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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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Thoracic-net: Explainable artificial intelligence (XAI) based few shots learning feature fusion technique for multi-classifying thoracic diseases using medical imaging

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Multimedia Tools and Applications 2025年第9期84卷 5397-5433页

作者： Bhosale, Yogesh H. Patnaik, K. Sridhar Zanwar, S.R. Singh, Saket Kr. Singh, Vandana Shinde, U.B. Department of Computer Science & Engineering Birla Institute of Technology Ranchi Mesra India India India India

Background & Need: The early detection of thoracic diseases and COVID-19 (coronavirus disease) significantly limits propagation and increases therapeutic outcomes. This article focuses on swiftly distinguishing COVID-19 patients with 10 chronic thoracic illnesses from healthy examples. The death rates of COVID-19-confirmed patients are rising due to chronic thoracic illnesses. Method: To identify thoracic illnesses (Consolidation, Tuberculosis, Edema, Fibrosis, Hernia, Mass, Nodule, Plural-thickening, Pneumonia, Healthy) from X-ray images with COVID-19, we provide an ensemble-feature-fusion (FFT) deep learning (DL) model. 14,400 chest X-ray images (CXRI) of COVID-19 and other thoracic illnesses were obtained from five public sources and applied UNet-based data augmentation. High-quality images were intended to be provided under the CXR standard. To provide model parameters and feature extractors, four deep convolutional neural networks (CNNs) with a proprietary CapsNet as the backbone were employed. To generate the ensemble-fusion classifiers, we suggested five additional USweA (Unified Stacking weighted Averaging)-based comparative ensemble models as an alternative to depending solely on the findings of the single base model. Additionally, USweA enhanced the models' performance and reduced the base error-rate. USweA models were knowledgeable of the principles of multiple DL evaluations on distinct labels. Results: The results demonstrated that the feature-fusion strategy performed better than the standalone DL models in terms of overall classification effectiveness. According to study results, Thoracic-Net significantly improves COVID-19 context recognition for thoracic infections. It achieves superior results to existing CNNs, with a 99.75% accuracy, 97.89% precision, 98.69% recall, 98.27% F1-score, shallow 28 CXR zero-one loss, 99.27% ROC-AUC-score, 1.45% error rate, 0.9838 MCC, (0.98001, 0.99076) 95% CI, and 5.708 s to test individual CXR. This suggested USweA m

关键词： COVID-19

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Automatic Query Generation Based on Adaptive Naked Mole-Rate Algorithm

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Multimedia Tools and Applications 2025年第16期84卷 16249-16280页

作者： Kinikar, Mayura Saleena, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Pune India

In the growing information retrieval (IR) world, selecting suitable keywords and generating queries is important for effective retrieval. Modern database applications need a sophisticated interface for automatically updating the connections between users and databases. Most database applications are intelligent;however, some may be complex to understand when generating queries for effective retrieval. Therefore, this paper develops a new Adaptive Naked Mole-Rate algorithm (ANMR) for an automatic query generation (AQG) based IR system. The query generation approach primarily generates the query with expanded keywords to enhance IR. Modified sealion optimization (MSO) is applied to select several features. The selected features are combined in the feature fusion process using the Fuzzy based Search and Rescue approach (FSR). Similarity matching is performed using the hybrid cosine and Jaccard similarity measures. At last, the ranking process is performed using the Dynamic Global Local Attention Network based on Capsules (DGLANC). The developed AQG-ANMR system improves the performance of the entire information retrieval system. Then, to analyze the performance, the proposed approach is implemented in the python platform and evaluated in terms of accuracy, recall, precision, and F1-Score by employing TREC-3 and CISI datasets. Besides, the performance of the proposed approach is compared with that of state-of-the-art approaches. Finally, the simulated results clearly showed that the proposed approach outperformed the state-of-the-art approaches better. The maximum accuracy attained by the proposed approach is 97.2% with AQG-ANMR and 94.69% without AQG-ANMR for the TREC 3 dataset. In the same way, using the CISI dataset, the proposed approach reached a maximum accuracy of 98.6% with AQG-ANMR and 95.8% without AQG-ANMR, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Information retrieval

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Bidirectional Legendre memory unit: bidirectional memory for person authentication combining voice and online signature

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Neural Computing and Applications 2025年第3期37卷 1541-1563页

作者： Kumar, Rohitesh Ghosh, Rajib Department of Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Patna800005 India

