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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Pai, HAO-Ting Chung, Wen-Cheng Fang, Xin-Hong Hsu, Yu-Hsin Huang, Shu-Ting Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Bachelor Program of Artificial Intelligence National Yunlin University of Science and Technology Douliou Taiwan

ISBN: (纸本)9798400716874

Current AI-driven methods in healthcare show significant limitations in addressing misdiagnoses, often leading to serious consequences. This work highlights the inadequacy of state-of-the-art AI in managing diagnostic errors through experiments with public real-world datasets, namely "Bone Marrow Transplant: Children" and "Breast Cancer Wisconsin (Original)". We propose two novel scoring measures—Local-score and Global-score—to enhance the accuracy of Transparent Classification (TC). Experimental results indicate that Global-score improves recall but may reduce precision, whereas Local-score slightly lowers recall for better precision. These findings suggest that both Local-score and Global-score can effectively identify misdiagnoses, marking a substantial improvement over traditional AI methods. This research offers a new perspective in employing explainable AI to address the challenges of misdiagnoses, thereby improving patient care and medical diagnostic accuracy. © 2024 ACM.

关键词： Explainable artificial intelligence

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Comments on a Key Agreement Scheme for the V2G System 14th

Comments on a Key Agreement Scheme for the V2G System

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Ya-Fen Chang, Wen-Ting Tai, Wei-Liang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Vehicle-to-grid (V2G) technology supporting bidirectional power transfer allows electric vehicles (EVs) to contribute and consume energy bidirectionally. Because the specific properties and requirements of V2G, Khan et al. proposed a key agreement scheme for the V2G system to ensure the security of communication between a vehicle user and the grid server. However, after analyzing Khan et al.’s scheme, we find that it suffers from some flaws such that it cannot ensure anonymity, resist impersonation attack, man-in-the-middle attack and denial-of-service attack, and work as expected. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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An Improved Cryptocurrency Predictive Method by Using Bi-Long Short-Term Memory Based on News Technical Indicators 14th

An Improved Cryptocurrency Predictive Method by Using Bi-Lon...

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14th International Conference on Frontier Computing, FC 2024

作者： Chiang, Mao-Lun Hsieh, Hui-Ching Kung, Hsiang-Wei Department of Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan Department of New Media Communication Hsing Wu University New Taipei City Taiwan

ISBN: (纸本)9789819623945

Due to the trend of smart contracts and digital banking, more and more people pay attention to cryptocurrencies in recent years, and Bitcoin and Ethereum are currently the most concerned projects. Therefore, there are more and more researches on deep learning technology that apply the technology originally applicable to the stock market to this problem. Among them, the effective one is the Recurrent Neural Network (RNN) used to solve the prediction of time series problem. However, as time goes by, the vanishing gradient problem will be generated. Therefore, a Long Short-Term Memory (LSTM) model improved by RNN is proposed to solve vanishing gradient problem, but it still cannot completely solve this problem. In addition, since the price of Bitcoin is more volatile compared with the stock market, it is difficult to make accurate predictions by using traditional LSTM. Therefore, this paper proposes a new "Bi-Long Short-Term Memory based on News Technical indicators, Bi-NTLSTM" to combines news technical analysis indicators to solve improve the model’s learning ability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Long short-term memory

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Reconfigurable Hardware Accelerator of Morphological Image Processor 3

Reconfigurable Hardware Accelerator of Morphological Image P...

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3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Bachelor's Program in Artificial Intelligence and Mechatronics National Pingtung University of Science and Technology Pingtung Taiwan

ISBN: (纸本)9798350333862

Researchers emphasize the importance of hardware accelerators for mathematical morphology. If there are any issues, the hardware architecture may need to be redesigned. Thus, we propose a novel, reconfigurable hardware architecture with pipeline techniques for acceleration. With the order control register, there is no need to modify the architecture even if different results are expected. Furthermore, the hardware architecture caters to specific purposes instead of requiring a purpose-built hardware design. © 2023 IEEE.

