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Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

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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

Opinion dynamics with backfire effect and biased assimilation

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arXiv 2019年

作者： Chen, Xi Tsaparas, Panayiotis Lijffijt, Jefrey de Bie, Tijl Dept. of Electronics and Information Systems IDLab Ghent University Department of Computer Science and Engineering University of Ioannina

The democratization of AI tools for content generation, combined with unrestricted access to mass media for all (e.g. through microblogging and social media), makes it increasingly hard for people to distinguish fact from fiction. This raises the question of how individual opinions evolve in such a networked environment without grounding in a known reality. The dominant approach to studying this problem uses simple models from the social sciences on how individuals change their opinions when exposed to their social neighborhood, and applies them on large social networks. We propose a novel model that incorporates two known social phenomena: (i) Biased Assimilation: the tendency of individuals to adopt other opinions if they are similar to their own;(ii) Backfire Effect: the fact that an opposite opinion may further entrench someone in their stance, making their opinion more extreme instead of moderating it. To the best of our knowledge this is the first model that captures the Backfire Effect. A thorough theoretical and empirical analysis of the proposed model reveals intuitive conditions for polarization and consensus to exist, as well as the properties of the resulting opinions. Copyright © 2019, The Authors. All rights reserved.

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Adaswarm: augmenting gradient-based optimizers in deep learning with swarm intelligence

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arXiv 2020年

作者： Mohapatra, Rohan Saha, Snehanshu Coello, Carlos A. Coello Bhattacharya, Anwesh Dhavala, Soma S. Saha, Sriparna Bengaluru India The Dept. of Computer Science and Information Systems APPCAIR BITS Pilani K K Birla Goa Goa Campus India The Department of Computer Science CINVESTAVIPN Mexico City Mexico Ikerbasque Spain The Department of Physics CSIS BITS Pilani Pilani India ML Square Bengaluru India Department of Computer Science and Engineering Indian Institute of Technology Patna India

This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially weighted Momentum Particle Swarm Optimizer (EMPSO), is proposed. The ability of AdaSwarm to tackle optimization problems is attributed to its capability to perform good gradient approximations. We show that, the gradient of any function, differentiable or not, can be approximated by using the parameters of EMPSO. This is a novel technique to simulate GD which lies at the boundary between numerical methods and swarm intelligence. Mathematical proofs of the gradient approximation produced are also provided. AdaSwarm competes closely with several state-of-the-art (SOTA) optimizers. We also show that AdaSwarm is able to handle a variety of loss functions during backpropagation, including the maximum absolute error (MAE). Copyright © 2020, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

Extending signature-based intrusion detection systems with Bayesian abductive reasoning

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arXiv 2019年

作者： Ganesan, Ashwinkumar Parameshwarappa, Pooja Peshave, Akshay Chen, Zhiyuan Oates, Tim Dept. of Computer Science & Electrical Engineering UMBC Maryland United States Dept. Of Information Systems UMBC Maryland United States

Evolving cybersecurity threats are a persistent challenge for system administrators and security experts as new malwares are continually released. Attackers may look for vulnerabilities in commercial products or execute sophisticated reconnaissance campaigns to understand a target's network and gather information on security products like firewalls and intrusion detection / prevention systems (network or host-based). Many new attacks tend to be modifications of existing ones. In such a scenario, rule-based systems fail to detect the attack, even though there are minor differences in conditions / attributes between rules to identify the new and existing attack. To detect these differences the IDS must be able to isolate the subset of conditions that are true and predict the likely conditions (different from the original) that must be observed. In this paper, we propose a probabilistic abductive reasoning approach that augments an existing rule-based IDS (snort [29]) to detect these evolved attacks by (a) Predicting rule conditions that are likely to occur (based on existing rules) and (b) able to generate new snort rules when provided with seed rule (i.e. a starting rule) to reduce the burden on experts to constantly update them. We demonstrate the effectiveness of the approach by generating new rules from the snort 2012 rules set and testing it on the MACCDC 2012 dataset [6]. Copyright © 2019, The Authors. All rights reserved.

