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13th Annual IEEE Global Humanitarian technology Conference, GHTC 2023

作者： Koga, Motoya Kaneko, Tetsuya Leleito, Emanuel Karashima, Kazuki Aharari, Ari Sojo University Department of Architecture Kumamoto Japan Nagoya University Graduate School of Engineering Nagoya Japan Maebashi Institute of Technology Dept.of Architecture Maebashi Japan Sojo University Department of Computer and Information Sciences Kumamoto Japan

ISBN: (纸本)9798350321326

Collaborative action in disaster mitigation efforts contributes to knowledge and experience sharing, resource sharing, improvement of response and coordination, and standardization of good practice. In this study, we propose a workshop model and develop a virtual collaboration tool to support and promote cross-community knowledge sharing and collaboration focusing on disaster mitigation efforts. The workshop model involves pre-fieldwork training, actual fieldwork, and post-fieldwork information exchange. The collaboration tool enables information collection, sharing and record keeping. The effectiveness of the workshop model and the collaboration tool is demonstrated through use in a citizen participatory disaster preparedness workshops linking two elementary schools located in the central and southern regions of Japan. © 2023 IEEE.

关键词： Disaster prevention

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ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for Rapid Intraoperative Histology 7

ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shi, Lulin Hou, Xingzhong Wong, Ivy H.M. Chan, Simon C.K. Chen, Zhenghui Lo, Claudia T.K. Wong, Terence T.W. Translational and Advanced Bioimaging Laboratory Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Virtual staining has shown great promise in realizing a rapid and low-cost clinical alternative for pathological examinations, eliminating the need for chemical reagents and laborious staining procedures. However, most of the previous studies mainly focus on thin slice samples, which still require tissue sectioning and are unsuitable for intraoperative use. In this paper, we propose a multi-scale model to virtually stain label-free and slide-free biological tissues, allowing hematoxylin- and eosin- (H&E) staining generation in less than a minute for an image with 100 million pixels. We name this ThickV-Stain model, specifically developed to virtually stain intricated and unprocessed thick tissues. We harness the ability of a multi-scale network to encourage the model to capture multiple-level micromorphological characteristics from low-resolution images. Experimental results highlight the advantages of our method for virtual staining on unprocessed thick samples. We also show the effectiveness of ThickV-Stain on thin sections, showing generalizability to other clinical workflows. The proposed method enables us to obtain virtually stained images from unstained samples within minutes and can be seamlessly integrated with downstream pathological analysis tasks, providing an efficient alternative scheme for intraoperative assessment as well as general pathological examination. © 2024 CC-BY 4.0, L. Shi, X. Hou, I.H.M. Wong, S.C.K. Chan, Z. Chen, C.T.K. Lo & T.T.W. Wong.

关键词： Tissue

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Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (case study: protein-peptide region residue-level interaction) using structural information 29

Integrating Structural Information: Comparing Classification...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Shafiee, Shima Fathi, Abdolhossein Safari, Faezeh Department of Computer Engineering and Information Technology Razi University Kermanshah Iran School of Electrical Eng. and Computer Science Faculty of Eng. Architecture and Info. Tech University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798331523114

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method. © 2025 IEEE.

关键词： Bioinformatics

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Performance Evaluation of Infrastructure as a Service across Cloud Service Providers

Performance Evaluation of Infrastructure as a Service across...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Sithiyopasakul, Saran Archevapanich, Tuanjai Purahong, Boonchana Sithiyopasakul, Paisan Lasakul, Attasit Benjangkaprasert, Chawalit School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Computer Engineering Bangkok Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Communication Engineering Nonthaburi Thailand

ISBN: (纸本)9798350332360

The purpose of this research aims to monitor, analyze, and compare the performance of infrastructure as a service (IaaS) between the selective cloud providers. To assure which cloud provider has more stability, reliability, and scalability. This paper focuses on performance testing based on a deployed web server in the cloud environment. The main feature of cloud computing is scalability thus most common IaaS cloud service providers (CSPs) have Auto Scaling features for instances or virtual machines. Not only does this paper gives the experimental results of the scaling scalability testing, but it also provides the results of recovery testing to inspect how long a web server is able to recover from failures and load testing which simulated traffic requests. Testing was conducted in the major public clouds of Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). Azure performs the most efficiently of almost all testing but hardest to configure. © 2023 IEEE.

关键词： Virtual machine

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Image super resolution using Enhanced Attention Block (EAB) for Grape Leaf Disease Detection 7

Image super resolution using Enhanced Attention Block (EAB) ...

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7th International Conference on computer Applications in Electrical Engineering-Recent Advances, CERA 2023

作者： Yeswanth, P.V. Thanya, Y. Deivalakshmi, S. Lokesh Kumar, K.M. George, Johaan National Institute of Technology Department of Electronics and Communication Engineering Tiruchirappalli India Amrita Vishwa Vidyapeetham Department of Computer and Communication Engineering Chennai India Indian Insitute of Technology Department of Ocean Engineering and Naval Architecture Kharagpur India

ISBN: (纸本)9798350305005

Accurate disease diagnosis in grape leaf analysis is heavily reliant on high-resolution (HR) images. To meet this need, we propose a model that incorporates the Enhanced Attention Block (EAB) to generate HR images for improved disease detection. The EAB employs advanced attention mechanisms and residual learning to effectively capture critical, spatial and channel-wise relationships. Proposed model is assessed using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) metrics, yielding PSNR scores of 31. 902dB, 32.785dB, and 34. 853dB, and SSIM score of 0.8709, 0.9058, and 97.46 for factors 2,4 and 6 respectively. This proposed approach holds great potential for widespread implementation in disease detection across various plant species, allowing for timely and accurate interventions for plant health management and increasing industry productivity. © 2023 IEEE.

