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arXiv 2023年

作者： Nambiar, Ananthan Pan, Chao rana, Vishal Cheraghchi, Mahdi ribeiro, João Maslov, Sergei Milenkovic, Olgica Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL United States Center for Artificial Intelligence and Modeling Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States NOVA LINCS and NOVA School of Science and Technology Caparica Portugal

Pathogenic infections pose a significant thr.a. to global health, affecting millions of people every year.a.d presenting substantial challenges to healthcare systems worldwide. Efficient and timely testing plays a critical role in disease control and transmission prevention. Group testing is a well-established method for reducing the number of tests needed to screen large populations when the disease prevalence is low. However, it does not fully utilize the quantitative infor.a.ion provided by qPCr methods, nor is it able to accommodate a wide range of pathogen loads. To address these issues, we introduce a novel adaptive semi-quantitative group testing (SQGT) scheme to efficiently screen populations via two-stage qPCr testing. The SQGT method quantizes cycle threshold (Ct) values into multiple bins, leveraging the infor.a.ion from the first stage of screening to improve the detection sensitivity. Dynamic Ct threshold adjustments mitigate dilution effects and enhance test accuracy. Comparisons with traditional binary outcome GT methods show that SQGT reduces the number of tests by 24% while maintaining a negligible false negative rate. © 2023, CC BY.

关键词： Disease control

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Data congestion control framework in Wireless Sensor Network in IoT enabled intelligent transpor.a.ion system

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Measurement: Sensors 2022年 24卷

作者： Kavitha, T. Pandeeswari, N. Shobana, r. Vinothini, V.r. Sakthisudhan, K. Jeyam, A. Malar, A. Jasmine Gnana Department of Electronics and Communication Engineering Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai600054 India Department of Information Technology PSNA College of Engineering and Technology Dindigul624622 India Department of Computer Science and Engineering S.A. Engineering College Chennai600077 India Department of Mathematics Bannari Amman Insitute of Technology Sathyamangalam638401 India Department of Electronics and Communication Engineering Dr. N. G. P. Institute of Technology Coimbatore641 048 India Nuclear Power Corporation of India Limited Kudankulam PO Radhapuram627106 India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli627152 India

Intelligent Transpor.a.ion System (ITS) holds an inevitable concern in r.a. safety and efficient transpor.a.ion. Data communication is enforced by wireless sensor nodes and is compatible with traffic monitoring and control capabilities. Congestion in such a system will carry off serious constraints and effects on the intelligent transpor.a.ion system. Congestion problems can severely limit the perfor.a.ce of Wireless Sensor Network (WSN)-based IoT, resulting in higher packet loss ratios, longer delays, and lower throughputs. To resolve such constraints, a novel particle swarm optimization algorithm-based Dynamic deep neural network (DDNN-PSO) is proposed. To enhance the DDNN perfor.a.ce, its weight parameters are optimized using the PSO algorithm. The perfor.a.ce analysis of the proposed DDNN-PSO is performed by estimating the Delivery ratio, Packet delay, Throughput, Overhead, and Energy consumption with the existing Genetic Algorithm based DNN (DNN-GA) and DNN techniques. The experimental findings show that the proposed DDNN-PSO sur.a.ses models such as DNN and DNN-GA. The proposed method has an overall perfor.a.ce of 5.69% and 8.01% better than DNN-GA and DNN respectively. © 2022

关键词： Deep neural networks

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Transformers in medical image analysis

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Intelligent Medicine 2023年第1期3卷 59-78页

作者： Kelei He Chen Gan Zhuoyuan Li Islem rekik Zihao Yin Wen Ji Yang Gao Qian Wang Junfeng Zhang Dinggang Shen Medical School of Nanjing University NanjingJiangsu 210093China National Institute of Healthcare Data Science at Nanjing University NanjingJiangsu 210093China BASIRA Laboratory Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey School of Science and Engineering ComputingUniversity of DundeeUK State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingJiangsu 210093China School of Biomedical Engineering ShanghaiTech UniversityShanghai 201210China Department of Research and Development Shanghai United Imaging Intelligence Co.Ltd.Shanghai 200030China Shanghai Clinical Research and Trial Center Shanghai 201703China

