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Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction

Annals of Data science 2022年第2期9卷 393-428页

作者： Emara, Heba M. Elwekeil, Mohamed Taha, Taha E. El-Fishawy, Adel S. El-Rabaie, El-Sayed M. El-Shafai, Walid El Banby, Ghada M. Alotaiby, Turky Alshebeili, Saleh A. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt KACST Riyadh Saudi Arabia King Saud University Riyadh Saudi Arabia Department of Electrical Engineering King Saud University Riyadh11421 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh21974 Saudi Arabia

Seizure detection and prediction are a very hot topics in medical signal processing due to their importance in automatic medical diagnosis. This paper presents three efficient frameworks for applications related to electroencephalogram (EEG) signal processing. The first one is an automatic seizure detection framework based on the utilization of scale-invariant feature transform (SIFT) as an extraction tool. The second one depends on the utilization of the fast Fourier transform (FFT) and an artificial neural network for epileptic seizure prediction. Finally, an automated patient-specific framework for channel selection and seizure prediction is presented based on FFT. The simulation results show the success of the proposed frameworks for automated medical diagnosis. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fast Fourier transforms

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Integration of ANN for Accurate Estimation and control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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AeroGTO: An Efficient Graph-Transformer Operator for Learning Large-Scale Aerodynamics of 3D Vehicle Geometries 39

AeroGTO: An Efficient Graph-Transformer Operator for Learnin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Pengwei Wang, Pengkai Ren, Xingyu Yuan, Hangjie Hao, Zhongkai Xu, Chao Cai, Shengze Ni, Dong College of Control Science and Engineering Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Obtaining high-precision aerodynamics in the automotive industry relies on large-scale simulations with computational fluid dynamics, which are generally time-consuming and computationally expensive. Recent advances in operator learning for partial differential equations offer promising improvements in terms of efficiency. However, capturing intricate physical correlations from extensive and varying geometries while balancing large-scale discretization and computational costs remains a significant challenge. To address these issues, we propose AeroGTO, an efficient graph-transformer operator designed specifically for learning large-scale aerodynamics in engineering applications. AeroGTO combines local feature extraction through message passing and global correlation capturing via projection-inspired attention, employing a frequency-enhanced graph neural network augmented with k-nearest neighbors to handle three-dimensional (3D) irregular geometries. Moreover, the transformer architecture adeptly manages multi-level dependencies with only linear complexity concerning the number of mesh points, enabling fast inference of the model. Given a car's 3D mesh, AeroGTO accurately predicts surface pressure and estimates drag. In comparisons with five advanced models, AeroGTO is extensively tested on two industry-standard benchmarks, Ahmed-Body and DrivAerNet, achieving a 7.36% improvement in surface pressure prediction and a 10.71% boost in drag coefficient estimation, with fewer FLOPs and only 1% of the parameters used by the prior leading method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Super Resolution of Unpaired MR Images Based on Domain Migration 18th

Super Resolution of Unpaired MR Images Based on Domain Migra...

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Liu, Xiaodong Guo, Haipeng Liu, Huanyu Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819901043

High-resolution magnetic resonance imaging (MRI) is very important for doctors to judge the condition. At present, the depth learning method has been widely studied in the super-resolution reconstruction of MR images. However, due to the distribution difference between the real low-resolution MR images and the down sampled MR images used to train the super-resolution model under experimental conditions, the super-resolution performance of the super-resolution method is often seriously degraded when super-resolution is applied to the real low-resolution MR images. Blind super-resolution methods used to solve such problems in natural image super-resolution are lack of image content constraints. It is not advisable to introduce additional information that will interfere with doctors’ diagnosis in the super-resolution process of MR images. These two problems hinder the application of super-resolution method in real MR images. In order to solve these problems, an unpaired MR image super-resolution network based on domain migration is proposed. The proposed DMN can use a real set of low to high-resolution unpaired MR images as input, migrate the real low-resolution MR images to the down sampled MR image domain, and then super-resolution reconstruction is carried out through the paired super-resolution network obtained through supervision training. Experiments on fast MRI datasets verify the effectiveness of this method in solving the super-resolution problem of real MR images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Image Inversion: A Survey from GANs to Diffusion and Beyond

arXiv

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arXiv 2025年

作者： Chen, Yinan Zhang, Jiangning Bi, Yali Hu, Xiaobin Hu, Teng Xue, Zhucun Yi, Ran Liu, Yong Tai, Ying College of Control Science and Engineering Zhejiang University China YouTu Lab Tencent China College of Computer and Information Science Southwest University China Department of Computer Science & Engineering Shanghai Jiao Tong University China School of Intelligence Science and Technology Nanjing University China