关键词： Diagnosis

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Implementation of Cloud Computing and Internet of Things (IoT) by Performance Evaluation 12

Implementation of Cloud Computing and Internet of Things (Io...

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12th International Electrical Engineering Congress, iEECON 2024

作者： Sithiyopasakul, Jiran Archevapanich, Tuanjai Sithiyopasakul, Saran Lasakul, Attasit Purahong, Boonchana Benjangkaprasert, Chawalit School Of Engineering King Mongkut's Institute Of Technology Ladkrabang Bangkok Thailand Rajamangala University Of Technology Suvarnabhumi Faculty Of Engineering And Architecture Department Of Electronics And Communication Engineering Nonthaburi Thailand School Of Engineering King Mongkut's Institute Of Technology Ladkrabang Department Of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350383591

The integration of cloud computing and the Internet of Things (IoT) holds transformative potential across diverse industries. Performance assessment is essential to gauge the quality and efficiency of cloud computing and IoT systems. This paper presents a comprehensive performance evaluation of cloud computing and IoT systems, focusing on three major platforms: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Experimental results encompass various scenarios, including normal operation, heavy load conditions, IoT applications, and scalability testing. The outcomes reveal distinct performance metrics such as response time, throughput, latency, and reliability for each cloud platform. © 2024 IEEE.

关键词： Internet of things

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Countering DoS Threats in WSN: Inventive Machine Learning Strategies with a Minimalistic Twist 1

Countering DoS Threats in WSN: Inventive Machine Learning St...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Chidambaranathan, Senthilnathan Vanitha Sasireka, C.S. Jose, Naduvathezhath Nessariose Rao, K. Madhusudhana Devi, T. Lakshmi Architecture and Design Virtusa India Sri Sairam Institute of Technolgoy Department of Computer Science and Engineering India Srm Institute of Science and Technology Department of Computer Science and Engineering India Regent Strategic Solutions Rochester United States Kkr&ksr Institute Of Technology & Science A.P Guntur India Department of Data Science Telangana Hyderabad501506 India

ISBN: (纸本)9798350377002

In the era of expanding wireless sensor network (WSN) applications, safeguarding these networks against a spectrum of threats, including Denial-of-Service (DoS) attacks, is paramount. The innate resource limitations of sensor nodes often render conventional security measures ineffective. This study introduces a pioneering approach integrating minimalist machine learning techniques to combat DoS attacks within WSNs. Our methodology adeptly leverages machine learning capabilities while strategically curbing computational and resource demands. By distilling essential features from network traffic patterns, our model adeptly identifies aberrations signaling DoS attacks, all while preserving the efficiency of resource-limited sensor nodes. Its minimalist framework seamlessly integrates with existing WSN architectures, negating the need for substantial hardware upgrades and maintaining network performance. Results underscore the superior detection accuracy and efficiency of our minimalist machine learning strategy compared to traditional methods. Moreover, its scalability and adaptability render it ideal for deployment across varied WSN setups, fortifying them against DoS threats while safeguarding core network *** mitigating DoS attack impacts without burdening computational resources, our approach marks a significant stride towards bolstering the security and dependability of wireless sensor networks amid evolving threat landscapes. © 2024 IEEE.

关键词： Sensor nodes

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Light the Sprite: Pixel Art Dynamic Light Map Generation

Light the Sprite: Pixel Art Dynamic Light Map Generation

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46th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2025

作者： Nikolov, I. Computer Graphics Group Department of Architecture Design and Media Technology Aalborg University Denmark

ISBN: (纸本)9783038682684

Correct lighting and shading are vital for pixel art design. Automating texture generation, such as normal, depth, and occlusion maps, has been a long-standing focus. We extend this by proposing a deep learning model that generates point and directional light maps from RGB pixel art sprites and specified light vectors. Our approach modifies a UNet architecture with CIN layers to incorporate positional and directional information, using ZoeDepth for training depth data. Testing on a popular pixel art dataset shows that the generated light maps closely match those from depth or normal maps, as well as from manual programs. The model effectively relights complex sprites across styles and functions in real time, enhancing artist workflows. The code and dataset are here - https://***/IvanNik17/light-sprite. © 2025 The Authors.

关键词： Mapping

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ECG Pre-processing and Feature Extraction Tool for Intelligent Simulation Systems 15th

ECG Pre-processing and Feature Extraction Tool for Intelli...

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15th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2023

作者： Domínguez-Morales, Manuel Muñoz-Macho, Adolfo Sevillano, José L. Architecture and Computer Technology Department E.T.S. Ingeniería Informática Universidad de Sevilla Sevilla Spain Computer Science Research Institute Universidad de Sevilla Sevilla Spain Performance and Medical Department at RCD Mallorca SAD Palma de Mallorca Spain

ISBN: (纸本)9783031575228

Sudden cardiac death events and fatal cardiac problems are a field of vital importance for physicians working with elite athletes. For this reason, it is common to periodically perform cardiac monitoring with professional ECG devices to detect certain risk markers. As these doctors often work with many athletes (as is the case with professional football teams), an artificial intelligence-based system would help mass screening and allow these exams to be carried out more regularly. Because physicians often evaluate the printed reports generated by ECG devices, few manufacturers provide powerful and configurable software tools. Moreover, for teaching purposes, a simulation tool that would allow working with previously collected ECG files would be very useful. In this paper, we present a software tool to be used with General Electric CardioSoft 12SL electrocardiograph. This tool allows importing the XML files generated by this device, perform a manual or automatic signal filtering process and PQRST peak detection, and finally generate a customisable report as a CSV file containing the features obtained after signal analysis. This pre-processed information can be used as input of ECG simulators and in artificial intelligence systems to develop diagnostic support systems. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Electrocardiograms

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