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Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network

COGNITIVE COMPUTATION 2024年第4期16卷 1666-1681页

作者： Goel, Tripti Murugan, R. Mirjalili, Seyedali Chakrabartty, Deba Kumar Natl Inst Technol Silchar Dept Elect & Commun Engn Biomed Imaging Lab BIOMIL Silchar 788010 Assam India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea Silchar Med Coll & Hosp Dept Radiol Silchar 788014 Assam India

The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis. Automatic identification software tools are essential for helping to screen COVID-19 using CT images. However, there are few datasets available, making it difficult to train deep learning (DL) networks. To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images. The whale optimization algorithm (WOA) is used to optimize the hyperparameters of GAN's generator. The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images. The performance metrics of the proposed optimized model, including accuracy (99.22%), sensitivity (99.78%), specificity (97.78%), F1-score (98.79%), positive predictive value (97.82%), and negative predictive value (99.77%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods. This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks.

关键词： Automatic diagnosis Coronavirus COVID-19 Generative Adversarial Network whale optimization algorithm Deep learning

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Fusion of machine learning and blockchain-based privacy-preserving approach for healthcare data in the Internet of Things

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JOURNAL OF SUPERCOMPUTING 2024年第17期80卷 24975-25003页

作者： Bezanjani, Behnam Rezaei Ghafouri, Seyyed Hamid Gholamrezaei, Reza Islamic Azad Univ Dept Comp Engn Kerman Branch Kerman Iran

In recent years, the rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management. While these technological innovations hold immense promise, they concurrently raise critical security concerns, particularly in safeguarding medical data against potential cyber threats. The sensitive nature of health-related information requires robust measures to ensure patient data's confidentiality, integrity, and availability within IoT-enabled medical environments. Addressing the imperative need for enhanced security in IoT-based healthcare systems, we propose a comprehensive method encompassing three distinct phases. In the first phase, we implement blockchain-enabled request and transaction encryption to fortify the security of data transactions, providing an immutable and transparent framework. Subsequently, in the second phase, we introduce request pattern recognition check, leveraging diverse data sources to identify and thwart potential unauthorized access attempts. Finally, the third phase incorporates feature selection and the BiLSTM network to enhance the accuracy and efficiency of intrusion detection through advanced machine-learning techniques. We compared the simulation results of the proposed method with three recent related methods, namely AIBPSF-IoMT, OMLIDS-PBIoT, and AIMMFIDS. The evaluation criteria encompass detection rates, false alarm rates, precision, recall, and accuracy, crucial benchmarks in assessing the overall performance of intrusion detection systems. Notably, our findings reveal that the proposed method outperforms these existing methods across all evaluated criteria, underscoring its superiority in enhancing the security posture of IoT-based healthcare systems.

关键词： Blockchain Privacy-preserving Healthcare Machine learning whale optimization algorithm

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Refined Analysis of Spatial Three-Curved Steel Box Girder Bridge and Temperature Stress Prediction Based on WOA-BPNN

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BUILDINGS 2024年第2期14卷 415页

作者： Hu, Wei Zhang, Zhongyong Shi, Junwei Chen, Yulun Li, Yixuan Feng, Qian Hangzhou Zhongmeihuadong Pharmaceut Co Ltd Hangzhou 310011 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

Bridges often improve the visual appeal of urban landscapes by incorporating curve elements to create iconic forms. However, it is noteworthy that curved bridges have unique mechanical properties under loads compared to straight bridges. This study analyzes a spatial three-curved steel box girder bridge based on an actual engineering case with a complex configuration. Initially, the finite element software Midas/Civil 2021 is utilized to establish a beam element model and a plate element model to examine the structural responses under dead loads in detail. Then, two different temperature gradient distribution models are employed for the temperature effect analysis. The backpropagation neural network (BPNN) optimized by the WOA algorithm is trained as a surrogate model for finite element models based on the results of temperature stress simulation. The results reveal that the bending-torsion coupling effect in the second span of the spatial three-curved steel box girder bridge is pronounced, with the maximum torque reaching 40% of the bending moment. The uneven distribution of cross-section stress is particularly significant at the vertices, where the shear lag coefficient exceeds 3. Under the action of temperature gradients, the bridge displays a warped stress state;the stress results obtained from the exponential model exhibit a 21% increase compared to BS-5400. optimization of the weights by the WOA algorithm results in a significant improvement in prediction accuracy, and the convergence speed is improved by 30%. The coefficient of determination (R2) for predicting temperature stress can reach as high as 0.99.

