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A Survey of Distributed Algorithms for Aggregative Games

IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 859-871页

作者： Li, Huaqing Li, Jun Ran, Liang Zheng, Lifeng Huang, Tingwen College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518107 China

Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals. In non-cooperative settings, aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players. In such scenarios, each player's decision is influenced by an aggregation of all players' decisions. Nash equilibrium (NE) seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property. This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology. A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks, directed networks, and time-varying networks. Furthermore, it sorts out the challenges and compares the algorithms' convergence performance. It also delves into real-world applications of distributed optimization techniques grounded in aggregative games. Finally, it proposes several challenges that can guide future research directions. © 2014 Chinese Association of Automation.

关键词： Nash equilibrium

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Drought Prediction in Agriculture with Support Vector Neural Networks: Enhancing Accuracy 8th

Drought Prediction in Agriculture with Support Vector Neural...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Patil, Mithun B. Adhatrao, Ashlesha S. Computer Science and Engineering N. K. Orchid College of Engineering and Technology Maharashtra Solapur India Computer Science and Engineering N K Orchid College of Engineering and Technology Solapur India

ISBN: (纸本)9789819757855

Accurate drought prediction plays a pivotal role in water resource management and agricultural planning. This study delves into the realm of machine learning algorithms to enhance the accuracy of such predictions. The core focus centers on the integration of support vector neural networks (SVNNs) as a means to predict drought occurrences, leveraging data from diverse sources including meteorological, hydrological, and remote sensing. The unique characteristic of SVNNs lies in their amalgamation of support vector machines and neural networks, allowing for the nuanced capture of intricate data relationships. The refinement of feature engineering strategies is instrumental in optimizing the predictive models. These strategies address the challenges posed by imbalanced datasets through the implementation of resampling techniques, paired with careful selection of evaluation metrics. The performance evaluation of SVNNs is conducted through a rigorous assessment against historical drought events, further accentuated by a comparison against conventional methods. To expand the scope, the integration of remote sensing data enriches the models with comprehensive spatiotemporal insights, enhancing their predictive capabilities. Moreover, the research extends its exploration into ensemble techniques and hybrid models, showcasing the versatile potential of machine learning in the domain of drought prediction. The findings of this study unequivocally demonstrate the superiority of the projected method when compared to existing methodologies, particularly in terms of accuracy, precision, and F1-score. The novel drought prediction models put forward in this research hold the potential for unprecedented advancements, significantly enhancing the efficacy of drought mitigation strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

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Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Quantum visual multi-tone secret sharing scheme based on color encoding

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Signal, Image and Video Processing 2025年第7期19卷 1-13页

作者： Liu, Meng-Yao Zhao, Pei-Jun Yan, Bin Pan, Jeng-Shyang Yang, Hong-Mei College of Electronic and Information Engineering Shandong University of Science and Technology Shandong Qingdao266590 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China

Current quantum visual secret sharing (QVSS) schemes are mainly limited to encrypting black-and-white binary images, which is one of their major key problems. To solve this problem, we propose a new quantum visual multi-tone secret sharing (QVMTSS) scheme. In the sharing phase, we first encrypt the whole quantum image using a novel enhanced quantum representation (NEQR). This avoids individual pixel processing while achieving the encryption of the four-tone image. Subsequently, we encode the binary color values to drastically reduce qubit usage during the sharing phase and achieve the encryption of a 2t-tone image. In the recovery phase, the secret image is recovered by performing quantum XOR operations based on position. Theoretical analysis and experimental findings confirm the scheme’s security and feasibility. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Color image processing

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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis

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IEEE Access 2025年 13卷 74078-74091页

作者： Jeba Priya, S. Anand Deva Durai, C. Subathra, M.S.P. Thomas George, S. Jeyabose, Andrew Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India King Khalid University College of Computer Science Department of Computer Science Abha Saudi Arabia Karunya Institute of Technology and Sciences Department of Biomedical Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Robotics Engineering Tamil Nadu Coimbatore India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal India

