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HPC+ in the medical field: Overview and current examples

Technology and Health Care 2023年第4期31卷 1509-1523页

作者： Koch, Miriam Arlandini, Claudio Antonopoulos, Gregory Baretta, Alessia Beaujean, Pierre Bex, Geert Jan Biancolini, Marco Evangelos Celi, Simona Costa, Emiliano Drescher, Lukas Eleftheriadis, Vasileios Fadel, Nur A. Fink, Andreas Galbiati, Federica Hatzakis, Ilias Hompis, Georgios Lewandowski, Natalie Memmolo, Antonio Mensch, Carl Obrist, Dominik Paneta, Valentina Papadimitroulas, Panagiotis Petropoulos, Konstantinos Porziani, Stefano Savvidis, Georgios Sethia, Khyati Strakos, Petr Svobodova, Petra Vignali, Emanuele High-Performance Computing Center Stuttgart (HLRS) Stuttgart Germany CINECA Casalecchio di Reno Italy iKnowHow Athens Greece InSilicoTrials Trieste Italy Laboratory of Theoretical Chemistry Namur Institute of Structured Matter University of Namur Namur Belgium Data Science Institute Hasselt University Hasselt Belgium RBF Morph Rome Italy BioCardioLab Fondazione Toscana G Monasterio Massa Italy RINA Rome Italy Swiss National Supercomputing Centre (CSCS) Lugano Switzerland BIOEMTECH Athens Greece GRNET Athens Greece Department of Mathematics Faculty of Science University of Antwerp Antwerp Belgium University of Bern Bern Switzerland IT4Innovations VSB - Technical University of Ostrava Ostrava-Poruba Czech Republic

Background: To say data is revolutionising the medical sector would be a vast understatement. The amount of medical data available today is unprecedented and has the potential to enable to date unseen forms of healthcare. To process this huge amount of data, an equally huge amount of computing power is required, which cannot be provided by regular desktop computers. These areas can be (and already are) supported by High-Performance-computing (HPC), High-Performance data Analytics (HPDA), and AI (together 'HPC+'). Objective: This overview article aims to show state-of-the-art examples of studies supported by the National Competence Centres (NCCs) in HPC+ within the EuroCC project, employing HPC, HPDA and AI for medical applications. Method: The included studies on different applications of HPC in the medical sector were sourced from the National Competence Centres in HPC and compiled into an overview article. Methods include the application of HPC+ for medical image processing, high-performance medical and pharmaceutical data analytics, an application for pediatric dosimetry, and a cloud-based HPC platform to support systemic pulmonary shunting procedures. Results: This article showcases state-of-the-art applications and large-scale data analytics in the medical sector employing HPC+ within surgery, medical image processing in diagnostics, nutritional support of patients in hospitals, treating congenital heart diseases in children, and within basic research. Conclusion: HPC+ support scientific fields from research to industrial applications in the medical area, enabling researchers to run faster and more complex calculations, simulations and data analyses for the direct benefit of patients, doctors, clinicians and as an accelerator for medical research. © 2023 - The authors. Published by IOS Press.

关键词： AI (artificial intelligence) computational modeling Computer simulation data analysis diagnosis medicine therapeutics

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Paddy Diseases Multi-Class Classification using CNN Variants

Paddy Diseases Multi-Class Classification using CNN Variants

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Information Technology (ACIT)

作者： Danial Izzat Anwar Bin Abdul Aziz Marina Yusoff Nurain Ibrahim Raed Alazaidah College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively.

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Neural Network Driven by Density and Parallel Features for Field-Road Mode Mining

Neural Network Driven by Density and Parallel Features for F...

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International Conference on Computer Supported Cooperative Work in Design

作者： Fengqi Hao Xiyuan Zhao Cunxiang Bian Jinqiang Bai Qingyan Ding Faculty of Data Science City University of Macau Macau China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite System (GNSS) is widely applied in agricultural machinery, many methods based on motion and spatial-temporal features of GNSS trajectory data have been proposed for FRMM. However, these methods ignore the density and parallel features of GNSS points. The two features are useful for FRMM because field points usually have a higher density and more parallel points than road points. Therefore, a neural network driven by density and parallel features is proposed for accurate FRMM. Firstly, a statistical method is designed to extract the density feature (i.e., the number of its neighbor points) and parallel feature (i.e., the number of approximately parallel points in its neighbors) of each point. Then, the two features and eight motion features (e.g., speed, direction, etc.) are fed into a neural network to extract valuable latent features. Finally, a linear classifier is used to identify the field and road categories of GNSS points based on the latent features. Experimental results show that our method outperforms state-of-the-art methods and achieves the accuracy of 91.69% and 86.44% on public Wheat and Paddy datasets, respectively.

