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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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Hybrid approach for the detection of epileptic seizure using electroencephalography input

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International Journal of Information technology (Singapore) 2024年第1期16卷 569-575页

作者： Basha, Niha Kamal Surendiran, B. Benzikar, Amutha Joyal, S. School of Computer Science and Engineering Vellore Institute of Technology (VIT) Vellore India Department of Computer Science and Engineering National Institute of Technology (NIT) Puducherry Karaikal India School of Computer Science and Engineering Vellore Institute of Technology (VIT) Chennai India Saveetha Engineering College Chennai India

In the early days, it was difficult to study bio-electric signals, but now a days these problems have been solved by many hardware devices which are available at low cost. Even then there is a need for technical improvements to process bio-electric signals. Enormous effort has been taken for the detection of epileptic seizure from EEG signal. Considerable evidence available on Bonn university EEG dataset for epileptic seizure detection. However, deep learning algorithms have not applied often on Bonn university data like other machine learning algorithms for the detection of epileptic seizure due to the less availability of data. This work adopted machine learning and deep learning models for the detection of epileptic seizure. The data consists of a hundred subjects with sampling rate as 173.61 Hz of ‘S’ (ictal) and ‘Z’ (normal) dataset. The results show how the choice of healthy and ictic subjects through high order functional math decision like variance (STD), Power, Skewness, and Kurtosis values are analyzed to extract epileptic features. The performance of classifiers has been evaluated based on the evaluation metrics within which CGRU-SVM outperforms all other models with 97.54% accuracy. The features are extracted based on the statistical measures to detect epileptic seizure. Further, the proposed work provides more evidence on the epileptic seizure features, and it shows the new possibility for using deep learning models for epileptic seizure detection. In future, an analysis must be performed with dynamic sets of input and different analysis techniques. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Confusion matrix Deep learning model Electroencephalography Epilepsy Ictal subject Rhythmic frequency Statistical feature

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A new hybrid MobileNet-MLP model for secured data communication using signature verification and deep learning

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Iran Journal of computer science 2024年第1期8卷 233-252页

作者： Pandiyaraju, V. Ganapathy, Sannasi Thangaramya, K. Nahar, R. Raunak Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department Computer Science and Engineering Education National Institute of Technical Teachers Training and Research (NITTTR) Bhopal India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Signature verification plays a critical role in various industries, including finance and document authentication. However, traditional verification techniques have limitations, such as a lack of robustness and an inability to effectively capture intricate patterns and relationships within the signature images. This paper introduces an innovative approach that addresses the drawbacks by integrating conventional machine learning methods with deep learning techniques. For this purpose, we design a new hybrid MobileNet model using deep learning to extract features from the signature images. Here, the Multilayer Perceptron (MLP) classifier is also used to carry out the binary classification over the extracted features. The experimental results of the proposed model demonstrate that the amalgamated technique surpasses the conventional methods and underscores the possibilities of merging traditional machine learning and deep learning in signature verification. Moreover, the proposed model overcomes the limitations of the traditional signature verification techniques by leveraging the strengths of both deep learning and traditional machine learning. Finally, it obtains a more robust and efficient solution and proves it by providing high security to the data in terms of accuracy. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural network Feature extraction MobileNet Multilayer perceptron Signature verification

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A Construction of Object Detection Model for Acute Myeloid Leukemia

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Intelligent Automation & Soft Computing 2023年第4期36卷 543-560页

作者： K.Venkatesh S.Pasupathy S.P.Raja Department of Computer Science and Engineering Annamalai UniversityChidambaram608002India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India

The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was signiﬁcant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classiﬁ*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).

关键词： Acute myeloid leukemia(AML) convolutional neural network(CNN) and nvidia tesla v100 gpu

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Integrating Advanced Neural Network Architectures for Effective Skin Disorder Classification

Integrating Advanced Neural Network Architectures for Effect...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Shreya, A. Gautam, G. Dhivyaa, C.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350374933

Skin illnesses, such as vitiligo, provide considerable hurdles in proper diagnosis due to their resemblance to other conditions, necessitating time-consuming examinations by medical personnel. Taking advantage of advances in deep learning and medical image processing, this study suggests a novel way to swiftly and accurately classify these disorders. The proposed model uses many convolutional layers to extract detailed features from skin pictures, followed by Long Short-Term Memory (LSTM) layers to capture temporal dependencies and improve feature representation. This hybrid architecture consists of six convolutional layers that gradually increase in depth and spatial reduction via max-pooling, culminating in two LSTM layers and three fully connected layers for precise classification. By combining CNNs’ spatial feature extraction powers with LSTMs’ sequence processing skills, our model obtains a high training accuracy of 93.40% and precision of 94.48%, proving its usefulness in distinguishing between vitiligo and scar images. This approach shows potential for improving diagnostic accuracy and efficiency in clinical settings, resulting in better patient outcomes and healthcare delivery. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

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Multimedia Tools and Applications 2024年第16期84卷 1-32页

作者： Kinikar, Mayura Saleena, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Pune India

