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European Journal of Radiology Artificial Intelligence 2025年 2卷 100016-100016页

作者： Le, Minh Huu Nhat Kha, Hien Quang Tran, Nghia Minh Nguyen, ph.t Ky Huynh, Han H. Huynh, ph.t Kim Lam, Han Le, Nguyen Quoc Khanh International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan AIBioMed Research Group Taipei Medical University Taipei 110 Taiwan Translational Science and Precision Medicine Talent Cultivation Program College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan International Master Program for Translational Science College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan Department of Industrial and Systems Engineering North Carolina A&T State University USA Industrial & Management Systems Engineering Department University of South Florida Tampa FL 33620 USA In-Service Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei 110 Taiwan

AbstractThis narrative review explores radiomics in the broader context of liver disease research, an interdisciplinary field bridging medical imaging, oncology, and data science. It begins with an introduction to data analysis techniques, from foundational methods like descriptive statistics, comparative and regression analysis, and survival analysis, to advanced approaches like machine learning (ML), meta-analysis, and bioinformatics. Contextualizing these methodologies within liver disease research enhances our understanding of liver diseases. The focus is on radiomics, an emerging field, which is first placed in historical context before its applications are explained across fatty liver diseases (FLd), fibrosis, cirrhosis, and liver cancers. The role of radiomics in early detection, disease staging, and outcome prediction, particularly as a non-invasive alternative to liver biopsy, is analyzed in-depth. Current imaging modalities for liver cancer diagnosis are also examined, highlighting their advantages and limitations. Alongside radiomics, these imaging tools enable comprehensive liver assessment. Lastly, the integration of radiomics with other data types, especially genomic data, is explored, emph.sizing its role in a holistic approach to liver disease management. The review concludes with a forward-looking perspective on future directions and advancements in radiomics and liver cancer research, addressing the challenges and opportunities in the field. It underscores the transformative impact of these developments on liver cancer research, patient care, personalized medicine, and clinical decision-making.

关键词： AFP alph.-fetoprotein AI artificial intelligence AST aspartate transaminase AUC area under the curve CCrCC clear cell renal cell carcinoma CEUS contrast-enhanced ultrasound CNNs convolutional neural networks dCNN deep convoluted neural network dOR diagnostic odds ratio FLd fatty liver disease GWAS genome-wide association studies HCC hepatocellular carcinoma MAFLd metabolic associated fatty liver disease ML machine learning MRE magnetic resonance elastograph. NAFLd nonalcoholic fatty liver disease NASH nonalcoholic steatohepatitis NPV negative predictive value PPV positive predictive value VCTE vibration-controlled transient elastograph. Radiomics Liver disease Cancer Non-invasive diagnostic techniques Presicion medicine deep learning Machine learning

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Semantic Visual Simultaneous Localization and Mapping: A Survey

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7426-7449页

作者： Chen, Kaiqi Xiao, Junhao Liu, Jialing Tong, Qiyi Zhang, Heng Liu, Ruyu Zhang, Jianhua Ajoudani, Arash Chen, Shengyong Zhejiang University of Technology Institute of Computer Vision College of Computer Science and Technology Hangzhou 310023 China Istituto Italiano di Tecnologia Human-Robot Interfaces and Interaction Laboratory Genoa 16163 Italy National University of Defense Technology College of Intelligence Science and Technology Changsha 410073 China Universitã di Genova Ph.D. Program of National Interest in Robotics and Intelligent Machines (DRIM) Genoa 16126 Italy Hangzhou Normal University School of Information Science and Technology Hangzhou 311121 China Technical University of Denmark Department of Technology Management and Economics Kongens Lyngby 2800 Denmark Tianjin University of Technology Institute of Computer Vision School of Computer Science and Engineering Tianjin 300384 China

