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

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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Detection of Wrong Direction Vehicles on Two-Way Traffic 13

Detection of Wrong Direction Vehicles on Two-Way Traffic

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13th International Conference Knowledge and Systems engineering, KSE 2021

作者： Suttiponpisarn, Pintusorn Charnsripinyo, Chalermpol Usanavasin, Sasiporn Nakahara, Hiro Thammasat University Ictes Program Taist Tokyo Tech Sirindhorn International Institute of Technology Pathum Thani Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand Thammasat University School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Pathum Thani Thailand Tokyo Institute of Technology Dept. of Information and Communications Engineering Tokyo Japan

ISBN: (纸本)9781665499750

In 2018, around 22,000 people were killed in road accidents in Thailand and more than 80% of road accidents involved motorcycles. There are several reasons and one of them is motorcycles driving in the wrong direction. Our paper proposes a system for road surveillance to detect and track moving vehicle's direction on two-way traffic roads from CCTV viewpoint using deep learning where the roadside noise is filtered out. The system can determine whether the vehicles drive in the correct direction in their lane. In this case, we will focus on motorcycles. To set up the system, there are two main steps: draw the area of interest and validate the correct direction. In the direction validation process, we detect and track vehicles by using YOLOv4-tiny and Deep SORT tracking algorithm. The system returns a satisfactory and accurate result for detecting direction without noise, such as moving objects on the sidewalk or moving vehicles in an unwanted area. © 2021 IEEE.

关键词： Motorcycles

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A Multidisciplinary Effort for Smart Health Assistive Technology in a Children's Psychiatric Unit

A Multidisciplinary Effort for Smart Health Assistive Techno...

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IEEE International Conference on Microwaves, communications, Antennas and Electronic Systems, COMCAS

作者： Itai Dabran Meira Levy Michal Pauzner Mariela Mosheva Tom Sofer Tal Shilton Eitan Eldar Doron Gothelf Computer Science Faculty Technion - Israel Institute of Technology Haifa Israel Department of Industrial Engineering and Management Shenkar College Ramat Gan Israel Department of Visual Communication Azrieli Faculty of Design Shenkar College Ramat Gan Israel The Edmond and Lily Safra Children's Hospital Chaim Sheba Medical Centre Tel HaShomer Israel Applied Behavior Analysis in Psychiatry Program Chaim Sheba Medical Centre Tel HaShomer Israel

ISBN: (数字)9798350348187

ISBN: (纸本)9798350348194

This paper demonstrates the importance of interdisciplinary collaboration between teams of medical researchers, product design researchers, and computer science students. Their combined work resulted in both a concept and a product, a Smart Health Assistive Bag for children being hospitalized. The Child Psychiatry Inpatient Unit at Sheba Medical Center provides mental health services to children suffering from a variety of psychopathological conditions. The multidisciplinary staff takes a family-oriented approach where parents and caregivers are encouraged to be active participants in the treatment team throughout their child's stay. Our work focused on the design and development of a Smart Bag, which supports this family-oriented approach, encourages the involvement of the family during the child's stay and collects new data for future functional analysis. engineering and design students from both Shenkar and the Technion worked with Sheba's clinicians to create a conceptual prototype that advances the defined theoretical needs and build a working prototype. Our Smart Bag aimed at aiding child hospitalizations in the Psychiatrist Inpatient Unit is an important innovative tool for Sheba's Smart Health facility. It is designed to help the child in adjusting to the unit and be an active participant in their daily routine while in the unit. Additionally, because it has IoT sensors and is linked to a network through a digital platform, it supports the child's family and caregivers in staying updated regarding the child's progress. The Smart Bag can apprise the family about the child's daily achievements by informing them of tokens given to the child for desirable behavior. It updates both family and caregivers regarding the current stage in the child's treatment. Such on-going communication involving the child, caregivers and clinicians reduces the stress and worry that families often feel when their child is hospitalized in the Psychiatrist Unit and improves the therap

关键词： Microwave antennas computer science Pediatrics Hospitals Databases Smart healthcare Authentication

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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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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A novel tri-bandpass filter side-coupled square ring based

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Journal of Microwaves, Optoelectronics and Electromagnetic Applications 2020年第2期19卷 265-275页

