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arXiv 2024年

作者： Chen, Boyu Liu, Junjie Li, Zhu Yang, Mengyue Institute of Health Informatics University College London United Kingdom Department of Aeronautics Imperial College London United Kingdom Gatsby Computational Neuroscience Unit University College London United Kingdom Department of Computer Science University College London United Kingdom School of Engineering Mathematics and Technology University of Bristol United Kingdom

Probability of necessity and sufficiency (PNS) measures the likelihood of a feature set being both necessary and sufficient for predicting an outcome. It has proven effective in guiding representation learning for unimodal data, enhancing both predictive performance and model robustness. Despite these benefits, extending PNS to multimodal settings remains unexplored. This extension presents unique challenges, as the conditions for PNS estimation—exogeneity and monotonicity—need to be reconsidered in a multimodal context. We address these challenges by first conceptualizing multimodal representations as comprising modality-invariant and modality-specific components. We then analyze how to compute PNS for each component while ensuring non-trivial PNS estimation. Based on these analyses, we formulate tractable optimization objectives that enable multimodal models to learn high-PNS representations. Experiments demonstrate the effectiveness of our method on both synthetic and real-world data. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Multi-Objective Deep Reinforcement Learning for 5G Base Station Placement to Support Localisation for Future Sustainable Traffic

Multi-Objective Deep Reinforcement Learning for 5G Base Stat...

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European Conference on Networks and Communications (EuCNC)

作者： Ahmed Al-Tahmeesschi Jukka Talvitie Miguel López-Benítez Hamed Ahmadi Laura Ruotsalainen School of Physics Engineering and Technology University of York United Kingdom Unit of Electrical Engineering Tampere University Tampere Finland Dept. of Electrical Engineering and Electronics University of Liverpool United Kingdom ARIES Research Centre Antonio de Nebrija University Spain Dept. of Computer Science University of Helsinki Helsinki Finland

ISBN: (数字)9798350344998

ISBN: (纸本)9798350345001

Millimeter-wave (mmWave) is a key enabler for next-generation transportation systems. However, in an urban city scenario, mmWave is highly susceptible to blockages and shadowing. Therefore, base station (BS) placement is a crucial task in the infrastructure design where coverage requirements need to be met while simultaneously supporting localisation. This work assumes a pre-deployed BS and another BS is required to be added to support both localisation accuracy and coverage rate in an urban city scenario. To solve this complex multi-objective optimisation problem, we utilise deep reinforcement learning (DRL). Concretely, this work proposes: 1) a three-layered grid for state representation as the input of the DRL, which enables it to adapt to the changes in the wireless environment represented by changing the position of the pre-deployed BS, and 2) the design of a suitable reward function for the DRL agent to solve the multi-objective problem. Numerical analysis shows that the proposed deep Q-network (DQN) model can learn/adapt from the complex radio environment represented by the terrain map and provides the same/similar solution to the exhaustive search, which is used as a benchmark. In addition, we show that an exclusive optimisation of coverage rate does not result in improved localisation accuracy, and thus there is a trade-off between the two solutions.

关键词： Wireless communication Base stations Accuracy Urban areas Transportation Deep reinforcement learning Millimeter wave communication

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Enhanced Rheumatoid Arthritis Treatment Optimization Through Deep Learning 3

Enhanced Rheumatoid Arthritis Treatment Optimization Through...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Arcot, Siva Venkatesh Buvaneswari, T. Kavitha, K.R. Manickavasagam, R. Suguna, M. Muthulekshmi, M. Contact Center Business Unit Cisco Systems Inc Little ElmTX United States Department of Computer Science and Engineering Tamil Nadu Salem India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Alpha College of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331519582

Inflammation and joint destruction are hallmarks of Rheumatoid Arthritis (RA), a chronic autoimmune condition. To improve patient outcomes, it is essential to optimize treatment plans. According to this research, integrating deep learning (DL) methods into a Convolutional Neural Network (CNN) provides a new method to optimize RA treatments. The CNN model is used to decipher intricate medical data, such as imaging and patient records, find trends, and predict how patients react to treatment. To assess the efficacy of the proposed model in predicting ideal treatment methods, it was trained on a large dataset that included patient images and clinical data. The CNN model offers a potential tool for doctors to adapt RA management plans since results show it significantly improves prediction accuracy and treatment personalization. This method allows for data-driven decisions in RA management, improving patient care and enhancing treatment performance. © 2025 IEEE.

关键词： Theranostics

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Some hybrid Riemannian conjugate gradient methods with restart strategy

Franklin Open

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Franklin Open 2025年 10卷

作者： Salihu, Nasiru Kumam, Poom Salisu, Sani Khammahawong, Konrawut Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) Bangkok 10140 Thailand Department of Mathematics Faculty of Sciences Modibbo Adama University Yola 652105 Nigeria KMUTT-Fixed Point Research Laboratory Room SCL 802 Science Laboratory Building Department of Mathematics Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) Bangkok 10140 Thailand Department of Mathematics Faculty of Natural and Applied Sciences Sule Lamido University Kafin Hausa Jigawa Nigeria Applied Mathematics for Science and Engineering Research Unit (AMSERU) Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand

Hybrid nonlinear conjugate gradient (CG) methods are known to be efficient and have less memory requirement for solving optimization problems in Euclidean spaces. The formulations strategically switch between two or more CG parameters to enhance their general structures. In this work, some hybrid Riemannian CG methods are presented, which are sufficiently descent irrespective of the line search technique employed and converges globally using scaled version of the nonexpensive condition. Finally, the numerical experiments are performed to confirm the strength of the schemes by considering some Riemannian optimization problems. The numerical results obtained when compared to some classical Riemannian CG methods indicated the efficiency of the proposed hybrid Riemannian CG methods. © 2025 The Authors

