Authors - Gauri S. Bhagat, Nitin S. More Abstract - Cardiovascular disorders continue to be a major global health issue, with arrhythmias presenting significant hurdles in both diagnosis and treatment. This article presents a groundbreaking and thorough framework for ECG feature assessment that incorporates morphological, temporal, and frequency-domain elements, all improved by advanced processing techniques and smart classification methods. By utilizing diverse features and tailoring approaches to individual patients, the proposed system enhances diagnostic accuracy and dependability. Evaluations on standard datasets indicate improved classification efficacy, marking a substantial advancement in automated systems for arrhythmia diagnosis. The framework’s adaptability further positions it as a strong prospect for integration into mobile and telemedicine platforms,thus facilitating diagnostic processes to near real-time clinical application.
Authors - Ann Mary Francis, C. Rojalin Patri, A. Varun Raj, B. Harijith M Abstract - This study evaluates the extent of customer satisfaction with private logistics service providers, where courier services are widely used. The study aims to comprehend customer perception of service quality by implementing a dual survey method: a customer service call survey and a walk-through audit. The key is to determine the areas of improvement and increase overall customer satisfaction. The findings indicate that more than 40% of the customers are dissatisfied with the service delivered. Findings indicate major problem areas are delivery experience, communication channels, and customer service interactions. The findings indicate that Order and tracking (improving order accuracy, transparency, and real-time tracking ability), Delivery (enhancing delivery timeliness, reliability, and communication during the delivery process), and Facility Factors (streamlining facility layout, staffing, and processes to facilitate smooth operations and effective service delivery) must be enhanced. Through the enhancement of these three aspects, private logistics service providers can enhance overall customer satisfaction.
Tuesday August 25, 2026 12:30pm - 2:30pm IST Virtual Room AGOA, India
Authors - Sanjeev Kumar Punia, Karanjeet Singh, Fahar Imran Abstract - The exponential growth of the World Wide Web has increased the need for efficient Web-page prediction models to reduce access latency and enhance user experience. Traditional methods, such as k-order Markov models, struggle with balancing prediction accuracy and complexity. In this work, we propose an ensemble model that combines Logistic Regression, Naive Bayes, and a First-Order Markov model to improve Web-page prediction accuracy. Logistic Regression identifies relationships in Web-log datasets, Naive Bayes applies probabilistic reasoning, and the Markov model captures transition probabilities between pages. By combining these models using a stacking classifier, we aim to leverage their strengths for more robust predictions. Our results demonstrate that the ensemble model outperforms individual models, achieving higher accuracy, precision, and recall. This hybrid approach can significantly improve Web navigation efficiency, making it a promising solution for real-time Web-page prediction.
Authors - Rupali Vairagade, Shailesh Hiralal Yadav, Harshal Raju Ismulwar, Manoj Ramashish Gupta, Nilakshi Jain, Shwetambari Borade Abstract - Digital statistics develop at an extraordinary pace, making the demand for cozy, scalable, green records more than ever before. Traditional encryption methods, including symmetric and asymmetric encryption, have long been extremely important for protected tag marks. However, these traditional methods face major challenges consisting of complex mathematical calculations, difficult intrinsic management, and obstacles to scalability. These issues can be enjoyed by improving aid, poor overall performance, and bad users. Hybrid encryption structures provide an effective solution with the help of a combination of stable security of heterogeneous encryption for critical changes and administration and the efficiency of symmetric encryption. This twin technology allows for faster processing of records, but the encryption key is protected and protected from unauthorized entries. By using the power of both encryption strategies, hybrid encryption deals with scalability and overall performance issues. This is often seen in traditional systems and is ideal for large packages in a wide range of different fields. In the long run, hybrid encryption is actually the main break in protection technology, providing a balanced solution that improves security, improves machine efficiency and ensures scalability. This approach is suitable for current desired developments for virtual environments, allowing businesses to protect sensitive statistics without compromising performance or enjoying users.
Authors - Dhruva R. Rinku, Parimi Hema Sree, D. Nagajyothi, Anita Kulkarni Abstract - In recent years, the frequency of floods has surged due to climate change and unchecked urbanization. In developing nations such as India, floods wreak havoc that can take decades to recover from. To effectively mitigate the impact of flood disasters, precise flood inundation warnings are essential. This system employs a Geographic Information System (GIS) model, constructed using a Digital Elevation Model (DEM) and building shape-files, to analyze land behavior during floods and identify inundation risks in various locations. This information is crucial for taking proactive measures to reduce flood-related destruction promptly. The Global Precipitation Measurement’s (GPM) half-hourly rain data is utilized to assess the current influence of rainfall on flood conditions across different regions, thereby enabling timely warnings to residents in nearby areas. Furthermore, this system incorporates a flood relief system, which is web-based and developed using PHP as the web application and a MySQL database. This platform prepares donors to assist flood victims with various types of donations. Immediately following the release of a flood warning through a GSM module that sends SMS alerts, donors are informed to be ready with their contributions. This timely communication equips authorities to provide refuge to flood victims. This system is specifically designed for Mumbai, India’s largest city, which is frequently affected by floods, resulting in significant property and life damage annually.
