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IEEE Access 2025年 13卷 31659-31659页

作者： Khaoula Taji Ali Sohail Tariq Shahzad Bilal Shoaib Khan Muhammad Adnan Khan Khmaies Ouahada Electronic Systems Information Processing Mechanics and Energy Laboratory Kenitra Faculty of Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science Minhaj University Lahore Lahore Pakistan Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Computer Science Green International University Lahore Pakistan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si Republic of Korea

Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.

关键词： Plant diseases Metaheuristics

Control of Type 1 Diabetes Mellitus using direct reinforcement learning based controller

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Control of Type 1 Diabetes Mellitus using direct reinforceme...

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Denes-Fazakas, Lehel Siket, Mate Kertesz, Gabor Szilagyi, Laszlo Kovacs, Levente Eigner, Gyorgy Óbuda University Physiological Controls Research Center Bécsi street 96/B BudapestH-1034 Hungary Óbuda University Biomatics and Applied Artificial Intelligence Institute John von Neumann Faculty of Informatics Bécsi street 96/B BudapestH-1034 Hungary Software Engineering Institute John von Neumann Faculty of Informatics Óbuda University Hungary Óbuda University Applied Informatics and Applied Mathematics Doctoral School Bécsi street 96/B BudapestH-1034 Hungary Institute for Computer Science and Control Kende utca 13-17 BudapestH-1111 Hungary

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

One of the most challenging area of diabetes research is to provide such automated insulin delivery systems - so called artificial pancreas systems - that have robust and adaptive capabilities in a highly sophysticated way. I.e. they are able to provide robust insulin delivery actions at the beginning of the therapy to satisfy the requirements of the patients without knowing the users daily lifestyle and preferences however adaptive on the short-term to learn these patient specifics to increase the quality of the therapy. One possible solution is the closed-loop systems that have self-learning features. In the present study, we have examined a glucose regulatory problem using direct reinforcement learning based controller. The approach represents the fully automatic insulin administration as the timepoint and the carbohydrate content of the meals were unknown and randomized. We constructed a virtual environment of the patient with type 1 diabetes by applying a mathematical model. Proximal policy optimization learning model with continuous action space was used. Furthermore, we evaluated the effect of different training lengths on the test scenario. © 2022 IEEE.

关键词： Reinforcement learning

Uncertain multi-conceptual information acquisition and fusion for hierarchical classification

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Information Fusion 2026年 125卷

作者： Xiaoyuan Deng Jinhai Li Weiping Ding Xizhao Wang Faculty of Science Kunming University of Science and Technology Kunming 650500 China Data Science Research Center Kunming University of Science and Technology Kunming 650500 China School of Artificial Intelligence and Computer Science Nantong University Nantong 226019 China Faculty of Data Science City University of Macau Macau 999078 China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China

Recently, hierarchical classification has become a hot research problem due to the wide existence of hierarchically structured data in the real world. However, some existing studies on hierarchical classification proposed a series of feature selection methods without considering the design of a stopping mechanism, while others designed the stopping mechanism without taking the feature selection into account. Despite the simplicity of integrating the above two parts, they are usually not compatible with each other, which lead to reduced performance of hierarchical classification models. In this work, we obtain a novel hierarchical classifier by constructing a hierarchical fuzzy concept-cognitive learning model (HFCCLM), in which incremental hierarchical feature selection is realized by the update of inclusion degree of fuzzy concepts, and a stopping mechanism is designed by learning uncertainties of samples matching nodes in a hierarchical tree structure. That is, feature selection and design of a stopping mechanism can be unified in the fuzzy concept-cognitive learning framework. Furthermore, by integrating the uncertainties of set approximation of clue based fuzzy reasoning and clue based fuzzy concept-cognitive learning for stopping samples at appropriate nodes, it significantly enhances the performance of the proposed hierarchical classifier. Finally, experiments are carried out to show the effectiveness of our model in achieving hierarchical classification tasks.

关键词： Hierarchical classification Uncertainty learning Fuzzy multi-concept fusion Feature selection Granular computing

Quantum-Computing for Corrosion Simulation and Prevention: Workflow and Resource Analysis

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arXiv 2024年

作者： Nguyen, Nam Watts, Thomas W. Link, Benjamin Williams, Kristen S. Sanders, Yuval R. Elman, J. Kieferova, Maria Bremner, Michael J. Morrell, Kaitlyn J. Elenewski, Justin Isaacs, Eric B. Johnson, Samuel D. Mathieson, Luke Obenland, Kevin M. Otten, Matthew Sundareswara, Rashmi Holmes, Adam Centre for Quantum Computation and Communication Technology University of Technology SydneyNSW2007 Australia Applied Mathematics Boeing Research & Technology United States HRL Laboratories LLC MalibuCA United States Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia MIT Lincoln Laboratory LexingtonMA United States Department of Physics University of Wisconsin – Madison MadisonWI United States

Corrosion is a pervasive issue that impacts the structural integrity and performance of materials across various industries, imposing a significant economic impact globally. In fields like aerospace and defense, developing corrosion-resistant materials is critical, but progress is often hindered by the complexities of material-environment interactions. While computational methods have advanced in designing corrosion inhibitors and corrosion-resistant materials, they fall short in understanding the fundamental corrosion mechanisms due to the highly correlated nature of the systems involved. This paper explores the potential of leveraging quantum computing to accelerate the design of corrosion inhibitors and corrosion-resistant materials, with a particular focus on magnesium and niobium alloys. We investigate the quantum computing resources required for high-fidelity electronic ground-state energy estimation (GSEE), which will be used in our hybrid classical-quantum workflow. Representative computational models for magnesium and niobium alloys show that 2292 to 38598 logical qubits and (1.04 to 1962) × 1013 T-gates are required for simulating the ground-state energy of these systems under the first quantization encoding using plane waves basis. © 2024, CC BY.

