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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

Computational Intelligence and Neuroscience 2022年第0期2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression

arXiv

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arXiv 2022年

作者： Vardhan, Harsh Timalsina, Umesh Volgyesi, Peter Sztipanovits, Janos Department of Computer Science Vanderbilt University 1400 18th Ave. S. NashvilleTN37212-2846 United States Institute for Software Integrated Systems Vanderbilt University 1025 16th Ave. S. NashvilleTN37212-2328 United States Department of Electrical and Computer Engineering Vanderbilt University 400 24th Ave. S. NashvilleTN37212 United States

In a computer-aided engineering design optimization problem that involves notoriously complex and time-consuming simulator, the prevalent approach is to replace these simulations with a data-driven surrogate that approximates the simulator’s behavior at a much cheaper cost. The main challenge in creating an inexpensive data-driven surrogate is the generation of a sheer number of data using these computationally expensive numerical simulations. In such cases, Active Learning (AL) methods have been used that attempt to learn an input–output behavior while labeling the fewest samples possible. The current trend in AL for a regression problem is dominated by the Bayesian framework that needs training an ensemble of learning models that makes surrogate training computationally tedious if the underlying learning model is Deep Neural Networks (DNNs). However, DNNs have an excellent capability to learn highly nonlinear and complex relationships even for a very high dimensional problem. To leverage the excellent learning capability of deep networks along with avoiding the computational complexity of the Bayesian paradigm, in this work we propose a simple and scalable approach for active learning that works in a student-teacher manner to train a surrogate model. By using this proposed approach, we are able to achieve the same level of surrogate accuracy as the other baselines like DBAL and Monte Carlo sampling with up to 40% fewer samples. We empirically evaluated this method on multiple use cases including three different engineering design domains:finite element analysis, computational fluid dynamics, and propeller design. © 2022, CC BY.

关键词： Deep neural networks

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A Consensus Privacy Metrics Framework for Synthetic Data

arXiv

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arXiv 2025年

作者： Pilgram, Lisa Dankar, Fida K. Drechsler, Jörg Elliot, Mark Domingo-Ferrer, Josep Francis, Paul Kantarcioglu, Murat Kong, Linglong Malin, Bradley Muralidhar, Krishnamurty Myles, Puja Prasser, Fabian Raisaro, Jean Louis Yan, Chao El Emam, Khaled School of Epidemiology and Public Health University of Ottawa ON Canada CHEO Research Institute ON Canada Department of Nephrology and Medical Intensive Care Charité – Universitätsmedizin Berlin Berlin Germany Department for Statistical Methods Institute for Employment Research Nuernberg Germany Institute for Statistics Ludwig-Maximilians-Universität Munich Germany Joint Program in Survey Methodology University of Maryland United States The Cathie Marsh Institute Research School of Social Sciences University of Manchester Manchester United Kingdom Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Catalonia Tarragona Spain Max Planck Institute for Software Systems Germany Department of Computer Science Virginia Tech United States Department of Mathematical and Statistical Sciences University of Alberta Alberta Canada Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Department of Marketing and Supply Chain Management University of Oklahoma Oklahoma United States Medicines and Healthcare products Regulatory Agency London United Kingdom Berlin Institute of Health at Charité Universitätsmedizin Berlin Medical Informatics Group Berlin Germany Biomedical Data Science Center University Hospital Lausanne Lausanne Switzerland

Synthetic data generation is one approach for sharing individual-level data. However, to meet legislative requirements, it is necessary to demonstrate that the individuals’ privacy is adequately protected. There is no consolidated standard for measuring privacy in synthetic data. Through an expert panel and consensus process, we developed a framework for evaluating privacy in synthetic data. Our findings indicate that current similarity metrics fail to measure identity disclosure, and their use is discouraged. For differentially private synthetic data, a privacy budget other than close to zero was not considered interpretable. There was consensus on the importance of membership and attribute disclosure, both of which involve inferring personal information about an individual without necessarily revealing their identity. The resultant framework provides precise recommendations for metrics that address these types of disclosures effectively. Our findings further present specific opportunities for future research that can help with widespread adoption of synthetic data. © 2025, CC BY.

