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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Brown, Michael Scott Momtaz, Maliha Mai, Aaron Modi, Dhruvil University of Maryland Baltimore County Information Systems Department BaltimoreMD United States Hood College Computer Science and It Department FrederickMD United States Texas A&m College Station Computer Science and Engineering Department College StationTX United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering BaltimoreMD United States

ISBN: (纸本)9798350351507

This work is a full research-to-practice paper that describes a predictive method to improve the prediction of student test scores. Predicting student test scores is difficult. However, doing so can improve education greatly by improving advising, scheduling, tutoring assignment and other educational processes. This research extends previous research by using a domain space reduction technique to improve accuracy. Factor Analysis is used to reduce the number of domain attributes for improving the accuracy of a Neural Network to predict student test scores. In this research datasets for Mathematics and Language of high school student test scores were used. Test scores were predicted using a Neural Network computing the Mean Absolute Error as a measurement of accuracy. The datasets have 30 domain attributes each. Factor Analysis was used to reduce the domain size from between 1 to 29, each time using it to train the Neural Network. Because the Mean Absolute Error may vary depending upon which records in the dataset are used for training versus testing, 50 trials of each dataset size were executed producing an Average Mean Absolute Error for each domain size. A statistical test was used to show statistical significance between the Neural Network without Factor Analysis and the Neural Network with varying domain sizes using Factor Analysis. Results were very promising and correspond to previous research that used Principal Component Analysis. Numerous domain sizes had significantly better Average Mean Absolute Errors than the accuracy of the Neural Network without Factor Analysis. This research shows that reducing the domain size using Factor Analysis can greatly improve the accuracy of Neural Networks when predicting student test scores. The best improvements occurred when domain sizes were very small ranging from 2 to 6. Domain reduction techniques, such as Factor Analysis, have been shown to improve predictive models for student test score prediction. Future research

关键词： Support vector machines

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EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network

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Mathematical Biosciences and engineering 2024年第4期21卷 5712-5734页

作者： Qamar, Hafiz Ghulam Murtza Qureshi, Muhammad Farrukh Mushtaq, Zohaib Zubariah, Zubariah Rehman, Muhammad Zia ur Samee, Nagwan Abdel Mahmoud, Noha F. Gu, Yeong Hyeon Al-Masni, Mohammed A. School of Electrical Engineering Yanshan University Hebei Qinhuangdao066104 China Department of Electrical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems College of Engineering and Technology University of Sargodha Sargodha40100 Pakistan Department of Physiotherapy Isfandyar Bukhari Civil Hospital District Headquarter Hospital Attock43600 Pakistan Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Health Science and Technology Aalborg University Aalborg9220 Denmark Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Artificial Intelligence Data Science College of Software & Convergence Technology Sejong University Seoul05006 Korea Republic of

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN’s superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control. © 2024 the Author(s), licensee AIMS Press.

关键词： Convolutional neural networks

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An Implementation of Naming Rule Checking Function and Its Applications to Java Programming Codes

An Implementation of Naming Rule Checking Function and Its A...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Wai, Khaing Hsu Funabiki, Nobuo Mentari, Mustika Aung, Soe Thandar Kyaw, Htoo Htoo Sandi Yamaguchi, Shingo Kao, Wen-Chung Graduate School of Science and Engineering Akita University Akita Japan Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Graduate School of Sciences and Technology for Innovation Yamaguchi University Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798331521165

Currently, a lot of universities are offering Java programming courses for teaching the first-step object-oriented programming language. For novice students, to master writing readable codes using proper names for variables, classes, and methods is crucial in Java programming, to improve understandability and maintainability. In this paper, we consider four naming rules to write a readable code and implement the naming rule checking function to check whether the given code follows them. For evaluations, we applied the proposal to 2, 908 source codes submitted in Java programming courses from students in Okayama University, Japan, Malang State Polytechnic, Indonesia, and Yamaguchi University, Japan. The results confirm the effectiveness of the proposal. © 2025 IEEE.