关键词： refined analysis spatial curve steel box girder bridge temperature stress whale optimization algorithm neural network

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Robust optimization of resource-constrained assembly line balancing problems with uncertain operation times

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ENGINEERING COMPUTATIONS 2022年第3期39卷 813-836页

作者： Jin, Wenrui He, Zhaoxu Wu, Qiong Tongji Univ Sch Mech Engn Shanghai Peoples R China Tongji Univ Sinogerman Coll Appl Sci Shanghai Peoples R China Tongji Univ Sch Mech Engn Shanghai Peoples R China

Purpose Due to the market trend of low-volume and high-variety, the manufacturing industry is paying close attention to improve the ability to hedge against variability. Therefore, in this paper the assembly line with limited resources is balanced in a robust way that has good performance under all possible scenarios. The proposed model allows decision makers to minimize a posteriori regret of the selected choice and hedge against the high cost caused by variability. Design/methodology/approach A generalized resource-constrained assembly line balancing problem (GRCALBP) with an interval data of task times is modeled and the objective is to find an assignment of tasks and resources to the workstations such that the maximum regret among all the possible scenarios is minimized. To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, whale optimization algorithm, are proposed and analyzed. A problem-specific coding scheme and search mechanisms are incorporated. Findings Theory analysis and computational experiments are conducted to evaluated the proposed methods and their superiority. Satisfactory results show that the constraint generation technique-based exact method can efficiently solve instances of moderate size to optimality, and the performance of WOA is enhanced due to the modified searching strategy. Originality/value For the first time a minmax regret model is considered in a resource-constrained assembly line balancing problem. The traditional whale optimization algorithm is modified to overcome the inferior capability and applied in discrete and constrained assembly line balancing problems.

关键词： Assembly line balancing Robust optimization Resource-constrained Minmax regret whale optimization algorithm

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Hunting Based optimization Techniques Used in Controlling an Active Magnetic Bearing System

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IEEE ACCESS 2022年 10卷 62702-62721页

作者： Gupta, Suraj Biswas, Pabitra Kumar Babu, Thanikanti Sudhakar Alhelou, Hassan Haes Natl Inst Technol Mizoram Aizawl 796012 India Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Tishreen Univ Fac Mech & Elect Engn Dept Elect Power Engn Latakia 2230 Syria

This paper proposes a closed-loop and implements some metaheuristic optimization approaches to regulate an unstable active magnetic bearing (AMB) system. First of all, a hardware model of an AMB is fabricated in the laboratory. Mathematical analysis is carried out and a linearized open-loop transfer function is obtained for an equilibrium point of operation, using the parameters of the hardware model. Then, a closed loop is proposed for this AMB system, which comprises a PID controller, power amplifier, and position sensor. Further, three different metaheuristic nature-inspired hunting-based optimization algorithms i.e., Ant lion optimization (ALO), Grey wolf optimization (GWO), and whale optimization algorithm (WOA) are implemented individually to calculate the gain parameters of the PID controller. Separately, the performance of these optimization algorithms is evaluated and observed on four different performance indices: integral of absolute error (IAE), integral of squared error (ISE), and an integral of time multiplied absolute error (ITAE) and integral of time multiplied squared error (ITSE). For a stable, efficient, and reliable bearing operation, it is vital to perform an analysis of the performance of optimization techniques with different objective functions for the proposed system. Therefore, few comparisons are conducted, first based on data obtained from statistical analysis. The second is based on data obtained from transient state performance and phase margin. Third on the scale of algorithm execution time. Finally, with the assistance of observed data effectiveness of each optimization technique to control the proposed AMB system is concluded which can serve as theoretical and experimental foundations for the continued use of AMB in high-speed applications.

