Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm

As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%.

New Binary Archimedes Optimization Algorithm and its application

Optimization problem, as a hot research field, is applied to many industries in the real world. Due to the complexity of different search spaces, metaheuristic optimization algorithms are proposed to solve this problem. As a recently introduced optimization method inspired by physics, Archimedes Optimization Algorithm (AOA) is an efficient metaheuristic algorithm based on Archimedes' law. It has the advantages of fast convergence speed and balance between local and global search ability when solving continuous problems. However, discrete problems exist more in practical applications. AOA needs to be further improved in dealing with such problems. On this basis, to make Archimedes Optimization Algorithm better applied to solve discrete problems, a Binary Archimedes Optimization Algorithm (BAOA) is proposed in this paper, which incorporates a novel V-shaped transfer function. The proposed method applies the BAOA to COVID-19 classification of medical data, segmentation of real brain lesion, and the knapsack problem. The experimental results show that the proposed BAOA can solve the discrete problem well.

Detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra-low-dose CT images

In this article, the detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra-low-dose CT (ULDCT) images is proposed. Here, the ultra-low-dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto-encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI-Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID-19 ULDCT images classification as COVID-19 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN-AOA-ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%;precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet-HHO-ULDCT, ELM-DNN-ULDCT, EDL-ULDCT, ResNet 50-ULDCT, SDL-ULDCT, CNN-ULDCT, and DRNN-ULDCT, respectively. © 2023 John Wiley & Sons, Ltd.

Real-Time Health Monitoring in WBANs using Ensemble Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP)

Wireless body area networks (WBANs) are helpful for remote health monitoring, especially during the COVID-19 pandemic. Due to the limited batteries of bio-sensors, energy-efficient routing is vital to achieve load-balancing and prolong the network's lifetime. Although many routing techniques have been presented for WBANs, they were designed for an application, and their performance may be degraded in other applications. In this paper, an ensemble Metaheuristic-Driven Machine Learning Routing Protocol (MDML-RP) is introduced as an adaptive real-time remote health monitoring in WBANs. The motivation behind this technique is to utilize the superior route optimization solutions offered by metaheuristics and to integrate them with the real-time routing capability of machine learning. The proposed method involves two phases: offline model tuning and online routing. During the offline pre-processing step, a metaheuristic algorithm based on the whale optimization algorithm (WOA) is used to optimize routes across various WBAN configurations. By applying WOA for multiple WBANs, a comprehensive dataset is generated. This dataset is then used to train and test a machine learning regressor that is based on support vector regression (SVR). Next, the optimized MDML-RP model is applied as an adaptive real-time protocol, which can efficiently respond to just-in-time requests in new, previously unseen WBANs. Simulation results in various WBANs demonstrate the superiority of the MDML-RP model in terms of application-specific performance measures when compared with the existing heuristic, metaheuristic, and machine learning protocols. The findings indicate that the proposed MDML-RP model achieves noteworthy improvement rates across various performance metrics when compared to the existing techniques, with an average improvement of 42.3% for the network lifetime, 15.4% for reliability, 31.3% for path loss, and 31.7% for hot-spot temperature.

Predicting COVID-19 using lioness optimization algorithm and graph convolution network.

In this paper, a graph convolution network prediction model based on the lioness optimization algorithm (LsOA-GCN) is proposed to predict the cumulative number of confirmed COVID-19 cases in 17 regions of Hubei Province from March 23 to March 29, 2020, according to the transmission characteristics of COVID-19. On the one hand, Spearman correlation analysis with delay days and LsOA are used to capture the dynamic changes of feature information to obtain the temporal features. On the other hand, the graph convolutional network is used to capture the topological structure of the city network, so as to obtain spatial information and finally realize the prediction task. Then, we evaluate this model through performance evaluation indicators and statistical test methods and compare the results of LsOA-GCN with 10 representative prediction methods in the current epidemic prediction study. The experimental results show that the LsOA-GCN prediction model is significantly better than other prediction methods in all indicators and can successfully capture spatio-temporal information from feature data, thereby achieving accurate prediction of epidemic trends in different regions of Hubei Province.

