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arXiv 2021年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Nodehi, Issa Mashmool, Amir Zadegan, Zeynab Kiani Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Tamadon, Tahereh Khosravi, Samiyeh Kohnehshari, Mitra Akbari Hassannatajjeloudari, Edris Sharifrazi, Danial Mosavi, Amir Loh, Hui Wen Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Information Technology Engineering Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering University of Qom Qom Iran Italy Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia Computer Engineering Department Engineering Faculty Bu-Ali Sina University Hamedan Iran Department of Nursing School of Nursing and Allied Medical Sciences Maragheh Faculty of Medical Sciences Maragheh Iran Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Faculty of Informatics Obuda University Budapest Hungary School of Science and Technology Singapore University of Social Sciences Singapore Department of Cardiology National Heart Centre Singapore Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals. © 2021, CC BY-SA.

关键词： Deep neural networks

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On the importance of AI research beyond disciplines

arXiv

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arXiv 2023年

作者： Dignum, Virginia Casey, Donal Cerratto-Pargman, Teresa Dignum, Frank Fantasia, Valentina Formark, Bodil Hammarfelt, Björn Holmberg, Gunnar Holzapfel, André Larsson, Stefan Lagerkvist, Amanda Lakemond, Nicolette Lindgren, Helena Lorig, Fabian Marusic, Ana Rahm, Lina Razmetaeva, Yulia Sikström, Sverker Tatar, Kıvanç Tucker, Jason Department of Computing Science Umeå University Sweden Department of Philosophy Lund University Sweden Department of Business Studies Uppsala University Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Umeå University Sweden Swedish School of Library and Information Science University of Borås Sweden Department of Management and Engineering Linköping University Sweden Division of Media Technology and Interaction Design KTH Sweden Department of Technology and Society Lund University Sweden Department of Informatics and Media Uppsala University Sweden Department of Computer Science and Media Technology Malmö University Sweden University of Split School of Medicine Split Croatia Department of Philosophy and History KTH Sweden Department of Law Uppsala University Sweden Department of Psychology Lund University Sweden Chalmers University of Technology Sweden Department of Global Political Studies Malmö University Sweden

As the impact of AI on various scientific fields is increasing, it is crucial to embrace interdisciplinary knowledge to understand the impact of technology on society. The goal is to foster a research environment beyond disciplines that values diversity and creates, critiques and develops new conceptual and theoretical frameworks. Even though research beyond disciplines is essential for understanding complex societal issues and creating positive impact it is notoriously difficult to evaluate and is often not recognized by current academic career progression. The motivation for this paper is to engage in broad discussion across disciplines and identify guiding principles fir AI research beyond disciplines in a structured and inclusive way, revealing new perspectives and contributing to societal and human wellbeing and sustainability. © 2023, CC BY.

关键词： Sustainable development

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Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Mod...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ilaria Siviero Florian Helmhold Andreas M. Ray Carlos Bibián Gloria Menegaz Ander Ramos Murguialday Silvia F. Storti Dept. of Computer Science University of Verona Verona Italy Inst. of Medical Psychology and Behavioral Neurobiology University of Tübingen Tübingen Germany GTC of Neuroscience / IMPRS for Cognitive and Systems Neuroscience Tübingen Germany Dept. of Engineering for Innovation Medicine University of Verona Verona Italy TECNALIA Basque Research and Technology Alliance San Sebastian Spain Dept. of Neurology and Stroke University of Tübingen Tübingen Germany Athenea Neuroclinics San Sebastian Spain

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like electromyographic (EMG) and inertial measurement unit (IMU) data are crucial for decoding continuous human actions. Classical modeling methods, such as linear, adaptive, or static filters, lack the capacity to capture complex relationships between surface EMG and kinematics, as well as generalizability across subjects. We propose a deep learning (DL) model for the continuous decoding of hand motion. Custom-made wearable devices acquired EMG-IMU data from 27 healthy subjects performing a wrist flexion/extension task. Two regression models were compared with a hybrid convolutional neural network and a gated recurrent unit. To address inter-subject variability, a leave-one-subject-out cross-validation approach was implemented. The DL model showed a mean R 2 increase of 0.18 compared to the polynomial regressor. Our approach enhances wrist kinematics decoding providing a generalized model based on data captured with wearable devices. The findings hold potential for innovative home-based telemedicine solutions in motor rehabilitation.