Transformers have dominated the field of natural language processing and have recently made an impact in the ar.a.of computer *** the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and *** paper.a.med to promote awareness of the applications of transformers in medical image ***,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic ***,we reviewed various transformer.a.chitectures tailored for medical image applications and discuss their *** this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other *** hope this review would provide a comprehensive picture of transformers to r.a.ers with an interest in medical image analysis.

关键词： Transformer Medical image analysis Deep learning Diagnosis registration Segmentation Image synthesis Multi-task learning Multi-modal learning Weakly-supervised learning

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Diagnostic Framework for Mumps Based on Deep Learning with Enhanced Mayfly Optimization Algorithm

Diagnostic Framework for Mumps Based on Deep Learning with E...

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Ambient Intelligence, Knowledge informatics and Industr.a. Electronics (AIKIIE), Inter.a.ional Conference on

作者： S. Sudha Mercy S Madhavi Keerthika A Ch Prathima r T Prabhu Department of Computer Science & Design R.M.K Engineering College Chennai India Department of IT Vel tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Chennai India Department of ECE Aarupadai Veedu Institute of Technology Chennai India Department of Data Science School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of ECE Saveetha School of Engineering SIMATS Chennai India

The mumps virus causes a very infectious sickness. The enlargement of the parotid salivary glands can be quite uncomfor.a.le. Mumps tr.a.ment centers on symptom management. The illness must take its natural course. While most people experience very moderate symptoms, some develop severe consequences. The mumps virus can be avoided with the use of the MMr vaccination. In this research, we employ a convolutional neural network (CNN) technique to better.a.tomate mumps detection. In order to automate mumps detection, the authors of this research suggest a convolutional neural network (CNN) trained using XGBoost Classifier data. Incorporating machine learning (ML) technique comparisons. A mumps dataset consisting of 50×50 rGB picture patches served as the basis for.a.l the models. Quantitative findings from each appr.a.h were put through validation testing based on their respective effectiveness metrics. To improve the efficiency of the model, the Mayfly Optimization Algorithm is implemented. results show that the suggested method is effective, with 97% accuracy, suggesting that it might help decr.a.e diagnostic errors caused by human error. And the suggested system we've developed is so precise that it outperforms the CNN model, which only gets around 92% of the time. Consequently, the suggested method is 5% more accurate than the CNN Model.

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Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Appr.a.h

arXiv

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arXiv 2024年

作者： Liu, Xian-Xian Xu, Mingkun Wei, Yuanyuan Qin, Huafeng Song, Qun Fong, Simon Tien, Feng Luo, Wei Gao, Juntao Zhang, Zhihua Siu, Shirley The Department of Computer and Information Science University of Macau 999078 China Guangdong Institute of Intelligence Science and Technology Zhuhai519031 China Department of Biomedical Engineering The Chinese University of Hong Kong Shatin999077 China Department of Neurology David Geffen School of Medicine University of California Los AngelesCA90095 United States The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China Institute of Artificial Intelligence Chongqing Technology and Business University Chongqing400067 China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Hebei Province Handan056038 China The director of the Institute of Clinical Medicine The First People’s Hospital of Foshan Guangzhou510060 China Tsinghua University California Beijing100084 China Beijing Institute of Genomics Chinese Academy of Sciences Beijing100101 China Centre for Artificial Intelligence Driven Drug Discovery Macao Polytechnic University Rua de Luís Gonzaga Gomes 999078 China