关键词： Bayesian networks

State of the Art of Audio- and Video-Based Solutions for AAL

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arXiv 2022年

作者： Aleksic, Slavisa Atanasov, Michael Agius, Jean Calleja Camilleri, Kenneth Čartolovni, Anto Climent-Pérez, Pau Colantonio, Sara Cristina, Stefania Despotovic, Vladimir Ekenel, Hazım Kemal Erakin, Ekrem Florez-Revuelta, Francisco Germanese, Danila Grech, Nicole Sigurðardóttir, Steinunn Gróa Emirzeoğlu, Murat Iliev, Ivo Jovanovic, Mladjan Kampel, Martin Kearns, William Klimczuk, Andrzej Lambrinos, Lambros Lumetzberger, Jennifer Mucha, Wiktor Noiret, Sophie Pajalic, Zada Pérez, Rodrigo Rodriguez Petrova, Galidiya Petrovica, Sintija Pocta, Peter Poli, Angelica Pudane, Mara Spinsante, Susanna Salah, Albert Ali Santofimia, Maria Jose Islind, Anna Sigríður Stoicu-Tivadar, Lacramioara Tellioğlu, Hilda Zgank, Andrej Leipzig University of Applied Sciences Germany TU Wien Austria Department of Anatomy University of Malta Malta Centre for Biomedical Cybernetics Faculty of Engineering Department of Systems and Control Engineering Malta Catholic University of Croatia Croatia Universidad de Alicante Spain Institute of Information Science and Technologies The National Research Council of Italy Italy Department of Systems and Control Engineering University of Malta Malta University of Luxembourg Luxembourg Dept. of Computer Engineering Istanbul Technical University Turkey Reykjavik University School of Computer Science Iceland Sağlık Bilimleri Fakültesi Fizyoterapi ve Rehabilitasyon Karadeniz Technical University Research Information System Turkey TU Sofia Bulgaria Singidunum University Belgrade Serbia University of South Florida United States SGH Warsaw School of Economics Poland Department of Communication and Internet Studies Cyprus University of Technologies Cyprus Technical University of Vienna Austria VID Specialized University Oslo Norway Getafe University Hospital Spain Riga Technical University Latvia University of Zilina Slovakia Università Politecnica Marche Italy Utrecht University Netherlands Universidad de Castilla-La Mancha Spain Reykjavik University Iceland University Politehnica Timisoara Romania University of Maribor Slovenia

It is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may

关键词： computer vision

WiFi-based real-time breathing and heart rate monitoring during sleep

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arXiv 2019年

作者： Gu, Yu Zhang, Xiang Liu, Zhi Ren, Fuji School of Computer and Information Hefei University of Technology China Dept. of Mathematical and Systems Engineering Shizuoka University Japan Dept. of Information Science & Intelligent Systems Univ. of Tokushima Japan

Good quality sleep is essential for good health and sleep monitoring becomes a vital research topic. This paper provides a low cost, continuous and contactless WiFi-based vital signs (breathing and heart rate) monitoring method. In particular, we set up the antennas based on Fresnel diffraction model and signal propagation theory, which enhances the detection of weak breathing/heartbeat motion. We implement a prototype system using the off-shelf devices and a real-time processing system to monitor vital signs in real time. The experimental results indicate the accurate breathing rate and heart rate detection performance. To the best of our knowledge, this is the first work to use a pair of WiFi devices and omnidirectional antennas to achieve real-time individual breathing rate and heart rate monitoring in different sleeping postures. Copyright © 2019, The Authors. All rights reserved.

关键词： Patient monitoring

WiFi-based Real-time Breathing and Heart Rate Monitoring during Sleep

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WiFi-based Real-time Breathing and Heart Rate Monitoring dur...

IEEE Global Communications Conference

作者： Yu Gu Xiang Zhang Zhi Liu Fuji Ren School of Computer and Information Hefei University of Technology China Dept. of Mathematical and Systems Engineering Shizuoka University Japan Dept. of Information Science & Intelligent Systems Univ. of Tokushima Japan

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

关键词： Fresnel reflection Sleep apnea Monitoring Heart rate Wireless fidelity Real-time systems Antennas

Use of Wikipedia categories on information retrieval research: A brief review 18

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Use of Wikipedia categories on information retrieval researc...

5th Spanish Conference on information Retrieval, CERI 2018

作者： Tramullas, Jesús Garrido-Picazo, Piedad Sánchez-Casabón, Ana I. Dept. of Library and Information Science University of Zaragoza Spain Dept. of Computer Science and Systems Engineering University of Zaragoza Spain Dept. of Library and Information Science University of Zaragoza Spain

ISBN: (纸本)9781450365437

Wikipedia categories, a classification scheme built for organizing and describing Wikpedia articles, are being applied in computer science research. This paper adopts a systematic literature review approach, in order to identify different approaches and uses of Wikipedia categories in information retrieval research. Several types of work are identified, depending on the intrinsic study of the categories structure, or its use as a tool for the processing and analysis of other documentary corpus different to Wikipedia. information retrieval is identified as one of the major areas of use, in particular its application in the refinement and improvement of search expressions, and the construction of textual corpus. However, the set of available works shows that in many cases research approaches applied and results obtained can be integrated into a comprehensive and inclusive concept of information retrieval. © 2018 Copyright is held by the authors. Publication rights licensed to ACM.

关键词： Classification (of information)

Blended Multi-modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

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arXiv 2020年

作者： Bodapati, Jyostna Devi Veeranjaneyulu, N. Shareef, Shaik Nagur Hakak, Saqib Bilal, Muhammad Reddy, Praveen Kumar M. Jo, Ohyun Department of CSE Vignan’s Foundation for Science Technology and Research Guntur522213 India Department of IT Vignan’s Foundation for Science Technology and Research Guntur522213 India Faculty of Computer Science University of Northern British Columbia Canada Dept. of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Yongin-si17035 Korea Republic of School of Information Technology and Engineering VIT Vellore632014 India Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. Major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. In order to extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extract different features, fusing them using 1-D pooling, and cross pooling lead to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that, DNN with dropout at input layer converges faster when trained using blended features, than compared to the same model trained using uni-modal deep *** Codes 68T10, 62H30 Copyright © 2020, The Authors. All rights reserved.

关键词： Eye protection