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Optimized deep convolutional neural network with a long short-term memory framework for automated myocardial infraction detection in electrocardiogram images using Grey Wolf Optimizer

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JOURNAL OF ELECTRONIC IMAGING 2025年第1期34卷

作者： Murugan, Vinoth Panigrahy, Damodar SRM Inst Sci & Technol Coll Engn & Technol Dept Elect & Commun Engn Kattankulathur Tamil Nadu India

An electrocardiogram (ECG) serves as an essential tool in the medical evaluation of cardiac diseases. An ECG signal over a period may be utilized to detect a myocardial infarction (MI). This study is based on the development of an optimized deep convolutional neural network (deep CNN) with a long short-term memory (LSTM) framework for multilabel classification with the help of a single-lead ECG signal. The Chebyshev filter is used in the initial stage of the framework to remove the noise present in the ECG signal. The filtered signal is windowed and converted into images using optimum time-frequency domain conversion techniques. The images are given input to the optimized deep CNN with the LSTM framework to acquire the spatial and temporal attributes for the MI classification. The optimized deep CNN with LSTM is obtained by optimizing the important hyperparameters of CNN to provide a lesser fitness value using the Grey Wolf Optimization approach. The proposed optimized deep CNN with LSTM framework is compared with pre-trained models (AlexNet, SqueezeNet, GoogleNet, ResNet18, and MobileNetV2), deep CNN, deep CNN with gated recurrent unit, and existing techniques. The proposed optimized deep CNN with LSTM framework produced an overall classification accuracy of 99.21%, sensitivity of 99.28%, specificity of 99.13%, precision of 98.30%, recall of 99.28%, an F1 score of 98.74%, and a G-mean of 99.22% for 10-fold cross-validation, and it outperforms some of the existing methods.

关键词： myocardial infarction detection deep convolutional neural network long short-term memory pre-trained models Grey Wolf optimizer

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Vehicle load identification based on bridge response using deep convolutional neural network

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2025年第3期24卷 1235-1252页

作者： Hussain, Sadaqat Hussain, Syed M. Xin, Yu Wang, Zuo-Cai Hefei Univ Technol Dept Civil Engn Hefei Anhui Peoples R China Hefei Univ Technol Anhui Prov Rd & Bridge Inspect Engn Res Ctr Hefei Anhui Peoples R China Hefei Univ Technol Dept Civil Engn Hefei 230009 Anhui Peoples R China

Ensuring bridge safety and longevity through precise vehicle load characterization is crucial for maintaining structural integrity and efficiency. This research highlights the efficacy of deep convolutional neural networks (DCNNs) in analyzing bridge responses. The proposed methodology incorporates abridge model developed in ANSYS APDL, integrated subsequently with Universal Mechanism (UM) software for simulating vehicle-bridge interactions, considering factors such as vehicle load, speed, road surface roughness, and ambient noise. To validate the accuracy and robustness of the DCNN models, the study involved training and testing them on diverse datasets, each comprising 250 randomized samples for various parameters such as load, speed, and surface roughness. Two popular deep learning frameworks, ResNet and Inception models, were employed to assess their capability to identify these critical vehicle-bridge interaction characteristics accurately. The results reveal that the Inception model excelled in vehicle load identification, achieving an impressive validation accuracy of 99.9%. In contrast, ResNet outperformed in vehicle speed identification with a99% validation accuracy, surpassing Inception's 97.9%. Both models exhibited remarkable resilience to noise, with road surface roughness identification demonstrating exceptional performance, including aperfect 100% training accuracy for ResNet and 99.9% for Inception. Furthermore, to validate the noise robustness of the DCNN model, the load identification is conducted under the effects of measurement noise, and the identification results show that the DCNN model can accurately identify vehicle loads with accuracies of 97.9% at 10 dB and 99.7% at 20 dB, respectively. These findings firmly establish the DCNNs' capability to accurately identify vehicle load, speed, and road surface roughness, emphasizing their potential to significantly improve bridge safety and longevity.