关键词： Wrist Deep learning Adaptation models Biological system modeling Telemedicine Kinematics Motors Electromyography Decoding Wearable devices

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Pigmented skin lesions classification using convolutional neural networks

Pigmented skin lesions classification using convolutional ne...

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2019 International Conference on Biomedical Innovations and Applications, BIA 2019

作者： Naronglerdrit, Prasitthichai Mporas, Iosif Perikos, Isidoros Paraskevas, Michael Kasetsart University Sriracha Campus Dept. of Computer Engineering Chonburi Thailand University of Hertfordshire Dept. of Engineering Hatfield United Kingdom University of Patras Computer Engineering and Informatics Dept. Patras Greece Computer Technology Institute and Press 'Diophantus Patras Greece University of Peloponnesse Dept. of Electrical and Computer Engineering Patras Greece

ISBN: (纸本)9781728147543

In this paper we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image pre-processing for natural hair removal and image segmentation for extraction of the skin lesion area. The segmented images were processed by a convolutional neural network classifier. The training process was done by using the Keras and TensorFlow python packets with CUDA supported. The best performance was achieved by a convolutional neural network architecture with three convolution layers and the classification accuracy was equal to 76.83%. © 2019 IEEE.

关键词： Image classification

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The Rendezvous Between Extreme Value Theory and Next-generation Networks

arXiv

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arXiv 2024年

作者： Sagar, Srinivas Subhash, Athira Liu, Chen-Feng Elzanaty, Ahmed Al-Badarneh, Yazan H. Kalyani, Sheetal Alouini, Mohamed-Slim Bennis, Mehdi Hanzo, Lajos Dept. of Electrical Engg. IIT Madras India Department of Informatics New Jersey Institute of Technology NewarkNJ07102 United States University of Surrey GuildfordGU2 7XH United Kingdom Department of Electrical Engineering The University of Jordan Amman11942 Jordan Makkah Province Thuwal Saudi Arabia Centre for Wireless Communications University of Oulu Oulu90014 Finland School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Promising technologies such as massive multiple-input and multiple-output, reconfigurable intelligent reflecting surfaces, non-terrestrial networks, millimetre wave communication, ultra-reliable low-latency communication are envisioned as the enablers for next-generation (NG) networks. In contrast to conventional communication systems meeting specific average performance requirements, NG systems are expected to meet quality-of-service requirements in extreme scenarios as well. In this regard, extreme value theory (EVT) provides a powerful framework for the design of communication systems. In this paper, we provide a comprehensive survey of advances in communication that utilize EVT to characterize the extreme order statistics of interest. We first give an overview of the history of EVT and then elaborate on the fundamental theorems. Subsequently, we discuss different problems of interest in NG communication systems and how EVT can be utilized for their analysis. We finally point out the open challenges and future directions of EVT in NG communication systems. Copyright © 2024, The Authors. All rights reserved.

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Cost optimization in CNN for big data of share price

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AIP Conference Proceedings 2023年第1期2587卷

作者： R. Saravanakumar Gokul Talele Ali Hazim Alyamoor Kaushal Kishor Bhatt Prashant Vats Malik Bader Alazzam Dept. of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India Dept. of Data Science IIIT Banglore India Dept. of Accounting Economic and Administration College University of Mosul Ninawa Iraq Dept. of Computer Science and Engineering Birla Institute of Applied Science Bhimtal Nainital Uttarakhand India Dept. of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Faculty of Computer Science and Informatics Amman Arab University Mubis-Amman Jordan

Optimization of share price in order to evaluate whether to invest in specific share or not is subject of study paper. Optimized value has been utilized to enhance the performance and accuracy when dataset is trained using CNN. Review has been given on big cap, mid cap & small cap fund which are accessible in large, medium and small amount for the purpose of investing. It has been found that share in big cap are more dependable but offers limited return. However share in case of mid cap are considered comparatively more hazardous than big cap yet return may be substantial. The suggested study has given mechanism that will make use of optimization mechanism to filter the data set considering PSO and MVO mechanism. The suggested CNN based training model is claimed high performance system with improved accuracy.

