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Corrigendum to “Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems” Appl. Soft Comput. 132 (2023) 109909

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Applied Soft Computing 2025年 169卷

作者： Hafiz Tayyab Rauf Jiechao Gao Ahmad Almadhor Ali Haider Yu-Dong Zhang Fadi Al-Turjman Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Department of Computer Science University of Virginia USA Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia Department of Computer Science University of Gujrat Gujrat Pakistan School of Informatics University of Leicester Leicester LE1 7RH UK Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin 10 Turkey

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

Search@Home: A commercial off-the-shelf environment for investigating optimization problems

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arXiv 2020年

作者： Fredericks, Erik M. Moore, Jared M. Dept. of Computer Science & Engineering Oakland University RochesterMI48309 United States School of Computing and Information Systems Grand Valley State University AllendaleMI49401 United States

Search heuristics, particularly those that are evaluation-driven (e.g., evolutionary computation), are often performed in simulation, enabling exploration of large solution spaces. Yet simulation may not truly replicate real-world conditions. However, search heuristics have been proven to be successful when executed in real-world constrained environments that limit searching ability even with broad solution spaces. Moreover, searching in situ provides the added benefit of exposing the search heuristic to the exact conditions and uncertainties that the deployed application will face. Software engineering problems can benefit from in situ search via instantiation and analysis in real-world environments. This paper introduces Search@Home, an environment comprising heterogeneous commercial off-the-shelf devices enabling rapid prototyping of optimization strategies for real-world problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Heuristic algorithms

Mind Drone Chasing Using EEG-based Brain computer Interface

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Mind Drone Chasing Using EEG-based Brain Computer Interface

International Conference on Intelligent Environments (IE)

作者： Fatima Ali Al-Nuaimi Rauda Jasem Al-Nuaimi Sara Saaed Al-Dhaheri Sofia Ouhbi Abdelkader Nasreddine Belkacem Dept. Information Systems and Security CIT UAE University Al Ain UAE Dept. Comp. & Network Engineering CIT UAE University Al Ain UAE Dept. Computer Science & Software Eng. CIT United Arab Emirates University Al Ain UAE

ISBN: (数字)9781728161587

ISBN: (纸本)9781728161594

In this paper, we present a new way of controlling drone by using a P300-based brain-computer interface that supports the military field as assistive technology. The main idea is that the drone can be controlled by the soldier's brain activity using electroencephalogram (EEG) to chase other drones and discover hidden enemy areas. We assumed that we have two able-bodied users, the first role played by a user as a soldier aims to control the drone by using brain activity and the other role played by another user as an enemy aims to control manually the drone using Python program. This scenario allowed us to test the ability of chasing the enemy's drone. The results for this application was evaluated by the ability of the user to calibrate very well with the software and the ability of the program to receive and send commands using EEG signal to the drone for execution.

关键词： Drones Electroencephalography Portable computers Electrodes Visualization Brain Software

Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

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Bayesian Neural Networks Uncertainty Quantification with Cub...

International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

Correction to: LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

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Remote Sensing in Earth systems.sciences 2025年第2期8卷 473-473页

作者： Krushnasamy, V. S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Guntur India

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

Hybrid training data for historical text OCR 15

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Hybrid training data for historical text OCR

15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Martinek, Jiri Lenc, Ladislav Kral, Pavel Nicolaou, Anguelos Christlein, Vincent Dept. of Computer Science & Engineering University of West Bohemia Plzen Czech Republic NTIS-New Technologies for the Information Society University of West Bohemia Plzen Czech Republic Pattern Recognition Lab Friedrich-Alexander University Erlangen-Nurnberg Germany

ISBN: (纸本)9781728128610

Current optical character recognition (OCR) systems.commonly make use of recurrent neural networks (RNN) that process whole text lines. Such systems.avoid the task of character segmentation necessary for character-based approaches. A disadvantage of this approach is a need of a large amount of annotated data. This can be solved by sing generated synthetic data instead of costly manually annotated ones. Unfortunately, such data is often not suitable for historical documents particularly for quality reasons. This work presents a hybrid approach for generating annotated data for OCR at a low cost. We first collect a small dataset of isolated characters from historical document images. Then, we generate historical looking text lines from the generated characters. Another contribution lies in the design and implementation of an OCR system based on a convolutional-LSTM network. We first pre-train this system on hybrid data. Afterwards, the network is fine-tuned with real printed text lines. We demonstrate that this training strategy is efficient for obtaining state-of-the-art results. We also show that the score of the proposed system is comparable or even better in comparison to several state-of-the-art systems. © 2019 IEEE.

关键词： Optical character recognition

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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