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The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things

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computer Methods and Programs in Biomedicine 2023年第1期241卷 107745-107745页

作者： Aminizadeh, Sarina Heidari, Arash Toumaj, Shiva Darbandi, Mehdi Navimipour, Nima Jafari Rezaei, Mahsa Talebi, Samira Azad, Poupak Unal, Mehmet Medical Faculty of Islamic Azad University of Tabriz Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul Turkey Urmia University of Medical Sciences Urmia Iran Department of Electrical and Electronic Engineering Eastern Mediterranean University Gazimagusa99628 Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Tabriz University of Medical Sciences Faculty of Surgery Tabriz Iran Department of Computer Science University of Texas at San Antonio TX United States Department of Computer Science University of Manitoba Winnipeg Canada Department of Computer Engineering Nisantasi University Istanbul Turkey

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL

关键词： Deep learning

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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

arXiv

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arXiv 2024年

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Collaborative Innovation Center of Novel Software Technology and Industrialization Suzhou China School of Computer Science and Technology Soochow University Suzhou China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University Auburn United States Department of Electrical and Computer Engineering North Carolina State University NC United States BEDI Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this paper, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent trade-off between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024, CC BY.

关键词： Federated learning

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Output Feedback Control of Nondeterministic Finite–State Systems with Reach–Avoid Specifications

Output Feedback Control of Nondeterministic Finite–State Sy...

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Mediterranean Conference on Control and Automation (MED)

作者： Daniel Ajedamola Ajeleye Tommaso Masciulli Giordano Pola Department of Information Engineering Computer Science and Mathematics Center of Excellence DEWS University of L&#x2019 Aquila L&#x2019 Aquila Italy

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

In this paper we address control design of nondeterministic finite state systems with reach–avoid specifications. A general class of controllers is considered, which combines feedforward and output feedback schemes. Results proposed here extend those of [1] in two directions: first, here we consider reach avoid–specifications instead of simply reachability specifications; second, we propose algorithms exhibiting better time computational complexity than those given in [1].

关键词： Sufficient conditions Automation Control design Feedforward systems Computational complexity Output feedback

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Exhaled volatile organic compounds for cholangiocarcinoma diagnosis

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Liver Research 2022年第3期6卷 191-197页

作者： Nanicha Siriwong Thanikan Sukaram Rossarin Tansawat Terapap Apiparakoon Thodsawit Tiyarattanachai Sanparith Marukatat Rungsun Rerknimitr Roongruedee Chaiteerakij Department of Medicine Faculty of MedicineChulalongkorn UniversityBangkokThailand Division of Gastroenterology Department of MedicineFaculty of MedicineChulalongkorn Universityand King Chulalongkorn Memorial HospitalThai Red Cross SocietyBangkokThailand Department of Food and Pharmaceutical Chemistry Faculty of Pharmaceutical SciencesChulalongkorn UniversityBangkokThailand Department of Computer Engineering Faculty of EngineeringChulalongkorn UniversityBangkokThailand Clinical Sciences PhD International Program Faculty of MedicineChulalongkorn UniversityThailand Image Processing and Understanding Team Artificial Intelligence Research GroupNational Electronics and Computer Technology Center(NECTEC)Thailand Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology Chulalongkorn UniversityBangkokThailand

Objectives:The difficulties in the early detection consequent to the lack of sensitive biomarkers render patients with cholangiocarcinoma(CCA)to have poor ***,sensitive and specific volatile organic compounds(VOCs)were identified in several ***,the VOC profiles in CCA are not ***,we investigated the VOC profiles in exhaled breath of CCA patients and ***:We prospectively collected exhaled breath samples from 30 consecutive patients newly diagnosed with CCA and 30 controls who did not have CCA(seven had benign biliary strictures and 23 had other medical conditions).Exhaled VOCs were identified using gas chromatography mass spectrometry Triple Quadrupoles *** of the significant differences in VOCs between cases and controls was conducted using supervised multivariate regression *** validation was performed for these VOCs in another cohort of 18 CCA patients and 22 ***:Levels of six compounds were significantly different between CCA patients and controls,namely,acetone,isopropyl alcohol,dimethyl sulfide,1,4-pentadiene,allyl methyl sulfide,and N,*** and dimethyl sulfide were independently associated with CCA as demonstrated in the multivariate *** the cut-off value of 8.59107 arbitrary unit(AU),acetone had a sensitivity and specificity of 82.1%and 75.8%,respectively,with an area under the receiving operator curve(AUROC)of 0.85 for the CCA *** level was also significantly different between cases and controls in the validation *** the same cut-off value,the sensitivity,specificity,and AUROC was 59.1%,66.7%,and 0.85,***:Breath analysis may potentially be useful for CCA diagnosis.A cohort of patients with earlystage CCA in further studies is needed to confirm the ability of exhaled VOCs for the early detection of CCA.