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

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arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Opportunities for machine learning in scientific discovery

arXiv

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arXiv 2024年

作者： Vinuesa, Ricardo Rabault, Jean Azizpour, Hossein Bauer, Stefan Brunton, Bingni W. Elofsson, Arne Jarlebring, Elias Kjellström, Hedvig Markidis, Stefano Marlevi, David Cinnella, Paola Brunton, Steven L. FLOW Engineering Mechanics KTH Royal Institute of Technology Stockholm Sweden Stockholm Sweden IT Department Norwegian Meteorological Institute Oslo0313 Norway KTH Royal Institute of Technology Stockholm Sweden TUM School of Computation Information and Technology Technical University Munich Munich Germany Helmholtz AI Helmholtz Center Munich Munich Germany Department of Biology University of Washington SeattleWA98195 United States Dept. of Biochemistry and Biophysics Science for Life Laboratory Stockholm University Solna171 21 Sweden Dept. Mathematics KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Computer Science KTH Royal Institute of Technology Stockholm Sweden Dept. Molecular Medicine and Surgery Karolinska Institutet Stockholm171 77 Sweden Inst. for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA02139 United States Institut Jean le Rond D’Alembert Sorbonne Université France Department of Mechanical Engineering University of Washington SeattleWA98195 United States

Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields. However, our ability to leverage ML methods for scientific discovery, i.e. to obtain fundamental and formalized knowledge about natural processes, is still in its infancy. In this review, we explore how the scientific community can increasingly leverage ML techniques to achieve scientific discoveries. We observe that the applicability and opportunity of ML depends strongly on the nature of the problem domain, and whether we have full (e.g., turbulence), partial (e.g., computational biochemistry), or no (e.g., neuroscience) a-priori knowledge about the governing equations and physical properties of the system. Although challenges remain, principled use of ML is opening up new avenues for fundamental scientific discoveries. Throughout these diverse fields, there is a theme that ML is enabling researchers to embrace complexity in observational data that was previously intractable to classic analysis and numerical investigations. © 2024, CC BY.

关键词： Deep learning

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Clustering big data for novel health care system

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AIP Conference Proceedings 2023年第1期2587卷

作者： R. Saravanakumar S. P. Maniraj Anindita Das Barshan Santanu Das Hasibul Hasan Malik Bader Alazzam Dept. of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India Dept. of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Tamilnadu Chennai India Dept. of Medicine Dhaka Medical College Hospital Dhaka Bangladesh Dept. of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Dept. of Applied Nutrition and Food Technology Islamic University Bangladesh Faculty of Computer Science and Informatics Amman Arab University Jordan

In research, the relevance of data science in health care is being explored. It has been proposed that the health-care sector benefit from a Nutrition Prescription System. In the proposed model, the Map Reduce, Enhanced K-Mean clustering technique was used. Actual datasets are represented by rectangular rectangles, which contain the health care centre dataset and frequency dataset generated by the map reduction method. Operations in original data flow are represented by the elliptical boxes. Patients will be able to use this technique to access specific and specialized health care institution. As a consequence, consumers will get better services that are tailored to their specific requirements. Patients would have secure access to planned system because of backup facilities. Because diagnosis has no side effects, the concept of nutrition prescription system is better than that of medicine prescription system. In suggested dietary prescription system, patient selects symptoms. The patient is provided information on his nutritional deficits, requirements and food supply. In addition, the patient is provided additional nutritional data stored in an unstructured dataset. The outcome was derived from the data set. In milliseconds, the report also shows the time spent on traditional and contemporary duties.

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Local Competition and Uncertainty for Adversarial Robustness in Deep Learning

arXiv

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arXiv 2020年

作者： Alexos, Antonios Panousis, Konstantinos P. Chatzis, Sotirios Department of Electrical and Computer Engineering University of Thessaly Volos Greece Dept. of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Dept. of Electrical Eng. Computer Eng. Informatics Cyprus University of Technology Limassol Cyprus

This work attempts to address adversarial robustness of deep networks by means of novel learning arguments. Specifically, inspired from results in neuroscience, we propose a local competition principle as a means of adversarially-robust deep learning. We argue that novel local winner-takes-all (LWTA) nonlinearities, combined with posterior sampling schemes, can greatly improve the adversarial robustness of traditional deep networks against difficult adversarial attack schemes. We combine these LWTA arguments with tools from the field of Bayesian non-parametrics, specifically the stick-breaking construction of the Indian Buffet Process, to flexibly account for the inherent uncertainty in data-driven modeling. As we experimentally show, the new proposed model achieves high robustness to adversarial perturbations on MNIST and CIFAR10 datasets. Our model achieves state-of-the-art results in powerful white-box attacks, while at the same time retaining its benign accuracy to a high degree. Equally importantly, our approach achieves this result while requiring far less trainable model parameters than the existing state-of-the-art. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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