关键词： Differential privacy

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Natidroid: Cross-language android permission specification

arXiv

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arXiv 2021年

作者： Li, Chaoran Chen, Xiao Sun, Ruoxi Xue, Jason Wen, Sheng Ahmed, Muhammad Ejaz Camtepe, Seyit Xiang, Yang School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC3122 Australia Department of Software Systems and Cybersecurity Faculty of IT Monash University ClaytonVIC3800 Australia School of Computer Science University of Adelaide North TerraceSA5005 Australia CSIRO Data61 Australia

The Android system manages access to sensitive APIs by permission enforcement. An application (app) must declare proper permissions before invoking specific Android APIs. However, there is no official documentation providing the complete list of permission-protected APIs and the corresponding permissions to date. Researchers have spent significant efforts extracting such API protection mapping from the Android API framework, which leverages static code analysis to determine if specific permissions are required before accessing an API. Nevertheless, none of them has attempted to analyze the protection mapping in the native library (i.e., code written in C and C++), an essential component of the Android framework that handles communication with the lower-level hardware, such as cameras and sensors. While the protection mapping can be utilized to detect various security vulnerabilities in Android apps, such as permission over-privilege and component hijacking, imprecise mapping will lead to false results in detecting such security vulnerabilities. To fill this gap, we thereby propose to construct the protection mapping involved in the native libraries of the Android framework to present a complete and accurate specification of Android API protection. We develop a prototype system, named NATIDROID, to facilitate the cross-language static analysis to benchmark against two state-of-the-art tools, termed AXPLORER [1] and ARCADE [2]. We evaluate NATIDROID on more than 11,000 Android apps, including system apps from custom Android ROMs and third-party apps from the Google Play. Our NATIDROID can identify up to 464 new API-permission mappings, in contrast to the worst-case results derived from both AXPLORER and ARCADE, where approximately 71% apps have at least one false positive in permission over-privilege and up to 3.6% apps have at least one false negative in component hijacking. Additionally, we identify that 24 components with at least one Native-triggered component hij

关键词： Mobile security

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Flow-Based Programming For Machine Learning

Research Square

引用

Research Square 2021年

作者： Mahapatra, Tanmaya Banoo, Syeeda Nilofer Lehrstuhl für Software und Systems Engineering Fakultät für Informatik Technische Universität München Boltzmannstraße 03 Garching b München85748 Germany Birla Institute of Technology and Science Department of Computer Science and Information Systems Vidya Vihar Pilani333031 India

Machine Learning (ML) has gained prominence and has tremendous applications in fields like medicine, biology, geography and astrophysics, to name a few. Arguably, in such areas it is used by domain experts, who are not necessarily skilled-programmers. Thus, it presents a steep learning curve for such domain experts in programming ML applications. To overcome this and foster widespread adoption of ML techniques, we propose to equip them with domain-specific graphical tools. Such tools, based on the principles of flow-based programming paradigm, would support the graphical composition of ML applications at a higher level of abstraction and auto-generation of target code. Accordingly, (i) we have modelled ML algorithms as composable components;(ii) described an approach to parse a flow created by connecting several such composable components and use an API-based code generation technique to generate the ML application. To demonstrate the feasibility of our conceptual approach, we have modelled the APIs of Apache Spark ML as composable components and validated it in three use-cases. The use-cases are designed to capture the ease of program specification at a higher abstraction level, easy parametrisation of ML APIs, auto-generation of the ML application and auto-validation of the generated model for better prediction accuracy. © 2021, CC BY.

关键词： Machine learning

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A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation 2

A Time Effect based Collaborative Filtering Approach for Use...

引用

2019 2nd International Conference on computer Information science and Artificial Intelligence, CISAI 2019

作者： Chen, Yuxi Zhang, Xiaotong Zhao, Qing Akhtar, Faheem School of Statistics and Mathematics Country Central University of Finance and Economics Beijing100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York13902 United States Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Vrije Universiteit Amsterdam1081 HV Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing100124 China Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect;next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

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science of design for societal-scale cyber-physical systems: challenges and opportunities

引用

Cyber-Physical systems 2019年第3期5卷 145-172页

作者： Sztipanovits, Janos Koutsoukos, Xenofon Karsai, Gabor Sastry, Shankar Tomlin, Claire Damm, Werner Fränzle, Martin Rieger, Jochem Pretschner, Alexander Köster, Frank Institute for Software Integrated Systems Vanderbilt University NashvilleTN United States Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Department of Computer Science University of Oldenburg Oldenburg Germany Computer Science Technical University of Munich München Germany Braunschweig Germany

Emerging industrial platforms such as the Internet of Things (IoT), Industrial Internet (II) in the US and Industrie 4.0 in Europe have tremendously accelerated the development of new generations of Cyber-Physical systems (CPS) that integrate humans and human organizations (H-CPS) with physical and computation processes and extend to societal-scale systems such as traffic networks, electric grids, or networks of autonomous systems where control is dynamically shifted between humans and machines. Although such societal-scale CPS can potentially affect many aspect of our lives, significant societal strains have emerged about the new technology trends and their impact on how we live. Emerging tensions extend to regulations, certification, insurance, and other societal constructs that are necessary for the widespread adoption of new technologies. If these systems evolve independently in different parts of the world, they will ‘hard-wire’ the social context in which they are created, making interoperation hard or impossible, decreasing reusability, and narrowing markets for products and services. While impacts of new technology trends on social policies have received attention, the other side of the coin–to make systems adaptable to social policies–is nearly absent from engineering and computer science design practice. This paper focuses on technologies that can be adapted to varying public policies and presents (1) hard problems and technical challenges and (2) some recent research approaches and opportunities. The central goal of this paper is to discuss the challenges and opportunities for constructing H-CPS that can be parameterized by social context. The focus in on three major application domains: connected vehicles, transactive energy systems, and unmanned aerial vehicles. Abbreviations: CPS: Cyber-physical systems;H-CPS: Human-cyber-physical systems;CV: Connected vehicle;II: Industrial Internet;IoT: Internet of Things. © 2019, © 2019 Informa UK Limited, trading a

