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Research Paper

Vehicle Breakdown Assistance Management System

Vehicle breakdown situations often create inconvenience, delays, and safety concerns for drivers. In many traditional cases, assistance is arranged manually through phone calls or local contacts, which may consume time and create communication gaps. This paper presents a Vehicle Breakdown Assistance Management System (VBAMS) developed using the Python Django framework and MySQL database, based directly on the implemented project model. The system provides a centralized platform where customers can submit assistance requests, administrators can manage bookings and drivers, and assigned drivers can update service progress. The project follows Django’s Model-View-Template (MVT) architecture and includes secure authentication, role-based access, driver management, booking records, and request tracking. The developed system improves operational efficiency, organizes service data, and reduces manual coordination. Future scope of the project includes GPS tracking, artificial intelligence based dispatching, mobile application support, and automated notifications.

Published by: Sonia, Er Bablu Jaipal

Author: Sonia

Paper ID: V12I3-1215

Paper Status: published

Published: June 13, 2026

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Research Paper

Deep Learning Based Non-Invasive Screening of Autism Spectrum Disorder Using Transfer Learning

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social communication. Early intervention is paramount; however, traditional diagnostic pathways often take years due to a lack of specialized clinicians. This research proposes an automated screening tool using facial image analysis. By employing the VGG16 architecture via Transfer Learning, we extract high-level spatial features from facial landmarks to identify markers associated with ASD. Our findings indicate that computational models can provide a significant preliminary screening layer, reducing the burden on clinical resources.

Published by: Nisha Sharma, Bablu Jaipal

Author: Nisha Sharma

Paper ID: V12I3-1213

Paper Status: published

Published: June 10, 2026

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Research Paper

Lane Detection System using Python OpenCV

Lane detection is an important perception module in advanced driver-assistance and autonomous driving because it helps a vehicle interpret road geometry and remain centered within the lane. This paper presents a compact lane detection pipeline developed in Python with OpenCV using classical image-processing techniques. The method processes each video frame in sequence and applies grayscale conversion, Gaussian smoothing, Canny edge detection, a region-of-interest mask, and Probabilistic Hough Transform line extraction. The detected segments are separated into left- and right-lane candidates using slope-based rules, averaged to reduce noise, and drawn on the original frame to create an annotated road view. The system was tested on real driving video captured from a front-facing camera under normal daylight conditions. The results indicate that the approach performs well on straight roads and moderate curves when lane markings are visible, but its robustness decreases under shadows, glare, faded paint, and partial occlusion. Because the pipeline is lightweight, deterministic, and capable of near real-time execution on standard hardware, it is a useful baseline for educational and prototype intelligent transportation systems. The paper also discusses the problem context, design objectives, implementation steps, results, limitations, and future extensions such as adaptive thresholding, temporal tracking, and learning-based lane recognition.

Published by: Yash Bhardwaj

Author: Yash Bhardwaj

Paper ID: V12I3-1211

Paper Status: published

Published: June 9, 2026

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Review Paper

Lean Manufacturing Techniques to Enhance the Productivity of Base Coat Process in Automotive Lighting System

Kaizen, a Japanese term meaning "change for the better," is a core Lean management philosophy focused on continuous, incremental improvement. Unlike radical overhauls, Kaizen emphasizes small, daily changes made by every employee—from the shop floor to the executive suite—to cumulatively drive significant gains in productivity and quality. As a Lean technique, Kaizen functions as the primary engine for eliminating waste (Muda), reducing variability, and optimizing process flow. It relies on a participatory culture where frontline workers are empowered to identify inefficiencies and propose solutions at the source of work, known as the Gemba. The methodology is typically operationalized through the Plan-Do-Check-Act (PDCA) cycle, ensuring that improvements are data-driven, tested, and standardized to prevent regression. The implementation of Kaizen within a Lean framework results in measurable benefits, including reduced lead times, lower defect rates, and enhanced employee morale. Ultimately, Kaizen transforms an organization into a "learning enterprise" capable of sustaining long-term competitive advantages without requiring massive capital investment.

Published by: Majaharali Shikalgar, Dr. Javed G. Khan

Author: Majaharali Shikalgar

Paper ID: V12I3-1206

Paper Status: published

Published: June 5, 2026

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Research Paper

Real-Time Smart Road Accident Detection System Using IoT and Sensors for Faster Emergency Response

This literature review examines studies related to real-time smart road accident detection systems using IoT, sensors, communication technologies, and intelligent emergency response methods. The reviewed studies show that accident detection systems commonly use accelerometers, gyroscopes, GPS, GSM/GPRS, microcontrollers, cloud platforms, dashboards, and real-time alerting mechanisms to reduce delays in emergency notification. Some studies focus on low-cost vehicle-mounted systems, while others explore broader emergency response platforms, V2V/V2I communication, smart traffic lights, driver monitoring, and AI-based accident or incident detection. The reviewed studies also show that machine learning, deep learning, computer vision, YOLO-based models, and multi-agent systems can support accident recognition and traffic monitoring. However, several limitations remain, including limited real-world testing, false alarms, threshold sensitivity, weak emergency coordination, connectivity problems, limited sensor fusion, and security or privacy concerns. Overall, the reviewed studies support the need for a practical hybrid system that combines IoT-based sensing, location tracking, reliable communication, emergency alerting, and, where suitable, AI-based validation for faster and more reliable road accident response.

Published by: Qutaiba Almassri

Author: Qutaiba Almassri

Paper ID: V12I3-1171

Paper Status: published

Published: June 3, 2026

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Research Paper

AI-Driven Breath Analysis for Early Lung Cancer Detection Using Optimized Ensemble Learning of VOC Biomarkers

Lung cancer is one of the most prevalent and deadly diseases worldwide, primarily due to late-stage diagnosis and the limitations of conventional detection methods. Early and accurate identification is crucial for improving patient survival rates. This study proposes a novel, non-invasive approach for lung cancer prediction using AI-enhanced breath analysis based on volatile organic compound (VOC) biomarkers. The proposed system utilizes sensor-based breath data to capture VOC patterns associated with lung cancer. Advanced Preprocessing and feature selection techniques are applied to identify the most relevant biomarkers, reducing data complexity and improving model efficiency. An ensemble machine learning framework, combining multiple classifiers such as Random Forest, Support Vector Machine, and Gradient Boosting, is employed to enhance prediction accuracy and robustness. Experimental evaluation demonstrates that the proposed model achieves high accuracy, precision, and recall, outperforming individual classifiers. The system also shows strong potential for real-time implementation due to its computational efficiency. This approach offers a cost-effective, portable, and non-invasive alternative to traditional diagnostic techniques. Overall, the integration of VOC biomarker analysis with feature-selected ensemble learning provides a promising solution for early lung cancer detection, paving the way for improved screening methods and better clinical outcomes.

Published by: Syed Naseemtaj, Syed Nafeesa Thehseen

Author: Syed Naseemtaj

Paper ID: V12I3-1201

Paper Status: published

Published: June 2, 2026

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