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Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

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NeuroImage 2025年 314卷 121256-121256页

作者： Qin, Jianmei Wu, Haoting Wu, Chenqing Guo, Tao Zhou, Cheng Duanmu, Xiaojie Tan, Sijia Wen, Jiaqi Zheng, Qianshi Yuan, Weijin Zhu, Zihao Chen, Jingwen Wu, Jingjing He, Chenyu Ma, Yiran Liu, Chunlei Xu, Xiaojun Guan, Xiaojun Zhang, Minming Department of Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Joint Laboratory of Clinical Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China State Key Laboratory of Computer-aided Design & Computer Graphics Zhejiang University College of Computer Science and technology Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University College of Control Science and Engineering Hangzhou China Department of Electrical Engineering and Computer Sciences University of California Berkeley CA United States Helen Wills Neuroscience Institute University of California Berkeley CA United States

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), *** consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87–0.90, FUNC2T1: 0.67–0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinso

关键词： Parkinson's disease Quantitative susceptibility mapping Resting-state functional connectivity Subcortical nuclei

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Advanced Wound Care Training with IoT and LSTM for Dynamic Learning Environments

Advanced Wound Care Training with IoT and LSTM for Dynamic L...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： C. Viswanathan K. Lalitha J. Selvi J. Gnana Jeslin C. Chandru Vignesh S. Murugan Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tami Nadu India Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India Department of Civil Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India (Senior Grade – II) School of Computer Science and Engineering Vellore Institute of Technology Vellore Tami Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Wound treatment is an essential part of patient care that requires continuous training and education for medical personnel. Using Long Short-Term Memory (LSTM) networks for time-series feedback and Internet of Things (IoT), technologies provide a paradigm shift in wound education in this research. The platform combines LSTM-based learning algorithms with real-time data collected from IoT wound monitoring to provide healthcare providers with ongoing training and feedback. A battery of trials illustrates that strategy outperforms the status quo regarding wound assessment accuracy and treatment decision-making. This approach promotes ongoing professional growth in wound care management by allowing for individualized learning experiences. Its work shows that continuous training using LSTM powered by the IoT has great promise for better wound education and patient outcomes in healthcare.

关键词： Training Accuracy System performance Decision making Medical services Transforms Wounds Internet of Things Long short term memory Testing

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Scalable Verification of Strategy Logic through Three-valued Abstraction

arXiv

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arXiv 2023年

作者： Belardinelli, Francesco Ferrando, Angelo Jamroga, Wojciech Malvone, Vadim Murano, Aniello Imperial College London United Kingdom University of Genoa Italy SnT University of Luxembourg Institute of Computer Science Polish Academy of Sciences Luxembourg Telecom Paris France University of Naples Federico II Italy

The model checking problem for multi-agent systems against Strategy Logic specifications is known to be non-elementary. On this logic several fragments have been defined to tackle this issue but at the expense of expressiveness. In this paper, we propose a three-valued semantics for Strategy Logic upon which we define an abstraction method. We show that the latter semantics is an approximation of the classic two-valued one for Strategy Logic. Furthermore, we extend MCMAS, an open-source model checker for multi-agent specifications, to incorporate our abstraction method and present some promising experimental results. © 2023, CC BY.

关键词： Semantics

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Elevating Digital Experiences by Enhancing Service Delivery through Revolutionary Cloud Services and Applications

Elevating Digital Experiences by Enhancing Service Delivery ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V Asha S Sreekanth Ginni Nijhawan K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This research introduces an approach named “Cloud-Driven Personalized Experience Enhancement” to satisfy the escalating demand for enhanced digital connectivity. Via cutting-edge cloud services and applications, the CPExE framework provides a groundbreaking solution for digital platforms. The framework is built upon three core components: Together with the Quantum-Inspired Data Analytics and Evaluation (QIDAE) and Personalized Content Recommendations (PRA), Dynamic Resource Allocation and Adaptation (DRAA) completes the framework. By leveraging quantum-inspired approaches, QIDAE analyzes vast datasets with outstanding effectiveness and exactness improving processes for data handling and personalization. This strategy allows for swift detection of patterns while making certain that data-driven understandings are both on time and precise. By changing cloud resources according to user requests the DRAA module guarantees uninterrupted access to computing power and data storage. By managing resources effectively in real-time it decreases latency and improves service speeds. By using complex algorithms to assess user habits and choices the PRA system boosts the digital experience. PRA uses both content-focused filtering and collaborative screening strategies to provide users with engaging content that matches their unique preferences. By applying sophisticated analytics and resource management methods with content personalization CPExE boosts the user experience and develops cloud technology efficiency. This framework signifies an important progress toward developing dynamic and tailored online environments.

关键词： Heating systems Histograms Data analysis Heuristic algorithms Scalability Memory Dynamic scheduling Real-time systems User experience Resource management

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A Novel Approach for User Demand-aware Data Center Construction and Service Consolidation 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Lv, Yifei Guo, Kunyin Xia, Yunni Li, Yin Ma, Yong Li, Fan Zeng, Linchengxi Peng, Qinglan Long, Tingyan College of Computer Science Chongqing University Chongqing400030 China Guangzhou Institute of Software Application Technology Guangzhou510000 China School of Computers and Information Jiangxi Normal University Nanchang330022 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610000 China Chongqing China School of Artificial Intelligence Henan University Kaifeng475001 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers (DCs) to handle business, monetary, Internet and commercial applications of different enterprises. A cloud data center encompasses thousands of physical server nodes arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms. More and more cloud data centers are turning for adapting to dynamics of user demands and reducing operational cost. Therefore, in this paper, we propose a user demand-aware (UDA) method for servers selection and a modified adaptive large neighbourhood search (MALNS) algorithm for dynamic service consolidation. Experiments based on real-world datasets demonstrate our approach outperformed conventional strategies in terms of multiple metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Heuristic algorithms