关键词： deep convolutional neural network bridge response vehicle response noise detection vehicle load identification

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HiGMA-DADCN: Hirudinaria granulosa multitropic algorithm optimised double attention enabled deep convolutional neural network for psoriasis classification

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2025年第1期13卷

作者： Soumya, C. S. Jayanna, H. S. Ramaiah Inst Technol Dept Comp Sci & Engn MSRIT Post Bangalore 560054 India Visvesvaraya Technol Univ Belagavi India Visvesvaraya Technol Univ Siddaganga Inst Technol Belagavi India

Psoriasis, a chronic skin condition, holds particular importance in medical classification due to its impact on both physical and mental health. Its visible symptoms such as red, dry patches and persistent itchiness can severely affect a person's quality of life. Although many researchers have worked on psoriasis classification, existing methods often fall short. Common limitations include inaccurate classification, small or insufficient datasets, and high rates of false positives and false negatives. To overcome these challenges, this study introduces a novel approach: the Hirudinaria granulosa multitropic algorithm-based Double Attention deep convolutional neural network (HiGMA-DADCN). This model integrates two key components the HiGMA algorithm and a double attention module designed to enhance segmentation and boost overall classification performance. The HiGMA algorithm plays a crucial role in identifying and extracting the most relevant regions of affected skin through optimal segmentation. Experimental validation was conducted using the DermNet dataset. The proposed model achieved promising results: an accuracy of 95.80%, sensitivity of 95.15%, F1-score of 92.40%, MCC of 0.94, false positive rate of 0.04, and true positive rate of 0.96. Additionally, the model reduces computational complexity while significantly improving classification accuracy, outperforming existing techniques and offering a more reliable approach for psoriasis detection and diagnosis.

关键词： Skin diseases image segmentation psoriasis classification Hirudinaria granulosa multitropic algorithm deep convolutional neural network

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Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques

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JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY 2025年 19卷

作者： Huong, Phan Thi Hien, Lam Thanh Son, Nguyen Minh Tuan, Huynh Cao Nguyen, Thanh Q. Lac Hong Univ Fac Informat Technol Bien Hoa Vietnam Nguyen Tat Thanh Univ Inst Interdisciplinary Social Sci 300A Nguyen Tat Thanh StWard 13Dist 4 Ho Chi Minh City 700000 Vietnam

This study introduces significant improvements in the construction of deep convolutional neural network models for classifying agricultural products, specifically oranges, based on their shape, size, and color. Utilizing the MobileNetV2 architecture, this research leverages its efficiency and lightweight nature, making it suitable for mobile and embedded applications. Key techniques such as depthwise separable convolutions, linear bottlenecks, and inverted residuals help reduce the number of parameters and computational load while maintaining high performance in feature extraction. Additionally, the study employs comprehensive data augmentation methods, including horizontal and vertical flips, grayscale transformations, hue adjustments, brightness adjustments, and noise addition to enhance the model's robustness and generalization capabilities. The proposed model demonstrates superior performance, achieving an overall accuracy of 99.53%similar to 100% with nearly perfect precision, recall of 95.7%, and F1-score of 94.6% for both "orange_good" and "orange_bad" classes, significantly outperforming previous models which typically achieved accuracies between 70% and 90%. While the classification performance was near-perfect in some aspects, there were minor errors in specific detection tasks. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. Finally, this study highlights the practical applicability of the proposed model, particularly its easy deployment on resource-constrained devices and its effectiveness in agricultural product quality control processes. These findings affirm the model in this research as a reliable and highly efficient tool for agricultural product classification, surpassing the capabilities of traditional models in this field.

关键词： deep convolutional neural network MobileNetV2 data augmentation feature extraction depthwise separable convolutions linear bottlenecks inverted residuals image classification

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deep convolutional neural network based fractional-order terminal sliding-mode control for robotic manipulators

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NEUROCOMPUTING 2020年 416卷 143-151页

作者： Zhou, Minghao Feng, Yong Xue, Chen Han, Fengling Harbin Univ Sci & Technol Coll Elect & Elect Engn Harbin 150080 Peoples R China Harbin Inst Technol Dept Elect Engn Harbin 150001 Peoples R China RMIT Univ Sch Sci Melbourne Vic 3001 Australia