关键词： Global convergence Retraction Riemannian conjugate gradient method Riemannian optimization Riemannian wolfe conditions Vector transport

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Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model

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computers in Biology and Medicine 2024年 173卷 108294-108294页

作者： Zhang, Lufang Yu, Renyue Chen, Keya Zhang, Ying Li, Qiang Chen, Yu The First Clinical College Wenzhou Medical University Wenzhou325000 China Cardiac Care Unit Sir RUN RUN Shaw Hospital Hangzhou310000 China Wenzhou Medical University School of Nursing 325000 Wenzhou325000 China Cixi Biomedical Research Institute Wenzhou Medical University Cixi315300 China School of Computer Science and Technology Beijing Institute of Technology Beijing China Nursing Department The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China

Background: Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes. Methods: This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability. Results: Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07%. Conclusions: The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches. © 2024 Elsevier Ltd

关键词： COVID-19

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The Recoverability of Network Controllability

The Recoverability of Network Controllability

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International Conference on System Reliability and science (ICSRS)

作者： Anqi Chen Peng Sun Robert E. Kooi Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands TNO Unit ICT The Netherlands

ISBN: (纸本)9781665400503

Network recoverability refers to the ability of a network to recover to a desired performance level after suffering topological perturbations such as link failures. The minimum number of driver nodes is a typical metric to denote the network controllability. In this paper, we propose closed-form analytic approximations for the minimum number of driver nodes to investigate the recoverability of network controllability under link-based perturbations in two scenarios: 1) only the links which are damaged in the failure process can be recovered and 2) links can be established between any pair of nodes that have no link between them after the failure process. Results show that our approximations fit well with simulation results both in synthetic networks and real-world networks, such as swarm signaling networks and some communication networks.

关键词： Measurement Simulation Perturbation methods Process control Controllability Robustness Safety

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Dimitrios I. Zaridis Eugenia Mylona Nikolaos Tachos Charalampos Kalantzopoulos Vasileios C. Pezoulas Dimitrios D. Koutsouris George K. Matsopoulos Kostas Marias Manolis Tsiknakis Dimitrios I. Fotiadis Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens GR15773 Athens Greece Biomedical Research Institute FORTH Ioannina Greece University of Ioannina Ioannina Greece Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece FORTH-IMBB Ioannina Greece Materials Science and Engineering Department Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR 45110 Greece

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland’s complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model’s performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net’s deficiency leading to the development of more specialized models in respect to these factors.

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Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

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computers, Materials & Continua 2021年第10期69卷 1233-1252页

作者： Narjes Benameur Mazin Abed Mohammed Ramzi Mahmoudi Younes Arous Begonya Garcia-Zapirain Karrar Hameed Abdulkareem Mohamed Hedi Bedoui University of Tunis El Manar Higher Institute of Medical Technologies of TunisLaboratory of Biophysics and Medical TechnologiesTunisTunisia College of Computer Science and Information Technology University of Anbar31001AnbarIraq Faculty of Medicine of Monastir Medical Imaging Technology Lab-LTIM-LR12ES06University of Monastir5019MonastirTunisia Gaspard-Monge Computer-Science Laboratory Paris-Est UniversityMixed Unit CNRS-UMLV-ESIEE UMR8049BP99ESIEE Paris CitéDescartes93162 Noisy Le GrandFrance Military Hospital of Instruction of Tunis TunisTunisia eVIDA Lab University of DeustoAvda/Universidades 24Bilbao48007Spain College of Agriculture Al-Muthanna UniversitySamawah66001Iraq

Left ventricular(LV)dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac *** these indices give information about the presence or not of LV alteration,they are not able to identify the location and the size of such *** aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac *** proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal *** parametric images approaches:covariance analysis,parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of *** results show that parametric images generated from the monogenic signal were superior in term of sensitivity(89.69%),specificity(86.51%)and accuracy(89.06%)to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial ***,the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.

关键词： Covariance analysis cardiac MRI monogenic signal assessment Hilbert transform

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Event and activity recognition in video surveillance for cyber-physical systems

arXiv

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arXiv 2021年

作者： Bhaumik, Swarnabja Jana, Prithwish Mohanta, Partha Pratim Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

In this chapter, we aim to aid the development of Cyber-Physical Systems (CPS) in automated understanding of events and activities in various applications of video-surveillance. These events are mostly captured by drones, CCTVs or novice and unskilled individuals on low-end devices. Being unconstrained in nature, these videos are immensely challenging due to a number of quality factors. We present an extensive account of the various approaches taken to solve the problem over the years. This ranges from methods as early as Structure from Motion (SFM) based approaches to recent solution frameworks involving deep neural networks. We show that the long-term motion patterns alone play a pivotal role in the task of recognizing an event. Consequently each video is significantly represented by a fixed number of key-frames using a graph-based approach. Only the temporal features are exploited using a hybrid Convolutional Neural Network (CNN) + Recurrent Neural Network (RNN) architecture. The results we obtain are encouraging as they outperform standard temporal CNNs and are at par with those using spatial information along with motion cues. Further exploring multistream models, we conceive a multi-tier fusion strategy for the spatial and temporal wings of a network. A consolidated representation of the respective individual prediction vectors on video and frame levels is obtained using a biased conflation technique. The fusion strategy endows us with greater rise in precision on each stage as compared to the state-of-the-art methods, and thus a powerful consensus is achieved in classification. Results are recorded on four benchmark datasets widely used in the domain of action recognition, namely Columbia Consumer Videos (CCV), Human Motion Database (HMDB), UCF-101 and Kodak’s Consumer Video (KCV). It is inferable that focusing on better classification of the video sequences certainly leads to robust actuation of a system designed for event surveillance and object cum activit

关键词： Embedded systems

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A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

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Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

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