Authors: Jeevanantham D, Associate Professor Dr.S.Kapilan

Abstract: In many rural regions of India, low-level causeways serve as critical links between villages but often become unusable during monsoon seasons, leading to disruptions in connectivity, economic loss, and safety hazards. This study presents a techno-economic feasibility analysis focused on converting the existing causeway at Servaikaranpalayam village into a high-level bridge. The research emphasizes the real-life challenges faced by local residents due to traffic congestion, seasonal flooding, and inadequate infrastructure. Through a detailed case study, the project evaluates the design considerations specific to rural needs, including cost-effective materials, hydrological conditions, and traffic volume analysis. The proposed bridge is designed following relevant IRC and IS codes to ensure durability, flood resilience, and minimal maintenance. In addition to addressing transportation bottlenecks, the study highlights how improved connectivity fosters local economic growth, access to services, and enhanced quality of life. The findings demonstrate that a high-level bridge not only offers technical viability but also provides long-term socio-economic benefits for rural development.

Published by: vikaspatanker

Authors: L. Rahul Chandra, Kekkarla Madhu, V. Shirisha, Atla Sonya

 

 

Abstract: The future of loan approvals is increasingly driven by Artificial Intelligence (AI), offering faster and data-informed decisions. However, traditional machine learning (ML) models often lack transparency, making them unsuitable for high-stakes financial decisions. This paper presents an Explainable AI (XAI) framework based on a Belief Rule Base (BRB) to automate and enhance the loan underwriting process. The BRB model combines expert knowledge with supervised learning and supports both factual and heuristic rules within a hierarchical structure. The system provides clear, interpretable explanations by highlighting activated rules and the influence of input attributes, ensuring transparency and regulatory compliance. A case study on mortgage underwriting demonstrates the model’s ability to balance accuracy with explainability, outperforming conventional black-box approaches in trust and interpretability. This work underscores the potential of XAI to shape a fairer, more transparent future for automated loan approvals.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Poornima, Divya k, Assistant Professor Mrs. Parameswari R

Abstract: The global rise in mental health challenges has underscored the urgent need for accessible, affordable, and stigma-free psychological support systems. In response, this study presents a comprehensive Psychological Support website, a mobile-based solution developed to assist individuals in managing their mental well-being. The application functions as a virtual mental health companion, offering features such as mood tracking, AI-driven chatbot interactions, guided self-help resources & journaling tool. By leveraging cognitive behavioral therapy (CBT) principles and modern web technologies, the application aims to create an engaging and empathetic environment that promotes daily emotional awareness and self-care. It also includes quick access to emergency helplines for immediate support during crises. The system is designed with a focus on user privacy, simplicity, and customizability, ensuring it can adapt to individual needs and preferences. Overall, the proposed system bridges the gap between the growing demand for psychological support and the limited availability of traditional mental health services, particularly in underserved or remote areas. It serves as a proactive tool to promote mental wellness and resilience in today’s fast-paced, digitally connected world.

Published by: vikaspatanker

Authors: Manoj Parasa

Abstract: Exit interviews are an underutilized but critical tool for capturing organizational feedback, yet traditional analysis methods often fail to generate meaningful insights. This study investigates the application of artificial intelligence—specifically natural language processing, sentiment analysis, and topic modeling—to interpret qualitative exit interview data within SAP SuccessFactors. Using a mixed-methods design and data extracted from a large multinational enterprise over an 18-month period, the research reveals latent patterns in attrition reasons, identifies hidden organizational issues, and proposes actionable insights for HR leadership. Results demonstrate that AI-enhanced exit analytics uncover unstructured feedback trends more reliably than manual reviews, with significantly higher accuracy in detecting dissatisfaction themes. This paper contributes to social science research by positioning exit interviews as institutional diagnostic tools, offering a predictive lens into workforce behavior. The study concludes by recommending an integrative model for AI-powered offboarding intelligence that can be replicated across enterprise HR platforms.

DOI: https://doi.org/10.5281/zenodo.15718618

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Authors: Manoj Parasa

Abstract: Enterprise time and attendance tracking systems have historically relied on manual inputs and fragmented technologies, creating vulnerabilities in compliance, payroll accuracy, and employee trust. This paper investigates the integration of biometric Internet of Things (IoT) devices—including fingerprint scanners, RFID terminals, and facial recognition sensors—into SAP SuccessFactors Time Management using SAP Cloud Platform Integration (CPI). A mixed-methods methodology is applied, encompassing middleware simulation, data flow prototyping, and three real-world case studies across manufacturing, logistics, and healthcare. Quantitative results reveal a 27% improvement in attendance data accuracy and a 32% reduction in payroll discrepancies. Compliance audit readiness improved by 50% following integration. Drawing from interdisciplinary literature in IoT middleware, biometric security, and enterprise HRIS architecture, this study provides a scalable and secure blueprint for intelligent workforce time tracking.