关键词： Reconfigurable hardware

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AN INTERPRETABLE GENERALIZATION MECHANISM FOR ACCURATELY DETECTING ANOMALY AND IDENTIFYING NETWORKING INTRUSION TECHNIQUES

arXiv

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arXiv 2024年

作者： Pai, Hao-Ting Kang, Yu-Hsuan Chung, Wen-Cheng Bachelor Program of Big Data Applications in Business National Pingtung University Taiwan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Bachelor Program of Artificial Intelligence National Yunlin University of Science and Technology Taiwan

The increasing complexity of modern network environments presents formidable challenges to Intrusion Detection Systems (IDS) in effectively mitigating cyber-attacks. Recent advancements in IDS research, integrating Explainable AI (XAI) methodologies, have led to notable improvements in system performance via precise feature selection. However, a thorough understanding of cyber-attacks requires inherently explainable decision-making processes within IDS. In this paper, we present the Interpretable Generalization Mechanism (IG), poised to revolutionize IDS capabilities. IG discerns coherent patterns, making it interpretable in distinguishing between normal and anomalous network traffic. Further, the synthesis of coherent patterns sheds light on intricate intrusion pathways, providing essential insights for cybersecurity forensics. By experiments with real-world datasets NSL-KDD, UNSW-NB15, and UKM-IDS20, IG is accurate even at a low ratio of training-to-test. With 10%-to-90%, IG achieves Precision (PRE)=0.93, Recall (REC)=0.94, and Area Under Curve (AUC)=0.94 in NSL-KDD;PRE=0.98, REC=0.99, and AUC=0.99 in UNSW-NB15;and PRE=0.98, REC=0.98, and AUC=0.99 in UKM-IDS20. Notably, in UNSW-NB15, IG achieves REC=1.0 and at least PRE=0.98 since 40%-to-60%;in UKM-IDS20, IG achieves REC=1.0 and at least PRE=0.88 since 20%-to-80%. Importantly, in UKM-IDS20, IG successfully identifies all three anomalous instances without prior exposure, demonstrating its generalization capabilities. These results and inferences are reproducible. In sum, IG showcases superior generalization by consistently performing well across diverse datasets and training-to-test ratios (from 10%-to-90% to 90%-to-10%), and excels in identifying novel anomalies without prior exposure. Its interpretability is enhanced by coherent evidence that accurately distinguishes both normal and anomalous activities, significantly improving detection accuracy and reducing false alarms, thereby strengthening IDS reliability an

关键词： Network intrusion

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Comments on a Computation-Transferable Authenticated Key Agreement Protocol for Smart Healthcare

Comments on a Computation-Transferable Authenticated Key Agr...

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9th International Conference on Applied System Innovation, ICASI 2023

作者： Chang, Ya-Fen Tsai, Chung-Yi Tai, Wei-Liang National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung Taiwan National Taichung University of Science and Technology Bachelor Degree Program of Artificial Intelligence Taichung Taiwan

ISBN: (纸本)9798350398380

Recently, Wang et al. proposed a computationally transferable authenticated key agreement protocol for smart healthcare by adopting the certificateless public-key cryptography. They claimed that their protocol could ensure privacy, resist various attacks, and possess superior properties. After analyzing their protocol, we find that it suffers from some flaws. Firstly, user privacy is not ensured as claimed. Secondly, some statements are inaccurate or missing. Thirdly, it cannot resist DoS attack. In this paper, the details of how these flaws threaten Wang et al.'s protocol are shown. © 2023 IEEE.

关键词： Denial-of-service attack

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Dynamic weight-based connectivity recovery in wireless sensor and actor networks

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Journal of Supercomputing 2024年第1期80卷 734-760页

作者： Chiang, Mao-Lun Hsieh, Hui-Ching Lin, Tzu-Ling Chang, Tsui-Ping Chen, Hong-Wei Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan Department of New Media Communication Hsing Wu University New Taipei City Taiwan Department of Business Management National Taichung University of Science and Technology Taichung City Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung City Taiwan

