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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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Adaptive Time-Frequency Attention Network for Sleep Stage Classification Using Respiratory Signals

Adaptive Time-Frequency Attention Network for Sleep Stage Cl...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Zihang He, Kejing School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Sleep stage classification typically requires the uncomfortable and expensive polysomnography (PSG) test, which limits its widespread use in long-term monitoring and home-based environments. In this paper, we propose SleepTFANet, a novel deep learning model designed for automatic sleep stage classification using respiratory signals. SleepTFANet introduces two key modules: the Time Attention Module (TAM) and the Frequency Attention Module (FAM), to concurrently capture local and global dependencies in time-series data. TAM utilizes Transformer-based patch segmentation to capture temporal dependencies, while FAM leverages spectral analysis to extract frequency-based features. To further enhance performance, we propose the Enhanced Harmonic Energy Allocation (EHEA) method that dynamically adjusts the weighting between these two modules based on the periodicity of the input time series, allowing the model to better adapt to varying signal dynamics. Experimental results demonstrate that SleepTFANet consistently outperforms benchmark models and current state-of-the-art methods, showcasing superior robustness and generalization across multiple real-world datasets. Furthermore, ablation studies confirm the indispensability of each component in our proposed model, offering a promising approach for automatic sleep stage classification using respiratory signals. © 2025 IEEE.

关键词： deep learning frequency domain multi-head self-attention respiratory signal Sleep stage

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Enhanced Triple Layered Approach for Mitigating Security Risks in Cloud

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computers, Materials & Continua 2025年第4期83卷 719-738页

作者： Tajinder Kumar Purushottam Sharma Xiaochun Cheng Sachin Lalar Shubham Kumar Sandhya Bansal Computer Science&Engineering Department Jai Parkash Mukand Lal Innovative Engineering&Technology InstituteRadaur135133HaryanaIndia School of Computer Science&Engineering Galgotias UniversityGreater Noida203201Uttar PradeshIndia Computer Science Department Bay Campus Fabian WaySwansea UniversitySwanseaSA18ENUK Department of Engineering and Technology Gurugram UniversityGurugram122003India Presidency School of Computer Science Presidency UniversityBangalore560089India Maharishi Markendeshwar Engineering College Maharishi Markandeshwar(Deemed to be)UniversityMullanaAmbala133203India

With cloud computing,large chunks of data can be handled at a small ***,there are some reservations regarding the security and privacy of cloud data *** solving these issues and enhancing cloud computing security,this research provides a Three-Layered Security Access model(TLSA)aligned to an intrusion detection mechanism,access control mechanism,and data encryption *** TLSA underlines the need for the protection of sensitive *** proposed approach starts with Layer 1 data encryption using the Advanced Encryption Standard(AES).For data transfer and storage,this encryption guarantees the data’s authenticity and ***,the solution employs the AES encryption algorithm to secure essential data before storing them in the Cloud to minimize unauthorized ***-based access control(RBAC)implements the second strategic level,which ensures specific personnel access certain data and *** RBAC,each user is allowed a specific role and *** implies that permitted users can access some data stored in the *** layer assists in filtering granular access to data,reducing the risk that undesired data will be discovered during the *** 3 deals with intrusion detection systems(IDS),which detect and quickly deal with malicious actions and intrusion *** proposed TLSA security model of e-commerce includes conventional levels of security,such as encryption and access control,and encloses an insight intrusion detection *** method offers integrated solutions for most typical security issues of cloud computing,including data secrecy,method of access,and *** extensive performance test was carried out to confirm the efficiency of the proposed three-tier security *** have been made with state-of-art techniques,including DES,RSA,and DUAL-RSA,keeping into account Accuracy,QILV,F-Measure,Sensitivity,MSE,PSNR,SSIM,and computation time,encryption time,and decryption *** proposed

关键词： Cloud security:data encryption AES access control intrusion detection systems(IDS) role-based access control(RBAC)

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A Wavelet-based Diffusion Model for Low-light Image Enhancement in Raw Space 16