Timely and precise classification and segmentation of gastric bleeding in endoscopic imagery are pivotal for the rapid diagnosis and intervention of gastric complications, which is critical in life-saving medical procedures. Traditional methods grapple with the challenge posed by the indistinguishable intensity values of bleeding tissues adjacent to other gastric structures. Our study seeks to revolutionize this domain by introducing a novel deep learning model, the Dual Spatial Kernelized Constrained Fuzzy C-Means (Deep DuS-KFCM) clustering algorithm. This Hybrid Neuro-Fuzzy system synergizes Neural Networks with Fuzzy Logic to offer.a.highly precise and efficient identification of bleeding regions. Implementing a two-fold coarse-to-fine strategy for segmentation, this model initially employs the Spatial Kernelized Fuzzy C-Means (SKFCM) algorithm enhanced with spatial intensity profiles and subsequently harnesses the state-of-the-art DeepLabv3+ with resNet50 architecture to refine the segmentation output. Through extensive experiments across mainstr.a. gastric bleeding and red spots datasets, our Deep DuS-KFCM model demonstrated unprecedented accuracy rates of 87.95%, coupled with a specificity of 96.33%, outperforming contemporary segmentation methods. The findings underscore the model’s robustness against noise and its outstanding segmentation capabilities, particularly for identifying subtle bleeding symptoms, thereby presenting a significant leap for.a.d in medical image processing. © 2024, CC BY.

关键词： Medical image processing

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, robert Pinetz, Thomas radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar.a.ankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transfor.a.ive appr.a.h to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their.a.option in clinical practice. In this work, we or.a.ized the first inter.a.ional competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of perfor.a.ce booster.a.d reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly inter.a.e to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful r.a.-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Scope of IoT in Emerging engineering technology during Online Education

Scope of IoT in Emerging Engineering Technology during Onlin...

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Inter.a.ional Conference on Computing and Networking technology (ICCNT)

作者： Vivek Veeraiah Jayasri Kotti Vipin Jain Tripti Shar.a.Sumit Saini Ankur Gupta Dept. of R & D Computer Science Adichunchanagiri University Mandya Karnataka India Dept. of CSE GMR Institute of Technology Vizianagaram AP India Dept. of Management Teerthanker Mahaveer University Moradabad Uttar Pradesh India Department of CSE Rungta College of Engineering & Technology Bhilai Chhattisgarh India School of Engineering & Technology Central University of Haryana Mahendergarh Haryana India Department of CSE Vaish College of Engineering Rohtak Haryana India

Physical objects with built-in sensors, processors, software, and other technologies that can communicate with one another.a.d share data via the web are what the IoT refers to. Diverse devices that may cause security risks to your business, a new engineering method based on IoT safeguards Internet devices & networks they connect to against assaults and br.a.hes. The Internet of Things (IoT) can be protected against intrusion, its vulnerabilities can be monitored, and any existing holes may be patched with the help of cutting-edge engineering practises. IoT security guarantees the solution's availability, integrity, and privacy. With the help of mobile devices and the IoT, schools can better protect their campuses, keep an eye on vital resources, and improve students' access to infor.a.ion. In addition, teachers may use this technology to cr.a.e "smart lesson plans," which are more engaging for students than the traditional lesson plans of the past. One of the strategies advocated in this study is the use of IoT to protect the educational sector.a.ainst cyber thr.a.s. Existing research is being compared to proposed research in terms of safety, reliability, and effectiveness.

