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

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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Exploiting Retina Biometric Fused with Encoded Hash for Designing Watermarked Convolutional Hardware IP Against Piracy

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SN computer science 2024年第8期5卷 1-17页

作者： Chaurasia, Rahul Sengupta, Anirban Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal India Department of Computer Science and Engineering Indian Institute of Technology Indore India

The convolution layer in a convolutional neural network (CNN) is highly computationally intensive. It is crucial to design reusable low-cost hardware IP for convolutional layer for enabling hardware-based feature extraction. However, the involvement of fake IP vendor/untrustworthy broker in the integrated circuit (IC) supply chain, makes these IPs susceptible to the threat of piracy. The proposed approach presents high- level synthesis (HLS) driven watermarking methodology for designing low-cost and secure convolutional hardware IP. The presented watermarking approach employs complier-driven high-level transformation and exploits retinal signature fused with the encoded hash for piracy detective countermeasure. The proposed approach, therefore, firstly performs compiler-driven high-level transformation in order to optimize the design latency, followed by embedding the watermark of an authentic IP vendor. The generated watermark in the form of encoded hardware watermarking constraints (digital evidence) is covertly embedded into the resulting optimized design during the register allocation module of HLS. The proposed approach achieves the following: (i) optimized and secure design for convolutional hardware IP, (ii) robust detection of pirated IP at zero design cost overhead, (iii) significantly lower probability of coincidence (in the range of 1.3E−06 to 1.2E−09) indicating stronger digital evidence and higher tamper tolerance (in the range of 2.64E+460 to 9.60E+698) than recent approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional layer Encoded hash Hardware security HLS IP design Retina biometrics

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A bilateral assessment of human activity recognition using grid search based nonlinear multi-task least squares twin support vector machine

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Multimedia Tools and Applications 2024年第36期83卷 84121-84140页

作者： Thakur, Ujwala Prajapati, Amarjeet Vidyarthi, Ankit Department of Computer Science Engineering & amp Information Technology Jaypee Institute of Information Technology Noida Sector-62 Uttar Pradesh Noida201309 India

The recognition of individual activity has proven its importance in many application areas. Even after the pandemic crisis worldwide, the remote monitoring of human actions and their activities has increased a lot. In such situations, the smart sensors are utilized to capture the details remotely, share, and process intelligently using the computer-assisted machine. This paper introduces the new variant of the machine learning classifier, named Grid Search Based Nonlinear Multi-task Least Squares Twin Support Vector Machine (GMLT-SVM), to recognize human actions using sensory data. The proposed approach uses the grid search for hyperparameter tuning with a Nonlinear kernel for Multi-task classification in a twin support vector machine. This not only increases the efficacy of the model but also reduces the computation time for finding the optimal values of the hyper-parameters. The proposed approach when experimented with the sensory data of Human Action Recognition (HAR) gives an accuracy of 96.8%. The comparative analysis of the proposed method with existing other ML classifiers gives an increase in the accuracy of 94.2±2.6%. The experimental results suggest that the gained results are statistically significant enough to recognize human actions with sensory data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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FMeAR: FACS Driven Ensemble Model for Micro-Expression Action Unit Recognition

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SN computer science 2024年第5期5卷 598页

作者： Chauhan, Anjaly Jain, Shikha Department of Computer Science & amp Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Micro-expressions (MEs) are fleeting involuntary facial movements, which occur frequently when people attempt to conceal their emotions. Since human eyesight cannot detect fleeting and slight changes in facial expression, it is difficult to determine when spontaneous and low-intensity micro-expression occurs. The paper presents FMeAR which is a Facial action coding scheme (FACS) driven ensemble model for the micro-expression action unit recognition. Considering the simultaneous triggering of multiple facial AUs (action units), the problem is designed as a multiple-binary-class framework. First, each frame of the video is extracted and pre-processed thoroughly to eliminate any noise in the input. Then, features are extracted using an LBP descriptor from the pre-processed frames. To reduce the processing time, the size of the feature vector is reduced using PCA. Finally, reduced features are processed using a multiple ensemble model (comprising of Support Vector Machine, Linear Discriminant Analysis, and Decision Tree classifiers) to identify the micro-expressions action units. The presented model, FMeAR, outperforms several baseline frameworks and shows an accuracy of 95.3% for the CASME dataset, 95.9% for CASME-II, and 92.6% for MEVIEW. Intensive experiments demonstrated the effectiveness and generalization of FMeAR emphasizing its potential for various applications such as deception detection, depression detection, medical diagnosis, and many more. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Action units Ensemble-model FACS FMeAR Micro-expression

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Medical Image Classification Using DL-based Feature Extraction in IoMT

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Recent Patents on engineering 2024年第9期18卷 e220223213881-e220223213881页

作者： Thimmapuram, Madhuri Salvadi, Shoba Rani Pallati, Narsimhulu Aluvalu, Rajanikanth Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Computer Science & Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India

Aim: Recent advances in Artificial Intelligence (AI) and the addition of Deep Learning (DL) have made it possible to analyse both real-time and historical data from the Internet of Things (IoT). Recently, IoT technology has been implemented in healthcare schemes as IoMT to aid in medical diagnoses. Medical image classification is useful for predicting and identifying serious diseases at an early stage, which is crucial in the diagnostic process. Background: When it comes to managing, treating, and preventing illness, medical photographs are an essential element of a patient’s health record. However, it is a difficult issue in computer-based diagnostics to classify images using efficient characteristics. Objective: The patent study aimed to develop a deep learning-based classification model for feature extraction. Methods: Levy flight optimization is employed to pick the weight for the classification model optimally. At the end of the day, the optimal weight led to a better classification result and a higher degree of precision when analyzing medical photos for disease. Results: We tested the proposed results in MATLAB and compared them with conventional methods of classification. The suggested model’s best results include 97.71% accuracy on a brain dataset and 97.2% accuracy on an Alzheimer’s disease dataset. Conclusion: The proposed algorithm’s high rate of convergence proves that it can successfully balance the exploration and exploitation phases by avoiding capturing in local optimization and classifying thresholds rapidly. In light of the need for improved accuracy, precision, and computational speed in clinical picture classification, a novel approach based on soft sets has been presented. © 2024 Bentham science Publishers.

