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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Min-Shiang Hwang Hou-Wen Li Cheng-Ying Yang Department of Computer Science and Information Engineering Asia University Taichung Taiwan R.O.C. The Ph.D. Program in Artificial Intelligence Asia University Taichung Taiwan R.O.C. Department of Computer Science University of Taipei Taipei Taiwan R.O.C.

In NFC applications, user privacy information must be protected first. Cao and Liu recently proposed a lightweight NFC authentication scheme based on an improved hash function to ensure that the user’s private information will not be leaked. Although their method is highly efficient and has Mutual Authentication, Forward Security, Backward Security, and security against attacks such as Replay, Location, and Fake, once out of synchronization occurs, the method must reestablish synchronization data. Therefore, this article will propose a lightweight synchronization method.

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Exhaled volatile organic compounds for cholangiocarcinoma diagnosis

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Liver Research 2022年第3期6卷 191-197页

作者： Nanicha Siriwong Thanikan Sukaram Rossarin Tansawat Terapap Apiparakoon Thodsawit Tiyarattanachai Sanparith Marukatat Rungsun Rerknimitr Roongruedee Chaiteerakij Department of Medicine Faculty of MedicineChulalongkorn UniversityBangkokThailand Division of Gastroenterology Department of MedicineFaculty of MedicineChulalongkorn Universityand King Chulalongkorn Memorial HospitalThai Red Cross SocietyBangkokThailand Department of Food and Pharmaceutical Chemistry Faculty of Pharmaceutical SciencesChulalongkorn UniversityBangkokThailand Department of Computer Engineering Faculty of EngineeringChulalongkorn UniversityBangkokThailand Clinical Sciences PhD International Program Faculty of MedicineChulalongkorn UniversityThailand Image Processing and Understanding Team Artificial Intelligence Research GroupNational Electronics and Computer Technology Center(NECTEC)Thailand Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology Chulalongkorn UniversityBangkokThailand

Objectives:The difficulties in the early detection consequent to the lack of sensitive biomarkers render patients with cholangiocarcinoma(CCA)to have poor ***,sensitive and specific volatile organic compounds(VOCs)were identified in several ***,the VOC profiles in CCA are not ***,we investigated the VOC profiles in exhaled breath of CCA patients and ***:We prospectively collected exhaled breath samples from 30 consecutive patients newly diagnosed with CCA and 30 controls who did not have CCA(seven had benign biliary strictures and 23 had other medical conditions).Exhaled VOCs were identified using gas chromatography mass spectrometry Triple Quadrupoles *** of the significant differences in VOCs between cases and controls was conducted using supervised multivariate regression *** validation was performed for these VOCs in another cohort of 18 CCA patients and 22 ***:Levels of six compounds were significantly different between CCA patients and controls,namely,acetone,isopropyl alcohol,dimethyl sulfide,1,4-pentadiene,allyl methyl sulfide,and N,*** and dimethyl sulfide were independently associated with CCA as demonstrated in the multivariate *** the cut-off value of 8.59107 arbitrary unit(AU),acetone had a sensitivity and specificity of 82.1%and 75.8%,respectively,with an area under the receiving operator curve(AUROC)of 0.85 for the CCA *** level was also significantly different between cases and controls in the validation *** the same cut-off value,the sensitivity,specificity,and AUROC was 59.1%,66.7%,and 0.85,***:Breath analysis may potentially be useful for CCA diagnosis.A cohort of patients with earlystage CCA in further studies is needed to confirm the ability of exhaled VOCs for the early detection of CCA.

