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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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Named Entity Recognition for Protecting Sensitive Data using Hybrid CNN 5

Named Entity Recognition for Protecting Sensitive Data using...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Gowr, P. Sheela Kumar, N. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Named entity recognition is a natural language processing technique that effectively recognizes and categorizes named entities in a document. The named entity recognition helps to bring out dynamic information about a document or gather critical data to store in a database. Deep learning helps to develop over time, while NLP examines the structure and standards of language and produces an automated system that can discern meaning from text. Mining the essential entities in a text helps identify related data, which is vital when functioning with enormous datasets. The proposed system has a feature that can retrieve and identify sensitive data such as PAN numbers, bank account numbers, and Aadhar numbers from unstructured text data.. The proposed model is designed using Hybrid CNN and it attains 95% F1-Score, 97.5% precision, and 98.3% recall. © 2023 IEEE.

关键词： Natural language processing systems

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Machine learning electron-phonon interactions in two-dimensional semiconducting materials: The case of zero-point renormalization

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Physical Review Materials 2024年第10期8卷 L101001页

作者： Anubhab Haldar Quentin Clark Marios Zacharias Feliciano Giustino Sahar Sharifzadeh Department of Electrical and Computer Engineering Department of Philosophy Oden Institute for Computational Engineering and Sciences Department of Physics Division of Materials Science and Engineering

We utilize first-principles theory to investigate the role of electron-phonon interactions within a dataset of monolayer materials. Using density functional theory to describe excited-state transitions and the special displacement method to describe the role of phonons, we analyze the relationship between simple physical observables and electron-phonon coupling strength. For over 100 materials, we compute the band gap renormalization due to zero-point vibrational (ZPR) motion as a measure of electron-phonon interactions and train a machine learning model based on physical parameters. We demonstrate that the strength of electron-phonon interactions is highly dependent on the band gap, dielectric constant, and degree of ionicity, all of which can be physically justified. We then apply this model to 1302 2D materials, predicting the ZPR, which for five randomly selected materials tested agree well with the first-principles predictions. This work provides an approach for quantitatively predicting the ZPR as a measure of electron-phonon interactions in 2D materials.

关键词： Electron-phonon coupling Electronic structure 2-dimensional systems Machine learning

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Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction

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Neural Computing and Applications 2024年第23期36卷 14395-14407页

作者： Saraswathi, E. Banu, J. Faritha Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

Agriculture encompasses a way of life and a profession for the general population. Most global traditions and cultures revolve around agriculture. With the help of advanced farming, agriculture may become more profitable, dependable, and able to use resources and time more effectively. The proposed hybrid CGAN using OTSU and SURF (CGAN-OF) model provides a novel framework for plant leaf disease classification. In proposed CGAN-OF model, contrast-limited adaptive histogram equalization is used for image preprocessing and enhancement. The proposed model utilizes OTSU algorithm to speed up the image segmentation without prior knowledge of the images and SURF algorithm to extract the local features using scale-invariant feature transformation. CGAN increases the input plant village dataset using image generation method and identifies the various plant leaf diseases and classifies them. There are three classifications of leaf diseases: fungi, viruses, and bacteria. Furthermore, these classifications contain approximately 300 diseases. The proposed work identified a minimum of 200 diseases in 18,161 major and minor crop species. The investigational investigations are carried out using the Python Jupyter app with the Kaggle Plant Village Dataset and also leaf samples collected from farmers. The proposed framework achieves 99.2% accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image enhancement

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Utilizing attention mechanism with exemplar memory for improving domain adaptive person re-identification

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Multimedia Tools and Applications 2025年第16期84卷 16809-16834页

作者： Bhunia, Sugam Kr. Bakshi, Sambit Mukherjee, Imon Computer Science and Engineering Indian Institute of Information Technology Kalyani West Bengal Kalyani741235 India Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India

In an Unsupervised Domain Adaptation (UDA) task, extracted features from the entire image lead to a negative transfer of irrelevant knowledge. An attention mechanism may highlight the suitable transferable region of an image. Here in this paper, the representative power of the deep re-identification (re-ID) network of an exemplar memory-based domain adaptive unsupervised Person Re-identification (PRId) model is increased with our chosen optimal Bottleneck Attention Module (BAM). Performance enhancement of the deep re-ID network itself has been attempted by very few research works, suffering from a misinterpretation of global features or non-determination of local features due to the presence of occlusions, background interference, view-point variations, low camera resolution, etc. The proposed approach shows remarkable performance improvement in these specific challenging scenarios. Our attention-enabled unsupervised PRId model is evaluated in a cross-platform environment by labeled training in the DukeMTMC-reID dataset and unlabeled target testing in the Market-1501 dataset. The proposed method also enhances the overall performance of our baseline architecture. It provides a 2.12% relative increment in mean Average Precision (mAP) value, 0.93% increment in Top-1 value, 0.33% increment in Top-10 value, and 0.53% increment in Top-20 value respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Statistical tests

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Optimized Neural Network for Automated Cardiovascular Disease Classification: Meta-heuristic Enabled Model

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SN computer science 2025年第1期6卷 1-14页

