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10th E-Health and Bioengineering Conference, EHB 2022

作者： Motrescu, Emanuela Geman, Oana "Babes Bolyai" University Cluj Romania "Stefan cel Mare" University Faculty of Electric Engineering and Computer Science Department of Electronic Computers and Automation NeuroaestheticsLab Suceava Romania

ISBN: (纸本)9781665485579

The following case study targets the live deconstruction of a visual experience correlating attention-related body sensations that emerge with external visual stimuli resulting the improvement of attention. The attention deficit and hyperactivity disorder (ADHD) is one of the most common mental health disorders among children. It includes symptoms such as difficulty paying attention, hyperactivity and impulsive behavior. The disease appears before age of seven and lasts till adulthood. In contrast with traditional treatment (medication) our work focuses on the usage of biometric marker such as heart rate variability, applied in recovery through other treatments such as art therapy based on cinematic language techniques. We will be able to create a database that can be used to forecast pathologies within the autism spectrum and ADHD using various machine learning algorithm, by expanding the pilot project and including more participants. © 2022 IEEE.

关键词： Biometrics

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Integrated Extended SIR-type Epidemiological Modeling Platform for Covid-19 10

Integrated Extended SIR-type Epidemiological Modeling Platfo...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Brezulianu, Adrian Popa, Iolanda Geman, Oana Chiuchisan, Iuliana Nastase, Carmen GREENSOFT S.R.L. Iasi Romania Computer Science Electronics and Automation Department Faculty of Electrical Engineering and Computer Science "Stefan cel Mare" University of Suceava Suceava Romania Faculty of Economics Administration and Business "Stefan cel Mare" University of Suceava Romania

ISBN: (纸本)9781665485579

In these conditions of Covid-19 pandemic, the design and implementation of an epidemiological modeling platform with an API-type integration with beneficiary entities and data providers is a priority for the healthcare system and other organizations. An epidemiological platform (PLIS), replicable and adaptable for managing an epidemic, based on an integrated extended SIR-type model, is presented in this paper. This solution has an open architecture, respecting the scalability and interoperability, requirements, and is based on international communication standards and protocols, and SOA technology. © 2022 IEEE.

关键词： COVID-19

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An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

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computers, Materials & Continua 2021年第11期69卷 2475-2491页

作者： Saddam Bekhet Monagi H.Alkinani Reinel Tabares-Soto M.Hassaballah Faculty of Commerce South Valley UniversityQenaEgypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Electronics and Automation Universidad Autónoma de ManizalesManizales170001Colombia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and *** situation is very complex as the COVID-19 test kits are limited,therefore,more diagnostic methods must be developed urgently.A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography(CT),where any chest anomalies(e.g.,lung inflammation)can be easily *** hospitals possess X-ray or CT imaging equipments that can be used for early detection of *** by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT ***,the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs,which is not widely available in several *** paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks(CNNs),which does not require a custom hardware to run compared to currently available CNN *** proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU(0.54%of AlexNet parameters).This model is highly beneficial for countries where high-end GPUs are *** outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.

关键词： Artificial intelligence COVID-19 chest CT chest X-ray deep learning

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GreenChain: a Solana Blockchain-based Management Platform for Academic/Commercial Areas 10

GreenChain: a Solana Blockchain-based Management Platform fo...

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10th E-Health and Bioengineering Conference, EHB 2022

ISBN: (纸本)9781665485579

Technologies based on distributed chains (blockchain) are compared, by some authors, as being as innovative as the transition from analog to digital, with revolutionary consequences in the medium and long term regarding the strategies for generating, transmitting, and storing data/information and the relationships between them. This paper presents a proposal for a management platform solution based on Solana blockchain and Agile software methodologies, for academic areas (for example, for the dissemination of doctoral theses) and also, for the commercial area (various types of economic contracts, etc.). © 2022 IEEE.

关键词： Blockchain

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Grey Box Non-Recursive System Identification for a Third Order Electro-Hydraulic Actuator System: From Simulation to Practical Approaches 20

Grey Box Non-Recursive System Identification for a Third Ord...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Soon, Chong Chee Ghazali, Rozaimi Ghani, Muhamad Fadli Liew, Siaw-Hong Teng, Howe Cheng Zulkipli, Nur Farhanah Beh, Chong You Hanaffi, Mohd Saidi Department Of Engineering And Built Environment Penang Branch Campus Pulau Pinang Malaysia Centre For Robotics And Industrial Automation Fakulti Teknologi Dan Kejuruteraan Elektrik Melaka Malaysia Universiti Kuala Lumpur Perak Malaysia Faculty Of Computer Science And Information Technology Sarawak Kota Samarahan Malaysia

ISBN: (纸本)9798350382310

engineering systems heavily rely on mathematical modeling, significantly influencing both system performance and the design of control systems. This paper thoroughly investigates the establishment of a third-order mathematical model for heavy machinery systems, with a specific emphasis on the ElectroHydraulic Actuator (EHA) system. The formulation of a double-acting EHA system model using a grey box system identification approach is outlined, encompassing the system's dynamics and behavior. In addition, the details of the EHA system experimental setup that is used for system identification are described. The identification process demonstrates promising performance, achieving an impressive accuracy of 89.1%, as highlighted in the results and discussion sections. © 2024 IEEE.

