Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks

Noroozi-Ghaleini, Ebrahim; Shaibani, Mohammad Javad

doi:10.1016/j.heliyon.2023.e13672

Cited by 3 publications

(3 citation statements)

References 65 publications

(72 reference statements)

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“…Moreover, the choice of activation functions, learning rate, and other hyperparameters can significantly impact the network's performance, requiring rigorous experimentation for optimal results. The feedforward neural network is highly flexible and capable of learning complex mappings from inputs to outputs [28].…”

Section: D: Support Vector Classifiermentioning

confidence: 99%

EngineFaultDB: A Novel Dataset for Automotive Engine Fault Classification and Baseline Results

Vergara,

Ramos,

Rivera-Campoverde

et al. 2023

IEEE Access

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This paper introduces EngineFaultDB, a novel dataset capturing the intricacies of automotive engine diagnostics. Centered around the widely represented C14NE spark ignition engine, data was collected under controlled laboratory conditions, simulating various operational states, including normal and specific fault scenarios. Utilizing tools such as an NGA 6000 gas analyzer and a USB 6008 data acquisition card from National Instruments, we were able to monitor and capture a comprehensive range of engine parameters, from throttle position and fuel consumption to exhaust gas emissions. Our dataset, comprising 55,999 meticulously curated entries across 14 distinct variables, provides a holistic picture of engine behavior, making it an invaluable resource for automotive researchers and practitioners. For evaluation, several classifiers, including logistic regression, decision trees, random forests, support vector machines, knearest neighbors, and a feed-forward neural network, were trained on this dataset. Their performance, under standard configurations and a simple neural network architecture, offers foundational benchmarks for future explorations. Results underscore the dataset's potential in fostering advanced diagnostic algorithms. As a testament to our commitment to open research, EngineFaultDB is freely available for academic use. Future work involves expanding the dataset's diversity, exploring deeper neural architectures, and integrating realworld automotive conditions.

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Section: D: Support Vector Classifiermentioning

confidence: 99%

EngineFaultDB: A Novel Dataset for Automotive Engine Fault Classification and Baseline Results

Vergara,

Ramos,

Rivera-Campoverde

et al. 2023

IEEE Access

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“…Khurram Hussain et al used a hybrid of the WOA-ABC and proposed a CNN for intrusion detection systems in wireless sensor networks [15]. Ebrahim Noroozi-Ghaleini et al have also employed the artificial bee colony algorithm to optimize feedforward neural networks, thereby enhancing the accuracy of predictions [16]. In order to determine the optimal control parameters for RBFNN and mitigate the occurrence of excessive artifacts, we employed the artificial bee colony algorithm to optimize the three parameters of RBFNN, namely weight, center vector, and basis radius, to remove artifacts.…”

Section: Introductionmentioning

confidence: 99%

Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm

Zhu,

Li,

Luo

et al. 2023

Sensors

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In electrical impedance tomography (EIT) detection of industrial two-phase flows, the Gauss-Newton algorithm is often used for imaging. In complex cases with multiple bubbles, this method has poor imaging accuracy. To address this issue, a new algorithm called the artificial bee colony–optimized radial basis function neural network (ABC-RBFNN) is applied to industrial two-phase flow EIT for the first time. This algorithm aims to enhance the accuracy of image reconstruction in electrical impedance tomography (EIT) technology. The EIDORS-v3.10 software platform is utilized to generate electrode data for a 16-electrode EIT system with varying numbers of bubbles. This generated data is then employed as training data to effectively train the ABC-RBFNN model. The reconstructed electrical impedance image produced from this process is evaluated using the image correlation coefficient (ICC) and root mean square error (RMSE) criteria. Tests conducted on both noisy and noiseless test set data demonstrate that the ABC-RBFNN algorithm achieves a higher ICC value and a lower RMSE value compared to the Gauss–Newton algorithm and the radial basis function neural network (RBFNN) algorithm. These results validate that the ABC-RBFNN algorithm exhibits superior noise immunity. Tests conducted on bubble models of various sizes and quantities, as well as circular bubble models, demonstrate the ABC-RBFNN algorithm’s capability to accurately determine the size and shape of bubbles. This outcome confirms the algorithm’s generalization ability. Moreover, when experimental data collected from a 16-electrode EIT experimental device is employed as test data, the ABC-RBFNN algorithm consistently and accurately identifies the size and position of the target. This achievement establishes a solid foundation for the practical application of the algorithm.

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“…This study examined the linkage between human mobility and vaccination in the context of the COVID-19 pandemic [25] , [26] .…”

Section: Introductionmentioning

confidence: 99%

The impact of COVID-19 vaccination on human mobility: The London case

Bei

Zhi

et al. 2023

Heliyon

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Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks

Cited by 3 publications

References 65 publications

EngineFaultDB: A Novel Dataset for Automotive Engine Fault Classification and Baseline Results

EngineFaultDB: A Novel Dataset for Automotive Engine Fault Classification and Baseline Results

Electrical Impedance Tomography of Industrial Two-Phase Flow Based on Radial Basis Function Neural Network Optimized by the Artificial Bee Colony Algorithm

The impact of COVID-19 vaccination on human mobility: The London case

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