作者： Duarte, José Garibaldi Da Silva Neto, Valdemir Praxedes Postgraduate Program in Electrical and Computer Engineering PPGEEC UFRN Natal RN Brazil Communication Engineering Department DCO UFRN Natal RN Brazil

This work presents the design of a tri-bandpass filter based on the side-coupled square ring structure. The modeling is conceived from the classic approach of coupled transmission lines. The geometry consists of a square structure of electrical length equal to twice the wavelength of the main operating frequency, and the laterally coupled lines through which the power supply occurs. Expressions for the admittance and transformation matrix were obtained based on the impedances and admittances that model the geometry. Analyzis were performed from EM simulations. A prototype was built to validate the modeling and adjusted for operation at 0.78, 1.5 and 2.30 GHz. All results are discussed and commented, and the comparisons between simulated and measured results are coherent and in agreement, thus proving the effectiveness of the applied method. © 2020 SBMO/SBMag.

关键词： Electric lines

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Analysis and Comparative between Profile Matching and SAW Method in Decision Support 8

Analysis and Comparative between Profile Matching and SAW Me...

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8th International Conference on Cyber and IT Service Management, CITSM 2020

作者： Kurniawan, Helmi P Swondo, Ashari Purnama Sari, Eka Ummi, Khairul Rusdi Tanjung, Muhammad Yusfrizal Universitas Potensi Utama Faculty of Engineering and Computer Science Medan Indonesia English Education Study Program Universitas Potensi Utama Medan Indonesia Universitas Potensi Utama Faculty of Economy and Business Medan Indonesia Visual Communication Design Study Program Universitas Potensi Utama Medan Indonesia

ISBN: (纸本)9781728177014

A decision support system is a solution to help provide decision recommendations in determining which options to choose as a solution, in this decision support system research, namely analyzing and comparing the Matching Profile method and the SAW (Simple Additive Weighting) method in providing the best choice solution in the case of determining teaching staff acceptance. Determination of Admission, The selected teaching staff, are teaching staff who are in accordance with the criteria and quality. In an educational institution, namely TPQ Anbata, it is a very important issue to get elected teaching staff. One way to get teaching staff who is in accordance with the cafeteria is determined by selecting or determining prospective teaching staff by paying attention to various assessment criteria so that the selected teaching staff is feasible. From the tests that have been carried out, the results obtained include all test cases, and the testing is appropriate. The results of the system calculation accuracy test show that the calculation of Matching Profile and SAW (Simple Additive Weighting Method carried out by the system with manual calculations is correct and the feasibility test. Appropriate to use in making decisions determining the recruitment of teachers than the profile matching method in Anbata TPQ. The difference is in the highest value in each method. In the profile matching method, the highest value is Salman Al Fariz with a value of 4.70 while in the SAW method the highest value is Ali Gufron with a value of 8,778., it can be concluded that the most appropriate method in determining the recruitment of teaching staff at TPQ Anbata is the Profile Matching Method. © 2020 IEEE.

关键词： Decision support systems

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Bonifacius Vicky Indriyono Natalinda Pamungkas Fatima Hashim Abbas Ery Mintorini Mohammed Almohamadi Sarmad Nozad Mahmood Sameer Alani Ahmed H. Alkhayyat Eko Hari Rachmawanto Information System University of Dian Nuswantoro Kediri Indonesia Medical Laboratories Techniques Department Al-Mustaqbal University College Hillah Babil Iraq Visual Communication Design University of Dian Nuswantoro Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq Medical Laboratories Techniques University of Mashreq Research center Baghdad Iraq College of technical engineering University of Mashreq The Islamic University Najaf Iraq Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments; they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD.

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A Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach

A Platform for Monitoring Student Commuting in the Use of Sc...

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International Conference on Smart communications and Networking (SmartNets)

作者： Jessé Da Costa Rocha Marcela Alves de Souza Evelin Helena Silva Cardoso Nandamudi Vijaykumar Jasmine Priscyla Leite de Araújo Renato Lisboa Francês Graduate Program in Electrical Engineering Institute of Technology Federal University of Pará (UFPA) Belém Pará Brazil Information and Communication Technology Center Federal University of the South and Southeast of Para (Unifesspa) Marabá Pará Brazil Department of Computer Science Federal Rural University of the Amazon (UFRA) Capitão Poço Pará Brazil Coordination of Applied Research and Technological Development National Institute for Space Research (INPE) São Paulo Brazil

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics.

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