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Multiple Access Techniques for Intelligent and Multi-Functional 6G: Tutor.a., Survey, and Outlook

arXiv

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arXiv 2024年

作者： Clerckx, Bruno Mao, Yijie Yang, Zhaohui Chen, Mingzhe Alkhateeb, Ahmed Liu, Liang Qiu, Min Yuan, Jinhong Wong, Vincent W.S. Montojo, Juan The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The School of Information Science and Technology ShanghaiTech University Shanghai201210 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Corporate R&D Qualcomm Inc. San DiegoCA United States

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, message, etc) to serve multiple users/devices/machines/services, ideally in the most efficient way. Given the incr.a.ing needs of multi-functional wireless networks for integrated communications, sensing, localization, computing, coupled with the surge of machine learning / artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this paper, we provide a tutor.a., survey and outlook of past, emerging and future MA techniques and pay a particular.a.tention to how wireless network intelligence and multi-functionality will lead to a re-thinking of those techniques. The paper starts with an overview of orthogonal, physical layer multicasting, space domain, power domain, rate-splitting, code domain MAs, and MAs in other domains, and highlight the impor.a.ce of researching univer.a. multiple access to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, receiver designs, user behavior predictions for different MA schemes, and MA for.a. such as federated learning/edge intelligence and over the air computation. We then discuss MA for network multi-functionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications, metaverse, virtual r.a.it

关键词： Wireless networks

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Intelligent Distributed Grid's r.a.tive Overcurrent, or over Prevention: computer Intelligence in Power Grid

Intelligent Distributed Grid's Reactive Overcurrent, or over...

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Communication, Security and Artificial Intelligence (ICCSAI), Inter.a.ional Conference on

作者： K. Suneeta Preeti Ver.a.Pankaj Tripathi A. Sivasangari Pawan Kumar Veeraswamy Ammisetty Department of EEE Pallavi Engineering College Hyderabad School of Electronic and Electrical Engineering Uttarakhand University Uttarakhand India Department of Electrical Engineering Invertis University Bareilly Uttar Pradesh India Sathyabama Institute of Science and Technology Chennai Tamil Nadu School of Basic & Applied Sciences K. R. Mangalam University Gurugram Haryana India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Decentralized energy assets (DErs- DCE) that have substantial penetration pose a number of protection-related problems, including protection blindness, false falling, unsynchronized reclosing, et. Demand responses & reconfiguration of networks are two more active manager.a. technologies that pose a danger to current security measures. relay configurations are continuously adjusted upon online detection of network architectures plus the state of renewable energy sources in this research's mix of regional adaptable adaptive telecommunication helped centralized defense. The centrally located protection improvements relay environments during significant alterations in grid layouts, such as rewiring the network as well as transferring across islanded alongside networked transpor.a.ion methods. Moreover, the local adapting safeguards infor.a.ion communicate settings to reflect Distributed energy resources (status (ON/OFF) using locally gathered facts. These digital model (rTDS) simulation findings distribution grid is used to show the usefulness of the proposed method. The suggested algorithm offers effective fault detection & selection under various networking conditions, according to simulation findings.

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Security for eHealth system: data hiding in AMBTC compressed images via gradient-based coding

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Complex & Intelligent Systems 2021年第3期9卷 2699-2711页

作者： Chen, Yung-Yao Hu, Yu-Chen Kao, Hsiang-Yun Lin, Yu-Hsiu Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei 106 Taiwan (R.O.C.) Department of Computer Science and Information Management Providence University Taichung 433 Taiwan (R.O.C.) Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan (R.O.C.)

Various eHealth applications based on the Internet of Things (IoT) contain a considerable number of medical images and visual electronic health records, which are transmitted through the Internet everyday. Infor.a.ion forensics thus becomes a critical issue. This paper presents a data hiding algorithm for.a.solute moment block truncation coding (AMBTC) images, wherein secret data, or the authentication code, can be embedded in images to enhance security. Moreover, in view of the impor.a.ce of transmission efficiency in IoT, image compression is widely used in Internet-based applications. To cope with this challenge, we present a novel compression method named gradient-based (GB) compression, which is compatible with AMBTC compression. Therefore, after.a.plying the block classification scheme, GB compression and data hiding can be performed jointly for blocks with strong gradient effects, and AMBTC compression and data hiding can be performed jointly for the remaining blocks. From the experimental results, we demonstrate that the proposed method outperforms other state-of-the-art methods.

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