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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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The smart parking management system

arXiv

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arXiv 2020年

作者： Elsonbaty, Amira A. Shams El Den, Mahmoud Department of communication and electronics Higher institute of engineering and technology new Damietta New Damietta34517 Egypt Department of Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

With growing, Car parking increases with the number of car users. With the increased use of smartphones and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the server and are recovered by the mobile application which offers many options attractively and with no cost to users and lets the user check reservation details. With IoT technology, the smart parking system can be connected wirelessly to easily track available locations. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart city

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FedDAG: Federated DAG Structure learning

arXiv

引用

arXiv 2021年

作者： Gao, Erdun Chen, Junjia Shen, Li Liu, Tongliang Gong, Mingming Bondell, Howard School of Mathematics and Statistics The University of Melbourne Australia Faculty of Electronic and Information Engineering Xi'an Jiaotong University China JD Explore Academy China TML Lab Sydney AI Centre The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server. However, due to the consideration of privacy protection, data owners gradually refuse to share their personalized raw data to avoid private information leakage, making this task more troublesome by cutting off the first step. Thus, a puzzle arises: how do we discover the underlying DAG structure from decentralized data? In this paper, focusing on the additive noise models (ANMs) assumption of data generation, we take the first step in developing a gradient-based learning framework named FedDAG, which can learn the DAG structure without directly touching the local data and also can naturally handle the data heterogeneity. Our method benefits from a two-level structure of each local model. The first level structure learns the edges and directions of the graph and communicates with the server to get the model information from other clients during the learning procedure, while the second level structure approximates the mechanisms among variables and personally updates on its own data to accommodate the data heterogeneity. Moreover, FedDAG formulates the overall learning task as a continuous optimization problem by taking advantage of an equality acyclicity constraint, which can be solved by gradient descent methods to boost the searching efficiency. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

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Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design

arXiv

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arXiv 2023年

作者： Karandashev, Konstantin Weinreich, Jan Heinen, Stefan Arrieta, Daniel Jose Arismendi von Rudorff, Guido Falk Hermansson, Kersti von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry-Angström Laboratory Uppsala University Box 538 UppsalaSE-75121 Sweden University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Strase 40 Kassel34132 Germany Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast number of possible molecular systems. We propose an Evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favourable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap;optimization was done over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) datasets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus only feasible thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Electrolytes

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Physico-chemical properties extraction from the fluorescence spectrum with 1D-convolutional neural networks: application to olive oil

arXiv

引用

arXiv 2022年

作者： Venturini, Francesca Sperti, Michela Michelucci, Umberto Gucciardi, Arnaud Martos, Vanessa M. Deriu, Marco A. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Technikumstrasse 9 Winterthur8401 Switzerland PolitoBIOMed Lab Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy TOELT llc Machine Learning Research and Development Birchlenstr. 25 Dübendorf8600 Switzerland Artificial Intelligence Laboratory University of Ljubljana Ljubljana Slovenia Department of Plant Physiology Faculty of Sciences Biotechnology Institute University of Granada Campus Fuentenueva s/n 18071 Spain

One of the main challenge for olive oil producers is the ability to asses and control oil quality regularly during the production cycle. For this purpose, several parameters need to be determined, such as the acidity, UV absorption coefficients and the ethyl esters content. To achieve this, samples must be sent to an approved laboratory for chemical analysis. This approach is expensive, required costly equipment and scientific training and thus cannot be performed very frequently, making regular and low-cost quality control of olive oil a hard to solve challenge. This work describes a new approach based on fluorescence spectroscopy and artificial intelligence (namely, 1-D convolutional neural networks) to predict the five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast (less than one second) measurement from a low-cost fluorescence sensor. The measurement does not require any sample preparation or scientific training and can be easily performed even by completely untrained workers. The results indicate that the proposed approach gives exceptional results for quality determination through extraction of the relevant physico-chemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality, even for small producers. © 2022, CC BY-NC-ND.

关键词： Convolution

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Semantic Segmentation for Corrosion Detection in Archaeological Artefacts before Restoration

Semantic Segmentation for Corrosion Detection in Archaeologi...