With the increasing popularity of smart portable electronic gadgets, voice-based online person verification systems have become prevalent. However, these systems are susceptible to attacks where illegitimate individuals exploit the recorded voices of legitimate users, leading to false confirmations—spoofing attacks. To overcome this limitation, this article presents an innovative solution by combining speech and online handwritten signatures to mitigate the risks associated with spoofing attacks in voice-based authentication systems because a person has to be present in front of the system to produce an online handwritten signature. To accomplish this objective, this work proposes a novel bidirectional Legendre memory unit (BLMU), a type of recurrent neural network (RNN), for person authentication (verification) and recognition. The Legendre memory unit (LMU) is an innovative memory cell for RNNs that efficiently retains temporal/non-temporal sequential information over a long period with minimal resources. It achieves information orthogonalization by solving coupled ordinary differential equations (ODEs) and leveraging Legendre polynomials, ensuring effective data representation. The proposed framework for person authentication and recognition comprises seven convolution layers, four BLMU layers, two dense layers, and one output layer. The performance of the proposed BLMU-based deep learning framework has been evaluated on a self-generated/private dataset of combined feature matrix of voice signals and online handwritten signatures in the Devanagari script. To assess performance, experiments have also been conducted using various RNN architectures, such as LSTM, BLSTM, and ordinary differential equation recurrent neural network (ODE-RNN), to have a performance comparison with the proposed BLMU-based deep learning (DL) framework. The results demonstrate the superiority of the proposed BLMU-based DL framework in enhancing the accuracy of person verification systems,

关键词： Recurrent neural networks

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Automated breast nuclei feature extraction for segmentation in histopathology images using Deep-CNN-based gaussian mixture model and color optimization technique

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Multimedia Tools and Applications 2025年 1-27页

作者： Murmu, Anita Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Hematoxylin and eosin (H&E) staining are the key sources for identifying breast cancer patterns with different colors and shapes of nuclei cells for segmenting histopathology nucleus images. In nucleus cells, the color categorization of hematoxylin is stained blue, whereas cytoplasmic cells of eosin are stained pink. This color dissimilarity in the nucleus cells indicates different structures of the cellular and cancer-specific cells, which need to detect on time is a challenge for accurate diagnosis. To overcome this issue, a Deep Convolution Neural Network (Deep-CNN) based Unet framework with Transfer Learning (TL) is proposed for segmenting histopathology breast cancer images. Specifically, the Deep-CNN framework has been used to extract features automatically from input images, and select the features by using the TL technique for minimizing the target tasks to train it faster. Furthermore, a deep convolutional-based Gaussian Mixture Model (GMM) is used to normalize the shapes and merge the nucleus cell pixels for color optimization. The proposed method has been tested on Kaggle’s 2018 Data science Blow histopathology breast image dataset. The experimental results of the proposed scheme have been evaluated by performance evaluation matrices, namely accuracy, Jaccard coefficient, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and F1-score. The proposed approach outperforms compared with different state-of-the-art models on the same dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

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Verification of news and certificate, and plagiarism detector

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Multimedia Tools and Applications 2025年 1-28页

作者： Jain, Pushpak Anand, Dewansh Rajni Behal, Vansh Kapoor, Vipul Sharma, Neha Jindal, Neeru Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Patiala India

Fake news, Fake certification, and Plagiarism are the most common issues arising these days. During this COVID-19 situation, there are a lot of rumors and fake news spreading and some of us are using fake certification to avoid vaccination and plagiarism became the most common topic during online studies. It is found that several apps and websites are present for the detection of false news and plagiarism. However, there is no single website for the detection of fake certificates. Moreover, nothing is made that consists of all these features in one platform. So, this proposed work develops a website for detecting fake news, Fake certificate, and plagiarism detector. The proposed method uses Natural Language Processing techniques for detecting NPC (news, plagiarism, and certificate). The proposed work uses image processing with some Python libraries like OpenCV in all the detections and that makes it quite interesting. For detecting NPC (news, plagiarism, and certificate) the algorithm is built using a k-mean model, logistic regression, and neural network. The proposed work uses the datasets that are trained using the count vectorizer method for the detection and to get its accuracy which will be tested using machine learning algorithms. The proposed method has shown an accuracy rate of 94.28% on Logistics Regression in comparison to existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fake news Machine learning Natural language processing Plagiarism Social media platform

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BGRU-MTRA: bilinear GRU networks with multi-path temporal residual attention for suspicious activity recognition

