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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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An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 10302-10313页

作者： Pai, Hao-Ting Kang, Yu-Hsuan Chung, Wen-Cheng Natl Pingtung Univ Dept Big Data Business Analyt Pingtung 900392 Taiwan Natl Yunlin Univ Sci & Technol Int Grad Sch Artificial Intelligence Touliu 640301 Taiwan Natl Yunlin Univ Sci & Technol Bachelor Program Artificial Intelligence Touliu 640301 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 r

关键词： Accuracy NSL-KDD Training Forensics Explainable AI Decision making Mathematical models Feature extraction data preprocessing Principal component analysis Classification explainable AI (XAI) anomaly detection

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EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management 15

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Lina, Jia Banda, Shahab S. Paib, Hao-Ting Rawal, Bharat S. International Graduate School of Artificial Intelligence Yunlin County Douliu City Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science and Digital Technologies University GgramblingLA United States

ISBN: (纸本)9798350370249

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL's reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent's decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market. ©2024 IEEE.

关键词： Deep reinforcement learning

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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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EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jia Lina Shahab S. Banda Hao-Ting Paib Bharat S. Rawal International Graduate School of Artificial Intelligence Douliu City Yunlin Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science Digital Technologies University Ggrambling La USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL’s reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent’s decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market.

关键词： Uncertainty Correlation Additives Explainable AI Computational modeling Computer architecture Deep reinforcement learning Fraud Portfolios

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A Fault-Tolerant Cascaded Switched-Capacitor Multilevel Inverter for Domestic applications in Smart Grids

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IEEE ACCESS 2022年 10卷 110590-110602页

作者： Akbari, Ehsan Teimouri, Ali Reza Saki, Mojtaba Rezaei, Mohammad Amin Hu, Jiefeng Band, Shahab S. Pai, Hao-Ting Mosavi, Amir H. Mazandaran Univ Sci & Technol Dept Elect Engn Babol *** Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Univ Zagreb Dept Elect Engn & Comp FER Zagreb 10000 Croatia Federat Univ Australia Inst Innovat Sci & Sustainabil Ctr New Energy Transit Res Ballarat Vic 3353 Australia Natl Pingtung Univ Bachelor Program Big Data Applicat Business Pingtung 91201 Taiwan Slovak Univ Technol Bratislava Bratislava 81107 Slovakia Obuda Univ H-1034 Budapest Hungary

Cascaded multilevel inverters (MLIs) generate an output voltage using series-connected power modules that employ standard configurations of low-voltage components. Each module may employ one or more switched capacitors to double or quadruple its input voltage. The higher number of switched capacitors and semiconductor switches in MLIs compared to conventional two-level inverters has led to concerns about overall system reliability. A fault-tolerant design can mitigate this reliability issue. If one part of the system fails, the MLI can continue its planned operation at a reduced level rather than the entire system failing, which makes the fault tolerance of the MLI particularly important. In this paper, a novel fault location technique is presented that leads to a significant reduction in fault location detection time based on the reliability priority of the components of the proposed fault-tolerant switched capacitor cascaded MLI (CSCMLI). The main contribution of this paper is to reduce the number of MLI switches under fault conditions while operating at lower levels. The fault-tolerant inverter requires fewer switches at higher reliability, and the comparison with similar MLIs shows a faster dynamic response of fault detection and reduced fault location detection time. The experimental results confirm the effectiveness of the presented methods applied in the CSCMLI. Also, all experimental data including processor code, schematic, PCB, and video of CSCMLI operation are attached.

关键词： Voltage control Reliability Fault detection Switches Multilevel inverters Fault tolerant systems Capacitors Cascaded fault fault-tolerant multilevel inverter reliability smart grid switched-capacitor

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Explainable analytics: understanding causes, correcting errors, and achieving increasingly perfect accuracy from the nature of distinguishable patterns

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SCIENTIFIC REPORTS 2022年第1期12卷 1-8页

作者： Pai, Hao-Ting Hsu, Chung-Chian Natl Pingtung Univ Bachelor Program Big Data Applicat Business Pingtung Taiwan Natl Yunlin Univ Sci & Technol Dept Informat Management Touliu Taiwan NationalYunlin Univ Sci & Technol Int Grad Sch Artificial Intelligence Touliu Taiwan

In addition to pursuing accurate analytics, it is invaluable to clarify how and why inaccuracy exists. We propose a transparent classification (TC) method. In training, data consist of positive and negative observations. To obtain positive patterns, we find the intersection between each of the two positive observations. The negative patterns are obtained in the same manner. Next, pure positive and pure negative patterns are established by selecting patterns that appear in only one type. In testing, such pure positive and pure negative patterns are used for scoring observations. Next, an observation is classified as positive if its positive score is not zero or if both its positive and negative scores are zero;otherwise, it is classified as negative. By experiment, TC can identify all positive (e.g., malignant) observations at low ratios of training to testing data, e.g., 1:9 using the Breast Cancer Wisconsin (Original) and 3:7 using the Contraceptive Method Choice. Without fine-tuned parameters and random selection, the uncertainty of the methodology is eliminated when using TC. TC can visualize causes, and therefore, prediction errors in a network are traceable and can be corrected. Furthermore, TC shows potential in identifying whether the ground truth is incorrect (e.g., identifying diagnostic errors).