关键词： Extraction

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A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, Xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

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Secure data authentication and access control protocol for industrial healthcare system

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4853-4864页

作者： Gupta, Daya Sagar Mazumdar, Nabajyoti Nag, Amitava Singh, Jyoti Prakash Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais UP Amethi229304 India Department of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India Department of Computer Science and Engineering Central Institute of Technology Kokrajhar BTR Kokrajhar India Department of Computer Science and Engineering National Institute of Technology Patna Patna India

Because of recent COVID-19 epidemic, the Internet-of-Medical-Things (IoMT) has acquired a significant impetus to diagnose patients remotely, regulate medical equipment, and track quarantined patients via smart electronic devices installed at the patient’s end. Nevertheless, the IoMT confronts various security and privacy issues, such as entity authentication, confidentiality, and integrity of health-related data, among others, rendering this technology vulnerable to different attacks. To address these concerns, a number of security procedures based on traditional cryptographic approaches, such as discrete logarithm and integer factorization problems, have been developed. All of these protocols, however, are vulnerable to quantum attacks. This paper, in this context, presents a data authentication and access control protocol for IoMT systems that can withstand quantum attacks. A comprehensive formal security assessment demonstrates that the proposed algorithm can endure both current and future threats. In terms of data computing, transmission, and key storage overheads, it also surpasses other related techniques. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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A geometric-approach based Combinatorial Transformative Scalogram analysis for multiclass identification of pathologies in a voice signal

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Multimedia Tools and Applications 2024年 1-21页

作者： Khumukcham, Ranita Nongmeikapam, Kishorjit Department of Electronics and Communication Engineering Indian Institute of Information Technology Mantripukhri Manipur Imphal795002 India Department of Computer Science and Engineering Indian Institute of Information Technology Mantripukhri Manipur Imphal795002 India

Many researchers have preferred non-invasive techniques for recognizing the exact type of physiological abnormality in the vocal tract by training machine learning algorithms with feature descriptors extracted from the voice signal. However, until now, most techniques have been limited to classifying whether a voice is normal or abnormal. It is crucial that the trained Artificial Intelligence (AI) be able to identify the exact pathology associated with voice for implementation in a realistic environment. Another issue is the need to suppress the ambient noise that could be mixed up with the spectra of the voice. Current work proposes a robust, less time-consuming and non-invasive technique for the identification of pathology associated with a laryngeal voice signal. More specifically, a two-stage signal filtering approach that encompasses a score-based geometric approach and a glottal inverse filtering method is applied to the input voice signal. The aim here is to estimate the noise spectra, to regenerate a clean signal and finally to deliver a completely fundamental glottal flow-derived signal. For the next stage, clean glottal derivative signals are used in the formation of a novel fused-scalogram which is currently referred to as the "Combinatorial Transformative Scalogram (CTS)." The CTS is a time-frequency domain plot which is a combination of two time-frequency scalograms. There is a thorough investigation of the performance of the two individual scalograms as well as that of the CTS *** classification metrics are used to investigate performance, which are: sensitivity, mean accuracy, error, precision, false positive rate, specificity, Cohen’s kappa, Matthews Correlation Coefficient, and F1 score. Implementation of the VOice ICar fEDerico II (VOICED) standard database provided the highest mean accuracy of 94.12% with a sensitivity of 93.85% and a specificity of 97.96% against other existing techniques. The current method performed well despite the d

关键词： Frequency domain analysis

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Joint Rain Streaks & Haze Removal Network for Object Detection

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computers, Materials & Continua 2024年第6期79卷 4683-4702页

作者： Ragini Thatikonda Prakash Kodali Ramalingaswamy Cheruku Eswaramoorthy K.V Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Department of Computer Science and Engineering National Institute of TechnologyWarangal506004India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKurnool518008India

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

关键词： Image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)

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A study of learning models for COVID-19 disease prediction

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2581-2600页

作者： Jain, Sakshi Roy, Pradeep Kumar School of Information Technology Vellore Institute of Technology TN Vellore632014 India Department of Computer Science and Engineering Indian Institute of Information Technology Surat Gujarat Surat394190 India

Coronavirus belongs to the family of Coronaviridae. It is responsible for COVID-19 communicable disease, which has affected 213 countries and territories worldwide. Researchers in computational fields have been active in proposing techniques to filter the information and recommendations about this disease and provide surveillance in controlling this outbreak. Researchers used Chest X-ray images, abdominal Computed Tomography scans, and Tweet datasets for building machine learning and deep learning-based models for COVID-19 predictions and forecasting purposes. Accuracy, sensitivity, specificity, precision, and F1-measure are the five primary evaluation criteria researchers employ to evaluate the quality of their study. This article summarises research works on COVID-19 based on machine learning and deep learning models. The analysis of these research works, along with their limitations and source of datasets, will give a quick start for future research to arrive at a defined direction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Coronavirus

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