关键词： Hydraulic actuators

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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The Large Language Models on Biomedical Data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： Data consistency

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Multi-Virtual-Vector-Based Predictive Current Control for Fault-Tolerant Inverter-Fed Dual Three-Phase Permanent Magnet Synchronous Machines

Multi-Virtual-Vector-Based Predictive Current Control for Fa...

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2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023

作者： Li, Ze Rodriguez, Jose Xia, Jinhui Guo, Yuanbo Gao, Xiaonan Zhang, Xiaohua Hebei University College of Electronic and Information Engineering Baoding China Faculty of Engineering Universidad San Sebastian Santiag Chile Southeast University School of Automation Nanjing China Dalian University of Technology School of Electrical Engineering Dalian China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9798350396867

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has received extensive interests due to its inherent fault-tolerant capability. However, conventional single-virtual-vector-based predictive current control (SVV-PCC) for DTP-PMSM with open-phase fault (OPF) presents poor steady-state performance and high computational burden. To address these issues, a multi-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM with OPF is proposed in this paper. Therein, two virtual vectors and one zero vector with optimal duty cycles are determined in each sampling period to improve the steady-state performance. A simplification strategy for selecting the optimal virtual vectors and calculating the optimal duty cycles is established to reduce the computational burden in digital implementation. Extensive comparisons are conducted to verify the effectiveness of the proposed MVV-PCC scheme. © 2023 IEEE.

关键词： Vectors

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A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

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Chinese Physics C 2024年第9期48卷 176-187页

作者： Ming-Xiang Fu Yu Song Jia-Meng Lv Liang Cao Peng Jia Nan Li Xiang-Ru Li Ji-Feng Liu A-Li Luo Bo Qiu Shi-Yin Shen Liang-Ping Tu Li-Li Wang Shou-Lin Wei Hai-Feng Yang Zhen-Ping Yi Zhi-Qiang Zou National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan 030024China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Key lab of Space Astronomy and Technology National Astronomical ObservatoriesBeijing 100101China School of Computer Science South China Normal UniversityGuangzhou 510631China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesBeijing 100101China University of Chinese Academy of Sciences NanjingNanjing 211135China University of Science and Technology Beijing Beijing 100083China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai 200030China School of Computer and Information Dezhou UniversityDezhou 253023China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 30024China School of Mechanical Electrical and Information EngineeringShandong UniversityWeihai 264209China Nanjing University of Posts&Telecommunications Nanjing 210023China School of Science University of Science and Technology LiaoningAnshan 114051China

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the ***,effectively analyzing this vast amount of data poses a significant *** response,astronomers are turning to deep learning techniques,but these methods are limited by their specific training sets,leading to considerable duplicate *** overcome this issue,we built a framework for the general analysis of galaxy images based on a large vision model(LVM)plus downstream tasks(DST),including galaxy morphological classification,image restoration object detection,parameter extraction,and *** the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories,we designed our LVM to incorporate a Human-in-the-loop(HITL)module,which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images *** proposed framework exhibits notable fewshot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging *** particular,for the object detection task,which was trained using 1000 data points,our DST in the LVM achieved an accuracy of 96.7%,while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%.For morphological classification,to obtain an area under the curve(AUC)of~0.9,LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 *** addition,multimodal data can be integrated,which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.

关键词： artificial intelligence large vision model human-in-the-loop astronomy galaxies

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SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Yan, Ke Cai, Qing Zhang, Fan Cao, Ziyan Liu, Zhi Faculty of Computer Science and Technology Ocean University of China China School of Automation Northwestern Polytechnical University China School of Information Science and Engineering Shandong University China

Although semi-supervised learning has made significant advances in the field of medical image segmentation, fully annotating a volumetric sample slice by slice remains a costly and time-consuming task. Even worse, most of the existing approaches pay much attention to image-level information and ignore semantic features, resulting in the inability to perceive weak boundaries. To address these issues, we propose a novel Semantic-Guided Triplet Co-training (SGTC) framework, which achieves high-end medical image segmentation by only annotating three orthogonal slices of a few volumetric samples, significantly alleviating the burden of radiologists. Our method consist of two main components. Specifically, to enable semantic-aware, fine-granular segmentation and enhance the quality of pseudo-labels, a novel semantic-guided auxiliary learning mechanism is proposed based on the pretrained CLIP. In addition, focusing on a more challenging but clinically realistic scenario, a new triple-view disparity training strategy is proposed, which uses sparse annotations (i.e., only three labeled slices of a few volumes) to perform co-training between three sub-networks, significantly improving the robustness. Extensive experiments on three public medical datasets demonstrate that our method outperforms most state-of-the-art semi-supervised counterparts under sparse annotation settings. The source code is available at https://***/xmeimeimei/SGTC. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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