Parkinson's disease (PD) is a prevalent neurological disorder that significantly impacts posture and gait, leading to movement abnormalities due to malfunctions in the brain and nervous system. Gait signals are essential for identifying PD, and various techniques have been employed for classification, with a focus on spatiotemporal factors. Additionally, cognitive monitoring systems for PD symptoms have been developed. Recent advancements involve decomposing gait signals using techniques such as empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) to streamline data for improved computational efficiency. Machine learning (ML) and deep learning (DL) algorithms are widely used to enhance classification accuracy. This study integrates decomposition techniques with ML algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), decision trees (DTs), and k-nearest neighbors (k-NNs), as well as DL algorithms such as long short-term memory (LSTM), bidirectional long short-term memory (LSTM), and convolutional neural networks (CNNs), for PD classification. The combination of VMD with the 1D-CNN achieved the highest accuracy, sensitivity, and specificity, with values of 99.1 %, 100 %, and 100 %, respectively. This finding suggests a promising approach for further research in this field. The optimized VMD-1D-CNN combination demonstrated significant potential for accurately diagnosing PD based on gait dynamics. The successful application of these methods highlights the importance of advanced signal processing techniques in improving the detection and management of neurological disorders. © 2013 IEEE.

关键词： Wavelet decomposition

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Swin Transformer and Momentum Contrast (MoCo) in Leukemia Diagnostics: A New Paradigm in AI-Driven Blood Cell Cancer Classification

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IEEE Access 2025年 13卷 82441-82466页

作者： Jain, Eshika Kaushik, Pratham Kukreja, Vinay Ati, Modafar Hariharan, Shanmugasundaram Ahuja, Vandana Bhattacherjee, Abhishek Kumar Kaushal, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Abu Dhabi University Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Telangana Hyderabad501218 India Swami Vivekanand Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Banur140601 India Lovely Professional University Department of Computer Science Engineering Punjab Phagwara144411 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Applications Punjab Rajpura140401 India

Acute Lymphoblastic Leukemia (ALL) is a fast-growing blood cancer that requires prompt diagnosis for effective treatment. Automated image diagnostics offer potential solutions but often lack clinical robustness. Despite their widespread use in medical imaging, Convolutional Neural Networks (CNNs) struggle to differentiate morphologically similar ALL subtypes due to limited context and feature discrimination. Moreover, integrating contrastive self-supervised learning with hierarchical attention-based models remains underexplored in hematologic malignancy classification. This study aims to develop a robust, automated classification model for ALL subtypes using peripheral blood smear images, employing advanced feature extraction through the Swin Transformer framework, combined with Momentum Contrast (MoCo) for contrastive learning and a Bidirectional Encoder Transformer for classification. The Swin Transformer’s patch-based embedding and multi-level attention enhance feature discrimination across ALL subtypes, while MoCo generates distinct embeddings, minimizing overlap between cell types. BiET is employed to classify the refined feature vectors, leveraging self-attention mechanisms to improve classification accuracy. The model achieved an overall classification accuracy of 92.5%, with the precision of 90.3%, a recall of 91.1%, and an F1-score of 90.7% across four classes (Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B). Class-specific performance metrics indicate that Malignant Pre-B achieved the highest F1-score of 92.4%. The MoCo framework reduced contrastive loss from 0.5 to 0.097 for benign cells, enhancing feature discrimination. An ablation study revealed that omitting the dynamic queue decreased accuracy by 5%, underscoring its importance for effective feature learning. This approach can be extended to other hematologic malignancies, with potential for further improvement using larger datasets and real-time diagnostic workflows to support p

关键词： Diseases

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A computer-Aided Diagnosis System for the Detection of Parkinson’s Disease 8th

A Computer-Aided Diagnosis System for the Detection of Pa...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Abhijith, K.P. Sarath, R. Santhosh, Partha Mohan, Jesna Abraham, Bejoy Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Kerala Trivandrum India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Trivandrum India

ISBN: (纸本)9789819721467

Parkinson’s disease (PD) is a neurological condition that worsens over time and causes accidental or uncontrolled movements, stiffness, and problems with balance and coordination. Usually, symptoms are minor, to begin with, and worsen with time. As the condition develops, individuals may have trouble speaking and moving about. They could also have mental and psychological issues, such as fatigue, sadness, sleeplessness, and cognitive impairment. The development of useful, technologically supported methods for monitoring the development of PD symptoms in everyday life has the potential to change disease assessment and hasten diagnosis. The creation of simple, technology-based techniques for tracking PD symptoms over time in daily life has the potential to revolutionize disease evaluation and speed up diagnosis. The proposed work aims to detect Parkinson’s disease from a patient’s vocal features at a very early stage. This can be achieved by training machine learning models to detect the vocal characteristics distinct in patients with PD from the data of patients who are already diagnosed with Parkinson’s Disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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