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Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG systems Review

IEEE Journal of Biomedical and Health Informatics 2023年第8期27卷 3830-3843页

作者： He, Congying Chen, Yu-Yi Phang, Chun-Ren Stevenson, Cory Chen, I-Ping Jung, Tzyy-Ping Ko, Li-Wei Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Applied Arts National Yang Ming Chiao Tung University Hsinchu30010 Taiwan International Ph.D. Program in Interdisciplinary Neuroscience National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San DiegoCA90093 United States National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Biomedical Science and Environment Biology Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems. © 2023 IEEE.

关键词： Electroencephalography

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Correction to: NeXtSRGAN: enhancing super-resolution GAN with ConvNeXt discriminator for superior realism (The Visual Computer, (2025), 10.1007/s00371-024-03797-2)

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Visual Computer 2025年 1-1页

作者： Park, Sung-Wook Jung, Se-Hoon Sim, Chun-Bo Interdisciplinary Program in IT-Bio Convergence System Sunchon National University Suncheon57922 Korea Republic of Department of Computer Engineering Sunchon National University Suncheon57922 Korea Republic of

The publication of this article unfortunately contained mistakes. The assignment of the affiliations was not correct. The corrected affiliation is given below. Wrong assignment: Chun-Bo Sim 1,2 1 interdisciplinary program in IT-Bio Convergence System, Sunchon National University, Suncheon 57922, Republic of Korea 2 department of Computer engineering, Sunchon National University, Suncheon 57922, Republic of Korea Correct assignment: Chun-Bo Sim 1 1 interdisciplinary program in IT-Bio Convergence System, Sunchon National University, Suncheon 57922, Republic of Korea The original article has been corrected. © The Author(s) 2025.

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Bagging System Performance Analysis Using Artificial Neural Network, Random Forest Regression, Linear Regression, and Support Vector Regression

Bagging System Performance Analysis Using Artificial Neural ...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Ari Primantara Udisubakti Ciptomulyono Berlian Al Kindhi Interdisciplinary School of Management Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Industrial and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Automation Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Agriculture is one of the most important sectors in the world because it provides commodities such as rice, cassava, corn, rubber, and palm oil. Urea fertilizer is essential for use in crops, and the demand for this product increases every year due to rapid globalization. Fertilizer firms must reduce production costs and product losses to remain competitive. This study introduces the use of machine learning to predict which algorithm could give the best result in predicting fertilizer weight losses. The algorithms studied were linear regression, artificial neural network, random forest regression, and support vector regression. The results show R2, MAE, and RMSE for each method. The R2 values for ANN, Random Forest, Linear Regression, and Support Vector Regression are 0.9509; 0.9759; 0.9507; and 0.9367, respectively. Random Forest shows the best result compared with the three other algorithms, implying that it is the most accurate algorithm for this research, followed by the supporting result MAE value of 0.0829, which is smaller than the other.

关键词： Support vector machines Accuracy Machine learning algorithms Costs Linear regression Artificial neural networks Predictive models Prediction algorithms Random forests Fertilizers

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A Low Complexity ML-Based Methods for Malware Classification

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Computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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I.T. Professionals' Preference for Work From Home Setup: A Conjoint Analysis Approach 22

I.T. Professionals' Preference for Work From Home Setup: A C...

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8th International Conference on e-Society, e-Learning and e-Technologies, ICSLT 2022

作者： Praga, Dhan Joseph P. Prasetyo, Yogi Tri Nadlifatin, Reny Persada, Satria Fadil Chuenyindee, Thanatorn School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering and Aviation Management Navaminda Kasatriyadhiraj Royal Air Force Academy Bangkok Thailand

ISBN: (纸本)9781450396660

Due to constant growth of technology it results to increase efficiency to work remotely, as a result, there is an increase in percentage of I.T. professionals working from home. This study used Conjoint Analysis in order to determine the most preferred work from home setup attributes by I.T. professionals. Using the attributes such as having a dedicated workspace, type of internet connection, type of communication tool, audio tool, accessories for their computer or laptop, type of chair, and frequency of team socials. Using conjoint analysis with orthogonal design, this study was able to evaluate the preference among I.T. professionals. The result of the study showed that the type of Internet Connection is the most important (30.975%). Next is the preference on dedicated workspace (22.633%), the type of accessories (13.059%), Communication tool (10.982%), frequency of team socials (9.152%), type of chairs (6.723%), least is the type of audio tools (6.477%). The result of this study could be used by the employers to improve the work from home conditions of their employees. © 2022 Association for Computing Machinery.

