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14th International Conference on Frontier Computing, FC 2024

作者： Yang, Hsuan-Che Chang, Wen-Chih Huang, Wei-Han Hsu, Chia-Hao Lin, Yen-Chen Li, Jie-Syuan Lee, Yun-Cheng Luo, Zeng-Sheng Department of Information Management Chihlee University of Technology New Taipei City220 Taiwan International Master Program of Information Technology and Applications National Ping-Tung University Ping-Tung Taiwan

ISBN: (纸本)9789819623945

The worldwide aging population has been on the rise due to the advancement of medical science and decreasing birth rates. Unfortunately, this has led to an increase in falls among the elderly, which has emerged as one of the top ten causes of death for elderly individuals in Taiwan. To address this issue, our study aims to leverage the widespread use of smartphones and smartwatches to detect and prevent falls in older adults. Our machine learning model is trained using the UMAFall dataset, which collects data on daily activities and falling actions through five sensing devices. We used Azure Machine Learning Studio to test six different machine learning algorithms to evaluate suitable algorithms. In the end, the Two-Class Decision Tree algorithm achieved the best results. In our next step, this method will be programmed with the TensorFlow Lite and train the model. This model will be integrated into Android-based apps that can be installed on smartphones. By continuously connecting the smartwatch and smartphone Apps, the accelerometer, gyroscope, and magnetometer in both devices can be compared and analyzed in real time using the TensorFlow Lite model, accurately determining the wearer's actions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Path Planning Method for Mobile Robots in Pedestrian-Friendly Environments Using Drone Bird's-Eye View

Path Planning Method for Mobile Robots in Pedestrian-Friendl...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Ye, Linghao Chugo, Daisuke Muramatsu, Satoshi Yokota, Sho Hashimoto, Hiroshi Kwansei Gakuin University Graduate School of Science and Technology Hyogo Sanda6691330 Japan Tokai University School of Information Science and Technology Kanagawa Hiratsuka2591292 Japan Toyo University Faculty of Science and Engineering Saitama Kawagoe3508585 Japan Advanced Institute of Industrial Technology Master Program of Innovation for Design and Engineering Shinagawa Tokyo1400011 Japan

ISBN: (纸本)9798331531614

The objective of this paper is to develop a robot system that can transmit the video taken by the drone to the computer in real time, use YOLO to recognize the image of pedestrians, and enable the ground robot to plan the path to avoid pedestrians. External sensors, such as laser range finders (LRF) and CCD cameras, are often used on mobile robots. As we all know, these sensors are susceptible to occlusion. Therefore, even if the mobile robot avoids the pedestrian in front of it, it is likely to collide with other pedestrians that cannot be detected due to occlusion. In this study, we used a drone to shoot videos at high altitudes, transferred the videos to a computer, used YOLO on the computer to perform image recognition of pedestrians around the robot, and passed the recognition results to ROS, which enabled it to plan a path to avoid pedestrians around it. As a result, showed that the videos shot at high altitudes could effectively identify pedestrians around the robot after being recognized by YOLO. © 2025 IEEE.

关键词： Drones

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Classification and Prediction of Knee Osteoarthritis by Deep Learning Approach 3rd

Classification and Prediction of Knee Osteoarthritis by Deep...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Saraswat, Amit Tooley, Tanya Shrivastav, Snigdha Srivastava, Devesh Kumar Department of Electrical Engineering Manipal University Rajasthan Jaipur303007 India Senior Analyst EY Haryana Gurgaon India Master of Science in Data Science program at the University of Rochester New York United States Department of Information Technology Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819784288

The purpose of this research work on medical image analysis is to identify and diagnose knee osteoarthritis (OA) utilizing medical images by applying machine learning techniques. Knee OA is a degenerative joint disease that is becoming more prevalent in aged people worldwide. The study examines the effectiveness of several machine learning algorithms, including deep learning, to analyze medical images of knee OA, such as X-rays and magnetic resonance imaging (MRI). The research findings suggest that using machine learning algorithms might greatly improve the accuracy and efficacy of detecting knee OA, especially in its early stages. Moreover, machine learning algorithms can assist in the creation of specialized treatment plans by predicting the progression of the disease and recommending appropriate remedies. The findings of this study show how machine learning-based medical image analysis has the potential to enhance patient outcomes and quality of life for those with knee OA. Cancer detection diagnosis, neurological condition identification, and cardiovascular function analysis are a few instances of medical picture analysis employing machine learning. Moreover, image-guided treatments have been made more accurate and surgical planning has been made easier with the use of machine learning algorithms. Various methods are applied to achieve a test accuracy of 76.10% which is just 5% less than the accuracy provided by the Kellen-Lawrence Grading Scale. Overall, machine learning-based medical image analysis has the potential to revolutionize medical practice by facilitating earlier and more precise diagnosis, individualized treatment planning, and improved patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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BEST: AN INSTRUCTIONAL DESIGN MODEL TO EMPOWER GRADUATE STUDENT SELF-EFFICACY IN RESEARCH METHODOLOGY THROUGH TPACK INTEGRATION