Image inversion is a fundamental task in generative models, aiming to map images back to their latent representations to enable downstream applications such as editing, restoration, and style transfer. This paper provides a comprehensive review of the latest advancements in image inversion techniques, focusing on two main paradigms: Generative Adversarial Network (GAN) inversion and diffusion model inversion. We categorize these techniques based on their optimization methods. For GAN inversion, we systematically classify existing methods into encoder-based approaches, latent optimization approaches, and hybrid approaches, analyzing their theoretical foundations, technical innovations, and practical trade-offs. For diffusion model inversion, we explore training-free strategies, fine-tuning methods, and the design of additional trainable modules, highlighting their unique advantages and limitations. Additionally, we discuss several popular downstream applications and emerging applications beyond image tasks, identifying current challenges and future research directions. By synthesizing the latest developments, this paper aims to provide researchers and practitioners with a valuable reference resource, promoting further advancements in the field of image inversion. We keep track of the latest works at https://***/RyanChenYN/ImageInversion. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction

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IEEE Access 2025年 13卷 97529-97544页

作者： Santhosh, Sujith Chadaga, Krishnaraj Vijaya Arjunan, R. D'Souza, Sandra Manipal Academy of Higher Education Manipal Institute of Technology Department of Instrumentation and Control Engineering Karnataka Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal576104 India

The major contributor to global mortality is cardiovascular disease, posing a formidable challenge to the global healthcare system. Heart disease often develops and progresses without noticeable symptoms, emphasizing the need for earlier detection to prevent severe outcomes. AI models provide tools for accurate diagnosis, data analysis, and predictive diagnosis of people who may be suffering from heart disease. But increasingly, solutions like Explainable AI (XAI) are gaining traction. XAI demystifies the decisions of AI models, making them transparent and understandable to the point where they boost confidence in the context of AI diagnostics by healthcare professionals;the AI then provides support to professionals to decide the best course of action for each patient based on the analysis. XAI techniques such as SHAP, LIME, QLattice and Anchor are popular, and have been used in this study. We analysed a heart disease dataset collected from a multispecialty hospital in India, which is publicly available on Mendeley. We used ML models, including RF, Logistic Regression, KNN, XGBoost, DT and SVM. These models were optimized and stack-ensembled to form the 'CARDIACX' model to predict the risk of heart disease. The models were fine-tuned using Grid Search, Random Search, and Bayesian Optimization methods, achieving promising results with Random Forest achieving an AUC = 0.99 and accuracy of 98.5%, outperforming all the other models and demonstrating robust performance on various metrics such as AUC, PR Curve, Log Loss, Jaccard Score, and MCC. This study provides a reliable and transparent framework for early detection of heart disease, providing useful information and enabling patients to receive timely and personalized care. © 2013 IEEE.

关键词： Logistic regression

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Keynote Talk: Qualitative model based diagnosis of complex systems using decomposition 22

Keynote Talk: Qualitative model based diagnosis of complex s...

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Proceedings of the 7th International Conference on Sustainable Information engineering and Technology

作者： Katalin M. Hangos Systems and Control Laboratory Institute for Computer Science and Control Hungary and Department of Electrical Engineering and Information Systems University of Pannonia Hungary

ISBN: (纸本)9781450397117

Decomposition offers the potential to reduce the complexity of model-based optimization, prediction, control and diagnosis by accounting for the structure and sparsity of the describing model. Motivated by this fact, a rich and powerful collection of decomposition methods are available for model based diagnosis of large-scale complex dynamic systems, too. At the same time, one usually does not have enough information about a large-scale complex dynamic system to construct its precise enough model, so a kind of qualitative dynamic model is often used for the diagnosis [1]. Two structural decomposition based qualitative diagnostic methods are presented in this lecture, together with their component-driven system decomposition ***, a model-based diagnostic method is described that is able to detect and isolate non-technical losses (illegal loads) in low voltage electrical grids of one transformer area [2]. As a preliminary off-line step of the diagnosis, a powerful electrical decomposition method is proposed, which breaks down the overall network to subsystems with one feeder layout enabling to make the necessary computations efficient. The diagnostic method is based on analyzing the differences between the measured and model-predicted voltages. The uncertainty in the model parameters together with the measurement uncertainties are also taken into account to make the approach applicable in real-world cases. The proposed method is able to detect and localize multiple illegal loads, and the amount of the illegal consumption can also be *** a second case study, a high level decomposition approach for process system fault diagnosis using event traces is given [3], [4]. Using a simple component graph model behind the process system and the measured trace applied for the diagnosis, the method can find the root cause(s) of propagating failures between separate components. The method can connect individually operating lower-level component-specific diag

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Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications

arXiv

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arXiv 2024年

作者： Engelaar, Maico H.W. Zhang, Zengjie Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Lazar, Mircea Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into sub-specifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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