DOI: https://doi.org/10.5281/zenodo.15718647

Published by: vikaspatanker

Authors: Alim Shaikh, Dr.Santosh Gaikwad, Dr. A. A. Khan, Dr. R. S. Deshpande

 

Abstract: Speech-based interaction with large lan- guage models (LLMs) is revolutionizing human-computer communication by enabling natural, voice-driven inter- faces. This study explores methods to prompt LLMs through automatic speech recognition (ASR) while ad- dressing challenges such as transcription errors, noise interference, latency, and prompt optimization. The proposed framework integrates ASR with LLMs using noise reduction, structured prompt engineering, and contextual adaptation. Experimental evaluations using models like OpenAI Whisper and GPT-4 demonstrate improvements in performance metrics such as Word Error Rate (WER) and response latency. Applications span healthcare, accessibility, and customer support, and future work will focus on expanding multimodal capabilities and enhancing ethical and energy efficiency aspects.

DOI: http://doi.org/

 

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Authors: Swaranjali Santosh Wakure, Pooja Ambadas Hiwale, Professor Priyanka Ikhar

Abstract: River water pollution, particularly from floating debris such as plastics, poses a significant threat to aquatic ecosystems and public health. This paper presents a river water trash collector system that integrates Bluetooth technology and software for real- time monitoring and autonomous waste collection. The system consists of a floating collection device equipped with Bluetooth-enabled sensors and actuators, allowing it to autonomously detect and capture floating debris while communicating with a central control unit. The software platform allows remote monitoring, status updates, and real-time data collection via a user-friendly interface, providing valuable insights into waste types, quantities, and collection efficiency. The Bluetooth technology enables seamless communication between multiple collectors deployed in different river locations, facilitating coordinated waste collection efforts. Field tests conducted in various river environments demonstrate the effectiveness of the system in reducing floating debris, improving water quality, and offering scalability for large-scale implementations. This paper also explores the challenges of real-time data transmission, battery management, and system integration, highlighting the potential of this Bluetooth- based solution in advancing sustainable river management and contributing to global water pollution mitigation efforts.

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Authors: Hemant, Assistant Professor Mr. Ravi Kumar, Dr. Rajendra Khatana

Abstract: Cybersecurity is a critical pillar of the digital era, evolving in complexity and importance as technology advances. This paper explores the future of cybersecurity through three main lenses: emerging opportunities, ongoing and future challenges, and the ethical implications of increasingly sophisticated defense and attack capabilities. Drawing on current trends in artificial intelligence, quantum computing, data privacy, and cyber warfare, the paper identifies key areas for innovation and concern. It aims to provide a roadmap for navigating the cybersecurity landscape of the next decade while emphasizing the need for proactive governance, ethical standards, and global cooperation.

Published by: vikaspatanker

Authors: Ashumati Dahiwadakar

Abstract: Breast cancer (BC) is the most prominent form of cancer among females all over the world. Breast cancer develops from breast cells and is considered a leading cause of death in women. Breast cancer develops from breast cells and is a frequent malignancy in females worldwide. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k- nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques.

 

 

Published by: vikaspatanker

Authors: Diya Manoj, Dr. Asha K

Abstract: Electrocardiography (ECG) is an essential tool for diagnosing heart conditions, yet traditional manual interpretation takes more time and is subject to variability among experts. This research explores the application of deep learning techniques to automate ECG classification, aiming to enhance diagnostic correctness and speed and reliability. Using a dataset comprising 120,000 ECG images, a deep learning model based on the ResNet18 architecture was developed to categorize ECG signals into four classes: Myocardial Infarction, Abnormal Heartbeat, History of Myocardial Infarction (MI), and Normal. The study involved extensive pre-processing of ECG images, including normalization, augmentation, and noise reduction techniques to improve data quality. An exploratory data analysis (EDA) phase was conducted to visualize class distributions and identify potential challenges such as class imbalance. The model was trained for 40 epochs, achieving a training correctness of 99.85% and a best test correctness of 76.85%. Evaluation metrics such as precision, recall, and F1-score were used to assess performance, with confusion matrices revealing areas of improvement. Despite promising results, challenges such as class imbalances, overfitting, and the difficulty of distinguishing similar ECG patterns were encountered. Strategies such as weighted loss functions, dropout layers, and hyperparameter tuning were employed to mitigate these issues. The study concludes that deep learning models can serve as effective tools for ECG classification, providing a foundation for real-time clinical applications. Future work will focus on dataset expansion, model generalization, and real- time deployment to facilitate broader adoption in healthcare settings.

 

 

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