Wireless sensors and actor networks (WSANs) have been widely used in various fields, from basic data collection to precise real-time control and monitoring, including battlefield monitoring, rescue, and exploration. The actor nodes in a WSAN play a key role in processing and transmitting data, and because of the harsh environmental conditions encountered, network node failures are prone to occur. The failure of an actor causes a loss of connectivity with its neighbor nodes, resulting in network splits and failure to deliver messages between nodes. Therefore, determining how to quickly and autonomously recover network connectivity while minimizing the moving distance of backup nodes and the number of relocated nodes is crucial. In this paper, we propose a dynamic weight-based connectivity recovery (DWCR) method to minimize the total moving distance of actor nodes, the number of relocated nodes, the total messages sent, and the coverage loss rate. The proposed DWCR method involves selecting a backup node for each critical node by dynamically adjusting the weight. If a critical node fails, the backup node starts to move to restore the connectivity of the failed node. The simulation results indicate that the proposed DWCR method outperforms the recovery through inward motion (RIM) method and other methods in terms of minimizing the total moving distance, the number of relocated nodes, and the total messages sent. However, because the RIM algorithm requires the relocation of more nodes during the network process, the DWCR algorithm is slightly inferior to the RIM algorithm in terms of coverage loss rate. Nevertheless, the DWCR algorithm still outperforms the distributed actor recovery algorithm and the distributed autonomous connectivity restoration method based on finite state machine algorithm. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Recovery

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An adaptive replica configuration mechanism based on predictive file popularity and queue balance in mobile edge computing environment

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Soft Computing 2023年第1期27卷 107-129页

作者： Chiang, Mao-Lun Hsieh, Hui-Ching Chang, Ting-Yi Lin, Tzu-Ling Chen, Hong-Wei Department of Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan Department of New Media Communication Hsing Wu University New Taipei City Taiwan Department of Industrial Education and Technology National Changhua University of Education Changhua City Taiwan Department of Business Management National Taichung University of Science and Technology Taichung404336 Taiwan

In the current internet of things era, various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause cloud core overload and significant network delays. To overcome these overload and delay problems, the mobile edge computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal. Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, while using the service, it may also encounter a large amount of popular data being accessed in a short period of time. Without proper handling of replica generation and deployment, even in low-latency environments, it can still kill the benefits of MEC due to increased access time. Although many scholars have proposed related issues for copy replication, there are *** that can be improved. To avoid the situation of insufficient availability of replicas, replica replication is performed, but infinite replicas may lead to a significant increase in traffic and waste of resources. And when deploying replicas, it is necessary to avoid placing them on congested nodes and consider how to achieve better load balancing. To improve the above problems, we synthesize the advantages of previous algorithms, make up for the shortcomings, and propose an adaptive replica configuration mechanism to predict the popularity of files and replicate replicas to low-blocking nodes. This method spreads the subsequent access workload by copying the popular file in advance to improve the system's overall performance. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Mobile edge computing

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Dynamic Numerical Studies of a Rotorcraft Entering and Exiting Rectangular Tunnel 19

Dynamic Numerical Studies of a Rotorcraft Entering and Exiti...

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19th IEEE International Conference on e-Business Engineering, ICEBE 2023

作者： Hsu, Uzu-Kuei Chang, Hong-Bin Tsai, Yao-Yu Tai, Chang-Hsien National Pingtung University of Science and Technology Bachelor Program in Artificial Intelligence and Mechatronics Kaohsiung City Taiwan National Pingtung University of Science and Technology Scerdc General Research Service Center Kaohsiung City Taiwan National Pingtung University of Science and Technology Dept. of Vehicle Engineering Kaohsiung City Taiwan

ISBN: (纸本)9798350325553

In recent years, the utilization of multi-rotor drones has seen a steady rise. Unmanned aerial vehicles (UAV) have found diverse applications, including aerial photography, transportation, military operations, and agriculture. The advent of these compact flying machines offers humanity a range of convenient and versatile choices across various sectors. Although unmanned aerial vehicles hold significant practical value, their stable flight is contingent on favorable weather conditions. Consequently, it becomes crucial to investigate the influence of environmental airflow on aircraft stability. This is particularly important for small machines like rotary-wing aircraft operating in confined spaces, where understanding the aerodynamic effects of the flow field is crucial to avert potential crashes. This study delves into the variations in the flow field during the entry and exit of a single rotor into a tunnel. Utilizing dynamic simulations, it examines how tunnels impact the rotor's flow field and analyzes thrust curves to understand dynamic rotor behavior. The study also explores the effect of vortex generation on the rotor and employs a simple square tunnel to assess how tunnel size and flight speed influence the rotor's flow field during flight. The findings hold potential as reference points to enhance flight stability during tunnel flights. © 2023 IEEE.

关键词： Vortex flow

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Mental Status Identification for Infertile Patients Using artificial intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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