In the growing information retrieval (IR) world, selecting suitable keywords and generating queries is important for effective retrieval. Modern database applications need a sophisticated interface for automatically updating the connections between users and databases. Most database applications are intelligent;however, some may be complex to understand when generating queries for effective retrieval. Therefore, this paper develops a new Adaptive Naked Mole-Rate algorithm (ANMR) for an automatic query generation (AQG) based IR system. The query generation approach primarily generates the query with expanded keywords to enhance IR. Modified sealion optimization (MSO) is applied to select several features. The selected features are combined in the feature fusion process using the Fuzzy based Search and Rescue approach (FSR). Similarity matching is performed using the hybrid cosine and Jaccard similarity measures. At last, the ranking process is performed using the Dynamic Global Local Attention Network based on Capsules (DGLANC). The developed AQG-ANMR system improves the performance of the entire information retrieval system. Then, to analyze the performance, the proposed approach is implemented in the python platform and evaluated in terms of accuracy, recall, precision, and F1-Score by employing TREC-3 and CISI datasets. Besides, the performance of the proposed approach is compared with that of state-of-the-art approaches. Finally, the simulated results clearly showed that the proposed approach outperformed the state-of-the-art approaches better. The maximum accuracy attained by the proposed approach is 97.2% with AQG-ANMR and 94.69% without AQG-ANMR for the TREC 3 dataset. In the same way, using the CISI dataset, the proposed approach reached a maximum accuracy of 98.6% with AQG-ANMR and 95.8% without AQG-ANMR, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Information retrieval

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AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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Lane Detection and Traffic Sign Detection for Autonomous Driving Systems 2

Lane Detection and Traffic Sign Detection for Autonomous Dri...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Biswas, Aishi Chowdhury, Anushka Roy Selvanambi, Ramani School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781837242672

In the realm of autonomous driving, this study focuses on two critical tasks for scene understanding: lane detection and traffic sign detection. These tasks are essential to ensuring safe and reliable navigation in self-driving systems. A combination of traditional computer vision techniques and modern deep learning methods are used to achieve accurate detection and analysis of lanes and traffic signs in real-world driving scenarios. For lane detection, we utilize edge detection and the Hough Transform, while for traffic sign detection, we apply machine learning algorithms to recognize and classify various signs. This hybrid approach demonstrates the potential of integrating classical computer vision algorithms with deep learning models to enhance the perception capabilities of autonomous vehicles. The model achieves a balanced performance, with accuracy metrics ranging from 80-90% across both tasks, indicating a robust ability to detect lanes and signs effectively under varied conditions. This work serves as a foundation for developing more advanced scene understanding systems in autonomous driving technology. In the future, we aim to enhance the model's adaptability through continuous learning and incorporating additional sensory data, such as LiDAR and radar. Additionally, expanding the dataset and testing the models in diverse environmental conditions, including different lighting and weather scenarios, will further improve the robustness and reliability. © The Institution of engineering & technology 2024.

关键词： Hough transforms

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Fostering Inclusive Communication: A Tool Integrating Machine Translation, NLP, and Audio-to-Sign-Language Conversion for the Deaf 2

Fostering Inclusive Communication: A Tool Integrating Machin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sen, Madhura Rajkumar, R. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350306415

Approximately 70 million individuals worldwide grapple with deafness or muteness, presenting challenges in communication. This article presents a novel solution: an audio-to-sign-language converter. Sign language, a visual medium for speech, is pivotal in bridging this communication gap. This Python-based converter interprets user input, transforming it into gestures, facilitating interaction between the hearing and the deaf. It also serves as a valuable tool for learning sign language. Live voice input, captured through a microphone, undergoes advanced Natural Language Processing techniques to be converted into text. Recognized phrases trigger relevant gesture videos or display corresponding letters. The application also features a termination command, "goodbye."With potential applications in web and mobile platforms, two-way interaction, and video integration, this converter holds promise in enhancing accessibility for the deaf community, fostering inclusivity and effective communication. This paper is a significant step towards facilitating meaningful connections between individuals with disabilities and their non-disabled peers. © 2024 IEEE.

关键词： accessibility facial expressions gestures GIF ISL vocabulary natural language processing Sign language speech recognition

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InceptionV3 infused Neural Network for Brain Tumor Detection 5

InceptionV3 infused Neural Network for Brain Tumor Detection

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bharargava, Kunal Nanda, Harshil Basha, Niha Kamal Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331504403

Automated identification and detection of brain tumors using Magnetic Resonance Imaging (MRI) is difficult, time-consuming and laborious because there exists many overlooked, misread, & deceptive lesions (similar to tumor) present in the brain tumor image scans. This paper evaluates the efficiency of the InceptionV3 infused convolutional neural network for brain tumor detection. Utilizing a comprehensive dataset Br35H with MRI brain tumor images, initially the model preprocessed and augmented the input to enhance training robustness. The novelty of our approach lies in the integration of advanced data augmentation techniques and a customized training pipeline. The proposed InceptionV3 infused neural network model achieved a validation accuracy of 98.50%, outperforming other models such as EfficientNetB4, VGG19, ResNet50, and MobileNetV2 in terms of key metrics. Our findings demonstrate that the proposed InceptionV3 infused neural network is highly effective and computationally efficient regarding brain tumor detection & classification, providing one reliable instrument for medical image analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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