text and voice interfaces and integrates with external platforms including email, calendar, document management, and social media. A local-first design philosophy ensures user privacy and independence from cloud infrastructure. Experimental evaluation across five task cate-gories demonstrates a Task Completion Rate (TCR) of 91.3%, an Automation Success Rate (ASR) of 88.6%, mean response latency of 2.8 s, and memory retrieval accuracy exceeding 93%, validating AVIA as a flexible, privacy-preserving foundation for next-generation intelligent personal assis-tants.
Neural Codec Language Models for Unified Speech Generation and Transformation: A Review
Authors: Aboli Ashok Ugale, Associate Professor Vijay B. More
Abstract: Neural codec language models (NCLMs) have re- cently emerged as a powerful paradigm for unified speech generation and transformation. By modeling discrete acoustic tokens extracted from neural audio codecs, these systems enable scalable solutions for text-to-speech (TTS), voice conversion, speech enhancement, and editing within a single generative framework. This paper presents a comprehensive review of representative models including AudioLM, VALL-E, Voice-box, NaturalSpeech 2, and SpeechX, analyzing their architectural design, probabilistic modeling strategies, computational complex- ity, and task generalization capabilities. A comparative study highlights the tradeoff between perceptual quality and inference efficiency across autoregressive and diffusion-based approaches. Furthermore, existing research gaps in discrete representation fidelity, evaluation standardization, and multi-task optimization are identified. Finally, a conceptu-al extension termed SpeechX++ is discussed to address limitations through emotion conditioning, multilingual adaptation, and efficient inference strategies. The review demonstrates the ongoing transition toward general- purpose speech foundation models capable of robust, scalable, and ethically responsible deployment.
An AI-Driven Personalized Learning Pace Opti-mizer Using Reinforcement Learning And Self-Paced Curriculum Design
Authors: Sara Saroj Pathan, Mrunal Jitendra Palaskar, Chetan Hiraman Satpute, S. N. Jadhav
Abstract: The rapid expansion of e-learning platforms has enabled large-scale access to education; however, most existing systems continue to employ static content pacing strategies that fail to accommodate individual learner differences. Such one- size-fits-all approaches often result in learner disen-gagement, inefficient knowledge acquisition, and high dropout rates. This work presents an AI-driven personalized learning pace optimizer that integrates Reinforcement Learning (RL) with Self-Paced Learning (SPL) to dynamically adapt instructional pacing based on a learner’s evolv-ing knowledge state. SPL is used to structure educational content from easy to hard, providing pedagogical stability and robustness to noisy learner data, while RL models the pacing decision as a sequential optimization problem. A multi-objective reward formulation is adopted to balance learner engagement, knowledge retention, and learning efficiency. The proposed approach pro-vides a technically robust and pedagogi- cally safe architecture for adaptive e-learning systems and serves as a foundation for future real-world deployment and evaluation.
DOI: http://doi.org/10.5281/zenodo.20927889
Performance Analysis Of Traditional And IoT-Based Irrigation Systems For Pomegranate Crops
Authors: Priyanka Agnihotri, Utsav Bhapkar, Madhuri Nimse, Aniket Nagare, Tanish Torpe, Vaibhav Kothawade
Abstract: Pomegranate cultivation in many regions of India continues to rely on traditional flood irrigation because of its simplicity and low initial cost. However, this method often leads to excessive water use, non-uniform soil moisture distribution, and nutrient losses through runoff and deep percola-tion. These conditions adversely affect root health and increase the incidence of fruit cracking, reduced fruit size, and yield variability. To overcome these limitations, drip irrigation has emerged as a more efficient alternative for pomegranate farming. Drip irrigation delivers water directly to the root zone at a controlled rate, allowing irrigation to closely match crop water requirements and maintain soil moisture within the optimal range. Uniform moisture supply during critical stages such as flowering and fruit development reduces moisture fluctuations, improves nutrient uptake, and enhances plant vigor. Field observations indicate that drip irrigation can reduce water con-sumption by 35–45%, lower energy use, and decrease labor requirements compared to flood irrigation. Overall, the adoption of drip irrigation improves water-use efficiency, yield stability, and sustainability of pomegranate production under water-limited conditions.
DOI: http://doi.org/10.5281/zenodo.20927977
Exploring The Role Of NF-MQL In Sustainable Machining Of Challenging Materials: A Review
Authors: Amol P Vadnere, Shyamkumar D Kalpande, Nandkishor O Warbhe, Sachin B Mahale
Abstract: Modern manufacturing industries are increasingly focused on achieving high quality, dimensional accuracy, surface finish, production efficiency, and cost reduction while minimizing environmen-tal impact. Cutting fluids play a vital role in machining operations by cooling the cutting tool and workpiece, and by removing chips from the heat-affected zone. However, in high-speed machin-ing, conventional fluid applications often fail to dissipate heat effectively, and their improper use and disposal adversely affect both human health and the environment. The Minimum Quantity Lubrication (MQL) technique offers a viable alternative, achieving effective cooling and lubrica-tion using minimal quantities of lubricant. Recent studies have shown that combining MQL with nanofluids (NF-MQL) significantly enhances machinability and sustainability, particularly in the case of challenging materials. This paper presents a comprehensive review of the research on NF-MQL-assisted machining processes. The study concludes by identifying research gaps and pro-posing directions for future work in the field of sustainable machining.