关键词： Java programming language

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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Updating Top-k Dominate Individuals with Incomplete Data Addition

Updating Top-k Dominate Individuals with Incomplete Data Add...

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2023 IEEE International Symposium on information Theory, ISIT 2023

作者： Wu, Jimmy Ming-Tai Wang, Ke Yan, Huizhen Chen, Chao-Chun Chen, Pei-Wei Lin, Jerry Chun-Wei Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China National Cheng Kung University Institute of Manufacturing Information and Systems Tainan70101 Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665475549

Top-k dominance (TKD) query is an extended query method of skyline query and top-k query, which reveals the top-k dominant individuals in an incomplete dataset by analyzing the dominance relationships between individuals and is a common decision tool in intelligent recommendation applications. This research proposes two parallel query algorithms based on Spark computing engine to address the shortcomings of the parallel top-k-dominated query algorithms for dynamic incomplete datasets. The designed model achieves good performance in terms of runtime performance compared to previous studies. © 2023 IEEE.

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Use of IoT sensor devices for efficient management of healthcare systems: a review

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Discover Internet of Things 2024年第1期4卷 8页

作者： Gopichand, G. Sarath, T. Dumka, Ankur Goyal, Himanshu Rai Singh, Rajesh Gehlot, Anita Gupta, Lovi Raj Thakur, Amit Kumar Priyadarshi, Neeraj Twala, Bhekisipho School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science Women Institute of Technology VMSBUTU Uttarakhand Dehradun India Department of CSE Graphic Era Deemed to Be University Uttarakhand Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Lovely Professional University Punjab Phagwara Jalandhar144001 India Department of Electrical Engineering JIS College of Engineering Kolkata741235 India Digital Transformation Portfolio Tshwane University of Technology Staatsartillerie Rd Pretoria West Pretoria0183 South Africa

With an aging population and an increase in chronic sickness, healthcare systems are under increasing pressure. Internet of Things (IoT) technology has recently garnered a lot of interest for its ability to alleviate this burden. The IoT is a network of interconnected wireless digital devices that can gather, transmit, and store data automatically, without the need for human or computer intervention. In order to better anticipate health problems, diagnose, treat, and monitor patients in and out of the hospital, the IoT offers numerous advantages that could streamline and improve health care delivery. As a matter of urgency, decision-makers and heads of state around the world are enacting policies to provide healthcare services through the use of technology. Thanks to recent advancements in the IoT, healthcare has been able to evolve at a rapid pace. Research on Internet IoT uses in healthcare and newly developed emergency equipment are summarized in this article. © The Author(s) 2024.

关键词： Internet of things

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Do we need watchful eyes on our workers? Ethics of using computer vision for workplace surveillance

AI and Ethics

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AI and Ethics 2025年 1-21页

作者： Sebastian, Rinu Ann Ehinger, Kris Miller, Tim School of Computing and Information Systems The University of Melbourne Carlton Australia School of Electrical Engineering and Computer Science The University of Queensland St Lucia Australia

In this paper, we critically examine the relevant ethical concerns of using computer vision-based surveillance in workplaces and propose an intent—and priority-based ethical framework for such systems. With the growing capabilities of computer vision technologies, its application in monitoring workplaces brings forth significant concerns. Organisations increasingly leverage computer vision for workplace surveillance to improve productivity, safety, and security. Unlike electronic surveillance techniques that monitor workers’ wire or electronic communication, computer vision-based workplace surveillance (CVWS) captures highly detailed visual and personal information about workers, including body language, emotional state, and actions. This makes CVWS potentially more intrusive than traditional electronic surveillance, raising a more comprehensive range of ethical considerations. However, this topic has received minimal attention in the current literature. Our proposed framework combines the intention for deploying surveillance with the moral notions of privacy, data security, fairness, transparency, explainability, autonomy, beneficence, nonmaleficence, dignity, and reliability to morally scrutinise CVWS systems. The paper proposes a second framework that aims to establish accountability among key stakeholders of the CVWS system. Further, we discuss two critical questions to consider when evaluating the necessity for CVWS systems in a work environment. In practice, this work will serve as a groundwork for stakeholders such as technical developers, employers, and regulatory and advocacy teams to make ethical design decisions during the developmental, operational, and maintenance stages of CVWS systems and devise proactive strategies to minimise potential harm.

