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Epilepsy Seizure Detection based on EEG Signals using Machine Learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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Collision Avoidance System using YOLO-based Object Detection and Distance Estimation 3

Collision Avoidance System using YOLO-based Object Detection...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Shiv Akash Dhinakar Singh, Ashita Shekhawat, Tarakshraj Singh Manav Muthanna, M. Sivasankaran, K. School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

Road accidents are a significant global issue, with human error causing a substantial number of collisions. This emphasizes the need for advanced collision avoidance systems to improve road safety. In response, this study integrates object detection and distance estimation techniques to enhance vehicle safety. Utilizing recent iterations of the YOLO architecture—YOLOv6n, YOLOv8n, YOLOv9t, and YOLOv10n—the models were trained and evaluated on the KITTI dataset, a benchmark suite for driving applications. This research study provides a comparative analysis of these models, focusing on performance metrics such as precision, mean average precision, recall, and F1 score. A novel single-camera distance estimation method was also implemented to assess object proximity accurately. Among the evaluated models, YOLOv8n achieved the best results, with a F1 score of 0.74 and a mAP@0.5 of 0.782, demonstrating superior detection accuracy and reliability. Its swift convergence and low training losses make it well-suited for real-world use cases. While YOLOv9t and YOLOv10n also performed well, they were slightly less effective. The system enhances its capabilities by accurately estimating distances, which improves collision avoidance and road safety applications. Overall, YOLOv8n is the most efficient and reliable option for developing advanced safety features in vehicle systems. © 2025 IEEE.

关键词： Vehicle safety

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Predictive Maintenance of Aircraft Engines: Machine Learning Approaches for Remaining Useful Life Estimation 6

Predictive Maintenance of Aircraft Engines: Machine Learning...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Deepika, J. Reddy, Pappuru Manikanta Murari, Kothaluri Rahul, Boyana School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore India

ISBN: (纸本)9798331522667

In the realm of predictive maintenance for aircraft engines, ensuring precise and timely predictions of Remaining Useful Life (RUL) is paramount to enhancing safety, reducing costs, and minimizing downtime. While deep learning models dominate this field, their complexity, high computational demand, and lack of interpretability can hinder practical deployment in real-world scenarios. This study demonstrates the potential of machine learning models as an efficient and interpretable alternative for RUL prediction, leveraging the publicly available C-MAPSS dataset from NASA. Using C-MAPSS dataset, we integrated PostgreSQL for scalable data storage and Flask for interactive real-time visualization, creating a seamless pipeline from data ingestion to result interpretation. Preprocessing steps, including normalization, noise removal via box plots, and feature engineering, ensured a clean and reliable dataset. Among the models evaluated—XGBoost, Random Forest, SVM, KNN, and Linear Regression—XGBoost emerged as the best performer, achieving an RMSE of 23.8 and an R2 score of 0.67. These results nearly matches the accuracy typically associated with deep learning models while maintaining computational efficiency. Feature importance analysis through Explainable AI (XAI) further revealed critical insights into sensor contributions, enhancing interpretability and trust in the predictions. This work underscores the practicality and effectiveness of machine learning in predictive maintenance, offering a robust, transparent, and resource-efficient approach to RUL prediction. The findings highlight its potential to meet industry needs where simplicity, accuracy, and interpretability are critical for decision-making and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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Leveraging Artificial Neural Networks for Breast Cancer Detection and Prognosis

Leveraging Artificial Neural Networks for Breast Cancer Dete...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Goel, Aadya Mishra, Pallavi Bhatia, Rachna School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331512965

Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection and accurate classification critical for improving patient out-comes. This study explores the application of Artificial Neural Networks (ANN) for breast cancer multiclass classification and prediction using the CBIS-DDSM: Breast Cancer Image Dataset. The model was trained to classify mammographic images into three categories: benign, malignant, and benign without callback, achieving an impressive accuracy of 99.6%. The choice of ANN was driven by its proven capability to model complex, nonlinear relationships within data, offering a significant improvement over traditional machine learning technique. Despite the challenges posed by the limited size and heterogeneity of the dataset, the ANN demonstrated robust performance, highlighting its potential for clinical applications. The high accuracy of the model underscores its value as a reliable tool for aiding radiologists in the early detection and prognosis of breast cancer, which could lead to more effective treatment planning and better patient outcomes. This study lays the foundation for integrating advanced AI technologies into clinical settings, offering promise for more precise, timely breast cancer diagnoses. © 2025 IEEE.

