Authors: Ragini Shivashetti, Nikita Waghamare, Pranita Bhosale, Namrata Shinde, Pranoti Hukkire, Professor Ms. Savita Kadam

Abstract: An online booking system is a web-based platform that automates scheduling and reservations, allowing customers to book services or events (like movies, appointments, or travel) 24/7, while providing administrators tools to manage availability, bookings, and payments efficiently, reducing manual work and improving customer experience through features like user registration, seat selection, payment integration, and real-time confirmations.

Published by: vikaspatanker

Authors: Nagender Yamsani

Abstract: This study examines how enterprise master data design can be operationalized as a primary mechanism for regulatory governance within highly regulated financial environments. The research addresses a persistent industry challenge where regulatory obligations such as OFAC screening, customer due diligence, and personal data protection are often implemented as isolated compliance processes rather than embedded into core data architectures. The purpose of this work is to demonstrate how governance-first master data management can translate regulatory intent into enforceable, auditable, and scalable enterprise controls. Using a qualitative case-based methodology grounded in architectural analysis, control mapping, and operating model assessment, the study evaluates how regulatory requirements are structurally realized through master data domains, stewardship workflows, validation checkpoints, and exception handling mechanisms. The findings show that treating master data as a governed control layer enables consistent regulatory enforcement across operational systems, reduces manual remediation cycles, and strengthens audit readiness. The study further highlights how clear ownership models, policy-driven data validation, and controlled synchronization patterns contribute to sustained compliance without constraining business operations. From an academic perspective, the research extends governance and information systems literature by positioning master data architecture as a regulatory execution instrument rather than a purely technical capability. From an industry standpoint, the study provides practical guidance for financial institutions seeking to embed compliance obligations directly into enterprise data foundations, reinforcing trust, transparency, and operational resilience.

DOI: http://doi.org/10.5281/zenodo.19019592

Published by: vikaspatanker

Authors: Sri Raghuvardhan B, Srujan A U, Vinay Shankar H V, Willson Kumar, Dr. T N Anitha

Abstract: Road accidents remain a global concern, with human er- ror, road infrastructure defects, and environmental fac- tors contributing to millions of fatalities annually. This paper presents RoadGuardian, an integrated multi- modal AI framework designed to enhance road safety through real-time detection of three critical risk factors: driver drowsiness, road potholes, and surrounding ve- hicles. The system employs computer vision techniques with specialized architectures for each detection mod- ule. Drowsiness detection utilizes facial landmark anal- ysis with EAR (Eye Aspect Ratio) and MAR (Mouth Aspect Ratio) metrics. Pothole detection implements a custom YOLO architecture trained on augmented road datasets. Vehicle detection leverages YOLOv8 for ro- bust object recognition. These modules are integrated into a unified dashboard that provides real-time alerts, risk assessment scoring, and situational awareness visu- alization. Experimental results demonstrate high accu- racy rates: 96.8% for drowsiness detection, 94.2% for pothole detection, and 97.5% for vehicle detection with an average inference time of 45ms per frame on stan- dard hardware. The framework represents a significantadvancement in proactive road safety systems, offering a comprehensive solution to mitigate multiple accident risk factors simultaneously.

DOI: https://doi.org/10.5281/zenodo.18195863

 

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Authors: Pradhebaa S

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) models have become powerful tools for predictive analytics in medical and financial domains, enabling early diagnosis of disease, fraud detection, and risk forecasting with remarkable accuracy. Despite these advancements, most state-of-the-art models operate as complex black-box systems, offering minimal transparency into how predictions are formed. In healthcare, where predictions influence clinical decisions, lack of interpretability reduces clinician trust, raises ethical concerns, and limits real-world deployment. Similarly, in finance, opaque ML systems create challenges in regulatory audits, accountability, and fairness in automated risk scoring. These limitations motivate the need for Explainable AI (XAI) frameworks that provide human-interpretable reasoning without sacrificing predictive performance. This paper proposes a unified, model-agnostic explainable machine learning framework tailored for high-stakes prediction tasks. The system employs predictive models such as Random Forest, XGBoost, and LSTM for structured and longitudinal clinical data, integrated with XAI methods including SHAP, LIME, attention visualization, and counterfactual reasoning to generate both global and instance-level explanations. To enhance explanation reliability, the framework incorporates stability analysis, imbalance-aware training, and a composite trust scoring mechanism validated by domain experts. The approach aims to improve transparency, support clinician and analyst decision-making, and enable safer, auditable deployment of AI in medical prediction pipelines. Experimental results from existing research demonstrate that combining high-accuracy ML with robust explanation layers significantly improves stakeholder trust and practical adoption, positioning the framework as a step toward responsible and interpretable predictive intelligence in real-world applications.

Published by: vikaspatanker

Authors: Mohd saquib siddiqui

Abstract: Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

 

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Authors: Pranav Shelar, Om Shinde, Om Shinde, Omkar Solat, Prof. Italkar Sanika

Abstract: In conventional petrol pump systems, fuel dispensing and billing are carried out manually, which often leads to issues such as fuel theft, human errors, inaccurate billing, and increased waiting time for customers. With the growing demand for automation and secure cashless systems, there is a strong need for an efficient fuel management solution. This paper presents the design and implementation of an RFID-based automated petrol pump system using Arduino UNO, which ensures secure user authentication and accurate fuel dispensing with automatic balance deduction. Each user is provided with an RFID card containing unique identification and prepaid balance INFORMATION. When the card is scanned, the system verifies user credentials, checks available balance, and activates the DC pump accordingly. The pump automatically stops once the predefined fuel amount is dispensed or the balance limit is reached. The proposed system reduces manual intervention, prevents fuel fraud, and improves operational efficiency. Experimental results demonstrate reliable card detection, accurate fuel control, and real-time balance deduction, making the system suitable for modern smart petrol stations.