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A new online field feature selection algorithm based on streaming data

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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UWB Channel Modeling and Simulation with Continuous Frequency Response

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China Communications 2022年第11期19卷 88-98页

作者： Kai Mao Qiuming Zhu Xijuan Ye Ruirui Feng Fuqiao Duan Yang Miao Maozhong Song The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space College of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjing 211106China The Radio Systems Faculty of Electrical Engineering Computer Science and Mathematics(EEMCS)University of Twentethe Netherlands

Ultra-wideband(UWB)technology is a prospective technology for high-rate transmission and accurate localization in the future communication ***-of-art channel modeling approaches usually divide the UWB channel into several sub-band channels and model them *** considering frequency-dependent channel parameters,a novel analytical UWB channel model with continuous frequency response is *** composite effect of all frequency components within the UWB channel on the channel impulse response(CIR)of delay domain is derived based on the continuous channel transfer function(CTF)of frequency *** this basis,a closed-form simulation model for UWB channels and geometry-based parameter calculation method are developed,which can guarantee the continuity of channel characteristics on the frequency domain and greatly reduce the simulation ***,the proposed method is applied to generate UWB channel with 2 GHz bandwidth at sub-6GHz and millimeter wave(mmWave)bands,*** channel measurements are also carried out to validate the proposed *** simulated CIR and power gain are shown to be in good agreement with the measurement ***,the comparison results of power gain and Doppler power spectral density(DPSD)show that the proposed UWB channel model Received:Apr.23,2022 Revised:Jun.09,2022 Editor:Wei Fan achieves a good balance between the simulation accuracy and efficiency.

关键词： ultra-wideband(UWB)channel continuous frequency response frequency-dependent channel parameters simulation model channel measurement

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A Code-Switched Arabic-English Sentiment Analysis Approach Based on Deep-Learning 3

A Code-Switched Arabic-English Sentiment Analysis Approach B...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Almasah, Tasneem S. Ebrahim, Gamal A. Abdelaal, Marwa A. The British University in Egypt Faculty of Engineering Electrical Engineering Department Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Sadat Academy for Management Sciences Faculty of Computers and Information Computer Science Department Cairo Egypt

ISBN: (纸本)9798350306231

Sentiment analysis within Online Social Networks (OSNs) becomes a major challenge. Mainly, because of the large amount of data on social networks and the mix of different languages that can be used in these environments. Due to the lack of existing research and datasets on code-switched Arabic-English sentiment analysis, this study creates a new dataset collected from various online social networks. The data is subjected to several preprocessing steps before being analyzed using various deep learning architectures. Moreover, a thorough evaluation of the preprocessing techniques and the performance of the deep learning models have been conducted. This is achieved using comprehensive Twitter datasets in Arabic and English. The study reveals that individual deep learning architectures, specifically, Convolutional Neural Networks (CNNs), Bidirectional Long Short Term Memory (Bi-LSTM), and LSTM demonstrated superior results compared to their hybrid counterparts. Notably, CNN emerged as the most accurate model with an accuracy of 83%, followed closely by Bi-LSTM at 82.3%. Additionally, the paper presents the deployment of an automated machine learning tool, which is Auto-Keras. Furthermore, it designs an optimized sentiment analysis model that aims to enhance accuracy. This approach leads to an increase in the model accuracy to 86%. © 2023 IEEE.

关键词： Sentiment analysis

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