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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Nguyen Quoc Khanh Le Van-Nui Nguyen Thi-Tuyen Nguyen Thi-Xuan Tran Trang-Thi Ho Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan University of Information and Communication Technology Thai Nguyen University Thai Nguyen Vietnam University of Economics and Business Administration Thai Nguyen University Thai Nguyen Vietnam Department of Computer Science and Information Engineering TamKang University New Taipei Taiwan

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

In bioinformatics, classifying protein sequences into anticancer peptides (ACPs) and non-ACPs is crucial yet challenging due to the inherent uncertainties of biological data. This study introduces a novel fuzzy neural network (FNN) model that integrates fuzzy logic within neural network architectures, enhancing the handling of ambiguity and improving classification accuracy. Our model, tested against several conventional machine learning models and recent studies, demonstrated superior specificity (83.28%) and overall accuracy (79.14%), marking a significant advancement in the identification of therapeutically relevant peptides. The integration of fuzzy logic not only optimized the performance but also increased the interpretability of the results, making it a valuable tool for complex bioinformatic analyses. These findings underscore the potential of fuzzy systems to refine predictive capabilities in computational biology, aligning perfectly with the themes of enhancing fuzzy theory applications in practical and impactful ways.

关键词： Fuzzy logic Uncertainty Accuracy Biological system modeling Peptides Machine learning Fuzzy neural networks

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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Using a hybrid neural network architecture for DNA sequence representation: A study on N4-methylcytosine sites

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Computers in Biology and Medicine 2024年 178卷 108664-108664页

作者： Nguyen, Van-Nui Ho, Trang-Thi Doan, Thu-Dung Le, Nguyen Quoc Khanh University of Information and Communication Technology Thai Nguyen University Thai Nguyen Viet Nam Department of Computer Science and Information Engineering TamKang University New Taipei251301 Taiwan International Degree Program in Animal Vaccine Technology International College National Pingtung University of Science and Technology Pingtung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei110 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei110 Taiwan AIBioMed Research Group Taipei Medical University Taipei110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei110 Taiwan

N4-methylcytosine (4mC) is a modified form of cytosine found in DNA, contributing to epigenetic regulation. It exists in various genomes, including the Rosaceae family encompassing significant fruit crops like apples, cherries, and roses. Previous investigations have examined the distribution and functional implications of 4mC sites within the Rosaceae genome, focusing on their potential roles in gene expression regulation, environmental adaptation, and evolution. This research aims to improve the accuracy of predicting 4mC sites within the genome of Fragaria vesca, a Rosaceae plant species. Building upon the original 4mc-w2vec method, which combines word embedding processing and a convolutional neural network (CNN), we have incorporated additional feature encoding techniques and leveraged pre-trained natural language processing (NLP) models with different deep learning architectures including different forms of CNN, recurrent neural networks (RNN) and long short-term memory (LSTM). Our assessments have shown that the best model is derived from a CNN model using fastText encoding. This model demonstrates enhanced performance, achieving a sensitivity of 0.909, specificity of 0.77, and accuracy of 0.879 on an independent dataset. Furthermore, our model surpasses previously published works on the same dataset, thus showcasing its superior predictive capabilities. © 2024 Elsevier Ltd

关键词： Embeddings

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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Internet of Things in disaster management systems: A systematic review

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AIP Conference Proceedings 2023年第1期2594卷

作者： G. P. L. Barnabas H. Citrawati T. A. Santoso N. Surantha Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Department of Electrical Electronic and Communication Engineering Faculty of Engineering Tokyo City University Tamazutsumi 1-28-1 Setagaya City Tokyo 158-8557 Japan

Disasters are unexpected, sudden events that may cause monumental damages to both materials and human lives. Hence, the unpredictability and destruction that follow when a disaster happens to pose a dire challenge for researchers, scientists, and governments to find ways to effectively handle the situation and flow of information when such an event strikes. The Internet of Things (IoT) presents a solution that involves using one or more mobile devices connected over the network, thus making the transfer of data and information more effective and efficient. This paper presents a holistic analysis of the state-of-the-art IoT technologies which can answer the challenge posed above, in addition to the demand for efficiency, mobility, accuracy, as well as better communications between the command center and all actors involved in disaster operations. The applications of IoT have varied for many purposes. We divide those purposes into two broad strokes of pre-disaster management and post-disaster management IoT-aided systems. For pre-disaster management, the IoT-aided systems are mostly used to predict the possible disaster that might happen using sensors placed in strategic places. Finally, for post-disaster management, the IoT-aided systems aid in improving the effectiveness and accuracy of evacuation, relief, and victim-tracking efforts.

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Vision Application of LabVIEW: IGUI for Face and Pattern Detection in Real Time

Vision Application of LabVIEW: IGUI for Face and Pattern Det...

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International Conference on Information Management and Technology (ICIMTech)

作者： Faris Fawwaz Burhanudin Dunggio Putri Wulandari Irmawan Rahyadi Dwi Astharini Electrical Engineering Faculty of Science and Technology University of Al-azhar Jakarta Indonesia BINUS Graduate Program - Master of Strategic Marketing Communication Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

A well-prepared face detection has become a common thing in this era of technology There are so many applications that utilize face detection for example in smartphone, CCTV, and many more. This research proposed a developed system detection object (face and pattern) using LabVIEW software. LabVIEW is virtual instrument that mimics an instrument simulation software that can display object detectors in real time with IGUI design. The accuracy of the detection that can read object using IGUI in real time was also discussed. This research presents a face detection approach and pattern in a colorless image compare to pattern and face texture that are maintained by Image Capture. This is then used to identify faces and patterns, while other previous research on face detection using 3D stereo vision cameras.

