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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Report on the 12th International Workshop on Quantitative Approaches to software Quality (QuASoQ 2024) 12

Report on the 12th International Workshop on Quantitative Ap...

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12th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2024

作者： Lichter, Horst Wild, Nils Sunetnanta, Thanwadee Anwar, Toni Research Group Software Construction RWTH Aachen University Aachen Germany Computer Science Academic Group Faculty of Information And Communication Technology Mahidol University Bangkok Thailand Faculty of Science and Information Technology Chair Computer & Information Systems Universiti Teknologi Petronas Bandar Seri Iskandar Perak Malaysia

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Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

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The Level of Digital Literacy among Students in Uzbekistan: Assessing Skills and Perspectives 13

The Level of Digital Literacy among Students in Uzbekistan: ...

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13th International Symposium on Computer Science and Educational technology, ISCSET 2024

作者： Rasulova, Nasibakhon Yusufjonovna Zaripova, Dilnoza Anvarovna Otaxonova, Bahrixon Ibragimovna Zakirova, Feruza Maxmudovna Tashmukhamedova, Gulnora Khudayberdievna Anufriyeva, Yuliya Genadyevna Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Information Education Technology Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Software of Information Technologies Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi TV Studio Systems and Applications Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Information Library Systems Tashkent Uzbekistan

ISBN: (纸本)9798350390803

The aim of the article is to determine the level of digital literacy among students of the Muhammad al-Khwarizmi University of information Technologies in Tashkent and identify their strengths and weaknesses in this area. The article provides specific recommendations for improving students' digital literacy based on the findings and analysis of existing teaching methods. The study yielded both quantitative and qualitative results. Quantitative data include the percentage of students with varying levels of digital literacy in different areas such as information, communication, productivity, and security. Qualitative results encompass an analysis of students' strengths and weaknesses in digital skills identified during the study, along with suggestions for enhancing educational programs to improve digital literacy. The research findings can be applied in education to enhance students' digital literacy, providing a foundation for developing educational programs and courses and further exploring the issue. © 2024 IEEE.

关键词： Communication digital competence digital literacy digital technologies higher education information literacy productivity security students survey

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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chi, Haotian Ma, Qi Zhang, Yuxuan Fu, Chenglong Wang, Yuwei Geng, Haijun Du, Xiaojiang Shanxi University School of Automation and Software Engineering Taiyuan China University of North Carolina at Charlotte Department of Software and Information Systems CharlotteNC United States Stevens Institute of Technology Department of Electical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon. © 2024 IEEE.

关键词： Smart manufacturing

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A Novel Cryptocurrency Prediction Method Using Optimum CNN

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Computers, Materials & Continua 2022年第4期71卷 1051-1063页

作者： Syed H.Hasan Syeda Huyam Hasan Mohammed Salih Ahmed Syed Hamid Hasan Iowa State University AmesUSA University of Texas at Arlington USA Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

In recent years,cryptocurrency has become gradually more significant in economic regions *** cryptocurrencies,records are stored using a cryptographic *** main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social *** is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media ***,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet *** paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and *** results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.

关键词： Cryptocurrency litecoin monero bitcoin ethereum twitter optimal CNN price prediction sentiment analysis

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Towards Smart Farming: Simulation Framework to Exploit Network Connectivity for End-to-End Data Transmission 14

Towards Smart Farming: Simulation Framework to Exploit Netwo...

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14th International Conference on software, Knowledge, information Management and Applications, SKIMA 2022

作者： Huo, Dongyang Ravana, Sri Devi Malik, Asad Waqar Rahman, Anis Ur Ahmedy, Ismail University of Malaya Faculty of Computer Science & Information Technology Department of Information Systems Kuala Lumpur Malaysia Department of Computing Islamabad Pakistan University of Malaya Faculty of Computer Science & Information Technology Department of Computer Systems & Technology Kuala Lumpur Malaysia

ISBN: (纸本)9781665493345

Traditional agriculture is highly dependent on human participation and experience. Therefore, it is not possible to guarantee crop yield and quality while expanding the production scale. In recent years, based on the continuous development and innovation of IoT and ICT technologies, the concept of smart farming has started to receive the attention of researchers and farmers. However, many existing smart agriculture applications and research are conducted under controlled environments such as greenhouse farming, vertical farming, and hydroponic farming, etc. Few studies have shifted attention to large-scale arable agriculture, thus neglecting the scalability of the system. This work presents a simulation stack (digital mapping) in which the results of running the simulation model in a large-scale class of real-world scenarios are used to enhance the further performance of the proposed farming system in large-scale scenarios. © 2022 IEEE.

关键词： Hydroponics

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IoMT-Based Smart Healthcare of Elderly People Using Deep Extreme Learning Machine

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Computers, Materials & Continua 2023年第7期76卷 19-33页

作者： Muath Jarrah Hussam Al Hamadi Ahmed Abu-Khadrah Taher M.Ghazal School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia Center for Cyber-Physical Systems Khalifa University127788Abu DhabiUAE

The Internet of Medical Things(IoMT)enables digital devices to gather,infer,and broadcast health data via the cloud *** phenomenal growth of the IoMT is fueled by many factors,including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based *** is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising *** IoMT is a novel reality transforming our daily *** can renovate modern healthcare by delivering a more personalized,protective,and collaborative approach to ***,the current healthcare system for outdoor senior citizens faces new *** healthcare systems are inefficient and lack user-friendly technologies and interfaces appropriate for elderly people in an outdoor ***,in this research work,a IoMT based Smart Healthcare of Elderly people using Deep Extreme Learning Machine(SH-EDELM)is proposed to monitor the senior citizens’*** performance of the proposed SH-EDELM technique gives better results in terms of 0.9301 accuracy and 0.0699 miss rate,respectively.

关键词： ICT ML FN DELM SH-EDELM

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Enhancing IoT Botnet Attack Detection in SOCs with an Explainable Active Learning Framework

Enhancing IoT Botnet Attack Detection in SOCs with an Explai...

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AI IoT Congress (AIIoT), World

作者： Rajesh Kalakoti Sven Nõmm Hayretdin Bahsi Department of Software Science School of Information Technology TalTech Tallinn Estonia Department of Software Science School of Information Technology TalTech Estonia & School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (数字)9798350387803

ISBN: (纸本)9798350387810

The widespread use of Internet of Things (IoT) devices has raised the threat of botnet attacks, presenting significant challenges for security operations centres (SOCs). While machine learning techniques have shown promising results in detecting these attacks, their effectiveness is often limited by the lack of labeled data and the need for greater transparency in the decision-making process of labeling. We propose an explainable active learning framework incorporating post-hoc explainability methods, such as LIME and SHAP, into the active learning process for detecting IoT botnet attacks in a multi-class classification setting. Our framework enables SOC analysts to provide informed annotations, while the explainability methods offer insights into the model’s decision-making process. We employ uncertainty sampling and query-by-committee strategies to select the most informative instances for labeling, and we evaluate the quality of the explanations using various quantitative metrics. Experimental results demonstrate that our explainable active learning framework achieves high detection performance while enhancing the trust and transparency between the SOC analysts and the learning model.

关键词： Measurement Analytical models Uncertainty Botnet Decision making Network intrusion detection Internet of Things

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