关键词： Statistical methods

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On the maximum of cotangent sums related to the Riemann hypothesis in rational numbers in short intervals

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Moscow Journal of Combinatorics and Number Theory 2022年第4期10卷 303-313页

作者： Maier, Helmut Rassias, Michael Th. Department of Mathematics University of Ulm Ulm Germany Department of Mathematics and Engineering Sciences Hellenic Military Academy Athens Greece Moscow Institute of Physics and Technology Dolgoprudny Russian Federation Program in Interdisciplinary Studies Institute for Advanced Study Princeton NJ United States

Cotangent sums play a significant role in the Nyman–Beurling criterion for the Riemann hypothesis. Here we investigate the maximum of the values of these cotangent sums over various sets of rational numbers in short ... 详细信息

关键词： Cotangent sums Estermann’s zeta function Kloosterman sums Nyman–Beurling criterion Riemann hypothesis Riemann zeta function

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Association of the Factors Affecting Risk Score in HIRADC Form of a Thermal Power Plant 24

Association of the Factors Affecting Risk Score in HIRADC Fo...

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6th International Conference on Management Science and Industrial engineering, MSIE 2024

作者： Uayan, Christian Jay A. Prasetyo, Yogi Tri Novendah, Ierenne Ayuwati, Irene Dyah Cahigas, Maela Madel L. Nadlifatin, Reny School of Industrial and Engineering Management Mapúa University 658 Muralla St. Intramuros Manila1002 Philippines School of Graduate Studies Mapúa University 658 Muralla St. Intramuros Manila1002 Philippines International Bachelor Program in Engineering Yuan Ze University 135 Yuan-Tung Road Chung-Li32003 Taiwan Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road Chung-Li32003 Taiwan University of Surabaya Surabaya60284 Indonesia School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya60111 Indonesia

ISBN: (纸本)9798400717130

In devising an effective Hazard Identification, Risk Assessment and Determining Controls plan of a company, it is crucial to understand the factors that relate with the Inherent and Residual Risks to prioritize the implementation of controls. In this study, the Process Area, Activity, Hazard Category, Inherent Risk Score and Residual Risk Score are brought into Chi-Squared Test to identify which factors are closely associated with Inherent and Residual Risk Scores. Moreover, the factors and range of scores were assessed into categorical level through Phi coefficient analysis. Chi-Squared Test dictates that there is a close relationship between Process Area and the Inherent Risk Score while Activity is closely associated with the Residual Risk Score. Results suggest that the thermal power plant is putting more effort into activities that involve high-level risks especially those that directly affect the environment. © 2024 ACM.

关键词： Thermoelectric power plants

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Teaching Control Theory using Mobile Robot Obstacle Following/Avoidance with CoppeliaSim and MFO Algorithm.

Teaching Control Theory using Mobile Robot Obstacle Followin...

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IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana J. Bautista Jose Mendoza-Peñaloza L. H. Oliveira D. A. Mendes Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which students interact with simulations for control systems. The enhancement of visualization and interaction offered by modern computers presents an opportunity to teach the theoretical foundation with a more organic approach. In addition, there are optimization algorithms that can be employed to designing controllers in an optimal way without having extensive knowledge in the area of control theory. This paper delineates the utilization of CoppeliaSim software, the Moth Flame Optimization (MFO) algorithm, and the EVA mobile robot for teaching control theory with Single-Input, Single-Output systems (SISO), for mobile robot obstacle following/avoidance application. The approach employs an online multi-language (Spanish and Portuguese) methodology for students without knowledge of control theory.

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Distributed Mixed-Integer Quadratic programming for Mixed-Traffic Intersection Control

arXiv

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arXiv 2025年

作者： Le, Viet-Anh Malikopoulos, Andreas A. Department of Mechanical Engineering University of Delaware NewarkDE19716 United States Systems Engineering Program Cornell University IthacaNY14850 United States School of Civil and Environmental Engineering Cornell University IthacaNY14853 United States

In this paper, we present a distributed algorithm utilizing the proximal alternating direction method of multipliers (ADMM) in conjunction with sequential constraint tightening to address mixed-integer quadratic programming (MIQP) problems associated with traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We formulate a comprehensive MIQP model aimed at optimizing the coordination of traffic light systems and CAVs, thereby fully capitalizing on the advantages of CAV integration under conditions of high penetration rates. To effectively approximate the intricate multi-agent MIQP challenges, we develop a distributed algorithm that employs proximal ADMM for solving the convex relaxation of the MIQP while systematically tightening the constraint coefficients to uphold integrality requirements. The performance of our control framework and the efficacy of the distributed algorithm are rigorously validated through a series of simulations conducted across varying penetration rates and traffic volumes. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer programming

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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