关键词： Lung cancer

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A Scalable and Secure Facial Recognition Attendance System Leveraging CNN Algorithms for Real-Time Accuracy and Efficiency

A Scalable and Secure Facial Recognition Attendance System L...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kumar, Aakash Sharma, Jiya Renjith, P.N. Vellore Institute of Technology School of Computer Science and Engineering India

ISBN: (纸本)9798331529833

The facial recognition attendance system represents a modern approach to attendance tracking, designed to improve efficiency, accuracy, and security in various organizational settings. Advanced biometric technologies are leveraged to capture and encode facial features, creating unique digital identifiers for each individual. The methodology employs cutting-edge facial recognition algorithms powered by convolutional neural networks (CNNs) with a 99.5% recognition accuracy rate. These algorithms extract and encode facial vectors with sub-millisecond latency, which are then compared in real-time against a secure, encrypted database of preregistered individuals. Upon a successful match, the system automatically records the attendance, ensuring an accurate and efficient process. With an intuitive web interface built on ReactJS and a backend supported by *** and MongoDB, the system provides seamless access to attendance records and administrative functionalities. The entire attendance recording process is optimized for speed, typically completing within two seconds per individual. Additionally, advanced anti-spoofing measures and integration support with existing HR management systems redefine traditional attendance management practices with a scalable, robust, and secure solution. (Abstract) © 2025 IEEE.

关键词： Efficiency

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Crop weed separation through image-level segmentation: an ensemble of modified U-Net and encoder–decoder

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Neural Computing and Applications 2025年 1-24页

作者： Ganapathy, Sannasi Srinivasan, Srinitish Bhopal462 002 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600 127 India

Crop weed segmentation is one of the most challenging tasks in the field of computer vision. This is because, unlike other object detection or segmentation tasks, crop and weed are similar in terms of spectral features, shape, dimensions, etc. For precision agriculture to flourish in terms of smart spraying of crops, efficient systems to distinguish between crop and weed are the need of the hour, which if precise, will take a huge step toward solving the issue of food scarcity. To tackle this issue, we propose new ensemble architecture of two models—a U-Net with a modified backbone and an encoder–decoder. These networks learn to distinguish between soil and crop and soil and weed, respectively, whose ensemble gives state-of-the-art results on pixel-wise annotations of combined crop and weed images. Moreover, it also learns that the model captures un-annotated features since each component of the architecture learnt either crop or weed features to high precision. Finally, the proposed architecture is compared with the U-Net and SegNet, which are popular segmentation networks, and consistently achieved better results. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantic Segmentation

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Recommendation System using Emotion Detection 3

Recommendation System using Emotion Detection

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susindra, S. Sweta, B. Keerthikha Shri, R. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems. © 2025 IEEE.

关键词： Emotion Recognition

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Deep Learning-Based Enhanced Object Detection for Humanoid Robots 1

Deep Learning-Based Enhanced Object Detection for Humanoid R...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Negi, Akshit Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering & Information Systems Vellore India

ISBN: (纸本)9798331520540

In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed to assist people in accomplishing specific missions or sometimes substitute human beings in hazardous situations. To accomplish these missions effectively, humanoid robots should be very good at recognition and subsequent processing of objects. The main goal of this research is to enhance the object recognition capabilities Leverage the advancements in deep learning to provide the control of Robotis-Op3 humanoid robots. In this work, the researchers analyzed popular deep neural models, among which are VGG16 and ResNet, highly promoted for their robust object recognition ability, as well as the more recent CNN architectures such as SSD MobileNetV3. This research improves object recognition capabilities on hu-manoid robots as such to enhance performance. in different domains, allowing them to better recognize and interact with the objects around them. This development will then allow more efficient and effective utilization of humanoid robots, extending their application and contribution in society. © 2025 IEEE.

关键词： Anthropomorphic robots

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MediHealth: An AI-Driven Mobile Solution for Enhanced Health Care Decision-Making and Accessibility 3

MediHealth: An AI-Driven Mobile Solution for Enhanced Health...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Raj, Sukrutha Shekhar, Shashank Baskaran, P. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

ISBN: (纸本)9798331527549

Mobile health (mHealth) applications have revolutionized healthcare by enhancing patient engagement, providing tools for self-management, and supporting better health outcomes. This paper explores the development of an innovative mHealth application designed to empower patients in making informed healthcare decisions. The application leverages advanced features such as AI powered chatbots, personalized healthcare recommendations, and a report and prescription summarizer to simplify complex medical information. By allowing users to easily find appropriate healthcare professionals, access detailed prescription data, and receive symptom-based specialist recommendations, the app facilitates effective self-management of chronic conditions. Furthermore, it offers proactive care prompts, personalized advice, and health behavior reminders to promote better overall health outcomes. Despite challenges in data security, privacy, and equitable access, the study emphasizes the importance of a user-centered design approach in ensuring the widespread adoption and success of mHealth applications. Ultimately, the goal is to enhance healthcare delivery by improving patient engagement, facilitating seamless access to healthcare resources, and enabling well-informed decision-making. This application presents a holistic solution for advancing patient care across diverse healthcare settings. © 2025 IEEE.

关键词： Electronic health record

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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