A Wavelet-based Diffusion Model for Low-light Image Enhancem...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Zhang, Xin-Yu Xu, Yang Wei, Zhi-Hui Wu, Ze-Bin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Low-light image enhancement (LLIE) in Raw space has posed a challenge in the field of image processing and computational photography. Different from image processing in sRGB space, Raw images store more image information and thus have more space for image processing. Existing methods often struggle to approximate real images in terms of image detail enhancement and color restoration. Drawing inspiration from the latest advancements in denoising diffusion models, we introduce an innovative framework that leverages wavelet transform and diffusion models to tackle the low light image enhancement challenge in Raw space. In our method, the input low-light image is first decomposed into different frequency sub-bands by discrete wavelet transform (DWT) from both image and feature levels. Then a redesigned diffusion model is trained to adaptively denoise and enhance the different frequency sub-bands. In order to achieve better results in human eye perception, we also improved the loss function during training process. Finally, the enhanced result of the low-light image is finally reconstructed through inverse wavelet transform (IWT) and subsequent processing, transitioning from Raw space to sRGB space. Extensive experiments on SID and ELD datasets show that our refined method outperforms several existing methods, delivering superior performance both quantitatively and qualitatively. © 2025 SPIE.

关键词： Discrete wavelet transforms

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MTMDC-GAN: Self-Attention Driven Multi-Scale Temporal Synthesis with Multi-Domain Analysis and Contrastive Learning

MTMDC-GAN: Self-Attention Driven Multi-Scale Temporal Synthe...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhi, Weihai He, Kejing School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

The synthesis of high-quality multivariate time series (MTS) is critical for enhancing the performance of predictive models and data-driven decision-making. This paper introduces a novel approach to MTS generation, integrating multi-scale data fusion with self-attention mechanisms to capture complex interdependencies across temporal scales, which to some extent aids the generator in producing coherent MTS, improving the representational capacity of the data. The methodology extends beyond conventional spatial feature extraction by incorporating Fourier Transformation analysis in the frequency domain and Contrastive Learning in the time domain. This multi-domain approach uncovers deeper insights into the underlying patterns and cyclic behaviors of time series data. Extensive experimentation across different datasets and configurations demonstrate superiority over existing techniques, achieving greater accuracy and realism in synthesized MTS. © 2025 IEEE.

关键词： Contrastive Learning Fourier Transformation Multi-Scale Synthesis

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Architecture Decisions for Supporting Web-based Visualization of Distribution System Operating Envelopes

Architecture Decisions for Supporting Web-based Visualizatio...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Fernandez, Jorge Grijalva, Santiago School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331541125

Operating envelopes are dynamic limits on power injections, computed in advance based on the operational con-ditions of the network, such as load profiles. These dynamic limits can unlock the full potential of distributed energy resources (DERs) by maximizing the use of existing infrastructure while ensuring a reliable power supply. Visualizing operating envelopes presents a significant challenge, it requires effectively conveying spatio-temporal information for multiple connection sites simultaneously. This paper discusses, demonstrates, and evaluates a set of software architecture decisions to support web-based visualization of distribution system operating envelopes. We propose a microservice-oriented architecture with two processing modules: one for time series power flow and another for three-phase unbalanced multi-period optimal power flow (also known as energy scheduling) computations. Both modules are con-tainerized, ensuring strong separation of duties and portability. Additionally, a web-based visualization interface is developed to incorporate advanced interactive visualization techniques to support the analysis of complex spatio-temporal relationships and 'what if' scenarios. The performance of the approach is evaluated by measuring processing time and size from responses considering various distribution feeders, including the 3- Bus, IEEE 4-Bus, IEEE 13-Bus, and IEEE 123-Bus test feeders. © 2025 IEEE.

关键词： Electric load flow

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Comparative Assessment of Accessibility and Readability in Generative AI: A Case Study of GPT and Google BARD 4th

Comparative Assessment of Accessibility and Readability in G...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Afsal, C.P. Kuppusamy, K.S. Department of Computer Science School of Engineering and Technology Pondicherry University Puducherry India

ISBN: (纸本)9789819757022

The accessibility and readability of Generative Artificial Intelligence systems like GPT and Google BARD are crucial factors that require thorough examination. In today’s digitally connected world, where AI-generated content is becoming increasingly prevalent across various sectors, ensuring that such content is accessible to a broad audience, easy to understand, and user-friendly is essential. This research was motivated by the growing influence of AI-generated content on various user groups, including individuals with varying levels of familiarity with technology and diverse cognitive abilities. This paper comprehensively assesses and draws comparisons between the accessibility and readability of Google BARD and GPT to identify areas for enhancement. GPT exhibits various accessibility issues across different WCAG 2.0 conformance levels, contrasting with Google BARD, which shows no accessibility problems. The results underscore the importance of addressing accessibility concerns in AI models to ensure inclusivity and user-friendliness for individuals with disabilities. Google BARD consistently provides more readable content than GPT, underscoring the importance of selecting the appropriate tool based on the target audience and desired readability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ambient intelligence

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Point Cloud Classification Fall Detection Method Based on Millimeter Wave Radar 24

Point Cloud Classification Fall Detection Method Based on Mi...