DOI: https://doi.org/10.5281/zenodo.18182542

Published by: vikaspatanker

Authors: Prof. Keerti M, Mr.Narendra, Mr.Vishal, Mr.Kevin Dutt

Abstract: Deep fake detection technology has advanced rapidly with the progress of deep learning, enabling the generation of highly realistic manipulated images and videos. While such technology has beneficial applications in entertainment and media, its misuse poses serious threats including misinformation, identity fraud, political manipulation, and erosion of public trust. Traditional video authentication techniques are insufficient to detect subtle manipulations introduced by modern deepfake generation algorithms. This paper presents a deep learning–based deepfake detection system that analyzes both spatial and temporal inconsistencies in video frames. The proposed approach employs transfer learning–based convolutional neural networks for facial feature extraction and sequence-based models for capturing temporal variations across frames. Preprocessing techniques such as face detection, frame extraction, normalization, and data augmentation are applied to enhance detection robustness. Experimental evaluation using benchmark datasets demonstrates that the proposed system achieves reliable detection accuracy even for high-quality deepfake videos. The system provides an effective and scalable solution for digital forensics, cybersecurity, and social media content verification.

Published by: vikaspatanker

Authors: Md. Parvej

Abstract: The holistic development of children has become a central concern of contemporary primary education. In West Bengal, Ananda Parisar has been introduced as a joyful learning initiative in primary schools. The present study examines its impact on academic engagement and socio-emotional development of students in rural primary schools. Using a descriptive survey method, data were collected from selected rural blocks of Malda district. Findings reveal significant improvement in motivation, participation, social interaction, and emotional well-being. The study concludes that Ananda Parisar is an effective pedagogical intervention for rural primary education.

 

Published by: vikaspatanker

Authors: Ms. Achal A. Koyale, Ms. Shravani S. Golegaonkar, Ms. Maithili V. Mangalagiri

Abstract: In modern public and commercial environments, frequent physical contact with door handles increases hygiene risks and creates accessibility challenges for elderly and physically challenged individuals. To overcome these limitations, this paper presents the design and implementation of a contactless automatic door opening and closing system using ultrasonic distance sensing and microcontroller-based control logic. The proposed system employs an HC-SR04 ultrasonic sensor to continuously monitor the presence of approaching objects. When the detected distance falls below a predefined threshold, a servo motor is actuated to control the opening and closing of the door. A delay-based safety control algorithm is implemented to prevent unintended door closure and to ensure smooth and reliable operation. The system is developed using low-cost and easily available hardware components, making it suitable for small-scale and public applications such as hospitals, offices, shopping malls, and public washrooms. Experimental results demonstrate accurate object detection within a range of 2 cm to 80 cm, stable door operation, and minimal response delay. The proposed system provides an efficient, economical, and scalable solution for contactless door automation and can be further enhanced through IoT integration and advanced sensing technologies.

Published by: vikaspatanker

Authors: M. O. O. Ifesemen, Dr Dulari A Rajput

Abstract: This study investigates the intricate link between workplace operational incidents and administrative errors, emphasizing the critical role of organizational and leadership accountability in mitigating error-enforcing conditions that precipitate incidents and degrade performance. Employing a robust qualitative approach, the research integrates a mixed- methods design encompassing naturalistic observation—both participant and non- participant—and unstructured interviews conducted with over 300 personnel within a Nigerian-based transnational organization. Data were meticulously analyzed using descriptive and deductive reasoning frameworks to elucidate the impact of leadership decisions and organizational practices on the prevalence of workplace errors and related incidents. The findings reveal a compelling pattern: more than 80% of workplace incidents, encompassing both physical injuries and psychological harm, originate from administrative errors linked to leadership styles and organizational culture. Key error-enforcing conditions identified include pervasive blame culture, inadequate fatigue management, favoritism, bullying, flawed performance appraisal systems, and a pronounced lack of employee empowerment. Notably, psychological injuries arising from these administrative errors—such as diminished self-esteem, depression, and chronic stress—were found to be more detrimental than physical injuries, exerting profound negative effects on employee motivation, productivity, and overall organisational performance. The study further underscores the frequent misinterpretation of incident causality and highlights the paramount importance of objective evaluation and leadership accountability as mechanisms to reduce incident recurrence effectively. In conclusion, the research advocates cultivating accountability at all organisational levels, enhancing leadership competencies, and promoting a culture grounded in empathy and objectivity within performance appraisal and incident management processes. Implementation of these measures is projected to foster safer, more productive work environments, thereby driving improved organisational outcomes. The study also calls for integrating accountability principles into corporate governance frameworks. It emphasises the need for transformational learning through causal reasoning to address the root causes of workplace errors and incidents, ultimately contributing to sustainable organisational excellence.

DOI: https://doi.org/10.5281/zenodo.18172551

 

Published by: vikaspatanker