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