DOI: http://doi.org/10.5281/zenodo.20928158
Joint Contrastive Representation Learning For Road Networks And Trajectory Data: A Review
Authors: Shweta Santosh Bhoye, Namrata D. Ghuse
Abstract: Road network structures and trajectory data are essential components of intelligent transportation systems (ITS), as they represent spatial infrastructure and temporal movement patterns, respec-tively. While road networks capture structural relationships and contextual information, trajectory data re- flects dynamic mobility behavior over time. Recent research has increasingly focused on integrating these two data sources using self-supervised and contrastive learning techniques to produce unified and meaningful representations. This paper presents a comprehensive review of joint contrastive representa- tion learning methods that model both intra-domain relationships (road–road and trajectory–trajectory) and inter-domain interac- tions (road–trajectory). Findings reported across multiple real- world mobility datasets indicate that these approaches achieve im-proved performance in tasks such as traffic prediction, route optimization, and trajectory similarity analysis compared to tra- ditional non-contrastive methods. In addition, this study examines commonly used evaluation strategies, highlights scalability and ethical challenges, and suggests future research directions for building adaptive and multimodal transportation systems.
DOI: http://doi.org/10.5281/zenodo.20929314
Secret Chat Room With AI Summarization System
Authors: Jayshree Pansare, Karan Singh, Affan Ali Sayyed, Rushikesh Langhi, Prathamesh Dive
Abstract: The rapid expansion of digital communication platforms has significantly increased the need for secure and efficient messaging systems. Modern users rely heavily on chat-based applications for academic collaboration, professional coordination, and personal communication. However, tradi-tional messaging systems often fail to provide an optimal balance between data security and effi-cient information management. While some platforms emphasize usability, they frequently com-promise on privacy, whereas others focus on encryption but lack intelligent tools to manage large volumes of conversational data. This research presents a Secret Chat Room with AI Summariza-tion System, a web-based platform designed to address both security and usability challenges. The system integrates end-to-end encryption using AES and RSA algorithms to ensure confiden-tiality and protect messages from unauthorized access. Additionally, it employs WebSocket-based real-time communication to enable low-latency and efficient message exchange between users. A key contribution of this work is the integration of an AI-based summarization module that utilizes transformer-based model Gemini Summarization. This module processes chat histories and gen-erates concise summaries, allowing users to quickly understand lengthy discussions without manually reviewing all messages. This feature significantly reduces information overload and enhances productivity. The system follows a modular architecture consisting of authentication, encryption, messaging, and AI components. Experimental observations indicate that the system achieves efficient performance with minimal latency while maintaining strong security standards. The proposed solution is suitable for applications in education, enterprise communication, and collaborative environments.
DOI: http://doi.org/10.5281/zenodo.20954229
Metadata-Driven Data Governance Models for En-terprise Information Systems: A Strategic Frame-work for Data Lineage, Compliance, and Govern-ance
Authors: Associate Professor Sophie J. Bell, Professor Melissa A. Stewart, Associate Professor Eleanor F. Ross, Professor Aaron D. Hughes, Chaitanya Srinivas, Sai Nishil
Abstract: Metadata has become a foundational component of modern enterprise information systems by enabling organizations to effectively manage data assets, improve transparency, ensure regulatory compliance, and support data-driven decision-making. As enterprises increasingly operate across distributed, cloud-based, and hybrid data environments, the need for robust metadata-driven data governance models has grown significantly. This research presents a strategic framework for metadata-driven data governance that integrates data lineage, compliance management, and gov-ernance practices to enhance the reliability, accessibility, and security of enterprise information systems. The study examines the role of metadata in documenting data origins, transformations, ownership, relationships, and lifecycle management while facilitating end-to-end data traceability across complex enterprise architectures. It further explores governance mechanisms that incorpo-rate metadata repositories, business glossaries, data catalogs, stewardship, policy enforcement, and automated compliance monitoring to improve organizational accountability and regulatory adherence. The proposed framework emphasizes the integration of metadata management with data lineage visualization, master data management, data quality engineering, cloud computing, artificial intelligence, and enterprise analytics to establish a unified governance ecosystem. In addition, the research discusses the application of automation, machine learning, and intelligent metadata discovery techniques for continuous governance, impact analysis, risk assessment, and operational optimization. By implementing comprehensive metadata-driven governance models, organizations can improve data consistency, strengthen compliance with regulatory standards, enhance business intelligence capabilities, and increase trust in enterprise information assets. The findings demonstrate that metadata-driven governance serves as a strategic enabler of digital transformation by supporting scalable enterprise architectures, improving information transparen-cy, reducing governance risks, and fostering sustainable data management practices within large-scale enterprise information systems.