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Interdisciplinary Journal of information, Knowledge, and Management 2025年 20卷

作者： Sukma, Narongsak Pum, Wachiranun Master of Science Program in Professional Cyber Security Faculty of Digital Siam Technology College Bangkok Thailand Faculty of Management Science and Information Technology Nakhon Phanom University Nakhon Phanom Thailand

Aim/Purpose Traditional research methodology education faces challenges in developing student self-efficacy and integrating modern technology, necessitating innovative instructional approaches for graduate students. Background This study introduces the BEST model (begin with learner analysis, establish clear learning objectives, select engaging learning activities, and tailor instruction using technology) to address these challenges through systematic TPACK integration. Methodology A qualitative investigation was conducted with 10 graduate students through focus groups and in-depth interviews over one month, analyzed using narrative and content analysis approaches. Contribution Content analysis revealed improvements in self-efficacy (from four to eight participants), peer learning (from three to six), and critical thinking (from three to six) while maintaining active learning engagement throughout implementation. Findings Students showed significant improvements in self-efficacy (four to eight participants), peer learning (three to six), and critical thinking (three to six) while maintaining active learning engagement. Recommendations for Practitioners Institutions should implement robust technical support systems and flexible learning pathways while ensuring adequate infrastructure before adoption. A phased implementation approach with peer mentoring is recommended to address technology integration challenges. Recommendations for Researchers Future research should examine the BEST model’s effectiveness across different cultural contexts and disciplines through comparative and longitudinal studies while exploring the integration of emerging educational technologies. Impact on Society The BEST model enhances the quality of graduate research education by developing students’ confidence and competence, potentially leading to more capable researchers and improved research outcomes. Future Research Longitudinal studies should track the impact on research productivity and

关键词： Students

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A Hybrid Deep Learning and Natural Language Processing Model for Plant Ubiquitination Sites Prediction 3rd

A Hybrid Deep Learning and Natural Language Processing Model...

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3rd International Conference on Advances in information and Communication technology, ICTA 2024

作者： Tran, Thi-Xuan Nguyen, Thi-Tuyen Le, Nguyen Quoc Khanh Nguyen, Van-Nui University of Economics and Business Administration Thai Nguyen Viet Nam University of Information and Communication Technology Thai Nguyen Viet Nam Professional Master Program in Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan

ISBN: (纸本)9783031809422

Protein ubiquitination is a critical post-translational modification involved in numerous biological processes, playing a vital role in regulating both physiological and pathological mechanisms. Despite existing tools for predicting ubiquitination sites, variations in ubiquitination patterns among species remain inadequately understood. This study presents LBiPlantUbi, a novel hybrid deep learning model that incorporates Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) networks combined with natural language processing (NLP) techniques for predicting ubiquitination sites in plants. Our model uniquely learns features directly from raw protein sequences using word embedding and integrates feature extraction and prediction within a single architecture. LBiPlantUbi achieved superior performance with an accuracy of 84%, sensitivity of 88.9%, F1-score of 0.844, MCC of 0.675, and AUC of 0.902 on independent testing sets, outperforming existing methods. This advanced model opens new avenues for studying ubiquitination in plants and other species. The implementation is publicly accessible on GitHub at https://***/nuinvtnu/LBiPlantUbi. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Beyond the Scroll: Eye-Tracking Evaluations on the National Gallery of Indonesia’s Instagram 26th

Beyond the Scroll: Eye-Tracking Evaluations on the National ...