Authors - Nikita Patil, Basawaraj Patil Abstract - The goal of this paper is to leverage Field Programmable Gate Array (FPGA) technology to create a Lane Departure Warning and Correction System. Its Advanced Driver Assistance System (ADAS) design attempts to improve vehicle safety by avoiding in advertent lane changes. Under a variety of driving circumstances, such as changes in lighting, weather and road types, the system reliably detects lane boundaries by utilizing image processing techniques like Hough Transform and Canny Edge Detection. The Xilinx Zynq Ultra scale FPGA, which combines high-performance processors and peripherals for real- time processing and control, is used in the implementation. In order to guarantee that the car stays in its lane, the system is made to deal with issues like faded markers, shadows, and construction zones. It does this by promptly sending out alerts or taking remedial action. By addressing real-world issues in autonomous and semi-autonomous vehicles, this breakthrough highlights the emerging of strong hardware and cutting-edge algorithms, greatly lowering the risks associated with lane departure incidents.
Authors - Gauri Gautam, Vijay Kumar Sharma Abstract - This paper outlines an organized approach to creating a dataset and training an Artificial Neural Network (ANN) for autonomous vehicle driving systems. It utilizes the KITTI dataset and includes key preprocessing steps like converting binary files to CSV. Other steps comprise calibrating and plotting 3D point cloud data and creating 2D front-view projections with consistent sizes. A novel data preparation pipeline is presented, ensuring homogeneity while addressing challenges like variable point distributions. It also confronts cropping consistency to improve data quality. The results demonstrate a robust methodology for generating training-ready datasets. A three-layer ANN is trained effectively for autonomous driving tasks. This work contributes significantly to autonomous systems. It provides a scalable approach that can adapt to various datasets.
Authors - Jaimin Dave, Chintan Shah, Premal Patel Abstract - Implementing effective control over harmful actions in a network through an Intelligent Detection System (IDS) is necessary for modern digital security, but building robust techniques for coverage with high accuracy remains a challenge. To help overcome this challenge, the study's contribution proposes a hybrid deep learning approach combining convolution neural networks (CNN) and Long Short Term Memory (LSTM) networks for maximum coverage of detections in IDS. Tests have been conducted on various datasets and the model achieved best results of 75% detection accuracy for different attack scenarios. This was better than what's achieved using traditional methods as the legacy Intelligent Detection Systems (IDS) techniques, although increasing detection coverage, reduced the level of falsely identified cases and improved adaptability towards new patterns of attacks. The results open new frontiers for the development of hybrid machine learning architectures capable of addressing the shortcomings of traditional Intelligent Detection Systems (IDS) models and improving network security.
Authors - Safa Imtihaz Sayyad, Jyoti M Satihal, Ujwala Patil Abstract - Object detection in dark conditions at night is challenging due to poor visibility, leading to reduced accuracy and performance. We propose ODDNet, a framework for object detection in the dark that combines enhanced RGB images with event-based data, leveraging their complementary strengths. Using Zero-DCE, RGB images are enhanced for low-light, while event data is processed through a Temporal Multi-scale Aggregation to extract its temporal features. Attention mechanisms and specialized loss functions improve low-light imaging, preserve spatial details, and enhance detection accuracy. Ablation studies highlight the contributions of Zero-DCE and attention-based fusion. The proposed ODDNet model achieves a mean average precision (mAP) of 45.8% during the day and 28.3% at night at an intersection over Union (IoU) threshold of 0.5. These results demonstrate ODDNet’s capability to effectively address low-light challenges, indicating its potential for autonomous systems and night-time surveillance.
Authors - Aarti Amod Agarkar, Harshal Vijay Chaudhari, Sanskar Mukta Chaudhari, Astha Sachin Chaudhari, Om Yogesh Borse Abstract - The Personalized AI Doctor is a smart and user-friendly platform designed to make healthcare more accessible and efficient. It simplifies the process of booking medical appointments by using advanced technologies like artificial intelligence (AI), natural language processing (NLP), and secure cloud-based databases. Patients can easily register on the platform, share their symptoms through a chatbot or voice commands, and receive accurate disease predictions powered by AI. The system intelligently matches patients with the right doctors based on their specialization and availability, ensuring timely care. It also allows users to book appointments and attend virtual consultations through automatically generated Google Meet links, offering convenience and flexibility. Additionally, the platform provides detailed insights and reports for administrators, helping optimize doctor schedules and improve resource allocation. By combining AI, modern database systems, and cloud services, this system transforms the healthcare experience, making it simpler, faster, and more patient-centric.
