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Fractional discrete neural networks with variable order: solvability, finite time stability and synchronization

The European Physical Journal Special Topics 2024年 1-14页

作者： Hioual, Amel Alomari, Saleh Al-Tarawneh, Hassan Ouannas, Adel Grassi, Giuseppe Laboratory of Dynamical Systems and Control University of Oum El-Bouaghi Oum El Bouaghi Algeria Faculty of Science and Information Technology Software Engineering Jadara University Irbid Jordan Department of Data Sciences and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Department of Mathematics and Computer Science University of Oum EL-Bouaghi Oum El Bouaghi Algeria Dipartimento Ingegneria Innovazione Universita del Salento Lecce Italy

Research in the field of dynamic behaviors in neural networks with variable-order differences is currently a thriving area, marked by various significant discoveries. However, when it comes to discrete-time neural networks featuring fractional variable-order nonlocal and nonsingular kernels, there has been limited exploration. This paper stands as one of the initial contributions to this subject, focusing primarily on the topics of stability and synchronization in finite-time within discrete neural networks. The research employs the nabla ABC variable-order difference operator, with a primary approach involving the investigation of a novel Gronwall inequality using the Atangana-Baleanu difference variable-order sum operator. This analysis leads to the development of a uniqueness theorem and a criterion for the stability in finite-time of variable-order discrete neural networks. Furthermore, the requirements stemming from this type of stability and the novel Gronwall inequality serve as the foundation for establishing the conditions necessary for achieving finite-time synchronization in these networks, employing a specific control using state feedback method. Finally, the study utilizes numerical solutions to validate the obtained results.

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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Context-Aware Enhancement and Transformer Network for Image-Text Retrieval

Context-Aware Enhancement and Transformer Network for Image-...

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International Conference on computer Supported Cooperative Work in Design

作者： Zheng Dong Ruijia Zhang Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Image-text matching is an important and challenging task in the field of multimedia analysis, aimed at bridging the semantic gap between visual content and language descriptions. Although this field has significant implications for enhancing cross-modal interaction, most previous work still faces challenges in accurately aligning images with text descriptions, especially when dealing with images containing rich semantic information. To address this issue, we propose a novel context-aware image-text matching model that extracts and summarizes visual region information aligned with multiple text descriptions from a single image. Specifically, we designed an adaptive context-aware self-attention module to extract representations of visual regions and text words. By controlling the complementary semantic relationships within each modality, our model can adaptively capture contextual information for each modality. We then introduce a Transformer-based encoder layer to extract features at multiple levels and aggregate region-level features into image-level features. Finally, fine-grained word-region alignment is conducted to match image features with corresponding text features. To evaluate the effectiveness of our method, we conducted extensive experiments on two benchmark datasets, Flickr30K and MS-COCO. The experimental results show that our model outper-forms several state-of-the-art baselines in image-text matching tasks, demonstrating the effectiveness and practicality of our model in handline cross-modal retrieval tasks.

关键词： Measurement Visualization Adaptation models Federated learning Semantics Transformers Feature extraction Faces Context modeling Cross modal retrieval

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A Review of Machine Learning Algorithms for Detection of Breast Cancer

A Review of Machine Learning Algorithms for Detection of Bre...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Abhay Tale Aditya Barhate Prateek Verma Palash Gourshettiwar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of Machine Learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice

关键词： Support vector machines Machine learning algorithms Sensitivity Reviews Precision medicine Ubiquitous computing Breast cancer Vectors Convolutional neural networks Random forests

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Matching patterns with variables under hamming distance

arXiv

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arXiv 2021年

作者： Gawrychowski, Pawel Manea, Florin Siemer, Stefan University of Wroclaw Faculty of Mathematics and Computer Science Göttingen University Computer Science Department Campus-Institut Data Science Germany Göttingen University Computer Science Department Germany

A pattern α is a string of variables and terminal letters. We say that α matches a word w, consisting only of terminal letters, if w can be obtained by replacing the variables of α by terminal words. The matching problem, i.e., deciding whether a given pattern matches a given word, was heavily investigated: it is NP-complete in general, but can be solved efficiently for classes of patterns with restricted structure. In this paper, we approach this problem in a generalized setting, by considering approximate pattern matching under Hamming distance. More precisely, we are interested in what is the minimum Hamming distance between w and any word u obtained by replacing the variables of α by terminal words. Firstly, we address the class of regular patterns (in which no variable occurs twice) and propose efficient algorithms for this problem, as well as matching conditional lower bounds. We show that the problem can still be solved efficiently if we allow repeated variables, but restrict the way the different variables can be interleaved according to a locality parameter. However, as soon as we allow a variable to occur more than once and its occurrences can be interleaved arbitrarily with those of other variables, even if none of them occurs more than once, the problem becomes intractable. © 2021, CC BY.

关键词： Hamming distance

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RETRACTED ARTICLE: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in Engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in Engineering 2025年

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

The Editors-in-Chief have retracted this article. After publication, concerns were raised about the originality of the study reported. An investigation conducted by the Publisher confirmed that the article contains material that substantially overlaps with a previous study by different authors [1]. In particular, the research methodology, research questions, numerous formulations, and discussion are almost identical with those in [1]. The Editors-in-Chief therefore consider this article redundant. None of the authors have responded to correspondence from the Publisher about this retraction. © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2025.