Classification of COVID-19 Patient Based on Multilayer Perceptron Neural Networks Optimized with Garra Rufa Fish Optimization Using CT Scan Images

COVID-19 is known in recent times as a severe syndrome of respiratory organ (Lungs) and has gradually produced pneumonia, a lung disorder all around the world. As coronavirus is continually spreading rapidly globally, the computed tomography (CT) technique has been made important and essential for quick diagnosis of this dangerous syndrome. Hence, it is necessitated to develop a precise computer-based technique for assisting medical clinicians in identifying the COVID-19 influenced patients with the help of CT scan images. Therefore, the multilayer perceptron neural networks optimized with Garra Rufa Fish optimization using images of CT scan is proposed in this paper for the classification of COVID-19 patients (COV-19-MPNN-GRF-CTI). The input images are taken from SARS-COV-2 CT-scan dataset. Initially, the input images are pre-processed utilizing convolutional auto-encoder (CAE) to enhance the quality of the input images by eliminating noises. The pre-processed images are fed to Residual Network (ResNet-50) for extracting the global and statistical features. The extraction over the features of CT scan images is made through ResNet-50 and subsequently input to multilayer perceptron neural networks (MPNN) for CT images classification as COVID-19 and Non-COVID-19 patients. Here, the layer of Batch Normalization of the MPNN is separated and added with ResNet-50 layer. Generally, MPNN classifier does not divulge any adoption of optimization approach for calculating the optimal parameters and accurately classifying the extracted features of CT images. The Garra Rufa Fish (GRF) optimization algorithm performs to optimize the weight parameters of MPNN classifiers. The proposed approach is executed in MATLAB. The performance metrics, such as sensitivity, precision, specificity, F-measure, accuracy and error rate, are examined. Then the performance of the proposed COV-19-MPNN-GRF-CTI method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precision, 33.98%, 21.95%, 34.78% lower error rate compared with the existing methods, like multi-task deep learning using CT image analysis for COVID-19 pneumonia classification and segmentation (COV-19-MDP-CTI), COVID-19 classification utilizing CT scan depending on meta-classifier approach (COV-19-SEMC-CTI) and deep learning-based COVID-19 prediction utilizing CT scan images (COV-19-CNN-CTI), respectively. [ FROM AUTHOR] Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Jaya Honey Badger optimization-based deep neuro-fuzzy network structure for detection of (SARS-CoV) Covid-19 disease by using respiratory sound signals

PurposeThe Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approachThe major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.FindingsThe performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.Research limitations/implicationsThe JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implicationsThe proposed Covid-19 detection method is useful in various applications, like medical and so on.Originality/valueDeveloped JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.

Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes.

A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal transmission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and essential estimations for identifying optimal re-opening strategies with the lowest transmission risk.

Initialization and Local Search Methods Applied to the Set Covering Problem: A Systematic Mapping

The set covering problem (SCP) is a classical combination optimization problem part of Karp's 21 NP-complete problems. Many real-world applications can be modeled as set covering problems (SCPs), such as locating emergency services, military planning, and decision-making in a COVID-19 pandemic context. Among the approaches that this type of problem has solved are heuristic (H) and metaheuristic (MH) algorithms, which integrate iterative methods and procedures to explore and exploit the search space intelligently. In the present research, we carry out a systematic mapping of the literature focused on the initialization and local search methods used in these algorithms that have been applied to the SCP in order to identify them and that they can be applied in other algorithms. This mapping was carried out in three main stages: research planning, implementation, and documentation of results. The results indicate that the most used initialization method is random with heuristic search, and the inclusion of local search methods in MH algorithms improves the results obtained in comparison to those without local search. Moreover, initialization and local search methods can be used to modify other algorithms and evaluate the impact they generate on the results obtained.

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation.

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.

Design and Application of Vague Set Theory and Adaptive Grid Particle Swarm Optimization Algorithm in Resource Scheduling Optimization.

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

The Superiority of Coronavirus Optimization Algorithm for Optimal Designing of Photovoltaic/Wind/Fuel Cell Hybrid System Considering Cost Minimization Approach to Improve Reliability

This article proposes the best design for a hybrid system that incorporates wind turbines, solar panels, and fuel cells (FC) to satisfy the load requirement. The design's goal is to reduce the system's energy production costs considering the load supply's reliability. System costs include initial investment costs, operation and maintenance, replacement and replacement costs, and load loss costs. The optimal capacity of the hybrid system's equipment has been calculated with the help of the Coronavirus Optimization Algorithm (COVIDOA). The results obtained from the optimization have been compared and analyzed with those obtained from the Differential Evolution (DE) algorithm. The results have shown that the COVIDOA optimization method, like the DE optimization method, has obtained favourable results. In the COVIDOA method, the system's production costs have increased slightly, but the reliability of the load supply has been improved. Therefore, in the suggested approach, in addition to considering the economic aspect of the design, much attention has been paid to the technical aspect of the design, in other words, the reliability level of the system. © 2023 IEEE.