关键词： optimization Magnetic levitation Electromagnets Mathematical models Rotors Transfer functions Metaheuristics Active magnetic bearing metaheuristic nature-inspired hunting-based algorithms Ant lion optimization Grey wolf optimization whale optimization algorithm

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Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment

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COMPUTER COMMUNICATIONS 2022年 187卷 35-44页

作者： Manikandan, N. Gobalakrishnan, N. Pradeep, K. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Sri Venkateswara Coll Engn Dept Informat Technol Sriperumbudur TN India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India

Cloud computing is the computing technology that offers dynamically scalable and flexible computing resources. Task scheduling in the cloud system is the major problem that needs to be tackled for enhancing the system performance and cloud customer satisfaction level. The task scheduling scheme directly affects the execution time as well as the execution cost of the system. To overcome the above-stated issue, the novel hybrid whale optimization algorithm-based MBA algorithm is proposed for solving the multi-objective task scheduling problems in cloud computing environments. In the hybrid WOA based MBA algorithm, the multi-objective behavior decreases the makespan by maximizing the resource utilization. The output of the Random double adaptive whale optimization algorithm (RDWOA) is enhanced by utilizing the mutation operator of the Bees algorithm. The performance evaluation is conducted and compared with other algorithms using the platform of Cloudsim tool kit for various measures such as completion, time, and computational cost. The results are analyzed for the performance measures such as makespan, execution time, resource utilization and computational cost and the analysis proves that the proposed algorithm performs better than other algorithms such as IWC, MALO, BA-ABC and MGGS. The proposed HWOA based MBA algorithm converged faster than any other approach for large search spaces and makes it appropriate for large scheduling problems. The experimental results reveal that the HWOA based MBA algorithm effectively minimizes the task completion time and also execution time.

关键词： Task scheduling Cloud computing Task completion time Execution cost whale optimization algorithm MBA Multi-objective optimization Virtual machine

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Energy Efficiency in Semantic Networks: A Heuristic optimization Approach for Resource Allocation 28

Energy Efficiency in Semantic Networks: A Heuristic Optimiza...

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28th Asia-Pacific Conference on Communications (APCC)

作者： Xiao, Ao Zhao, Kaixuan Liu, Zhanjun Liang, Chengchao Chongqing Univ Posts & Telecom Sch Commun & Informat Engn Chongqing Peoples R China Carleton Univ Dept Syst & Comp Eng Ottawa ON Canada CQUPT Info & Comm Inst Chongqing Peoples R China

ISBN: (纸本)9798350382617

Anticipated to substantially enhance communication efficiency, semantic communication emerges as a novel communication paradigm. Considering the constraints of wireless resources, it becomes crucial to design resource allocation schemes that ensure efficient data transmission in a semantic communication system. This paper proposes a resource allocation scheme that maximizes the energy efficiency of the entire semantic network, ensuring the performance of semantic tasks within the confines of limited wireless resources. Specifically, we begin by defining the energy efficiency measurement metrics in semantic communication systems and subsequently optimize it through the joint allocation of semantic symbols, bandwidth, and power. This problem is formulated as an optimization problem. Given the absence of a mathematical closed-form expression for semantic similarity, an effective solution to the problem is proposed via a whale optimization algorithm with a penalty strategy, targeting joint semantic symbols assignment and resource allocation. Simulation results substantiate the effectiveness and feasibility of the proposed scheme.

关键词： Semantic communication resource allocation energy efficiency whale optimization algorithm

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Interpretable real-time monitoring of short-term rockbursts in underground spaces based on microseismic activities

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Kadkhodaei, Mohammad Hossein Ghasemi, Ebrahim Isfahan Univ Technol Dept Min Engn Esfahan *** Iran

