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Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data

Multimedia Tools and Applications 2025年第13期84卷 11451-11474页

作者： Moradi, MohammadMahdi Hassanzadeh, Alireza Haghanifar, Arman Ko, Seok Bum Faculty of Computer Science and Software Engineering Concordia University MontrealQC Canada Faculty of Electrical Engineering Shahid Beheshti University Tehran Tehran Iran Department of Electrical & Computer Engineering University of Saskatchewan SaskatoonSK Canada

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen

关键词： COVID-19

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Well-to-wheel Energy Consumption and CO2 Emission Comparison of Electric and Fossil Fuel Buses: Tehran Case Study

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International Journal of engineering, Transactions A: Basics 2024年第3期37卷 583-594页

作者： Nikzad, M. Khalilzadeh, R. Rabieic, A. Faculty of Engineering Lorestan University Khorramabad Iran School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Western University LondonON Canada

The development of public transportation is considered a vital issue in reducing traffic as well as urban pollution. City buses play an important role in the city transportation system. In Iran, due to the high average age of city buses, it is necessary to replace the old buses with new ones, To replace the old buses, diesel and CNG, hybrid, and electric buses are proposed as the main alternatives. Global warming and the energy crisis are now considered as two potential serious threats for the world. Therefore, energy consumption and CO2 emissions are examined as two outstanding criteria for comparing candidate buses in this paper. To make an accurate comparison, the amount of energy consumption and CO2 emissions have been calculated based on the well-to-wheel approach. The electric bus well-to-wheel analysis has been done for both electricity generation mix and renewable generation. To perform more accurate calculations and simulations, as a case study, a real driving cycle has been constructed for Tehran. For this approach, a modified micro trip method as a novel solution is presented to synthesize the driving cycle. The results show that due to the high share of fossil power plants (about 92%) in Iran, the use of electric buses in the bus fleet may not have much effect on reducing energy and CO2 eq emissions. By using renewable power plants, the amount of well-to-wheel energy consumption and CO2 emissions decrease significantly (about 56% and 93%, respectively) compared to that for the generation mix. ©2024 The author(s).

关键词： Electric buses

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Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems

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Soft Computing 2024年第7-8期28卷 6307-6326页

作者： Hsu, Chun-Fei Chen, Bo-Rui Department of Electrical Engineering Tamkang University New Taipei City25137 Taiwan Department of Electrical and Computer Engineering Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Although conventional control systems are simple and widely used, they may not be effective for complex and uncertain systems. This study proposes a Hermite broad-learning recurrent neural network (HBRNN) with a wide network structure and an internal feedback loop that enables good approximation of system dynamics without the need for a large number of training parameters. Furthermore, a Hermite broad-learning recurrent neural control (HBRNC) with HBRNN as the main controller is proposed, which requires no prior knowledge about the system dynamics and has no off-line learning phase. All the HBRNN network parameters are updated according to parameter learning laws through the gradient descent approach. To prevent network parameter overtraining of the HBRNN, a discrete-type Lyapunov function is used to determine the least upper bound for the learning rates. Additionally, an adaptive learning rate (ALR) approach is designed to dynamically fine-tune the learning rates within these specified limits, thereby achieving an optimal convergence speed between network parameters and tracking error. Finally, the HBRNC system with ALR is applied to a chaotic circuit and a reaction wheel pendulum, and its effectiveness is validated through simulation and experimentation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Learning systems

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The consequences of eye tracking on brain and heart coherence

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Multimedia Tools and Applications 2024年第38期83卷 86025-86035页

作者： Attar, Eyad Talal Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

When the eye uses the brain and heart, the cardiovascular and nervous systems integrate and interact. Because changes in retinal microcirculation are independent predictors of cardiovascular events, the eye serves as a "display" to the cardiovascular system and brain. The eye, which has two circulatory systems and a rich vascular supply, is a prime candidate for this study and benefits from early damage to the target organ. Eye movements performed during the visual search pose a challenge in identifying critical points in the eye scene. Because it uses different brain pathways and relates to the cardiac cycle, humans’ ability to spot anomalies under challenging circumstances means they are always needed for visual search. ECG (electrocardiogram), electroencephalogram (EEG), and eye tracking can improve visual search training and attention-tracking performance. EEG data can also be analyzed in real time using eye-tracking technology. Previous work has discussed the EEG or ECG concerning attraction during visual search. The eyeball’s movement combined with the ECG in the previous investigation and introduced large electroencephalographic (EEG) artifacts. This assessment aims to (a) identify brain–heart coherent features influenced by the visual search task and (b) discover the behavior of EEG frequency bands and heart rate variability (HRV) features. EEG and ECG were used to analyze and predict inattention in individuals during a visual search task. The EEG determines human brain function and considers to detect the variability in the EEG frequency band. The work proposed a visual search task with EEG and ECG analysis. Five participants recorded EEG and ECG recordings in three different scenarios: rest, gaze tracking, and normal. Statistical evaluation was used to compare EEG and ECG characteristics and Pearson’s correlation was employed for statistical analysis. Statistical ANOVA analysis revealed statistically significant (p > 0.05) differences between theta (F3) an

关键词： Electrocardiograms

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Wide-band high performance optical modulator based on a stack of graphene and h-BN layers with plasmonic edge mode

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Optical and Quantum Electronics 2025年第2期57卷 1-23页

作者： Karimkhani, Hossein Karim, Mohammad Ataul Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA02747 United States