Comparative Analysis of Swarm Algorithms to Classification of covid19 on X-Rays

The use of Chest radiograph (CXR) images in the examination and monitoring of different lung disorders like infiltration, tuberculosis, pneumonia, atelectasis, and hernia has long been known. The detection of COVID-19 can also be done with CXR images. COVID-19, a virus that results in an infection of the upper respiratory tract and lungs, was initially detected in late 2019 in China's Wuhan province and is considered to majorly damage the airway and, thus, the lungs of people afflicted. From that time, the virus has quickly spread over the world, with the number of mortalities and cases increasing daily. The COVID-19 effects on lung tissue can be monitored via CXR. As a result, This paper provides a comparison regarding k-nearest neighbors (KNN), Support-vector machine (SVM), and Extreme Gradient Boosting (XGboost) classification techniques depending on Harris Hawks optimization algorithm (HHO), Salp swarm optimization algorithm (SSA), Whale optimization algorithm (WOA), and Gray wolf optimizer (GWO) utilized in this domain and utilized for feature selection in the presented work. The dataset used in this analysis consists of 9000 2D X-ray images in Poster anterior chest view, which has been categorized by using valid tests into two categories: 5500 images of Normal lungs and 4044 images of COVID-19 patients. All of the image sizes were set to 200 × 200 pixels. this analysis used several quantitative evaluation metrics like precision, recall, and F1-score. © 2022 IEEE.

The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study.

Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more about their complexities. It is crucial to analyse and summarise optimization techniques methodically from a machine learning standpoint since this can provide direction for future work in both machine learning and optimization. The approaches under consideration include the Stochastic Gradient Descent (SGD), Stochastic Optimization Descent with Momentum, Rung Kutta, Adaptive Learning Rate, Root Mean Square Propagation, Adaptive Moment Estimation, Deep Ensembles, Feedback Alignment, Direct Feedback Alignment, Adfactor, AMSGrad, and Gravity. prove the ability of each optimizer applied to machine learning models. Firstly, tests on a skin cancer using the ISIC standard dataset for skin cancer detection were applied using three common optimizers (Adaptive Moment, SGD, and Root Mean Square Propagation) to explore the effect of the algorithms on the skin images. The optimal training results from the analysis indicate that the performance values are enhanced using the Adam optimizer, which achieved 97.30% accuracy. The second dataset is COVIDx CT images, and the results achieved are 99.07% accuracy based on the Adam optimizer. The result indicated that the utilisation of optimizers such as SGD and Adam improved the accuracy in training, testing, and validation stages.

Intelligent optimization design of electron barrier layer for AlGaN-based deep-ultraviolet light-emitting diodes

AlGaN-based deep-ultraviolet light-emitting diodes (DUV LEDs) are widely used in sterilization, sensing, water purification, medical treatment, non-line of sight (NLOS) communication and many other fields. Especially it has been reported that the global novel coronavirus (COVID-19) can be effectively inactivated by the DUV light with a wavelength below 280 nm (UVC) within a few seconds, which has also attracted great attention. However, the external quantum efficiency (EQE) of UVC LED is still at a low level, generally not more than 10%. As an important component of EQE, internal quantum efficiency (IQE) plays a crucial role in realizing high-performance DUV-LED. In order to improve the IQE of AlGaN-based DUV-LED, this work adopts an electron blocking layer (EBL) structure based on InAlGaN/AlGaN superlattice. The results show that the superlattice EBL structure can effectively improve the IQE compared with the traditional single-layer and double-layer EBL structure for the DUV-LED. On this basis, the optimization method based on JAYA intelligent algorithm for LED structure design is proposed in this work. Using the proposed design method, the InAlGaN/AlGaN superlattice EBL structure is further optimized to maximize the LED' s IQE. It is demonstrated that the optimized superlattice EBL structure is beneficial to not only the suppression of electron leakage but also the improvement of hole injection, leading to the increase of carrier recombination in the active region. As a result, the IQE of the DUV-LED at 200 mA injection current is 41.2% higher than that of the single-layer EBL structure. In addition, the optimized structure reduces IQE at high current from 25% to 4%. The optimization method based on intelligent algorithm can break through the limitation of the current LED structure design and provide a new method to improve the efficiency of AlGaN-based DUV-LED. © 2023 Chinese Physical Society.