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4th International Joint Conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Zhou, Manyue Gong, Pengcheng Wu, Yuntao Liu, Jianyu School of Computer Science and Engineering Wuhan Institute of Technology Hubei Wuhan China

ISBN: (纸本)9798400710100

Falling at home is the most common cause of injury among the elderly. Wearable devices are inconvenient and costly, while camera systems pose risks to privacy. A fall detection method employing millimeter-wave radar coupled with a classification network is proposed to address these issues. This approach leverages the benefits of millimeter-wave radar and deep learning. The raw data processed by the radar yields the required information, which is then rendered into a point cloud. Subsequent voxelization enhances feature learning, enabling the model to function even with sparse data. The information is then fed into our custom-designed network for time series extraction to classify behaviors. Finally, the time series extraction network utilized in this study is compared with several popular networks, demonstrating that our system offers an effective technological avenue for target detection and posture analysis, and boasts high algorithmic precision in millimeter-wave radar fall detection. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Fall detection

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Malicious Code Detection Based on Generative Adversarial Model 6th

Malicious Code Detection Based on Generative Adversarial Mo...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Zhang, Jinzhihao Yang, Jia Zhou, Weiqi School of Computer Science and Engineering Hubei University of Technology Wuhan430000 China

ISBN: (纸本)9789819645657

With the increasing diversity and complexity of malicious code, the threats posed by malicious code are continuously growing, making malicious code detection increasingly challenging. Currently, researchers typically extract malware features and use deep-learning models for malware detection. Using image-based features for classification can improve the accuracy and efficiency of malware detection. However, traditional deep learning-based malware detection faces challenges such as data imbalance, insufficient data volume, and malware obfuscation. We propose to augment the malicious code dataset using Generative Adversarial Network (GAN) technology to improve the accuracy and efficiency of malicious code detection. First, we converted malicious software binary files into grayscale images. We used Generative Adversarial Network (GAN), Wasserstein Generative Adversarial Network (WGAN), and Wasserstein Generative Adversarial Networks-Gradient Penalty (WGAN-GP) models to augment the grayscale image dataset of malicious code. Subsequently, we obtained three augmented datasets: w1, w2, and w3. These datasets were then fed into the ResNet50 neural network model for training. Experimental results indicate that using the GAN model for data augmentation improved ResNet’s recognition accuracy by approximately 10%. When using the WGAN model for data augmentation, ResNet’s recognition accuracy increased by 2%. Similarly, using the WGAN-GP model for data augmentation also increased ResNet’s recognition accuracy by 2%. Among the three models, the GAN model demonstrated the strongest improvement effect. We demonstrate that the ResNet50 network with GAN models can significantly improve malware code detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Multi-modal Knowledge Distillation-guided Artery-Vein Segmentation Network for OCT/OCTA Images 16

Multi-modal Knowledge Distillation-guided Artery-Vein Segmen...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Li, Shuo Zhang, Wenbo Chen, Qiang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The analysis of artery and vein differences in Optical Coherence Tomography Angiography (OCTA) is of great significance for diagnosing various eye diseases and systemic diseases (such as diabetic retinopathy, hypertension, and cardiovascular diseases). We propose a multi-modal knowledge distillation-guided Artery-Vein Segmentation Network for OCT/OCTA images (MKD-Net). Firstly, we introduce 2D projection image information in the process of segmenting from 3D to 2D to enhance the semantic representation capability in the horizontal direction of the network. Secondly, we transfer knowledge from a trained multi-modal (3D and 2D input) teacher network to a single-modal (3D input) student network, enabling the student network to not rely on the projection images generated by retinal layer segmentation, avoiding segmentation failure under diseased conditions, and enhancing its ability to capture details and feature changes in the images. Extensive evaluations on the OCTA500 and OCTA100 datasets demonstrate that the proposed method achieves superior performance, with an MIou of 84.96% and 84.25% on the OCTA500 and OCTA100 datasets respectively. © 2025 SPIE.

关键词： Optical coherence tomography

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