In this study, two novel hybrid intelligent models were developed to evaluate the short-term rockburst using the random forest (RF) method and two meta-heuristic algorithms, whale optimization algorithm (WOA) and coati optimization algorithm (COA), for hyperparameter tuning. Real-time predictive models of this phenomenon were created using a database comprising 93 case histories, taking into account various microseismic parameters. The results indicated that the WOA achieved the highest overall performance in hyperparameter tuning for the RF model, outperforming the COA. RF-WOA model accurately predicted the occurrence of this phenomenon with an accuracy of 0.944. Additionally, for this model, precision, recall and F1-score were obtained as 0.950, 0.944 and 0.943, respectively, indicating that the proposed model is robust in predicting damage severity of rockburst in deep underground projects. Subsequently, the Shapley additive explanations (SHAP) method was employed to interpret and explain the prediction process and assess the influence of input features based on RF-WOA model. The results showed that three parameters including cumulative seismic energy, cumulative microseismic events, and cumulative apparent volume have the greatest impact on the occurrence of rockburst events. This study provides an interpretable and transparent resource for accurately predicting rockburst events in real time. It can facilitate estimating project costs, selecting a suitable support system, and identifying essential ways to limit the danger of rockburst.

关键词： Short-term rockburst Microseismic monitoring Random forest whale optimization algorithm Coati optimization algorithm

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Enhancing Solar Irradiance Estimation for Pumped Storage Hydroelectric Power Plants Using Hybrid Deep Learning

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SMART GRIDS AND SUSTAINABLE ENERGY 2024年第2期9卷 40页

作者： Konduru, Sudharshan Naveen, C. Bansal, Ramesh C. SRM Inst Sci & Technol Dept Elect & Elect Engn Chennai 603203 India Univ Sharjah Elect Engn Dept Sharjah U Arab Emirates Univ Pretoria Elect Elect & Comp Engn Dept Pretoria South Africa

This research article explores the potential of Pumped Storage Hydroelectric Power Plants across diverse locations, aiming to establish a sustainable electric grid system and reduce per-unit energy costs. A distinctive feature of the study involves forecasting solar irradiance on large-scale hydroelectric dam locations to identify optimal sites for a PV-integrated hydropower system. The research focuses on advancing the integration of floating solar power modules on water storage systems in eight selected regions across India, emphasizing precise solar irradiance estimation. The paper introduces a state-of-the-art hybrid intelligent deep learning model, combining time series analysis and deep learning through residual ensembling to address these challenges. The primary objective is to pinpoint the optimal location for a Power Storage System (PSS) with the highest solar irradiation for PV-integrated hydro system integration. A secondary goal involves minimizing errors within computational time constraints by the proposed model. The study also employs various optimization techniques to enhance its effectiveness and fine-tune the model's performance, contributing to the advancement of sustainable energy solutions. The proposed model performs best with a whale optimization algorithm with mean absolute error varying from 0.34 to 3.63 W/m2 and root mean square error from 0.75 to 9.51 W/m2 on PSS locations. The analysis also confirms average solar irradiance is high on PSS 7 with 221.0 W/ m2 followed by PSS 1 with 221.1 W/ m2 among the eight designated sites.

关键词： Pumped storage systems Solar irradiance forecasting PV integrated hydro systems whale optimization algorithm Hybrid deep learning

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A deep learning approach for predicting and optimizing the V2X network parameters for sustainable smart transportation systems

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷

作者： Vinodhini, M. Rajkumar, Sujatha Vellore Inst Technol Sch Elect Engn Vellore India

The increasing prevalence of automated vehicles necessitates robust vehicle-to-everything (V2X) communication systems. However, challenges such as latency, reliability, transmission rates, data collisions, and security vulnerabilities require thorough investigation. To address these issues, this study proposes a deep learning-based hybrid model integrating long short-term memory (LSTM) neural networks and generative adversarial networks (GANs) to predict performance metrics in V2X communication networks. The GANs augment the sample vehicular dataset and facilitate proactive vehicular network management. The proposed approach comprises three key phases: data augmentation, feature extraction, and predictive analysis. To optimize the neural parameters, the whale optimization algorithm (WOA) is utilized and benchmarked against other optimization algorithms, including beetle swarm optimization, artificial bee colony optimization, and pelican optimization. The parameters in the developed hybrid GAN-LSTM model and autoencoder were tuned using the WO algorithm to minimize key performance metrics such as the mean absolute error, root mean square error, bit error rate, and mean square error. Results indicate that the predictive model enhances the sustainability and efficiency of transportation systems by accurately forecasting critical metrics, including latency, throughput, and reliability, within V2X networks.

关键词： long short-term memory generative adversarial network predictive modelling bit error rate throughput whale optimization algorithm sustainable transportation systems

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