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Sari, Desy Novita Prameswari, Clarissa Rahmi Anshori, Isa Master’s Program in Library Science Universitas Indonesia Kota Depok Indonesia Department of Library and Information Sciences Universitas Indonesia Kota Depok Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia National Gallery of Indonesia Central Jakarta Indonesia

ISBN: (纸本)9789819608676

Eye-tracking studies focused on art galleries and museums within the realm of Instagram, especially in the Indonesian context, are notably scant. This investigation elucidates the user interaction and experience of the art gallery’s Instagram account, @galerinasional. Employing a quantitative approach, we implemented eye-tracking experiments using the well-known online eye-tracking software RealEye. The study participants were followers of the National Gallery of Indonesia’s (Galnas) Instagram profile, seven women and three men, in two age brackets: 18–24 years (n = 5) and 25–34 years (n = 5). The experimental protocol required participants to execute five tasks centered on locating exhibition details and artwork on the Galnas Instagram platform, with a duration of 100 s allocated to each task. The Participant Quality Grade (PQG), examination duration, optimal browser dimensionality, K-factor mean, gaze and fixation frequencies, and heat maps were determined for the participants. The key findings are as follows. (1) The participants’ PQG scores were predominantly between 4 and 6. (2) Investigative browsing typically exceeded the 100-s limit. (3) The consensus on the ideal browser dimensions hovered around 1,368.6 × 814.8 px. (4) The K-factor mean across participants tended toward K > 0. (5) The average gaze occurrence was 27 items per second for most participants. (6) A noticeable disparity in fixation counts was observed between the two age groups, with individuals aged 25–34 demonstrating prolonged engagement with Galnas’ content. (7) Heat maps accentuated a pronounced concentration on visual content, encompassing photographs, videos, and their associated captions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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Improved Linear B-Cell Epitope Prediction Using CNN and BiLSTM 3rd

Improved Linear B-Cell Epitope Prediction Using CNN and BiLS...

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3rd International Conference on Advances in information and Communication technology, ICTA 2024

作者： Nguyen, Thi-Tuyen Nguyen, Van-Nui Tran, Thi-Xuan Le, Nguyen Quoc Khanh University of Information and Communication Technology Thai Nguyen Viet Nam University of Economics and Business Administration Thai Nguyen Viet Nam Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan AIBioMed Research Group Taipei Medical University Taipei110 Taiwan

ISBN: (纸本)9783031809422

Proteins contain epitopes that serve as their antigenic determinants, with antigenicity referring to an epitope's ability to react with an antibody. These antigenic determinants exist as either continuous or discontinuous epitopes and are pivotal in integrative biology. The presence of B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. Thus, identifying antigenic sites on proteins is critical for developing synthetic peptide vaccines, immunodiagnostic tests, and antibody production. Recent years have seen the development of several computational methods, particularly machine learning-based approaches, to predict B-cell epitopes. However, these methods often fall short in reliably predicting linear B-cell epitopes, presenting an ongoing challenge. In this study, we propose and develop a novel method that integrates multiple deep learning models, aiming to harness the strengths of each model type while mitigating their individual weaknesses. During the model construction process, we addressed data imbalance and fine-tuned the model parameters to achieve optimal performance for our dataset. Our model demonstrated promising results, achieving an ACC of 0.846, AUC of 0.808, BAC of 0.716, MCC of 0.568 and PR-AUC of 0.705 on an independent dataset. This represents a significant performance improvement over previous algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Epitopes

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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

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IEEE Access 2025年 13卷 87753-87776页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu880011 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

In the past decade, studies on illegal fishing have neglected to consider illegal underwater fishing. Traditionally, supervisor-based methods have been used to manually interpret underwater behavior;however, existing artificial intelligence (AI) and Internet of Things (IoT) methods are not applicable to illegal underwater fishing. Therefore, in this study, we used a computer vision AI method to perform the challenging task of illegal underwater fishing image recognition. Illegal underwater fishing images have unclear features and high feature similarity. In this study, we define normal underwater travel as wearing diving clothing. Abnormal underwater behavior includes underwater personnel with illegal fishing equipment. Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. The convolutional layers in our proposed FA-FENet include dilated convolutions, group convolutions, padded convolutions, pooling layers, and activation functions. We also comprehensively review the performance of the proposed FA-FENet. A series of ablation studies demonstrate that the proposed FA-FENet can be applied in shallow backbone networks. In addition, FA-FENet has potential in emerging networks with good performance in illegal underwater fishing identification tasks. Finally, we conducted experiments with an underwater fishing behavior dataset. The experimental results demonstrate that our proposed method outperforms recently reported methods with attention mechanisms. Specifically, FA-FENet-RegNet achieves an accuracy of 88.81%, FA-FENet-ResNet18 reaches 78.32%, and FA-FENet-ShuffleNetV2 attains 80.41%. © 2013 IEEE.

关键词： Convolutional neural networks

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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