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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： computerized tomography

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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Learning Local Representation by Gradient-Isolated Memorizing of Spiking Neural Network

Learning Local Representation by Gradient-Isolated Memorizin...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Man Wu Zheng Chen Yunpeng Yao Department of Information and Computer Science Keio University Yokohama Japan Graduate School of Engineering Science Osaka University Osaka Japan School of Information Science and Engineering Shandong University Shandong China

Spiking neural networks (SNNs) show promise in implementing low-power AI hardware due to their event-driven sparsity and leaky integrate-and-fire (LIF) neurons with binary spikes, rather than traditional activation functions with analog output. However, SNNs have a low capacity to process the local input due to their spike encoding paradigm. To address these problems, we propose a novel SNN architecture with a gradient-isolated memorizing mechanism, horizontal LIF, and vertical LIF neurons to learn local representation. Moreover, to take full advantage of local representations and global representations, we design a concurrent SNN architecture within the local-global joint representation. For proof-of-concept, we evaluate our proposal on MNIST, Fashion MNIST, and CIFAR-10 datasets, which achieve 98.68% and 90.37%, and 87.19% accuracy with 5 time steps, respectively, while maintaining low computational complexity.

关键词： Neurons Memory architecture Hardware Encoding Proposals Computational complexity Artificial intelligence

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Impact of Predictive Learning Analytics in Higher Education: A Systematic Literature Review

Impact of Predictive Learning Analytics in Higher Education:...

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International Conference on Educational and information Technology (ICEIT)

作者： John A. Bacus Rhodessa Cascaro University of Mindanao College of Engineering Education University of the Immaculate Conception Graduate School Davao City Philippines Mapua Malayan Colleges Mindanao College of Computer and Information Science Davao City Philippines

ISBN: (数字)9798350372663

ISBN: (纸本)9798350372670

This systematic literature review scrutinizes the role of Predictive Learning Analytics (PLA) in higher education from 2017 to 2023. Anchored by the PRISMA flowchart, the methodology involved a rigorous selection process from databases such as IEEE Xplore, Sage, ACM Digital Library, PubMed, Scopus, and ERIC. This systematic literature review filtered the studies based on inclusion criteria: peer-reviewed articles published in English, with full texts available, and excluded non-peer-reviewed sources, non-English publications, and those lacking definitive outcomes. The review addresses four questions in HEI's: the labeling and definition of PLA, its impact on student success, its role in enhancing teaching practices, and its influence on administrative functions. From the initial 142 records, 17 studies met the criteria, providing a comprehensive insight into PLA's role in shaping the academic landscape. The findings indicate that PLA is labeled as a strategic tool for leveraging student data to forecast and enhance learning outcomes. The analysis reveals PLA's significant contribution to student success through early identification of at-risk students and personalized intervention strategies. Furthermore, PLA has been instrumental in refining teaching methods and providing actionable insights to educators. For administrators, PLA informs resource allocation, curriculum development, and policy-making. This review emphasizes the transformative potential of PLA in optimizing higher education outcomes, signifying a paradigm shift towards data-driven educational strategies

关键词： Systematics Reviews Bibliographies Education Decision making Refining programmable logic arrays

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Uterine movement analysis based on velocity information by convolutional neural networks

Uterine movement analysis based on velocity information by c...

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2021 World Automation Congress, WAC 2021

作者： Mori, Kentaro Tokunaga, Yoshimitsu Sakumoto, Tetsuro Nakashima, Akira Komesu, Isamu Hata, Yutaka Electrical and Computer Engineering Maizuru Japan Sora No MORI CLINIC Shimajiri Japan University of Hyogo Graduate School of Information Science Kobe Japan

ISBN: (纸本)9781685241117

Female infertility patients have characteristic uterine movements. Since the movements are related to fertility, it is important to analyze the movements. We developed an automated analysis system for the uterine movements. The system was composed of convolutional neural networks. The system predicted the occurred movements from images with velocity information. The images were generated from Cine MRI images by calculating optical flow for each frame image. We confirmed that the accuracy of the model by the velocity images is higher than the model by the original images. We found that the uterine movement velocity is very important information to analyze the uterine movement patterns. © 2021 TSI Enterprises.

关键词： Velocity

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Misbehavior Detection Method by Time Series Change of Vehicle Position in Vehicle-to-Everything Communication

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Journal of Transportation Technologies 2021年第2期11卷 284-295页

作者： Toshiki Okamura Kenya Sato Computer and Information Science Graduate School of Science and Engineering Doshisha University Kyoto Japan

In recent years, research has been conducted on connected vehicles (CVs) that are equipped with communication devices and can be connected to networks. CVs share their own position information and surrounding information with other vehicles using Vehicle-to-Everything (V2X) communication. CVs can recognize obstacles on non-line-of-sight (NLoS), which cannot be recognized by autonomous vehicles, and reduce travel time to a destination by cooperative driving. Therefore, CVs are expected to provide safe and efficient transportation. On the other hand, problems of security of V2X communication by CVs have been discussed. Safe and efficient transportation by CVs is on the basis of the assumption that correct vehicle information is shared. If fake vehicle information is shared, it will affect the driving of CVs. In particular, vehicle position faking has been shown that it can induce traffic congestion and accidents, which is a serious problem. In this study, we define position faking by CV as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data composed of vehicle position information. We evaluated the proposed method using four different misbehavior models. F-measure of misbehavior models that CV sends random position information detected by the proposed method is higher than one by a related method. Therefore, the proposed method is suitable for detecting misbehavior in which the position information changes over time.