Visual Simultaneous Localization and Mapping (vSLAM) is a cornerstone technology in computer vision and robotics, underpinning applications such as autonomous vehicles and robot navigation. While traditional vSLAM systems have shown significant progress in indoor or outdoor environments, their performance often degrades in complex scenes, limiting their adaptability and robustness. Semantic vSLAM, which integrates high-level semantic information into vSLAM systems, has emerged as a promising solution to address these limitations by enabling a richer understanding of the environment. In this paper, we provide a comprehensive review of semantic vSLAM, offering a critical analysis of its evolution, methods, and challenges. We begin by revisiting the development of traditional vSLAM, emph.sizing its limitations and the motivation for incorporating semantic information. Subsequently, we delve into the core modules of semantic vSLAM, including semantic extraction, object association, semantic loop closing, back-end optimization, and semantic mapping. Then, we present a performance comparison of semantic vSLAM systems under two different datasets, indoor and outdoor, respectively. Furthermore, we also provide a comparative analysis of widely used SLAM datasets to provide guidance for performance testing and validation. To further enrich the discussion, we identify unresolved challenges in semantic vSLAM, such as long-term semantic perception and association, open and unstructured environments. We propose future research directions, including balancing computational resources and quantifying system risk, large model-based navigation and mapping, and embodied AI SLAM. By providing key insights and forward-looking perspectives, this work aims to stimulate future research and improve the capabilities of semantic vSLAM in real-world applications. © 2000-2011 IEEE.

关键词： localization mapping semantic information Semantic visual SLAM SLAM datasets

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Negative prognostic factors and clinical improvement prediction modeling for extracorporeal shockwave therapy in calcific shoulder tendinitis using artificial intelligence techniques

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Journal of Shoulder and Elbow Surgery 2025年

作者： Chou, Wen-Yi Huang, Tian-Hsiang Cheng, Jai-Hong Lien, Yu-Jui Ho, Wen-Hsien Chou, Paul Pei-Hsi Ph.D. Program in Biomedical Engineering College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopedic Surgery Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan Department of Leisure and Sport Management Cheng Shiu University Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Medical Research Center for Shockwave Medicine and Tissue Engineering Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan Precision Sports Medicine and Health Promotion Center Kaohsiung Medical University Kaohsiung Taiwan Department of Sports Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Sports Medicine Department of Orthopaedic Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Orthopaedics School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan

Background: The efficacy of extracorporeal shockwave therapy (ESWT) for treating shoulder calcific tendinitis can be influenced by various prognostic factors. This study aimed to identify prognostic factors associated with the failure of ESWT for symptom relief and to evaluate the predictive capability of the eXtreme Gradient Boosting (XGBoost) algorithm of artificial intelligence techniques in this context. Methods: This retrospective study enrolled patients with persistent shoulder pain attributed to calcific tendinitis who underwent ESWT after failed conservative treatment between January 1998 and december 2022. Age, sex, duration of symptoms, calcification classification and size, pre-ESWT visual analog scale (VAS), and pre-ESWT Constant-Murley score (CMS) served as potential input attributes. The difference in VAS and CMS were defined as the output attributes. The XGBoost model was used to predict treatment outcomes based on these factors. The dataset was balanced using the synthetic minority oversampling technique, and the model's performance was assessed using 10-fold cross-validation. Spearman's rank correlation coefficient analysis was adopted to explore the relationships between significant continuous input attributes and post-ESWT VAS and CMS scores. Results: A total of 296 patients with calcific tendinitis were enrolled and completed the 1-year follow-up. The findings revealed that a prolonged symptom duration (>10 months), severe pain (pre-ESWT VAS >5), and higher pre-ESWT CMS (>55) were significant prognostic factors for the failure of ESWT for symptom relief. Using these factors as inputs, the XGBoost model demonstrated high accuracy, precision, recall, and F1 score. By reducing the input attributes to age, calcification size, pre-ESWT CMS, and symptom duration, the model maintained a high prediction rate, suggesting that these factors are sufficient for effective prediction. discussion: The present study identified significant prognostic factors asso

关键词： Artificial intelligence Basic science Study calcific tendinitis CMS development of Predictive Computer Modeling Using AI/Machine Learning ESWT shoulder VAS XGBoost

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Application of the Internet of Everything in Educational Institutions

Application of the Internet of Everything in Educational Ins...

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Yuan Chou, Tsung Lai, Wen-Hsing National Kaohsiung University of Science and Technology Ph.D.Program In Enginering Seience and Technology College of Enginering Kaohsiung Taiwan National Kaohsiung University of Science and Technology Dept. of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665419512

developments in network infrastructure and progress in sensor devices have brought the rise of the Internet of Everything (IoE). The technologies and applications are characterized by overall perception, tight connection, and intelligent responses. This study explored the application of the IoE in education such that these features can be utilized in educational institutions and areas designated for educational services. Focus was placed on the use of the Smart Manager in school attendance tracking, notification system, and security monitoring system for more broad and comprehensive campus safety. © 2021 IEEE.