This paper proposes a deep convolutional neural network (DCNN) based fractional-order terminal sliding-mode (FOTSM) control strategy for tracking control of rigid robotic manipulators. The DCNN based on deep learning (DL) method is utilized to compensate the uncertainties of the system without requirement of presupposed knowledge of their upper-bounds, which makes the designed switching gain much smaller. The chattering phenomena are attenuated by combining the superiorities of a DCNN and a novel FOTSM manifold, and the control performance of rigid robotic manipulators is improved. Meanwhile, the singularity problem is solved by avoiding differentiating the exponential terms. The proposed chattering-free control strategy exhibits much robust performance against parametric uncertainties and external disturbances. Compared with the existing methods, simulations are carried out to validate the proposed methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： deep learning deep convolutional neural network Terminal sliding mode Fractional calculus Robotic manipulators

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deep convolutional neural network-based Henry gas solubility optimization for disease prediction in data from wireless sensor network

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SOFT COMPUTING 2023年 1-12页

作者： Goswami, Chandrashekhar Ragavan, V. K. Senthil Ramesh, Janjhyam Venkata Naga Balajee, J. Doni, A. Ronald Saravanan, T. R. Siva Shankar, S. Amity Univ Amity Sch Engn & Technol ASET Dept CSE Gwalior India St Martins Engn Coll Dept IT Secunderabad Telangana India Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram India Mother Theresa Inst Engn & Technol Dept CSE Chittoor Andhra Prades India Sathyabama Inst Sci & Technol Dept CSE Chennai India SRM Inst Sci & Technol Dept Computat Intelligence Kattankulathur India KG Reddy Coll Engn & Technol Dept CSE Hyderabad India

Wireless sensor networks (WSNs) are the most promising solutions in modern technology for increased reliability, remote monitoring, tracking of objects, etc., that are directly tied to sensor nodes. During transmission, if these data be stolen or manipulated, patients' privacy would be violated, and their life may be compromised. Several researchers have worked on encrypted personal health records (PHRs). Depending upon the leakage of training and deep learning models, certain clients, however, do not want their data to be exchanged with the analytics company, but there are still a few difficulties, such as data leakage during deep learning and training model leaks. A critical topic is how to secure privacy while using deep learning. Initially, the wireless sensors collect the patient's health data, which are encrypted through homomorphic encryption. Further, this study proposed a deep convolutional neural network (DCNN)-based Henry gas solubility (HGS) optimization for disease (COVID-19) prediction. The DCNN-HGS algorithm is used to assess and check the health data of patients to forecast disease and issue timely alerts. To ensure the privacy of health information, we deploy an assured data deletion method, wherein the data holder could choose to deny access to particular users' health data. Comprehensive study and empirical studies are offered to demonstrate the security and efficiency of our proposed strategy.

关键词： Wireless sensor network Image processing deep convolutional neural network Henry gas solubility COVID-19

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deep convolutional neural network model based chemical process fault diagnosis

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COMPUTERS & CHEMICAL ENGINEERING 2018年 115卷 185-197页

作者： Wu, Hao Zhao, Jinsong Tsinghua Univ Dept Chem Engn State Key Lab Chem Engn Beijing Peoples R China Tsinghua Univ Beijing Key Lab Ind Big Data Syst & Applicat Beijing Peoples R China

Numerous accidents in chemical processes have caused emergency shutdowns, property losses, casualties and/or environmental disruptions in the chemical process industry. Fault detection and diagnosis (FDD) can help operators timely detect and diagnose abnormal situations, and take right actions to avoid adverse consequences. However, FDD is still far from widely practical applications. Over the past few years, deep convolutional neural network (DCNN) has shown excellent performance on machine-learning tasks. In this paper, a fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed for chemical process fault diagnosis. The benchmark Tennessee Eastman (TE) process is utilized to verify the outstanding performance of the fault diagnosis method. (c) 2018 Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis deep convolutional neural network Alarm management Tennessee Eastman process

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deep convolutional neural network based secure wireless voice communication for underground mines