引用

International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Ruxandra Stoean Nebojsa Bacanin Leonard Ionescu Marinela Boicea Alina-Maria Gărău Cristina-Camelia Ghiţescu Faculty of Sciences University of Craiova Romania Artificial Intelligence and Machine Learning Team Romanian Institute of Science and Technology Cluj-Napoca Romania Faculty of Informatics and Computing Singidunum University Belgrade Serbia Restoration and Conservation Lab Oltenia Museum History and Archaeology Section Craiova Romania Restoration and Conservation Lab Oltenia Museum History and Archaeology SectionCraiovaRomania

ISBN: (纸本)9781665406512

From the moment of being excavated till they become a museum exhibit, archaeological artefacts undergo a careful process of restoration, elaborately conducted by human experts with the help of complex devices. After the chemical composition of the object is approximated, the next step of the pathway is to assess the degradation of the surface, i.e. the quantification of corrosion. While earlier work proposed an automation of the step related to the estimation of the chemical concentration, the current study attempts to further offer a computational solution for the detection of corroded areas of the artefact. Iron historical items were considered, stereo microscopy images were produced and the restorers manually roughly delineated the regions containing rust. An U-Net architecture was trained on the annotated collection to recognize rust from clean areas. Even with a preliminary minimal manual delineation of the degraded zones for training, the deep learning model was able to recognize the similar areas in new objects in the test phase.

关键词： Training Image segmentation Scientific computing Corrosion Microscopy Semantics Image restoration

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Cybertwin driven resource allocation using optimized proximal policy based federated learning in 6G enabled edge environment

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Digital Communications and Networks 2025年

作者： Sowmya Madhavan M.G. Aruna G.P. Ramesh Abdul Lateef Haroon Phulara Shaik Dhulipalla Ramya Krishna Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Yelahanka Bangalore India Department of Artificial Intelligence and Machine Learning Dayanand Sagar College of Engineering Bangalore India Department of Electronics and Communication Engineering St Peter's Institute of Higher Education and Research Chennai India Department of Electronics and Communication Engineering Ballari Institute of Technology and Management Ballari India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

Sixth-generation (6G) communication system promises unprecedented data density and transformative applications over different industries. However, managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient machine learning (ML) training and aggregation, often leading to increased energy consumption and reduced model generalization. To solve this problem, this research proposes a Weighted Proximal Policy-based Federated learning approach integrated with ResNet50 and Scaled Exponential Linear Unit activation function (WPPFL-RS). The proposed method optimizes resource allocation such as CPU and memory, through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds. The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption, solving complex challenges in 6G-enabled edge computing. This make sures that efficient resource utilization and enhanced performance in heterogeneous edge networks. The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks, a significant improvement over the baseline Deep Reinforcement learning (DRL), which recorded 11.39 s. This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.

关键词： Cybertwin Federated learning ResNet50 Resource allocation Scaled exponential linear Unit Weighted proximal policy

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Towards DMC accuracy across chemical space with scalable ∆-QML

arXiv

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arXiv 2022年

作者： Huang, Bing von Lilienfeld, O. Anatole Krogel, Jaron T. Benali, Anouar University of Vienna Faculty of Physics Kolingasse 14-16 Vienna1090 Austria Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Computational Sciences Division Argonne National Laboratory ArgonneIL60439 United States

In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to under-gird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for ∆-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Correction: Multivariate clustering for maximizing the small cell users’ performance based on the dynamic interference alignment

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Wireless Networks 2023年第1期30卷 593-593页

作者： Dakshinamoorthy, Prabakar Vaitilingam, Saminadan Sundar, Ramesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

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Dispatching and Control Information Freshness-Aware Federated learning for Simplified Power IoT

Dispatching and Control Information Freshness-Aware Federate...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Zehan Jia Ziqi Yu Haijun Liao Zhao Wang Zhenyu Zhou Xiaoyan Wang Guoqing He Shahid Mumtaz Mohsen Guizani Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Beijing Baoding Hebei China Graduate School of Science and Engineering Ibaraki University Ibaraki-ken Japan State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Instituto de Telecomunicações Aveiro Portugal Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (纸本)9781665435413

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

关键词： Training Computational modeling Simulation Decision making Minimization Dispatching Data models

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