关键词： Connected Vehicle V2X Communication Security Misbehavior Detection Anomaly Detection

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Improved YOLOv5 based on CBAM and BiFPN for Rice Pest and Disease Detection

Improved YOLOv5 based on CBAM and BiFPN for Rice Pest and Di...

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computer, Internet of Things and Smart City (CIoTSC), International Conference on

作者： Yanqin Zhao Yichao Cui Ziyi Wang Computer and Information Engineering Heilongjiang University of Science and Technology Harbin China Graduate School Heilongjiang University of Science and Technology Harbin China Fashion Design School Jiangxi Institute of Fashion Technology China

ISBN: (数字)9798350306569

ISBN: (纸本)9798350306576

Addressing the issue where traditional YOLOv5 algorithm struggles in accurately detecting pests and diseases of small to medium scale with dense leaf distribution in rice, this study introduces an enhanced YOLOv5-CW algorithm model, derived from the original YOLOv5, specifically for rice pest and disease detection. Firstly, the feature fusion is improved by introducing the improved three-layer BiFPN (Bidirectional Feature Pyramid Network) weighted bidirectional feature pyramid network BiFPN-W into the original YOLOv5 algorithm to replace the original Feature Pyramid Network (FPN) structure and Path Aggregation Network (PAN) structure in the Neck part, which can fuse more features without increasing the cost. Second, the introduction of Convolutional Block Attention Module (CBAM) attention mechanism will significantly improve the detection of small targets for the identification of small-scale pests and dis-eases in rice plants. Finally, experiments are conducted on the dataset. The experimental results showed that compared with the detection of rice pests using the traditional YOLOv5 algorithm, the precision (P) and recall (R) rates of the improved YOLOv5-CW algorithm model detection increased from 86.05% and 77.46% to 90.65% and 85.63%, respectively, and the mean Average Precision (mAP) also increased from 83.52% to 87.40%, while the detection speed reached 22.38ms, which is faster than the detection speed of the original YOLOv5 algorithm. Meanwhile, compared with target detection algorithms such as SSD, Faster R-CNN, and YOLOv4, this improved algorithm has certain superiority and is an ideal model in rice pests and diseases.

关键词： YOLO Accuracy Attention mechanisms Smart cities Feature extraction Real-time systems Neck Sensors Diseases Drones

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Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition

arXiv

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arXiv 2024年

作者： Wang, Xiao Zhu, Qian Jin, Jiandong Zhu, Jun Wang, Futian Jiang, Bo Wang, Yaowei Tian, Yonghong The School of Computer Science and Technology Anhui University Hefei230601 China The School of Artificial Intelligence Anhui University Hefei230601 China Peng Cheng Laboratory Shenzhen China Shenzhen China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image, however, the performance is unreliable in challenging scenarios, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can fully use temporal information by fine-tuning a pre-trained multimodal foundation model efficiently. Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt a pre-trained foundation model CLIP to extract the visual features. More importantly, we propose a novel spatiotemporal side-tuning strategy to achieve parameter-efficient optimization of the pre-trained vision foundation model. To better utilize the semantic information, we take the full attribute list that needs to be recognized as another input and transform the attribute words/phrases into the corresponding sentence via split, expand, and prompt operations. Then, the text encoder of CLIP is utilized for embedding processed attribute descriptions. The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning. The enhanced tokens will be fed into a classification head for pedestrian attribute prediction. Extensive experiments on two large-scale video-based PAR datasets fully validated the effectiveness of our proposed framework. The source code of this paper is available at https://***/Event-AHU/OpenPAR. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Optimizing Wireless Sensor Network Topology with Deep Reinforcement Learning for Multi-Source/Destination Scenarios

Optimizing Wireless Sensor Network Topology with Deep Reinfo...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Kosei Kobayashi Masayoshi Aritsugi Pedro Henrique González Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

Wireless sensor networks are widely valued for their effectiveness in real-time data collection. As the amount of data exchanged within such networks grows, designing a robust network topology that maximizes area coverage with minimal sensors has become a critical challenge. The choice of topology impacts key network metrics, including sensor coverage, communication range, connectivity, inference, and installation and management costsIn this paper, we address the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations, presenting an optimization approach based on deep reinforcement learning. This problem is noteworthy, as sensors in various applications are often required to share data within distinct destinationsWe leverage deep reinforcement learning to effectively address the complex task of selecting optimal sensor locations. Our reinforcement learning agent dynamically learns network structure by iteratively adding and removing sensors, optimizing both sensor coverage and the total number of sensors used. Experiment across diverse scenarios demonstrate the effectiveness of our method for network planning problems of varying scales, achieving full coverage with fewer sensors than traditional approaches. Additionally, our approach also produce solutions for large instances where Mixed Integer programming solvers were not able to. Overall, our method was able to reduce the number of sensors used by up to 22.3% compared to other methods.

关键词： Measurement Integer programming Wireless sensor networks Network topology Deep reinforcement learning Real-time systems Sensors Planning Topology Optimization

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HLPG-Based Bandwidth-Enhanced Flat-Top Filter and Its Application to OAM Mode Converter

HLPG-Based Bandwidth-Enhanced Flat-Top Filter and Its Applic...

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OptoElectronics and Communications Conference, OECC

作者： Zhang Meng Taiga Suzuki Hua Zhao Chengliang Zhu Peng Wang Hongpu Li Graduate School of the Science and Technology Shizuoka University Hamamatsu Japan School of the Computer and Electric Information anization Nanjing Normal University Nanjing China College of Information Science and Engineering Northeastern University Shenyang China College of the Electronic Engineering Xiaozhuang University Nanjing China

A bandwidth-enhanced flat-top OAM mode converter is proposed and demonstrated both theoretically and experimentally, which is realized by using a helical long-period fiber grating but operated at DTP wavelength of the...

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