关键词： Internet of Everything

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Cuisine Image Recognition and Recommender System using Convolutional Neural Network 5

Cuisine Image Recognition and Recommender System using Convo...

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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Kuan-Yun Kao, Hao-Xiang Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan Chung Hua University Program of Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

The fusion of technology and culinary exploration has allowed for the emergence of advanced online customer service systems. We developed a novel approach to enhance the dining experience. We used a Cuisine Image Recognition and Recommender System (CIRRS) powered by Convolutional Neural Network (CNN) to identify and suggest diverse cuisines based on visual inputs. CIRRS swiftly identified diverse cuisines from user-captured images, offering information on origin, ingredients, and variations by crawling websites. The system enhanced dining experiences by suggesting personalized menus based on individual preferences and past selections. Extensively tested across various culinary genres, CIRRS consistently demonstrated accuracy and adaptability. User feedback validated its potential to simplify dining choices and elevate satisfaction. This innovative system enriched dining experiences as a valuable tool for culinary enthusiasts, chefs, and restaurateurs, bridging the gap between technology and gastronomy, It also offers a unique way to explore Taiwanese cuisines. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system web crawler

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Non-destructive evaluation of a city roadway for pavement rehabilitation: A case study

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International Journal of Pavement Research and technology 2017年第7期11卷 667-678页

作者： Chao, Chien-Chiao Lin, deng-Fong Luo, Huan-Lin Wang, Yu-Kai Lo, Wei Ph. D. Program in Engineering Science and Technology College of Engineering National Kaohsiung First University of Science and Technology Kaohsiung City Taiwan Department of Civil and Ecological Engineering I-Shou University Kaohsiung City Taiwan Department of Construction Engineering National Kaohsiung First University of Science and Technology Kaohsiung City Taiwan

In this study, a pavement evaluation was carried out on a corridor that carried heavy truck traffic, connecting a harbor, a major high-way, an airport, and industries. The pavement of the corridor was distressed frequently, requiring constant maintenance. With tight maintenance budget and intention to minimize the effects on traffic, full reconstruction or major rehabilitation of the corridor was not practical. To develop proper pavement rehabilitation design within the practical constrains, pavement evaluation was performed in this study using non-destructive techniques, such as visual condition survey, falling weight deflectometer (FWd), ground penetrating radar (GPR), and dynamic cone penetrometer (dCP), and pavement coring. It is demonstrated that appropriate pavement rehabilitation design can be developed to offer practical solution to the repair/maintenance for city streets. © 2017 Chinese Society of Pavement engineering.

关键词： Ground penetrating radar systems

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A Big data-Based Target Cost Management System

A Big Data-Based Target Cost Management System

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International Symposium on Computer, Consumer and Control (IS3C)

作者： Yin-Chen Lin Jyun-Jie Wang Chi-Yuan Lin Sheng-Chih Yang Ph. D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung Taiwan National Chin-Yi University of Technology Taichung Taiwan

ISBN: (数字)9781728193625

ISBN: (纸本)9781728193632

This study proposed a novel big data-based target cost management (TCM) system that automatically calculates the cost table. MATLAB, Python, and Excel were used to realize the system. TCM can be performed using the content of the calculated cost table. The system is suitable for managing catering inventory. This study presented the theoretical model of the proposed system and revealed that the system outperformed the traditional cost table implementation.

关键词： Procurement databases programming Synchronization Mathematical model Python Matlab

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Properties of the mixing layer height retrieved from ceilometer measurements in Slovakia and its relationship to the air pollutant concentrations

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Environmental science and Pollution Research 2023年第54期30卷 115666-115682页

作者： Nguyen, duy-Hieu Štefánik, dušan Šedivá, Tereza Lin, Chitsan Ph.D. Program in Maritime Science and Technology College of Maritime National Kaohsiung University of Science and Technology Kaohsiung811213 Taiwan Slovak Hydrometeorological Institute Jeséniova 17 Bratislava833 15 Slovakia Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská Dolina F1 Bratislava842 48 Slovakia Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung811213 Taiwan