关键词： Faces Cameras Face detection Face recognition Real-time systems Software Streaming media

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A Vehicle Forensics framework with Post-Quantum Cryptography and Blockchain

A Vehicle Forensics framework with Post-Quantum Cryptography...

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IEEE International Symposium on Intelligent Signal Processing and communication Systems (ISPACS)

作者： Chien-Lung Hsu Yi-Cheih Hsu Yu-Jen Shih Cheng-Wei Wu Fu-Hau Hsu Graduate Institute of Management Chang Gung University Taiwan Taoyuan R.O.C Department of Information Management Chang Gung University Taiwan Taoyuan R.O.C Healthy Aging Research Center Chang Gung University Taiwan Taoyuan R.O.C Department of Visual Communication Design Ming-Chi University of Technology Taiwan Taoyuan R.O.C Chang-Gung Memorial Hospital Taiwan Taoyuan R.O.C Department of Master of Science Degree Program in Innovation for Smart Medicine Chang Gung University Taiwan Taoyuan R.O.C Computer Science& Information Engineering National Central University Taiwan Taoyuan R.O.C Information Management Chang Gung University Taiwan Taoyuan R.O.C

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

With the recent rapid development of the Internet of Vehicles and autonomous driving technology, the demand for digital vehicle identification has also increased. Coupled with the gradual maturity of quantum technology, the Internet of Vehicles, which mainly uses wireless communication technology for data transmission, will face the threat of quantum computing and privacy issues. This research proposes the Vehicle Forensics Cloud System (VFCS), a framework combining post-quantum cryptography and blockchain technology to address these issues and provide a comprehensive solution for vehicle digital forensics.

关键词： Wireless communication Quantum computing Digital forensics Insurance Signal processing Blockchains Safety Autonomous vehicles Interoperability Internet of Vehicles

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Land Suitability of Settlements in West Palu District, Palu City 4

Land Suitability of Settlements in West Palu District, Palu ...

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4th International Conference on Energy, Environment, Epidemiology and Information System, ICENIS 2019

作者： Santoso, Selamet Rudiarto, Iwan Luqman, Yanuar Master Program of Environmental Science School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Urban Regional Planning Faculty of Engineering Diponegoro University Semarang Indonesia Department of Communication Faculty of Social Science and Political Science Diponegoro University Semarang Indonesia

A settlement is an important requirement for humans. Increasing the number of residents has encouraged additional needs for residential land while the amount of land has not increased. It is necessary to analyze the suitability of residential land to create decent and safe settlements from disasters. This study analyzes the suitability of residential land in the West Palu District based on the criteria of terrain slope, distance to the road network, the distance of educational facilities, the distance of health facilities, distance of trade facilities, and disaster vulnerability. The results of this study obtained a percentage of the area for unsuitability class 28.84%, marginally suitable 18.44%, suitable 34.93%, and more suitable 17.79%. © The Authors, published by EDP Sciences, 2019.

关键词： Disasters

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The matter of deep reinforcement learning towards practical ai applications 12

The matter of deep reinforcement learning towards practical ...

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12th International Conference on Ubi-Media Computing, Ubi-Media 2019

作者： Thaipisutikul, Tipajin Chen, Yi-Cheng Hui, Lin Chen, Sheng-Chih Mongkolwat, Pattanasak Shih, Timothy K. Department of Computer Science and Information Engineering National Central University Taiwan Department of Information Management National Central University Taiwan Department of Innovative Information and Technology Tamkang University Taiwan Master's Program of Digital Content and Technologies College of Communication National ChengChi University Taiwan Faculty of Information and Communication Technology Mahidol University Thailand

ISBN: (纸本)9781728128207

Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised and unsupervised real-world problems. However, RL is one of state-of-the-art topic due to the opaque aspects in design and implementation. Also, in which situation we will get performance gain from RL is still unclear. Therefore, This study firstly examines the impact of Experience Replay in Deep Q-Learning agent with Self-Driving Car application. Secondly, The impact of Eligibility Trace in RNN A3C agents with Breakout AI game application is studied. Our results indicated that these two techniques enhance RL performance by more than 20 percent as compared with traditional RL methods. © 2019 IEEE.

关键词： Reinforcement learning

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Enhanced image encryption algorithm by quantum key distribution

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IOP Conference Series: Materials Science and engineering 2021年第1期1172卷

作者： M A Mahmoud T Mekkawy A D Elbayoumy NANO Master program Faculty of Engineering.Cairo UniversityGiza Egypt Avionics Department Military Technical Collage Cairo Egypt Communication Department Military Technical Collage Cairo Egypt

Quantum Key Distribution (QKD) is one type of Quantum Cryptography (QC) which is based on quantum mechanics fundamentals such as Heisenberg's uncertainty principle and No-cloning theory. The usage of QKD warns the legitimate communicated parties to any attack attempt and this is the most interesting security parameter. Therefore, QKD provides unconditional secure communication method and supports a powerful encryption scheme. The combination between classical communication and QKD creates a new technique called semi quantum key distribution SQKD. Unfortunately, SQKD increases the schemes complexity and requires two steps for ciphering, scramble and encryption. In this paper an enhance image encryption algorithm is proposed based on QKD that eliminates most of the drawbacks of SQKD. The proposed algorithm is simpler than other encryption schemes as it exploits only one encryption step based on the power and the randomness of the generated secret key, which decreases the chance to be cracked. The correctness and efficiency of the proposed algorithm are validated by numerical simulations.

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