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Optimizing Network-on-Chip using metaheuristic algorithms: A comprehensive survey

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MICROPROCESSORS AND MICROSYSTEMS 2023年 103卷

作者： Masdari, Mohammad Qasem, Sultan Noman Pai, Hao-Ting Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Comp Sci Dept Riyadh 11432 Saudi Arabia Taiz Univ Fac Appl Sci Comp Sci Dept Taizi 6803 Yemen Natl Pingtung Univ Bachelor Program Big Data Applicat Business Pingtung Taiwan

Network on Chip (NoC) is an interesting technology that benefits from several processing elements and the necessary communication facilities, to provide an answer to the ever-growing need for more processing power. Metaheuristic algorithms are important tools that have been used for dealing with various NP-hard problems in different domains. Such algorithms are also widely used in the NoC context by many frameworks for optimizing various characteristics of the NoC environments. Nonetheless, there is a lack of a comprehensive survey to put forward a thorough study of such schemes. To fill this gap, this article presents a comprehensive survey and classification of the metaheuristic-based schemes designed for various NoC topologies. For this purpose, first, some background knowledge is provided which helps to understand the studied schemes. Then, a taxonomy of the investigated approaches based on their applied metaheuristic algorithms is presented and in each category, schemes are studied and their main contributions and properties as well as their limitations are discussed. At last, a comparison of the techniques, tools, and methods that have been used in the studied schemes are provided along with the concluding remarks and future research directions.

关键词： Network on Chip Metaheuristic algorithms Nature-inspired PSO GA Multi-objective

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Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Levy-Jaya Optimization Algorithm

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WATER RESOURCES MANAGEMENT 2023年第10期37卷 3953-3972页

作者： Zhou, Jincheng Wang, Dan Band, Shahab S. Jun, Changhyun Bateni, Sayed M. Moslehpour, M. Pai, Hao-Ting Hsu, Chung-Chian Ameri, Rasoul Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guizh Duyun 558000 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Qiann Duyun 558000 Peoples R China Qiannan Normal Univ Nationalities Sch Math & Stat Duyun 558000 Guizhou Peoples R China Natl Yunlin Univ Sci & Technol Int Grad Inst Artificial Intelligence Dept Informat Management Touliu Taiwan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Taiwan Chung Ang Univ Coll Engn Dept Civil & Environm Engn Seoul South Korea Univ Hawaii Manoa Water Resources Res Ctr Dept Civil & Environm Engn Honolulu HI USA Asia Univ Coll Management Dept Business Adm Taichung 41354 Taiwan Natl Pingtung Univ Bachelor Program Big Data Applicat Business Pingtung Taiwan Natl Yunlin Univ Sci & Technol Dept Informat Management Touliu Taiwan

River discharge represents critical hydrological data that can be used to monitor the hydrological status of a river basin. The objective of this study was to forecast the monthly river discharge time-series of two gauging hydrometric sites (USGS 06054500 and USGS 06090800) located on the Missouri River, USA. The forecast was performed using two machine learning models based on extreme gradient boosting (XGB) and K-nearest neighbors (KNN). XGB outperformed the KNN framework in forecasting the river flow. Subsequently, wavelet (W) analysis was incorporated to develop the hybrid W-XGB and W-KNN approaches. Finally, two novel hybrid models were established through the hybridization of XGB and the Levy-Jaya optimization algorithm (LJA) and simultaneous integration of the wavelet analysis and LJA with the XGB, i.e., XGB-LJA and W-XGB-LJA, respectively. The performances of the models were evaluated using the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), determination coefficient (R ), and Nash-Sutcliffe efficiency (NSE). In the test phase, the best discharge forecasts at USGS 06054500 and USGS 06090800 were obtained using the hybrid WXGB2-LJA (RMSE = 41.303 m /s, MAE = 28.752 m /s, MBE = 3.377 m /s, R = 0.819, NSE = 0.800) and W-XGB4-LJA (RMSE = 39.310 m /s, MAE = 26.804 m /s, MBE = 1.489 m3/s, R = 0.897, NSE = 0.885), respectively.

关键词： River discharge Forecast Extreme gradient boosting Levy-Jaya algorithm Machine learning models

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Circulatory System Based Optimization (CSBO): an expert multilevel biologically inspired meta-heuristic algorithm

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ENGINEERING applications OF COMPUTATIONAL FLUID MECHANICS 2022年第1期16卷 1483-1525页

作者： Ghasemi, Mojtaba Akbari, Mohammad-Amin Jun, Changhyun Bateni, Sayed M. Zare, Mohsen Zahedi, Amir Pai, Hao-Ting Band, Shahab S. Moslehpour, Massoud Chau, Kwok-Wing Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Ajman Univ Artificial Intelligence Res Ctr Ajman U Arab Emirates Chung Ang Univ Coll Engn Dept Civil & Environm Engn Seoul South Korea Univ Hawaii Manoa Dept Civil & Environm Engn Honolulu HI 96822 USA Univ Hawaii Manoa Water Resources Res Ctr Honolulu HI 96822 USA Jahrom Univ Fac Engn Dept Elect Engn Jahrom Iran Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran Natl Pingtung Univ Bachelor Program Big Data Applicat Business Pingtung Taiwan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Taiwan Asia Univ Dept Business Adm Taichung Taiwan Calif State Univ San Bernardino Dept Management San Bernardino CA 92407 USA Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom Hong Kong Peoples R China

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body's blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at http://***/projects/csbo.

关键词： Circulatory System Based Optimization (CSBO) complex optimization functions optimization biologically inspired algorithm real-world problems

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