Letting Light Lead: The Role of Natural Light in Shaping Contemplative Space in Buddhist Monastery Architecture
Authors: Praniya Anil Lingayat, Under the Guidance of Prof. Dilip Jade, Prof. Radhika Raut
Abstract: Contemporary meditation and monastic buildings increasingly rely on standard glazing and artifi-cial illumination borrowed from ordinary institutional design, and in doing so they often lose the very quality that historically made monastic space feel sacred: a slow, changing, self-revealing daylight. This paper looks at what can be called the “Luminous Envelope” in Buddhist monastery design, and asks how the deliberate shaping of roofs, screens, and courtyards to filter and funnel natural light can support meditation, mark the passage of time, and give built form to core Bud-dhist ideas such as impermanence and inner clarity. The study connects historic precedent — the single, sun-tracking apertures of rock-cut chaitya halls and the deep, thick-walled windows of Himalayan monasteries — with present-day design practice, using the Upper Cloister in Aranya, Golden Mountain (designed by Atelier Deshaus, China, completed 2022) as its primary case study. The paper builds a qualitative, non-mathematical design framework that final-year architec-ture students and practitioners can apply when shaping daylight-responsive monastic and medita-tion spaces.
The Haptic Envelope: Evaluating Material Honesty and Earthy Textures in Non-Institutional Palliative Facades
Authors: Rutuja Nitin. Raipure, Under the Guidance of Prof. Gulfam B. Shaikh, Prof. Malini O. Nathe
Abstract: Most institutional palliative care centres end up making patients more anxious instead of less, mainly because of the stark, sterile look of their building envelopes. This paper looks at what I am calling "The Haptic Envelope" in the context of non-institutional hospice design, and tries to understand how macro-textured, natural building skins can help patients feel more grounded and emotionally secure. The study connects the idea of material honesty — specifically the deliberate use of exposed brick masonry and raw travertine texture — with the broader concept of saluto-genic, or health-promoting, architecture. Looking back at Indian vernacular construction, where raw earth and stone were used to represent stability and grounding (the Prithvi element), the paper builds a qualitative design framework that can be applied to present-day healthcare facades. It also studies how these macro-textures respond to changing daylight to create softer, glare-free visual environments that reduce sensory agitation for patients. The Benziger Hospice Home by Srijit Srinivas Architects is used as the primary case study, since it is a real, award-winning example of unplastered brick being used to turn a healthcare building into a calmer, more earth-bound space.
The Transition Threshold: Designing Spatial Buffers Between Special Education and Vocational Skill Training in Inclusive Learning Centres
Authors: Sahil Chandnani, Under the Guidance of Prof. Anand Pande, Prof. Sudhir V. Dhomane
Abstract: Inclusive education in India increasingly aims to carry a student with a disability from a protected special-education classroom into a functioning vocational identity, yet the architecture of most special schools stops designing at the classroom door and rarely gives thought to the space in between. This paper studies what is termed here “The Transition Threshold” — the corridor, courtyard, or buffer zone that physically and psychologically carries a learner from a slow-paced special-education wing into an open vocational-training wing where real tools, real deadlines, and real co-workers exist. The paper builds a qualitative framework connecting barrier-free circula-tion, graded independence, and sensory buffering, working from the position that architecture itself can rehearse a student for the transition into work. DISHA, A Resource Centre for the Disabled in Jaipur, designed by Ashok B. Lall Architects, is used as the primary case study, since its central courtyard and encircling ramp system is a documented, built precedent for exactly this kind of graded, self-paced movement between special-education classrooms and workshop-style activity spaces.
Modified Teacher Learning Algorithm Based Privacy Preserving Of Student Data For Grade Prediction
Authors: Sharjil Iqbal
Abstract: In educational organizations, early evaluation prediction is a significant place of enthusiasm as it permits educators to improve performance in their courses by giving uncommon consideration at the beginning stages. This paper has proposed a model that identifies the effective features from students data for grade prediction. Paper has hide the sensitive information in the dataset to provide the privacy for the student and organization data. Feature selection is done by Modified Teacher Learning Algorithm. Work has utilized the selected feature for the training of error back propagation model for grade prediction. Experiment was done on real dataset and result shows that proposed work has improved the grade prediction accuracy.
DOI: http://doi.org/10.5281/zenodo.21279545