关键词： Cholangiocarcinoma(CCA) Bile duct cancer Volatile organic compounds(VOCs) Biomarker Cancer screening Diagnostic model

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Segmenting Alzheimer's Disease MRI Slices for Hippocampal Volume Calculation

Segmenting Alzheimer's Disease MRI Slices for Hippocampal Vo...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Hsiao-Chuan Tan Gilang Titah Ramadhan Wei-Cheng Shen Wen-Thong Chang Chih-Yang Lin Ben-Yi Liau Yih-Kuen Jan Yori Pusparani Chi-Wen Lung Department of Artificial Intelligence PhD Program Asia University Taichung Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Creative Product Design Asia University Taichung Taiwan Department of Mechanical Engineering National Central University Taoyuan Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana America Department of Digital Media Design Asia University Taichung Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Alzheimer's is a neurodegenerative disease that quietly steals human memory. This study analyzed hippocampal volume in Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal Cognition (NC) using MRI image slices from the ADNI database and YOLOv8 instance segmentation. We used 300 images to segment the left and right hippocampal. This study focused on volume calculation from five MRI image slices. The results showed 0.98 (AD), 0.92 (MCI), and 0.90 (NC) and revealed significant volumetric differences showed in the NC class (P = 0.045). This suggests early neuroanatomical changes can occur without cognitive symptoms, highlighting the potential of deep learning in the early detection of neurodegenerative diseases.

关键词： Instance segmentation Deep learning Accuracy Databases Magnetic resonance imaging Cognition Alzheimer's disease

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Reducing information bottleneck for weakly supervised semantic segmentation 21

Reducing information bottleneck for weakly supervised semant...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Jungbeom Lee Jooyoung Choi Jisoo Mok Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Department of Electrical and Computer Engineering Seoul National University and Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (纸本)9781713845393

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes an information bottleneck, and as a result, only a subset of the task-relevant information is passed on to the output. We first support this argument through a simulated toy experiment and then propose a method to reduce the information bottleneck by removing the last activation function. In addition, we introduce a new pooling method that further encourages the transmission of information from non-discriminative regions to the classification. Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the PASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation.

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Out of Sight, Out of Mind: A Source-View-Wise Feature Aggregation for Multi-View Image-Based Rendering

arXiv

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arXiv 2022年

作者： Cha, Geonho Shin, Chaehun Yoon, Sungroh Wee, Dongyoon Clova AI NAVER Corp. Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

To estimate the volume density and color of a 3D point in the multi-view image-based rendering, a common approach is to inspect the consensus existence among the given source image features, which is one of the informative cues for the estimation procedure. To this end, most of the previous methods utilize equally-weighted aggregation features. However, this could make it hard to check the consensus existence when some outliers, which frequently occur by occlusions, are included in the source image feature set. In this paper, we propose a novel source-view-wise feature aggregation method, which facilitates us to find out the consensus in a robust way by leveraging local structures in the feature set. We first calculate the source-view-wise distance distribution for each source feature for the proposed aggregation. After that, the distance distribution is converted to several similarity distributions with the proposed learnable similarity mapping functions. Finally, for each element in the feature set, the aggregation features are extracted by calculating the weighted means and variances, where the weights are derived from the similarity distributions. In experiments, we validate the proposed method on various benchmark datasets, including synthetic and real image scenes. The experimental results demonstrate that incorporating the proposed features improves the performance by a large margin, resulting in the state-of-the-art performance. © 2022, CC BY-NC-ND.

关键词： Three dimensional computer graphics

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Software Project Management Effectiveness Evaluation Tool 9

Software Project Management Effectiveness Evaluation Tool

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9th International Conference on Software and Information engineering, ICSIE 2020

作者： Barghoth, Mohamed Ellithey Salah, Akram Ismail, Manal A Master in Software Engineering Program Egyptian E-Learning University Giza Egypt Faculty of Computer and Artificial Intelligence Cairo University Giza Egypt Department of Computer Engineering and Systems Faculty of Engineering Helwan University Helwan Egypt

ISBN: (纸本)9781450377218

Project management is important for the success of a software project. Enhancing software project management effectiveness measurements should lead the project manager to advance practices that contribute to the successful development of software projects. This study aims to enhance project management effectiveness measurements in the direction of increase the likelihood of software project success. An enhanced evaluation model of software project management effectiveness has been proposed in this study. Correspondingly, the model fed into a developed software project management effectiveness evaluation tool. This measurement tool helps software development managers to evaluate, monitor and improve project management effectiveness in software projects. The developed software project management effectiveness evaluation tool has been validated by a feedback survey. Feedback survey participants have been demonstrated the importance of the developed project management effectiveness measurement tool. © 2020 ACM.