关键词： Global navigation satellite system Accuracy Statistical analysis Federated learning Roads Neural networks Time series analysis Feature extraction Trajectory data mining

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Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

arXiv

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arXiv 2023年

作者： Ezugwu, Absalom E. Oyelade, Olaide N. Ikotun, Abiodun M. Agushaka, Jeffery O. Ho, Yuh-Shan Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Department of Computer Science Faculty of Physical Sciences Ahmadu Bello University Zaria Nigeria Trend Research Centre Asia University No. 500 Lioufeng Road Wufeng Taichung41354 Taiwan

The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa's most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent. © 2023, CC BY.

关键词： Image recognition

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EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CMR Segmentation

EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CM...

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International Conference on Computer Supported Cooperative Work in Design

作者： Huijuan Hao Wenpeng Wang Qingyan Ding Haibo Cheng Fengqi Hao Hu Zhang Jinqiang Bai Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Accurate and reproducible assessment of myocardial conditions is essential for the diagnosis of prior infarctions, cardiomyopathies, and inflammatory diseases. Although cardiac magnetic resonance imaging (CMR) is regarded as the gold standard for evaluating myocardial anatomy and function, manual segmentation remains labor-intensive and susceptible to variability. The demand for high-resolution images, dense predictions, and comprehensive segmentation across all phases of the cardiac cycle introduces significant challenges for Cine-CMR analysis. To address these issues, we present EML-SAM, an innovative interactive segmentation model based on the Segment Anything Model (SAM). EML-SAM incorporates an early-mid-late (EML) multi-scale fusion strategy, effectively mitigating the premature dilution of interaction information, thereby reducing segmentation errors and enhancing feature utilization. Furthermore, the model utilizes a multi-scale linear attention (MLA) transformer to establish an efficient global receptive field. Comprising 12 Transformer layers, the model integrates multi-scale attention with multi-scale perceptrons, striking a balance between computational efficiency and capacity. The proposed methodology offers a robust and efficient solution for high-resolution Cine-CMR segmentation, delivering accurate and real-time segmentation performance throughout the various stages of temporal changes within the cardiac cycle.

关键词： Image segmentation Accuracy Computational modeling Refining Myocardium Transformers Real-time systems Reproducibility of results Reliability Diseases

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Multi-Layer Transmitting RIS-Aided Receiver for Collaborative Jamming and Anti-Jamming Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Junjie Yang, Liang Hao, Wanming Ahmad, Ishtiaq Liu, Hongwu Shu, Feng Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering China Jishou University School of Communication and Electronic Engineering China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Czech Technical University in Prague Faculty of Electrical Engineering Department of Telecommunication Engineering Prague166 27 Czech Republic Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Hainan University School of Information and Communication Engineering Haikou570228 China Nanyang Technological University College of Computing and Data Science Singapore

In this paper, we propose a novel architecture for a multi-layer active-passive cascade transmitting reconfigurable intelligent surface (RIS)-aided receiver. We aim at enhancing the scalability of antenna dimensions and energy efficiency for user equipment (UE), as well as improving the amplitude freedom of antenna gain. This architecture integrates reflecting RIS for applications in both jamming and anti-jamming scenarios. We formulate a problem to maximize the worst-case spectral efficiency (SE) of the UE, based on imperfect channel state information (CSI) of the malicious device, thereby ensuring the SE of our UE while disrupting the signal reception of the illegal UE. To address the inherently non-convex nature of the formulated problem, we propose an alternating optimization framework, which decomposes the main problem into several subproblems. Specifically, we uniformly discretize the uncertain domains to obtain robust CSI, allowing us to determine an optimal receiver vector. To balance computational complexity and performance, we propose solutions for the subproblems of base station beamforming and coefficients with different patterns of RIS. Furthermore, to effectively disrupt malicious inter-device communication while avoiding detection and localization, we develop a collaborative interference pattern incorporating silent interference and non-interference protocols. Importantly, the pattern carefully balances performance with the overhead of CSI acquisition. Finally, simulation results validate the efficiency of the proposed algorithms, demonstrating that the proposed architecture achieves better jamming and anti-jamming performance. © 2002-2012 IEEE.

关键词： Jamming

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Innovative Urban Design Simulation: Utilizing Agent-Based Modelling through Reinforcement Learning 23

Innovative Urban Design Simulation: Utilizing Agent-Based Mo...