关键词： Cyber Physical System

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Design, monitoring, and testing of microservices systems: The Practitioners’ perspective

arXiv

引用

arXiv 2021年

作者： Waseem, Muhammad Liang, Peng Shahin, Mojtaba Salle, Amleto Di Márquez, Gastón School of Computer Science Wuhan University Wuhan430072 China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne3800 Australia Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’AquilaI-67100 Italy Department of Electronics and Informatics Federico Santa María Technical University Concepción4030000 Chile

Context: Microservices Architecture (MSA) has received significant attention in the software industry. However, little empirical evidence exists on design, monitoring, and testing of microservices systems. Objective: This research aims to gain a deep understanding of how microservices systems are designed, monitored, and tested in the industry. Method: A mixed-methods study was conducted with 106 survey responses and 6 interviews from microservices practitioners. Results: The main findings are: (1) a combination of domain-driven design and business capability is the most used strategy to decompose an application into microservices, (2) over half of the participants used architecture evaluation and architecture implementation when designing microservices systems, (3) API gateway and Backend for frontend patterns are the most used MSA patterns, (4) resource usage and load balancing as monitoring metrics, log management and exception tracking as monitoring practices are widely used, (5) unit and end-to-end testing are the most used testing strategies, and (6) the complexity of microservices systems poses challenges for their design, monitoring, and testing, for which there are no dedicated solutions. Conclusions: Our findings reveal that more research is needed to (1) deal with microservices complexity at the design level, (2) handle security in microservices systems, and (3) address the monitoring and testing challenges through dedicated solutions. © 2021, CC BY.

关键词： Surveys

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Review of Machine Learning Applications for Predicting the Quality Biomass Briquettes for Sustainable and Low-Carbon Energy Solutions

Green Energy and Resources

引用

Green Energy and Resources 2025年

作者： Constance Nakato Nakimuli Fred Kaggwa Johan De Greef David Kilama Okot Julien Blondeau Simon Kawuma Department of Information Technology Mbarara University of Science and Technology Uganda Brussels Institute for Thermal-Fluid Systems and Clean Energy(BRITE) Vrije Universiteit Brussel Belgium Thermodynamics and Fluid Mechanics Group Faculty of Engineering Vrije Universiteit Brussel (VUB) Belgium Department of Computer Science Mbarara University of Science and Technology Uganda Department of Materials Engineering – Leuven Group T Campus KU Leuven Leuven Belgium Department of Physics Mbarara University of Science and Technology Uganda Department of Software Engineering Mbarara University of Science and Technology Uganda

This review discusses how Machine Learning is applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper has reviewed literature covering various Machine Learning models applied for predicting and optimizing briquette quality parameters including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example Random Forest with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstock and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policy makers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

关键词： Machine learning briquettes sustainable energy low-carbon solutions agricultural waste municipal waste

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Semi-Supervised Domain-Adversarial Training for Intrusion Detection against False Data Injection in the Smart Grid

Semi-Supervised Domain-Adversarial Training for Intrusion De...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yongxuan Zhang Jun Yan Department of Computer Science and Software Engineering (CSSE) Concordia University Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montréal Canada

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The smart grid faces with increasingly sophisticated cyber-physical threats, against which machine learning (ML)-based intrusion detection systems have become a powerful and promising solution to smart grid security monitoring. However, many ML algorithms presume that training and testing data follow the same or similar data distributions, which may not hold in the dynamic time-varying systems like the smart grid. As operating points may change dramatically over time, the resulting data distribution shifts could lead to degraded detection performance and delayed incidence responses. To address this challenge, this paper proposes a semi-supervised framework based on domain-adversarial training to transfer the knowledge of known attack incidences to detect returning threats at different hours and load patterns. Using normal operation data of the ISO New England grids, the proposed framework leverages adversarial training to adapt learned models against new attacks launched at different times of the day. Effectiveness of the proposed detection framework is evaluated against the well-studied false data injection attacks synthesized on the IEEE 30-bus system, and the results demonstrated the superiority of the framework against persistent threats recurring in the highly dynamic smart grid.

关键词： Feature extraction Training Smart grids Data mining Intrusion detection Testing

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