Hybrid Particle Swarm Optimized and Fuzzy C Means Clustering based segmentation technique for investigation of COVID-19 infected chest CT

COVID-19 is the world's most serious threat, affecting billions of people worldwide. Medical imaging, such as CT, has a lot of potential as an alternative to RT-PCR approach for significant judgement and disease control. As a result, automatic image segmentation is in high demand as a therapeutic decision aid. According to studies, medical images may be very useful for early screening since certain aspects of the image imply the existence of virus of COVID-19 and hence may be used as an efficient scanning tool. The proposed work presents a hybrid approach for efficient screening of COVID-19 using chest CT images implementing Hybrid Particle Swarm Optimised-Fuzzy C Means Clustering. The proposed method is tested on 15 chest CT images of COVID-19-infected patients and the results have been validated quantitatively by metrices such as entropy, contrast and standard deviation, which clearly outperforms the conventional Fuzzy C Means Clustering.Copyright © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Optimal DG Allocation Based on Pay-back Period by a Proposed Modification for Coronavirus Herd Immunity Optimization

Distribued Generations (DG) have economic, financial, and environmental benefits. DG reduces power losses in the distribution system but has a negative impact on the protection devices. In this article, the IEEE 33 bus system will be used and tested by adding up to three DG units using MATLAB/SIMULINK software. the optimization techniques that will be used are Grey Wolf Optimizer, Whale Optimization Algorithm, Genetic Algorithm, and Coronavirus Herd Immunity or COVID-19 optimization techniques to select the optimal site and size of the DG units based on the lowest pay-back period considering the voltage limits and power losses. The paper proposes a modified mutation operator for COVID-19 based on Gaussian and Cauchy mutations to have better performance and lower variance. The proposed algorithm is compared with the other optimization techniques. The proposed algorithm achieved better results, which proved to have competitive performance with state-of-the-art evolutionary algorithms. © 2022 IEEE.

Optimization Model and Algorithm of Logistics Vehicle Routing Problem under Major Emergency

The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on the problems of insufficient timeliness and high total system cost of emergency logistics distribution in major epidemic situations, this paper takes the minimum vehicle distribution travel cost, time cost, early/late punishment cost, and fixed cost of the vehicle as the target, the soft time window for receiving goods at each demand point, the rated load of the vehicle, the volume, maximum travel of the vehicle in a single delivery as constraints, and an emergency logistics vehicle routing problem optimization model for major epidemics was constructed. The convergence speed improvement strategy, particle search improvement strategy, and elite retention improvement strategy were introduced to improve the particle swarm optimization (PSO) algorithm for it to be suitable for solving global optimization problems. The simulation results prove that the improved PSO algorithm required to solve the emergency medical supplies logistics vehicle routing problem for the major emergency can reach optimal results. Compared with the basic PSO algorithm, the total cost was reduced by 20.09%. © 2023 by the authors.

APPLYING HUNGER GAME SEARCH (HGS) FOR SELECTING SIGNIFICANT BLOOD INDICATORS FOR EARLY PREDICTION OF ICU COVID-19 SEVERITY

This paper introduces an early prognostic model for attempting to predict the severity of patients for ICU admission and detect the most significant features that affect the prediction process using clinical blood data. The proposed model predicts ICU admission for high-severity patients during the first two hours of hospital admission, which would help assist clinicians in decision-making and enable the efficient use of hospital resources. The Hunger Game search (HGS) meta-heuristic algorithm and a support vector machine (SVM) have been integrated to build the proposed prediction model. Furthermore, these have been used for selecting the most informative features from blood test data. Experiments have shown that using HGS for selecting features with the SVM classifier achieved excellent results as compared with four other meta-heuristic algorithms. The model that used the features that were selected by the HGS algorithm accomplished the topmost results (98.6 and 96.5%) for the best and mean accuracy, respectively, as compared to using all of the features that were selected by other popular optimization algorithms © 2023 Author(s). This is an open access publication, which can be used, distributed and reproduced in any medium according to the Creative Commons CC-BY 4.0 License