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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Ajoy Mahanta Md. Oshama Bin Nur Rakibul Islam Saiful Azad Shadman Wadith Mufti Mahmud M. Shamim Kaiser Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science Nottingham Trent University Nottingham UK Computing and Informatics Research Centre Nottingham Trent University Nottingham UK Medical Technologies Innovation Facility Nottingham Trent University Nottingham UK Institute of Information Technology Jahangirnagar University Savar Dhaka Bangladesh

Metaheuristic algorithms play a pivotal role in solving complex optimization problems. This study addresses a common challenge faced by such algorithms, i.e., getting trapped in local optima. This problem hampers the efficacy of metaheuristic algorithms in achieving globally optimal solutions. This limitation is also observed in Hybrid Henry Gas Solubility Optimization Algorithm (HHGSO)-a new metaheuristic based algorithm-which employs a cluster-based approach for population management. While this approach fosters exploration by diversifying the search process, it lacks adaptiveness in cluster assignments and the potentiality of hybridization with other algorithms. These shortcomings hinder the algorithm's ability to effectively escape local optima and harness the strengths of diverse optimization techniques. To mitigate these challenges, this study proposed an Enhanced HHGSO (EHHGSO) algorithm, an advanced hybridization of HHGSO. Here, a novel backtracking based technique is incorporated that can detect the trapping into local optima and can escape from it. Empirical findings substantiate the efficacy of EHHGSO in comparison to its ancestor, HHGSO for various benchmark functions.

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Implicit Neural Representation in Medical Imaging: A Comparative Survey

Implicit Neural Representation in Medical Imaging: A Compara...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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Attention-Based Multiscale Localization for Bone Age Assessment

Attention-Based Multiscale Localization for Bone Age Assessm...

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IEEE International Symposium on Biomedical Imaging

作者： Kyunghee Jung Toan Duc Nguyen Duc-Tai Le Junghyun Bum Chang-Hwan Son Hyunseung Choo Department of Superintelligence Engineering Sungkyunkwan University Korea College of Computing and Informatics Sungkyunkwan University Korea Sungkyun AI Research Institute Sungkyunkwan University Korea Department of Software Science & Engineering Kunsan National University Korea Department of Electrical and Computer Engineering Sungkyunkwan University Korea

Bone Age Assessment (BAA) is crucial for the biological maturity evaluation of children. Developing automated techniques of BAA has gained a lot of attention from both academia and medicine. This paper presents a novel deep-learning-based BAA method including refining and multi-scale processing of hand X-ray images. The refining step removes unnecessary background and noises in the images, resulting in a high-quality dataset. Such image refinement is beneficial for Region of Interest (ROI) localization step with self-attention mechanism. The localization model is trained with multiscale hand X-ray images separately to obtain multiple ROIs for each image. Eventually, the multiscale ROIs are used as complementary features of an image in training a regression model for BAA. We evaluate the performance of the proposed method using 2017 RSNA pediatric bone age challenge dataset. The results show mean absolute error of 3.52, which is 24.9% lower than state-of-the-art results.

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Yu Li Rana Das Cuong Duong Teng-Teeh Lim Timothy Haithcoat Ilker Ersoy Chi-Ren Shyu Department of Electrical Engineering and Computer Science University of Missouri Columbia MO US Biological Engineering University of Missouri Columbia MO US Plant Science and Technology Extension University of Missouri Columbia MO US Institute for Data Science and Informatics University of Missouri Columbia US Institute for Data Science and Informatics University of Missouri Columbia MO US Electrical Eng. and Computer Science Dept. Institute for Data Science and Informatics University of Missouri Columbia MO US

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making.

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iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence 2nd

iConDet2: An Improved Conjunctivitis Detection Portable Heal...

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2nd International Conference on Applied Intelligence and informatics, AII 2022

作者： Adak, Mainak Chatterjee, Aayushman Roy, Nilanjana Dutta Mahmud, Mufti Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9783031248009

Conjunctivitis is one of the common and contagious ocular diseases which affects the conjunctiva of the human eye. Both the bacterial and viral types of it can be treated with eye drops and other medicines. It is important to diagnose the disease at its early stage to realise the connection between it and other diseases, especially COVID-19. Mobile applications like iConDet is such a solution that performs well for the initial screening of Conjunctivitis. In this work, we present with iConDet2 which provides an advanced solution than the earlier version of it. It is faster with a higher accuracy level (95%) than the previously released iConDet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Routing on the Shortest Pairs of Disjoint Paths

Routing on the Shortest Pairs of Disjoint Paths

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IFIP Networking Conference

作者： Pé ter Babarczi Gá bor Ré tvá ri Lajos Ró nyai Janos Tapolcai Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Algebra Institute for Computer Science and Control Institute of Mathematics Budapest University of Technology and Economics E&#x00F6 tv&#x00F6 s Lorand Research Network Budapest Hungary

Recent trends point towards communication networks will be multi-path in nature to increase failure resilience, support load-balancing and provide alternate paths for congestion avoidance. We argue that the transition from single-path to multi-path routing should be as seamless as possible in order to lower the deployability barrier for network operators. Therefore, in this paper we are focusing on the problem of routing along the shortest pairs of disjoint paths between each source-destination pair over the currently deployed link-state routing architecture. We show that the union of disjoint path-pairs towards a given destination has a special structure, and we propose an efficient tag encoding scheme which requires only one extra forwarding table entry per router per destination. Our numerical evaluations demonstrate that in real-world topologies usually only 4 bit tags are sufficient in the packet headers to route on the disjoint path-pairs. Finally, we show that our tags automatically encode additional paths beyond the shortest pair of disjoint paths, including the shortest paths themselves, which enables incremental deployment of the proposed method.