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Cluster-Based Oversampling with Area Extraction from Representative Points for Class Imbalance Learning

SSRN

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SSRN 2023年

作者： Farou, Zakarya Wang, Yizhi Horváth, Tomáš ELTE – Eötvös Loránd University Faculty of Informatics Institute of Industry-Academia Innovation Department of Data Science and Engineering Pázmány Péter sétány 1/C BudapestH-1117 Hungary Pavol Jozef Šafárik University Faculty of Science Institute of Computer Science Jesenná 5 Košice040 01 Slovakia

Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class. Resampling methods have been used to adjust the class distribution, but they often have limitations for small disjunct minority subsets. This paper introduces AROSS, an adaptive cluster-based oversampling approach that addresses these limitations. AROSS utilizes an optimized agglomerative clustering algorithm with the Cophenetic Correlation Coefficient and the Bayesian Information Criterion to identify representative areas of the minority class. Safe and half-safe areas are obtained using an incremental k-Nearest Neighbor strategy, and oversampling is performed with a truncated hyperspherical Gaussian distribution. Experimental evaluations on 70 binary datasets demonstrate the effectiveness of AROSS in improving class imbalance learning performance, making it a promising solution for mitigating class imbalance challenges, especially for small disjunct minority subsets. © 2023, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Air Purification Robotics using Cloud and Deep Q Networks for Autonomous Systems

Air Purification Robotics using Cloud and Deep Q Networks fo...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Elangovan Guruva Reddy T. S. Balaji Damodhar Yuvaraj S B. Santha Vathani S. Sivakumar N. Malathi Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Management Studies Mahalashmi Women's College of Arts and Science Chennai Tamil Nadu India Department of Computer Science Thanthai Hans Roever College (Autonomous) Perambalur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Rising urban air pollution poses serious health dangers. Through robots, cloud computing, and deep reinforcement learning, this research proposes a new air purification method. The suggested autonomous system analyzes real-time air quality parameters and optimizes air purification tactics using cloud-based data processing. Advanced sensors and purification processes allow robotic agents to intelligently adapt to shifting pollution patterns in dynamic settings. Robots use Deep Q Networks (DQN) to learn and change their purifying tactics based on past data and environmental input. Cloud computing and deep learning improve air purification efficiency and enable real-time pollution response decision-making. The autonomous system is scalable and adaptable to varied urban environments since it requires little human involvement. The cloud-based design lets autonomous agents communicate and coordinate to improve air quality. Experimental findings show that the suggested strategy improves air purification over previous approaches. This system advances environmental robotics and lays the groundwork for intelligent, autonomous systems that handle urban air quality and pollution issues.

关键词： Cloud computing Technological innovation Purification Autonomous systems Robot kinematics Decision making Urban areas

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Feature selection in linear support vector machines via a hard cardinality constraint: A scalable conic decomposition approach

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European Journal of Operational Research 2025年

作者： Bomze, Immanuel D'Onofrio, Federico Palagi, Laura Peng, Bo Faculty of Mathematics and Research Network Data Science University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy VGSCO University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria

In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to an interpretable classification model. The problem is NP-hard due to the presence of the cardinality constraint, even though the original linear SVM amounts to a problem solvable in polynomial time. To handle the hard problem, we first introduce two mixed-integer formulations for which novel semidefinite relaxations are proposed. Exploiting the sparsity pattern of the relaxations, we decompose the problems and obtain equivalent relaxations in a much smaller cone, making the conic approaches scalable. To make the best usage of the decomposed relaxations, we propose heuristics using the information of its optimal solution. Moreover, an exact procedure is proposed by solving a sequence of mixed-integer decomposed semidefinite optimization problems. Numerical results on classical benchmarking datasets are reported, showing the efficiency and effectiveness of our approach. © 2025

关键词： Mixed-integer linear programming

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Virtual entanglement purification via noisy entanglement

arXiv

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arXiv 2024年

作者： Yamamoto, Kaoru Matsuzaki, Yuichiro Suzuki, Yasunari Tokunaga, Yuuki Endo, Suguru NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan Department of Electrical Electronic and Communication Engineering Faculty of Science and Engineering Chuo University 1-13-27 Kasuga Bunkyo-ku Tokyo112-8551 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan

Distributed quantum computation (DQC) is a promising approach for scalable quantum computing, where high-fidelity non-local operations among remote devices are required for universal quantum computation. These operations are typically implemented through state and gate teleportation with the consumption of high-fidelity entanglement prepared via entanglement purification. However, noisy local operations and classical communication (LOCC) limit the fidelity of purified entanglement, thereby degrading the quality of non-local operations. Meanwhile, circuit knitting and cutting, which simulate non-local operations by preparing separable states along with LOCC, has been considered for DQC as an alternative. Although this approach needs no entanglement among remote devices, it requires excessive circuit runs. Here, we present a protocol utilizing virtual operations that purifies noisy entanglement at the level of expectation values. Our protocol offers the following advantages over conventional methods: surpasses the fidelity limit of entanglement purification in the presence of noise in LOCC, requires fewer sampling shots than circuit knitting, and exhibits robustness against infidelity fluctuations in shared noisy Bell states, unlike probabilistic error cancellation. Our protocol bridges the gap between the entanglement-based and circuit-knitting DQC methods, offering a flexible approach to achieve further scalability despite hardware limitations. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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