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第10期12卷 9591-9610页

作者： Dey, Prasanjit Kumar, Chandan Mitra, Mitrabarun Mishra, Richa Chaulya, S. K. Prasad, G. M. Mandal, S. K. Banerjee, G. CSIR Cent Inst Min & Fuel Res Barwa Rd Dhanbad 826001 Bihar India

A secure wireless voice communication system for underground miners is an essential gadget for efficient and safe mining. Voice over internet protocol is a proven solution for wireless communication in underground mines where other cellular and satellite networks cannot be deployed. However, the wireless network's security is the major issue for the reliable operation of the system. A secure voice communication system has been developed by integrating voice over internet protocol system and deep convolutional neural network (DCNN) based trained model. Experimental results indicated that voice recognition accuracy of the DCNN based developed model was 93.7% for the noiseless environment. In contrast, it was 82.1 and 79% for the existing K-nearest-neighbour (KNN) and support vector machine (SVM) algorithms, respectively. Voice recognition response time of the DCNN, KNN, and SVM algorithms was 178, 220, and 228 ms, respectively. Thus, deployment of the developed secure and robust voice communication system would improve safety and productivity in underground mines.

关键词： convolutional auto-encoder deep convolutional neural network Underground mine VoIP Wireless communication

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deep convolutional neural network for Graphics Forgery Detection in Video

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WIRELESS PERSONAL COMMUNICATIONS 2020年第3期112卷 1763-1781页

作者： Kaur, Harpreet Jindal, Neeru Thapar Inst Engn & Technol ECE Patiala Punjab India

Doctored video generation with easily accessible editing software has proved to be a major problem in maintaining its authenticity. Nowadays, deep neural networks have been recognized as an effective technique in eradicating such troubles and classify the abnormal variations in the videos directly by learning significant features. This white paper is focused on a highly efficient method for the exposure of inter-frame tampering in the videos by means of deep convolutional neural network (DCNN). The proposed algorithm will detect forgery without requiring additional pre-embedded information of the frame. The other significance of pre-existing learning-techniques is that our algorithm classifies the forged frames on the basis of the correlation between the frames and the observed abnormalities using DCNN. The decoders used for batch normalization of input improve the training swiftness. Simulation results obtained on the REWIND and GRIP video dataset with an average accuracy of 98% shows the superiority of the proposed algorithm as compared to the existing one. The efficiency of the proposed algorithm is also tested on YouTube compressed videos.

关键词： deep convolutional neural network Doctored video Forged frames Accuracy

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deep convolutional neural network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第7期40卷 1805-1816页

作者： Gao, Mingjie Fessler, Jeffrey A. Chan, Heang-Ping Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Michigan Dept Radiol Ann Arbor MI 48109 USA

Digital breast tomosynthesis (DBT) is a quasi-three-dimensional imaging modality that can reduce false negatives and false positives in mass lesion detection caused by overlapping breast tissue in conventional two-dimensional (2D) mammography. The patient dose of a DBT scan is similar to that of a single 2D mammogram, while acquisition of each projection view adds detector readout noise. The noise is propagated to the reconstructed DBT volume, possibly obscuring subtle signs of breast cancer such as microcalcifications (MCs). This study developed a deep convolutional neural network (DCNN) framework for denoising DBT images with a focus on improving the conspicuity of MCs as well as preserving the ill-defined margins of spiculated masses and normal tissue textures. We trained the DCNN using a weighted combination of mean squared error (MSE) loss and adversarial loss. We configured a dedicated x-ray imaging simulator in combination with digital breast phantoms to generate realistic in silico DBT data for training. We compared the DCNN training between using digital phantoms and using real physical phantoms. The proposed denoising method improved the contrast-to-noise ratio (CNR) and detectability index (d') of the simulated MCs in the validation phantom DBTs. These performance measures improved with increasing training target dose and training sample size. Promising denoising results were observed on the transferability of the digital-phantom-trained denoiser to DBT reconstructed with different techniques and on a small independent test set of human subject DBT images.

关键词： Training Noise measurement Noise reduction Breast Image reconstruction Phantoms X-ray imaging deep convolutional neural network digital breast tomosynthesis generative adversarial network image denoising microcalcification

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