关键词： Focusing Tagging Routing Data structures Market research Load management Encoding

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A Bibliometric Analysis and research Landscape of Artificial Intelligence in Education

A Bibliometric Analysis and Research Landscape of Artificial...

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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Samuel-Soma M. Ajibade Bayan Issa Muhammed Basheer Jasser Farrukh Hassan Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Kayode Akinlekan Akintoye Almighty Cortezo Tabuena Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkiye Faculty of Informatics Engineering University of Aleppo Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia Department of Computer Science The Federal Polytechnic Ado Ekiti Ekiti State Nigeria College of Arts and Sciences University of the City of Valenzuela Philippines

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or $64.5 \%$ of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or $6.44 \%$ of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students’ learning patterns.

关键词： Deep learning Education Bibliometrics Neural networks Market research Prediction algorithms Stakeholders Artificial intelligence Socioeconomics Meteorology

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Integrated Artificial Intelligence System with existing system for Predicting the Disease

Integrated Artificial Intelligence System with existing syst...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Umamaheswaran, S.K. Gehlot, Anita Singh, Bhagwant Alanya-Beltran, Joel Dixit, Nrupura Bansal, Rohit Sri Sai Ram Engineering College Department of Mathematics TN Chennai India Uttaranchal University Uttaranchal Institute of Technology India India SS Informatics Cluster School of Computer Science Uttarakhand Dehradun248007 India Universidad Tecnológica Del Perú Electronic Department Peru Kalyan421301 India Vaish College of Engineering Department of Management Studies Rohtak India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

It is crucial to employ AI technology when developing beneficial uses. It may help with the definition of several useful software packages that will be used to create the healthcare prediction process. computer vision and virtualization are important components in the creation of intelligent healthcare apps. It may help in the collecting and analyzing of various healthcare data so that prompt responses can be given. AI is crucial in the detection and forecasting of illnesses. This plan well. coming up method may improve the precision of illness diagnosis. Sensors connected to wireless networking may help identify each person's behaviour. The detection of muscle activity and body language may be assisted by sensor systems and neural networks. It also comprises a range of technological advancements designed to enhance the data processing procedure. The effectiveness of using numerous efficient technologies to build a heath predictive model is the main focus of this research. In order to increase its effectiveness for application areas, AI employs a range of sensing approaches. The health-care system is significantly impacted by another research subject. The advancement of modelling techniques for identifying biomedical mistakes may be helped by deep learning. Transceivers that gather current environmental data allow for things that tend. © 2022 IEEE.

关键词： Forecasting

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Foundation Models in Electrocardiogram: A Review

arXiv

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arXiv 2024年

作者： Han, Yu Liu, Xiaofeng Zhang, Xiang Ding, Cheng University of Chinese Academy of Sciences Beijing China Department of Biomedical Informatics and Data Science Yale University Connecticut United States Department of Computer Science University of North Carolina at Charlotte Charlotte United States Department of Biomedical Engineering Georgia Institute of Technology Atlanta United States

The electrocardiogram (ECG) is ubiquitous across various healthcare domains, such as cardiac arrhythmia detection and sleep monitoring, making ECG analysis critically essential. Traditional deep learning models for ECG are task-specific, with a narrow scope of functionality and limited generalization capabilities. Recently, foundation models (FMs), also known as large pre-training models, have fundamentally reshaped the scheme of model design and representation learning, enhancing the performance across a variety of downstream tasks. This success has drawn interest in the exploration of FMs to address ECG-based medical challenges concurrently. This survey provides a timely, comprehensive and up-to-date overview of FMs for large-scale ECG-FMs. First, we offer a brief background introduction to FMs. Then, we discuss the model architectures, pre-training methods, and adaptation approaches of ECG-FMs from a methodology perspective. Despite the promising opportunities of ECG-FMs, we also outline the challenges and potential future directions. Overall, this survey aims to provide researchers and practitioners with insights into the research of ECG-FMs on theoretical underpinnings, domain-specific applications, and avenues for future exploration. © 2024, CC BY.

关键词： Electrocardiograms

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Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery

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IEEE Access 2025年 13卷 103880-103897页

作者： Ching-Chun Chang Kai Gao Shuying Xu Anastasia Kordoni Christopher Leckie Isao Echizen Information and Society Research Division National Institute of Informatics Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan Department of Psychology Lancaster University Lancaster U.K. Department of Computing and Information Systems University of Melbourne Melbourne VIC Australia Graduate School of Information Science and Technology University of Tokyo Tokyo Japan School of Multidisciplinary Sciences Graduate University for Advanced Studies (SOKENDAI) Kanagawa Japan

Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject’s subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes a conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoor threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine’s behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern as the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability.

关键词： Data models Soft sensors Vehicle dynamics Reverse engineering Predictive models Optimization Cyberspace Cybernetics Weapons Trojan horses

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