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Proceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems

作者： Ayse Glass Jorg R. Noennig Burak Bek Roman Glass Eylul K. Menges Iryna Okhrin Pramod Baddam Mariela Rossana Sanchez Gunalan Senthil René Jäkel Digital City Science Hafencity Universitat Hamburg Germany University Grenoble Alpes France Faculty of Architecture izmir Institute of Technology Turkiye ScaDS.AI - Center for Scalable Data Analytics and Artificial Intelligence Technical University of Dresden Germany and Center for Information Services and High-Performance Computing (ZIH)Technical University of Dresden Germany Center for Information Services and High-Performance Computing (ZIH) Technical University of Dresden Germany

ISBN: (纸本)9798400709067

data-driven design for cities is improving the quality of everyday life of citizens and optimizes the usage of resources. A new aspect is artificial intelligence, which Smart Cities could greatly benefit from. A central problem for urban designers is the unavailability of data to make relevant decisions. Agent-based simulations enable a view of the dynamic properties of the urban system, generating data in its course. However, the simulation must remain sufficiently simple to remain in the realm of computability. The research question of this paper is: How can we make agents behave more realistically to analyze citizens’ mobility behavior? To solve this problem, we first created a simulated virtual environment, where agents can move freely in a small part of a city, the harbor area in Hamburg, Germany. We assumed that happiness is a crucial motivating factor for the movement of citizens. A survey of 130 citizens provided the weights that govern the simulated environment and the happiness score assignation of places. As an AI method, we then used Reinforcement Learning as a general model and Q-learning as an algorithm to generate a baseline. Through randomly traversing the model environment a baseline was created. We are in the process of enhancing Reinforcement Learning with a Deep Q-Network to make the actors learn. Early experiments show a significant improvement over a tabular Q-learning approach. This paper contributes to the literature of urban planning, and data-driven architectural design. The main contribution is replacing the inefficient search for a global maximum of the happiness function, with an efficient local solution global maximum. This has implications for further research in the generation of synthetic data through simulations.

关键词： agent-based modeling artificial intelligence city simulations smart cities synthetic data urban design

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A Novel Defocus-blur Region Detection Approach Based on DCT Feature and PCNN Structure

arXiv

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arXiv 2023年

作者： Basar, Sadia Ali, Mushtaq Waheed, Abdul Ahmad, Muneer Miraz, Mahdi H. Department of CS & IT Hazara University Mansehra21120 Pakistan Higher Education Department Govt. Girls Degree College Palosai KPK Peshawar25121 Pakistan Department of Computer Science Women University Swabi Swabi23430 Pakistan School of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Human and Digital Interface Woosong University Daejeon Korea Republic of School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia School of Computing Faculty of ArtsScience and Technology Wrexham Glyndwr University Wrexham United Kingdom Faculty of Computing Engineering and Science University of South Wales Pontypridd United Kingdom

The motion or out-of-focus effect in digital images is the main reason for the blurred regions in defocused-blurred images. It may adversely affect various image features such as texture, pixel, and region. Therefore, it is important to detect in-focused objects in defocused-blurred images after the segmentation of blurred and non-blurred regions. The state-of-the-art techniques are prone to noisy pixels, and their local descriptors for developing segmentation metrics are also complex. To address these issues, this research, therefore, proposed a novel and hybrid-focused detection approach based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN) structure. The proposed approach partially resolves the limitations of the existing contrast schemes to detect in-focused smooth objects from the out-of-focused smooth regions in the defocus dataset. The visual and quantitative evaluation illustrates that the proposed approach outperformed in terms of accuracy and efficiency to referenced algorithms. The highest Fα-score of the proposed approach on Zhao's dataset is 0.7940 whereas on Shi's dataset is 0.9178. © 2023, CC BY.

关键词： Neural networks

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Markov chain quasi-Monte Carlo method for forecasting fire hotspots in Sarawak, Malaysia

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Environmental science and Pollution Research 2024年第35期31卷 48608-48619页

作者： Zakaria, Nurul Nnadiah Daud, Hanita Sokkalingam, Rajalingam Othman, Mahmod Abdul Kadir, Evizal Mohd Aris, Muhammad Naeim Muhammad, Noryanti Maharani, Warih Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Perak Darul Ridzuan Bandar Seri Iskandar32610 Malaysia Information System Department Universitas Islam Indragiri Tembilahan29212 Indonesia Informatics Engineering Department Universitas Islam Riau Pekanbaru28284 Indonesia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Darul Ehsan Bangi43600 Malaysia Centre of Excellence in Artificial Intelligence & amp Data Science Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob Pahang Darul Makmur Kuantan26300 Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob Pahang Darul Makmur Kuantan26300 Malaysia School of Computing Telkom University Bandung40257 Indonesia

Stochastic modeling approaches have attracted many researchers to the field. However, fire hotspot detection suffers from not using a Markov chain quasi-Monte Carlo (MCQMC) as a forecasting methodology. This paper proposes improvements to the computational time by combining the strengths of the Markov chain Monte Carlo (MCMC) and quasi-Monte Carlo (QMC) methods. The proposed method can lead to more precise and stable results, particularly in problems with high-dimensional integration or complex probability distributions. The proposed method is applied to a case study of fire hotspot detection in Sarawak, Malaysia. The outcome of this study reveals that the MCQMC method is more computationally efficient, taking only 0.0746 seconds compared to MCMC’s 0.0914 seconds and QMC’s 0.0994 seconds. It is shown that the best option derived by the proposed method is effective in predicting fire hotspots and providing quick solutions to protect the environment and communities from forest fires. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fires

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