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Neural Computing and Applications 2025年第1期37卷 185-212页

作者： Pandey, Ajeet Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Suspicious activity recognition (SAR) is an active research field in computer vision and image processing due to the rapid demand for intelligent video surveillance systems. However, current automated systems focus too much on the temporal dynamics of events in videos and ignore the importance of spatial dynamics. The existing methods for SAR are complex and demand substantial resources. Hence, this paper proposes a novel trade-off architecture for SAR integrating the strengths of the multi-path temporal residual attention (MTRA) module with the bilinear-gated recurrent unit (BGRU) module. The MTRA module combines spatial feature extraction (SFE) with temporal residual attention network (TRAN) blocks for improving SAR by enhancing resilience to variations in object sizes, viewpoints, and motion patterns. It selects relevant action features, addresses vanishing gradient issues, and reduces spatial dimension. The BGRU module preserves spatial features, improving the model’s ability to recognize fine-grained features and complex temporal patterns for effectively recognizing suspicious activities. The BGRU-MTRA system yields recognition accuracy of 93.28%, 98.97%, 99.86%, and 99.66%, and 48.42% on the benchmark hybrid crime action (HCA), UT interaction (UTI), CAVIER, hockey fight (HF), and the most challenging UCF-crime datasets, respectively. Through comprehensive experiments, it is clear that the proposed method outperforms state-of-the-art methods over benchmark datasets with reduced parameters. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Spatio-temporal data

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A moment-based pooling approach in convolutional neural networks for breast cancer histopathology image classification

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Neural Computing and Applications 2025年第2期37卷 1127-1156页

作者： Kumar, Arvind Singh, Chandan Sachan, Manoj Kumar Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India Department of Computer Science Punjabi University Punjab Patiala147002 India

Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variability in the tissue images, often resulting in misdiagnosis. We propose four CNN models using a novel Zernike moments (ZMs)-based pooling approach, which replaces the existing global average pooling for the effective classification, referred to as CZP-CNN, MZP-CNN, SPMZP-CNN, and SPCZP-CNN. We utilize the ZMs-based pooling at three intermediate and last convolution layers. Of the four models, the two models, SPMZP-CNN and SPCZP-CNN, employ spatial pyramidal structures of feature maps to represent global and local characteristics of the feature maps. They effectively integrate the ZMs of various orders derived from the pyramidal representation of a feature map, representing the global and local aspects of shape and fine morphological details. The ability of ZMs is fully utilized to capture shape and morphological information available in the feature map using only a few low-order moments, which adds to the robustness of the proposed approach. Detailed experimental results on the publicly available BreaKHis dataset with four magnification levels and on the high-resolution BACH dataset demonstrate the proposed models’ effectiveness, robustness, and generalization ability. Among these proposed models, the complex ZMs-based SPCZP-CNN model achieves an impressive mean classification accuracy of 94.16% on the BreaKHis dataset and overall best binary and multiclass classification accuracy of 96.25% and 93.75% on the BACH dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Breast Cancer Histopathological Image Classification Using PIP-Net 1

Breast Cancer Histopathological Image Classification Using P...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Aryan Titoriya, Ankit Kumar Singh, Maheshwari Prasad SRM Institute of Science &Technology Computer Science and Engineering Chennai India National Institute of Technology Computer Science and Engineering Patna India

ISBN: (纸本)9798350365016

Histopathological images serve as pivotal assets within the domain of breast cancer diagnosis, demanding profound comprehension for precise interpretation. This paper introduces a histopathological image classification framework employing the Patch-Based Intuitive Prototype Network (PIP-Net). PIP-Net finds important features without the need for manual part annotations by using prototype patches and self-supervised learning mechanisms. Consequently, PIP-Net adeptly discerns novel patterns within images, thereby facilitating informed therapeutic interventions. Leveraging the 'BrakeHis' dataset, encompassing histopathological images of breast cancer, this paper evaluates PIP-Net's efficacy across varying magnification levels, specifically at 40x, 100x, 200x, and 400x. The classification accuracies achieved are 98.49%, 94.53%, 99.33%, and 98.08% for each respective magnification level. The integration of prototypical elements within images serves to enhance correlation with human visual perception. This shows the paper's dual commitment to not only attaining superior classification performance but also aligning with interpretability and intuitive principles integral to medical image analysis. © 2024 IEEE.

关键词： Self-supervised learning

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