关键词： Qubits

New Moments Based Fuzzy Similarity Measure for Text Detection in Distorted Social Media Images 5th

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New Moments Based Fuzzy Similarity Measure for Text Detectio...

5th Asian Conference on Pattern Recognition, ACPR 2019

作者： Roy, Soumyadip Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783030414030

A trend towards capturing or filming images using cellphone and sharing images on social media is a part and parcel of day to day activities of humans. When an image is forwarded several times in social media it may be distorted a lot due to several different devices. This work deals with text detection from such distorted images. In this work, we consider images pass through three mobile devices on WhatsApp social media, which results in four images (including the original image) Unlike the existing methods that aim at developing new ways, we utilize the results detected by the existing ones to improve performances. The proposed method extracts Hu moments and fuzzy logic from detected texts of images. The similarity between text detection results given by three existing text detection methods is studied for determining the best pair of texts. The same similarity estimation is then used in a novel way to remove extra background or non-texts and restoring missing text information. Experimental results on own dataset and benchmark datasets of natural scene images, namely, MSRA-TD500, ICDAR2017-MLT, Total-Text, CTW1500 dataset and COCO datasets, show that the proposed method outperforms the existing methods. © Springer Nature Switzerland AG 2020.

关键词： Fuzzy logic

Local-photon model of the momentum of light

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arXiv 2024年

作者： Waite, Gabriel Hodgson, Daniel Lang, Ben Alapatt, Varghese Beige, Almut The School of Physics and Astronomy University of Leeds LeedsLS2 9JT United Kingdom Centre for Quantum Computation and Communication Technology Australia Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia The School of Physics and Astronomy University of Nottingham NottinghamNG7 2RD United Kingdom Universitat de València Paterna Spain

Recently we introduced a local photon approach for modelling the quantised electromagnetic field in position space. Using this approach, we define the momentum of light in this paper as in quantum mechanics as the generator for spatial translation. Afterwards, we analyse the momentum dynamics of photonic wave packets which transition from air into a denser dielectric medium. Our analysis shines new light onto the Abraham-Minkowski controversy which highlights the intricacies involved in the characterisation of the momentum of the electromagnetic field. Although our results align with Minkowski’s theory and with the definition of the canonical momentum of light in quantum electrodynamics, there are also some crucial differences. © 2024, CC BY.

关键词： Momentum

AUTOMATIC GENERATION OF A LARGE DICTIONARY WITH CONCRETENESS/ABSTRACTNESS RATINGS BASED ON A SMALL HUMAN DICTIONARY

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arXiv 2022年

作者： Ivanov, Vladimir Solovyev, Valery Faculty of Computer Science and Software Engineering Innopolis University 1 Universitetskaya st. Republic of Tatarstan Innopolis420500 Russia Linguistic Research and Education Center Research Laboratory 'Intellectual Technologies of Text Management' Kazan Federal University 2 Tatarstan Street Republic of Tatarstan Kazan420021 Russia

Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods. © 2022, CC BY-NC-ND.

关键词： Extrapolation

A Brief Review of Explainable Artificial Intelligence in Healthcare

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SSRN

SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis

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arXiv 2025年

作者： Zubair, Muhammad Hussain, Muzammil Al-Bashrawi, Mousa Ahmad Bendechache, Malika Owais, Muhammad Interdisciplinary Research Center for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Information Systems and Operations Management King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia ADAPT Research Centre School of Computer Science University of Galway GalwayH91 TK33 Ireland Department of Mechanical and Nuclear Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF’s vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF’s broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance. This review organizes key knowledge, outlines challenges, and highlights opportunities, guiding researchers, clinicians, and developers in advancing MMIF for routine clinical use and promotin

关键词： Neurophysiology

A Synthesis-based Tool Path Planning Approach for Machining Operations

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Procedia CIRP 2021年 104卷 918-923页

作者： Tristan Schäfer Jim A. Bergmann Rafael Garcia Carballo Jakob Rehof Petra Wiederkehr Software Engineering by Algorithms and Logic Department of Computer Science TU Dortmund University Otto-Hahn-Straße 12 44227 Dortmund Germany Virtual Machining Department of Computer Science TU Dortmund University Otto-Hahn-Straße 12 44227 Dortmund Germany Faculty of Mechanical Engineering TU Dortmund University Leonhard-Euler-Straße 5 44227 Dortmund Germany

The planning of machining operations for components using CAM requires careful consideration by highly trained personnel, with rising complexity in part requirements. This work proposes a novel domain-specific planning technique using Combinatory Logic Synthesis to generate a multitude of tool paths. By analyzing current state CNC path planning algorithms, motion primitives are identified and implemented in a modular representation using software components. Thus, a large variety of path planning strategies can be generated, while maintaining a multitude of different boundary constraints and productivity metrics. These solutions are subjected to geometric physically-based simulations in order to validate their ranking and compliance.

关键词： computer-Aided Manufacturing path planning simulation Combinatory Logic Synthesis