作者： Kusuma, S. Jothi, K.R. Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) signals. The model consists of two main steps: feature extraction using a refined Lion Algorithm with New Mutated evaluation (LA-NM) and classification through an ensemble approach combined with a Neural Network (NN). The proposed model significantly outperforms traditional classifiers. At 80% training data, it achieves accuracy improvements of 8.4% to 3.11% over Neural Network (NN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and ensemble models. At 90% training data, it shows precision improvements ranging from 1.03% to 0.31% better than the comparison models and specificity improvements ranging from 13.92% to 0.52%. These results underscore the effectiveness of the model and suggest its potential for further development in personalized treatment recommendations based on patient history. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ensemble models Feature extraction Heart disease classification Neural network Optimization

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Cryptography with optimal deep learning-based authentication scheme for preserving anonymity in telecare medical information system

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Multimedia Tools and Applications 2025年第10期84卷 8311-8330页

作者： Sheik, Syed Amma Durai, Saleem School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India

The Telecare Medicine Information System (TMIS) revolutionizes healthcare delivery by integrating medical equipment and sensors, facilitating proactive and cost-effective services. Accessible online, TMIS empowers patients and practitioners to manage medical data efficiently, ensuring timely care provision and streamlined record maintenance. Recognizing security challenges inherent in this interconnected healthcare landscape, researchers have devised a two-factor authentication solution, increasingly leveraging password-based authentication via smart cards. This article presents the development of a tailored remote authentication system, the Deep Learning-Based Online Threat Detection with Key Agreement (DLOTDKA-MAP), designed specifically for TMIS. Comprising two critical phases, the DLOTDKA-MAP framework employs an Arithmetic Optimization Algorithm (AOA) and Deep Belief Network (DBN) model in real-time online threat detection, ensuring swift identification and mitigation of potential security threats. The subsequent phase employs Elliptic Curve Cryptography (ECC) with Schnorr's signature to establish secure data transmission among collaborating entities, preemptively addressing security breaches and ensuring mutual authentication. A comprehensive experimental study validates the presented DLOTDKA-MAP technique, showcasing its superiority over existing approaches and underscoring its role in advancing TMIS security infrastructure. Achieving remarkable performance values, includes an accuracy, precision, recall, and F1 score of 98.57%, this research significantly contributes to safeguarding the confidentiality and integrity of medical data within TMIS, particularly in a federated cloud environment. © 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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Deep feature-support vector machine based hybrid model for multi-crop leaf disease identification in Corn, Rice, and Wheat

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Multimedia Tools and Applications 2025年第8期84卷 4751-4771页

作者： Bhola, Amit Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna Bihar India

Corn, Rice, and Wheat serve as primary staple foods globally, playing a pivotal role in the economies of numerous countries. Despite their paramount importance, these cereal crops face susceptibility to various diseases, posing a significant threat to production. Timely and accurate disease diagnosis is imperative to mitigate these risks. In response, a range of Machine Learning (ML) and Deep Learning (DL) models have been devised to automate the identification of diseases impacting cereal crops. While DL excels with abundant data, traditional ML may outperform DL with limited data. This study proposes a hybrid model integrating deep transfer learning and ML techniques for identifying crop diseases. The model utilizes pre-trained DenseNet201 weights to extract features, which are subsequently fed into a Support Vector Machine (SVM) for identification. Experimental results show that the hybrid model outperforms several benchmark counterparts. The DenseNet201 deep feature combined with SVM exhibits superior performance, achieving an impressive accuracy score of 87.23% with just 20.2M parameters. The model underwent comprehensive performance assessment using precision, recall, F1 score, and overall accuracy metrics. It effectively classifies healthy and diseased categories for each crop, achieving accuracies of 99.82%, 98.75%, and 84.15% for Corn, Wheat, and Rice, respectively. Moreover, the model’s remarkable performance and lightweight nature make it a practical and efficient real-time crop disease detection solution, emphasizing its relevance and significance for farmers and researchers in the agricultural community. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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Efficient breast cancer detection using neural networks and explainable artificial intelligence

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Neural Computing and Applications 2025年第5期37卷 3759-3776页

作者： Murugan, Tamilarasi Kathirvel Karthikeyan, Pritikaa Sekar, Pavithra School of Computer Science Engineering Vellore Institute of Technology Tamilnadu Chennai India

The growing dependence on deep learning models for medical diagnosis underscores the critical need for robust interpretability and transparency to instill trust and ensure responsible usage. This study investigates the efficacy of various explainable artificial intelligence (XAI) techniques in comprehending deep learning models utilized for breast cancer classification from down sampled histopathology images. A comparative assessment of multiple convolutional neural network (CNN) architectures, encompassing standard CNNs, ResNet, VGG-16, and VGG-19, on down sampled images was conducted. The primary goal is to pinpoint the model exhibiting the highest accuracy and subsequently employ three prominent XAI methods—LIME, SHAP, and Saliency Maps—to get insights into the top-performing model. This study identifies VGG-19 as the best-performing model with an accuracy of 92.59% and demonstrates that among various XAI techniques, LIME provides the most accurate and clinically relevant explanations for breast cancer classification from down sampled histopathology images. These findings, validated by medical professionals, enhance the interpretability and reliability of deep learning models in clinical settings, promoting their responsible integration into healthcare practices. This validation was further corroborated through consultation with medical professionals, including doctors specializing in breast cancer diagnosis. This research endeavors to deepen the understanding of the model’s rationale and instill confidence in its outputs. The outcomes of this study hold significant promise in elevating the interpretability and reliability of deep learning models tailored for breast cancer diagnosis, thus facilitating their responsible integration into clinical settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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