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Comparison of SVM and Random Forest for Detection of Malicious Node in Wireless Sensor Networks

Comparison of SVM and Random Forest for Detection of Malicio...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Sankara Reddy Thamma E. Gurumoorthi C.Om Prakash S K Muthusundar M.S. Nidhya Balajee Maram Deloitte Consulting LLP USA Information Technology DC Technical Delivery Master II Concord North Carolina USA School of Engineering Malla Reddy University CMR University Bengaluru Dept of AI & DS Chennai Institute of Technology college Chennai School of Computer Applications Dayananda Sagar University Bangalore School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Warangal

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Wireless Sensor Networks (WSNs) are used in various contexts and offer a wide range of applications, despite being a decentralized, resource-scarce network type; this is why this study deals with the problem of malicious nodes. Identifying such nodes is important to preventing the compromise of the network and all the nodes in it. In this paper, a comparative study of two popular classifiers named Support Vector Machine (SVM) and Random Forest (RF) in order to detect hostile nodes in WSNs. By nature, a malicious node in a network will cause significant problems to the users. There are number of models also there to detect the malicious node. Based on the above-discussed parameters the paper compares these algorithms in terms of detection accuracy, computational complexity, scalability and performance measure of imbalanced data. The comparison draws on different targets such as accuracy, capacity/large scale, speed, and small sample imbalance, and noise. As stated in this paper, both SVM and Random Forest have its own strengths where the selection between them mainly depends on the network environment and the features of the malicious activity.

关键词： Support vector machines Radio frequency Wireless sensor networks Accuracy Noise Network security Feature extraction Real-time systems Random forests Tuning

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Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

arXiv

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arXiv 2024年

作者： Rjoub, Gaith Islam, Saidul Bentahar, Jamal Almaiah, Mohammed Amin Alrawashdeh, Rana Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates King Abdullah II School of Information Technology The University of Jordan Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Information and Computer Science King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents’ capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decision-making efficiency and adaptability. Our contributions include a detailed exploration of the transformer’s role in processing heterogeneous IoT data, a comprehensive evaluation of the framework’s performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Coarse to fine vertebrae localization and segmentation with spatialconfiguration-Net and U-Net 15

Coarse to fine vertebrae localization and segmentation with ...

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15th International Joint Conference on computer Vision, Imaging and computer graphics Theory and Applications, VISIGRAPP 2020

作者： Payer, Christian Štern, Darko Bischof, Horst Urschler, Martin Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria School of Computer Science University of Auckland Auckland New Zealand

ISBN: (纸本)9789897584022

Localization and segmentation of vertebral bodies from spine CT volumes are crucial for pathological diagnosis, surgical planning, and postoperative assessment. However, fully automatic analysis of spine CT volumes is difficult due to the anatomical variation of pathologies, noise caused by screws and implants, and the large range of different field-of-views. We propose a fully automatic coarse to fine approach for vertebrae localization and segmentation based on fully convolutional CNNs. In a three-step approach, at first, a U-Net localizes the rough position of the spine. Then, the SpatialConfiguration-Net performs vertebrae localization and identification using heatmap regression. Finally, a U-Net performs binary segmentation of each identified vertebrae in a high resolution, before merging the individual predictions into the resulting multi-label vertebrae segmentation. The evaluation shows top performance of our approach, ranking first place and winning the MICCAI 2019 Large Scale Vertebrae Segmentation Challenge (VerSe 2019). Copyright © 2020 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： computerized tomography

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Periocular Biometrics: A Modality for Unconstrained Scenarios

arXiv

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arXiv 2022年

作者： Alonso-Fernandez, Fernando Bigun, Josef Fierrez, Julian Damer, Naser Proencą, Hugo Ross, Arun School of Information Technology Halmstad University Sweden School of Engineering Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Instituto de Telecomunicações University of Beira Interior Portugal Department of Computer Science and Engineering Michigan State University United States

Periocular refers to the externally visible region of the face that surrounds the eye socket. This feature-rich area can provide accurate identification in unconstrained or uncooperative scenarios, where the iris or face modalities may not offer sufficient biometric cues due to factors such as partial occlusion or high subject-to-camera distance. The COVID-19 pandemic has further highlighted its importance, as the ocular region remained the only visible facial area even in controlled settings due to the widespread use of masks. This paper discusses the state of the art in periocular biometrics, presenting an overall framework encompassing its most significant research aspects, which include: (a) ocular definition, acquisition, and detection;(b) identity recognition, including combination with other modalities and use of various spectra;and (c) ocular soft-biometric analysis. Finally, we conclude by addressing current challenges and proposing future directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Biometrics

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Application of binary classification of magnetic resonance therapy images to detect the threat of the disease due to COVID-19 3

Application of binary classification of magnetic resonance t...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Zalevska, Olga Vanin, Volodymyr Sydogenko, Julia Finogenov, Oleksii Demydenko, Olga Yablonskyi, Petro Ladogubets, Tetyana Oliinyk, Yurii Miroshnichenko, Ivan National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Automation of Power Processes and Systems Engineering Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Descriptive Geometry Engineering and Computer Graphics Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Computer-Aided Science and Data Processing Systems Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' The Department of Theory Practice and Translation of English Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Deprtament of Applied Mathematics Kyiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

The paper considers the use of a neural network for the binary classification of magnetic resonance imaging images to establish a possible disease of COVID-19. Processing of input data and their reduction to one format is provided, and neural network software for binary classification is developed and its training based on about 60,000 open patient images. The using of developed software is universal for medical doctors or ordinary Internet users and does not require additional training. © 2022 IEEE.

关键词： Magnetic resonance imaging

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