Mixing layer height (MLH) is an important meteorological parameter for air quality since it significantly affects ground-level pollution in the atmosph.re. This study examined the properties of the MLH on diurnal and seasonal timescales over a 3-year period (2020–2022) using high temporal resolution measurements from eight Vaisala CL31 ceilometers situated around Slovakia. Hourly averaged MLH data was retrieved from the BL-View software using merged method. The highest daily maxima for the MLH occurred mostly in summer and spring, while the lowest values occurred predominantly during winter and autumn. The average MLH daily maximum in summer was 2229 m, and just 859 m in winter. during summer, the spatial distribution of the MLH daily maxima was more uniform compared to winter, when the air masses within the individual valleys did not mix well. Correlations between ground-level pollutant concentrations and hourly mean/daily mean MLH were analyzed. The highest correlation, R≈0.6, was found for O3. For PM10, PM2.5, and NOx, the anticorrelations with MLH were found with maximum in winter (R ≈ − 0.3 for hourly data and R ≈ − 0.5 for daily mean data) but no relation in summer. Lastly, the ceilometer MLH was compared to the radiosonde retrieved MLH for various cloud covers. Our analysis is based on an extensive set of empirical data, which can improve the accuracy and effectiveness of meteorological and atmosph.ric chemistry models. The findings can support air pollution forecasting and warning systems, providing valuable insights for policymakers and researchers. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Air quality

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Ultrasensitive and selective electrochemical detection of hydroquinone using electropolymerized 7-amino ceph.losporanic acid modified GCE

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Surface and Coatings technology 2025年

作者： H. Sathya Pandiaraja Varatharajan M.A. Ashkar M. Chandhru Mani Govindasamy S. Kutti Rani N. Vasimalai Department of Chemistry B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai 600 048 India International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan Department of Physics B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai 600 048 India Centre for Applied Research Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai 602105 Tamil Nadu India Research Center for Intelligence Medical Devices Ming Chi University of Technology New Taipei City 243303 Taiwan

Industrial wastewater pollution harms ecosystems and hinders sustainable development growth. Particularly, hydroquinone (HQ) has transitioned from a commercial chemical to an environmental hazard because of its extensive use in beauty products, ph.rmaceuticals, chemicals, and food industries. Therefore, developing electrochemical sensors for the detection of HQ is very important for environmental safety. Herein, the 7-ACA monomer was electropolymerized on the glassy carbon electrode (GCE) surface to examine the electrochemical oxidation of HQ utilizing CV (cyclic voltammetry) and dPV (differential pulse voltammetry) techniques. Scanning electron microscopy (SEM) characterization verified the deposition of the p-7-ACA on the glassy carbon electrode (GCE) surface. The designed p-7-ACA@GCE sensor exhibited better electrocatalytic property. The acquired dPV observations for the p-7-ACA@GCE demonstrated excellent limit of detection (LOd) 8.86 nmol L −1 and broad linear range (0.33 μM to 955 μM) for the detection of HQ. The developed sensor exhibits excellent repeatability, reproducibility, selectivity and better cyclic stability. In addition, the fabricated p-7-ACA@GCE was used to determine the HQ in a different environmental water sample.

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deep Learning-Based Positioning Error Fault diagnosis of Wire Bonding Equipment and an Empirical Study for IC Packaging

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IEEE Transactions on Semiconductor Manufacturing 2023年第4期36卷 619-628页

作者： Kao, Sheng-Xiang Chien, Chen-Fu International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Intelligent Manufacturing and Circular Economy Research Center National Tsing Hua University Artificial Intelligence for Intelligent Manufacturing Systems Research Center National Science and Technology Council Department of Industrial Engineering and Engineering Management Hsinchu300044 Taiwan

Little research has been done for artificial intelligence applications of semiconductor backend. This study aims to develop a deep learning based fault diagnosis framework as prognostics and health management (ph.) solutions with a comprehensive analytics process. A linear encoder sensor is employed to measure position, while data preparation is conducted to convert position information into a signal. Then, signal processing is used to extract the features, in which high pass filter is applied to normalize the signal and emph.size the features. High-dimensional features including time domain statistical features, time-frequency domain features obtained by continuous wavelet transform (CWT), and frequency domain features converted by fast Fourier transform (FFT) are extracted to prepare the dataset. An ensemble neural network model that integrates deep neural network (dNN) and convolutional neural network (CNN) is employed to recognize the pattern and diagnose the positioning errors. The accuracy and false alarm rate are considered to support maintenance decisions. An empirical study was conducted in a world leading IC assembly and testing company for validation. The results have shown that the proposed approach can effectively detect inappropriate installation of the bond head in wire bonding equipment for predictive maintenance and cost reduction. © 1988-2012 IEEE.

关键词： Failure analysis

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