Modulation depth and its associated loss pose a significant challenge in electro-optical telecommunication systems. Optimal modulators strive to enhance modulation depth while minimizing loss rates. We propose a high-performance electro-optical hybrid plasmonic modulator based on graphene, hexagonal Boron Nitride (h-BN), and Molybdenum Disulfide (MoS2) layers. The substrate of the proposed modulator is SiO2 on a Silicon wafer, where Ag layers are embedded in the SiO2 layer and on top of the structure. Graphene layers at the edge of the upper and lower Ag layers and h-BN in between them create a waveguide capable of transmitting input light through the structure. Graphene and MoS2 layers increase the amount of light interaction increasing, in turn, modulation depth. The edge mode in the graphene layers confines light properly and increases the electrical field intensity in a narrow gap. The modulator’s performance is examined using a three-dimensional finite-difference time-domain (FDTD) method. The structure’s modulation depth, for a range of temperature, ranges between 40.54 dB/μm and 42.05 dB/μm. The maximum loss is estimated to be 5.723 dB/μm at 1.3 μm for 0.65 eV chemical potential, which yields a figure of merit (FoM) of 12.5 and extinction ratio (ER) of 99.51 dB. The equivalent circuit for the modulator is investigated in terms of parameters such as energy consumption and modulation bandwidth. The modulator demonstrates an impressively low energy consumption per bit, underscoring its efficiency and practicality. The modulator’s characteristics primarily arise from utilizing a thin layer of h-BN instead of thick dielectric layers. Unlike the previously examined configurations, applying voltage through the graphene layers substantially diminishes the insertion loss. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Silicon wafers

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Adaptive algorithm for controlling the power management system on offshore jack-up drilling rigs

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Energy Sources, Part A: Recovery, Utilization and Environmental Effects 2024年第1期46卷 16717-16737页

作者： Cemeljic, Hrvoje Havelka, Juraj Jeremic, Aleksandar Kuzle, Igor Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical & Computer Engineering McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The power systems of offshore jack-up drilling rigs consist of diesel generators running in parallel load-sharing mode, controlled by an automatic Power Management System (PMS). In this paper, the operational performance of the diesel generators (DG) and the PMS on a jack-up drilling rig is investigated, focusing on two critical offshore drilling operations: "pipe tripping" and "pulling/pumping out of the hole" (POOH). During these operations, large power swings occur in the system, subjecting the DGs to sudden load surges. These severe imbalances in the system cause the PMS to intermittently start and stop the engines throughout the operation, leading to various problematic conditions in the power plant. The paper provides a novel solution for intermittent starting and stopping of the DGs in the form of development and implementation of an adaptive PMS algorithm, a mathematical model of the system and a machine learning approach to event prediction of PMS operation in offshore drilling. Research demonstrates a noteworthy decrease in intermittent DG starting and stopping when the adaptive algorithm is implemented, with a 96% reduction during pipe tripping and an 87% reduction during POOH. This improvement comes at the expense of an 11% increase in engine running hours and a 1.5% rise in fuel consumption. Furthermore, by configuring the adaptive algorithm to economy mode, savings of up to 1700 liters of fuel per month are achievable. © 2024 Taylor & Francis Group, LLC.

关键词： Adaptive algorithms

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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Hybrid millimeter wave heterogeneous networks with spatially correlated user equipment

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Digital Communications and Networks 2024年第4期10卷 904-917页

作者： Arif Ullah Ziaul Haq Abbas Ghulam Abbas Fazal Muhammad Jae-Mo Kang Department of Computer Engineering College of IT ConvergenceChosun UniversityGwangju61452South Korea Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23640Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Artificial Intelligence Kyungpook National UniversityDaegu41566South Korea

In this paper,we analyze a hybrid Heterogeneous Cellular Network(HCNet)framework by deploying millimeter Wave(mmWave)small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data *** consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations(SBSs)are deployed in the areas with high User Equipment(UE)*** user centric deployment of mmWave SBSs inevitably incurs correlation between UE and *** a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave *** using tools from stochastic geometry,we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power *** UE clustering we considered Thomas cluster process and derive expressions for the association probability,coverage probability,area spectral efficiency,and energy *** also provide Monte Carlo simulation results to validate the accuracy of the derived ***,we analyze the impact of mmWave operating frequency,antenna gain,small cell biasing,and BSs density to get useful engineering insights into the performance of hybrid mmWave *** results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

关键词： Downlink cell association Heterogeneous cellular networks Integrated sub-6GHz and mmWave networks Millimeter wave communications Poisson cluster process

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Hybrid two-level protection system for preserving pre-trained DNN models ownership

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Neural Computing and Applications 2024年第34期36卷 21415-21449页

作者： Fkirin, Alaa Moursi, Ahmed Samy Attiya, Gamal El-Sayed, Ayman Shouman, Marwa A. Department of Electrical Engineering Faculty of Engineering Fayoum University Fayoum Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia Governorate Menouf32952 Egypt

Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands substantial time, vast datasets and high computational costs. However, these valuable models face significant risks. Attackers can steal and sell pre-trained DNN models for profit. Unauthorised sharing of these models poses a serious threat. Once sold, they can be easily copied and redistributed. Therefore, a well-built pre-trained DNN model is a valuable asset that requires protection. This paper introduces a robust hybrid two-level protection system for safeguarding the ownership of pre-trained DNN models. The first-level employs zero-bit watermarking. The second-level incorporates an adversarial attack as a watermark by using a perturbation technique to embed the watermark. The robustness of the proposed system is evaluated against seven types of attacks. These are Fast Gradient Method Attack, Auto Projected Gradient Descent Attack, Auto Conjugate Gradient Attack, Basic Iterative Method Attack, Momentum Iterative Method Attack, Square Attack and Auto Attack. The proposed two-level protection system withstands all seven attack types. It maintains accuracy and surpasses current state-of-the-art methods. © The Author(s) 2024.

关键词： Deep neural networks

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