关键词： Cholangiocarcinoma(CCA) Bile duct cancer Volatile organic compounds(VOCs) Biomarker Cancer screening Diagnostic model

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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yijia Chen Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model’s accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively.

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Joint Clustering and Analyze Single Cell Multi-omics Data by scMOGAN

Joint Clustering and Analyze Single Cell Multi-omics Data by...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Congcong An Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore SG Faculty of Engineering The University of Sydney Australia

Advancements in single-cell multi-omics sequencing technologies have dramatically transformed the analysis of cellular states at single-cell resolution. Cluster analyses leveraging single-cell transcriptome and epigenome data have enabled the characterization of cellular states and the delineation of transcriptomic regulatory programs associated with cellular heterogeneity. However, the high dimensionality, sparsity, and heterogeneity inherent to multi-omics data present significant challenges to their clustering analysis. In this study, we introduced single-cell Multi-Omics Generative Adversarial Networks (scMOGAN), an unsupervised clustering model specifically tailored for single-cell transcriptome and epigenome data. scMOGAN accomplishes this by efficaciously modeling the omics data and mining potential features through neural networks, ultimately utilizing Gaussian mixture models for cell type identification. To validate and scrutinize the performance of scMOGAN, we performed analyses on two actual multi-omics datasets and one simulated dataset. The results demonstrate that scMOGAN surpasses other existing methods in terms of clustering performance, underscoring its efficacy in navigating the complexities of multi-omics data.

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Quantitative and Qualitative Assessment of Indoor Exploration Algorithms for Autonomous UAVs

arXiv

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arXiv 2022年

作者： Farooq, Adil Laoudias, Christos Kolios, Panayiotis S. Theocharides, Theocharis KIOS Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

Indoor exploration is an important task in disaster relief, emergency response scenarios, and Search And Rescue (SAR) missions. Unmanned Aerial Vehicle (UAV) systems can aid first responders by maneuvering autonomously in areas inside buildings dangerous for humans to traverse, exploring the interior, and providing an accurate and reliable indoor map before the emergency response team takes action. Due to the challenging conditions in such scenarios and the inherent battery limitations and time constraints, we investigate 2D autonomous exploration strategies (e.g., based on 2D LiDAR) for mapping 3D indoor environments. First, we introduce a battery consumption model to consider the battery life aspect for the first time as a critical factor for evaluating the flight endurance of exploration strategies. Second, we perform extensive simulation experiments in diverse indoor environments using various state-of-the-art 2D LiDAR-based exploration strategies. We evaluate our findings in terms of various quantitative and qualitative performance indicators, suggesting that these strategies behave differently depending on the complexity of the environment and initial conditions, e.g., the entry point. © 2022, CC BY-NC-ND.

关键词： Antennas

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Virtual Reality-Based Random Dot Kinematogram

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computers, Materials & Continua 2021年第9期68卷 4205-4213页

作者： Jun Ma Hyo-Jung Kim Ji-Soo Kim Eek-Sung Lee Min Hong Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Research Administration Team Seoul National University Bundang HospitalSeongnam13620Korea Dizziness Center Clinical Neuroscience CenterDepartment of NeurologySeoul National University Bundang HospitalSeongnam13620Korea Department of Neurology Seoul National University College of MedicineSeoul03080Korea Department of Neurology College of MedicineSoonchunhyang UniversityBucheon14584Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

This research implements a random dot kinematogram(RDK)using virtual reality(VR)and analyzes the results based on normal *** motion perception is one of visual functions localized to a specific cortical area,the human motion perception area(human analogue for the middle temporal/middle superior temporal area)located in the parieto–occipito–temporal junction of the human *** RDK measures visual motion perception *** stimuli in conventional RDK methods are presented using a monitor screen,so these devices require a spacious dark room for installation and ***,VR technology has been implemented in different medical *** test method proposed in this study include a VR-based RDK that can independently measure human motion perception abilities without any spatial constraints via a VR head-mounted ***,the VR-based RDK was implemented,and the visual perception abilities of the normal subjects were measured based on varying *** both screen-and VR-based RDK tests,the easier the stimulus is,the higher the correct answer rate and the shorter the reaction *** significant differences in coherence thresholds were observed between the two test *** VRbased RDK proposed in this study can be used as a diagnosis tool for visual motion perception and neurodegenerative disorders affecting the posterior region of the brain.

关键词： Random dot kinematogram virtual reality visual psychophysics diagnostic device

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SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wen Zhu Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything" to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA’s LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS’s unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes.

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