关键词： Project management

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Comparative Analysis of Real-Time Multi-Person Pose Detection in Electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Pin-Hsuan Lin Chin-Hsiung Chen Wei Chien Yu-Cheng Chang School of Art and Design Sanming University Fujian China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Business Administration National Central University Taoyuan Taiwan Department of Electrical Engineering New Taipei Municipal Tamsui Commercial Industrial Vocational Senior High School New Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.

关键词： Training Accuracy Microcontrollers Neural networks Real-time systems Safety Internet of Things

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Mrf-Former: A Multi-Scale Resolution Fusion Approach for Addressing Scale Discrepancies in Remote Sensing Image Change Detection

SSRN

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SSRN 2024年

作者： Yang, Hualin Ouyang, Jiarui Cheng, Junhao Zhang, Wen Li, Hui Li, Xiying Chen, Calvin Yu-Chian Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China - Preferred Program Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong Provincial Key Laboratory of Intelligent Transportation System Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Institute of Artificial Intelligence Guangzhou University Guangzhou China

Dealing with resolution variations in dual-temporal remote sensing images is challenging due to differences in sensor capabilities, altitudes, footprints, resampling, and data fusion. Conventional methods for different-resolution change detection struggle to effectively address these challenges, primarily due to insufficient consideration of global and local feature interactions. In this paper, we propose a novel change detection model that leverages the power of multi-scale resolution fusion(MRF). By incorporating the MRF-Block, our approach extracts hierarchical multi-scale features from the input data, enabling effective capture of contextual information at various granularities. This facilitates improved handling of resolution disparities and promotes interactions between global and local features. Experimental evaluations conducted on diverse datasets, demonstrate the effectiveness of our proposed model in achieving accurate change detection in dual-temporal remote sensing imagery, even in scenarios characterized by varying resolutions. Code is available on-line at https://***/yanghl1914/MRF-Former. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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A probabilistic model for pathway-guided gene set selection

A probabilistic model for pathway-guided gene set selection

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Kim, Inyoung Lee, Sangseon Kim, Youngkuk Namkoong, Hugh Kim, Sun Artificial Intelligence Institute Seoul National University Seoul Korea Republic of Institute of Computer Technology Seoul National University Seoul Korea Republic of Seoul National University Department of Computer Science and Engineering Seoul Korea Republic of Institute of Engineering Research Seoul National University Seoul Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665401265

Breast cancer is classified into five intrinsic subtypes, with differing treatment methods and prognoses. Therefore, accurate identification of subtypes from patient transcriptome data is essential. Many gene signatures, including PAM50, have been developed to classify breast cancer subtypes. However, existing gene selection methods do not utilize biological pathways. Gene signature selection using biological pathways can explain signature genes in terms of biological functions. Thus, we propose a probabilistic model for pathway-guided gene set selection using gene expression data. First, we defined gene and pathway factors based on gene expression and pathway activation levels, and calculated the posterior probability. Second, we adopted the prediction strength to guide gene set selection. Third, the gene set was selected using the posterior probability and prediction strength values. Finally, on evaluating the selected gene set, it was experimentally confirmed that our gene set performed better on classification tasks than the PAM50 gene set, a gene set produced by the XGBoost classifier, and a random gene set. Among the genes selected by our method, it was confirmed that the genes included in the cell cycle and circadian rhythm pathways showed different expression patterns for each breast cancer subtype. Our selected gene set exhibited biological significance in terms of pathway activation. © 2021 IEEE.

关键词： Gene expression

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Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification

Enhancing Protein Sequence Classification with a Fuzzy Neura...

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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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