The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema...The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers.The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis,the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United States and Russian Federation.In this paper,we use the discrete logarithmic problem on prime finite fields,which has been implemented in the GOST R34.10-1994 digital signature standard,to build the proposed collective signature protocols.These protocols help to create collective signatures:Guaranteed internal integrity and fixed size,independent of the number of members involved in forming the signature.The signature built in this study,consisting of 3 components(U,R,S),stores the information of all relevant signers in the U components,thus tracking the signer and against the“disclaim of liability”of the signer later is possible.The idea of hiding the signer’s public key is also applied in the proposed protocols.This makes it easy for the signing group representative to specify which members are authorized to participate in the signature creation process.展开更多
Wearable biosensors have received great interest as patient-friendly diagnostic technologies because of their high flexibility and conformability.The growing research and utilization of novel materials in designing we...Wearable biosensors have received great interest as patient-friendly diagnostic technologies because of their high flexibility and conformability.The growing research and utilization of novel materials in designing wearable biosensors have accelerated the development of point-of-care sensing platforms and implantable biomedical devices in human health care.Among numerous potential materials,conjugated polymers(CPs)are emerging as ideal choices for constructing high-performance wearable biosensors because of their outstanding conductive and mechanical properties.Recently,CPs have been extensively incorporated into various wearable biosensors to monitor a range of target biomolecules.However,fabricating highly reliable CP-based wearable biosensors for practical applications remains a significant challenge,necessitating novel developmental strategies for enhancing the viability of such biosensors.Accordingly,this review aims to provide consolidated scientific evidence by summarizing and evaluating recent studies focused on designing and fabricating CP-based wearable biosensors,thereby facilitating future research.Emphasizing the superior properties and benefits of CPs,this review aims to clarify their potential applicability within this field.Furthermore,the fundamentals and main components of CP-based wearable biosensors and their sensing mechanisms are discussed in detail.The recent advancements in CP nanostructures and hybridizations for improved sensing performance,along with recent innovations in next-generation wearable biosensors are highlighted.CPbased wearable biosensors have been—and will continue to be—an ideal platform for developing effective and user-friendly diagnostic technologies for human health monitoring.展开更多
Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(I...Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.展开更多
Isogeometric analysis (IGA) is known to showadvanced features compared to traditional finite element approaches.Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functiona...Isogeometric analysis (IGA) is known to showadvanced features compared to traditional finite element approaches.Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functionalgrading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward adeep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complexIGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trainedusing the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationshipbetween the outputs and the inputs is constructed usingmachine learning so that the displacements can be directlyestimated by the deep learning network. To provide enough training data, we use S-FSDT Von-Karman IGA andobtain the displacement responses for different loads and gradient indexes. Results show that the recognition erroris low, and demonstrate the feasibility of deep learning technique as a fast and accurate alternative to IGA formodeling the geometrically nonlinear bending behavior of FG plates.展开更多
Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined wi...Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.展开更多
Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of ...Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.展开更多
A numerical analysis is performed to analyze the bioconvective double diffusive micropolar non-Newtonian nanofluid flow caused by stationary porous disks.The consequences of the current flow problem are further extend...A numerical analysis is performed to analyze the bioconvective double diffusive micropolar non-Newtonian nanofluid flow caused by stationary porous disks.The consequences of the current flow problem are further extended by incorporating the Brownian and thermophoresis aspects.The energy and mass species equations are developed by utilizing the Cattaneo and Christov model of heat-mass fluxes.The flow equations are converted into an ordinary differential model by employing the appropriate variables.The numerical solution is reported by using the MATLAB builtin bvp4c method.The consequences of engineering parameters on the flow velocity,the concentration,the microorganisms,and the temperature profiles are evaluated graphically.The numerical data for fascinating physical quantities,namely,the motile density number,the local Sherwood number,and the local Nusselt number,are calculated and executed against various parametric values.The microrotation magnitude reduces for increasing magnetic parameters.The intensity of the applied magnetic field may be utilized to reduce the angular rotation which occurs in the lubrication processes,especially in the suspension of flows.On the account of industrial applications,the constituted output can be useful to enhance the energy transport efficacy and microbial fuel cells.展开更多
This paper presents the calibration of a neutron dose rate meter and the evaluation of its calibration factors(CFs)in several neutron standard fields(i.e.,two standard fields with bare sources of252Cf and241Am-Be,and ...This paper presents the calibration of a neutron dose rate meter and the evaluation of its calibration factors(CFs)in several neutron standard fields(i.e.,two standard fields with bare sources of252Cf and241Am-Be,and five simulated workplace fields with241Am-Be moderated sources).The calibration in standard fields with bare sources was conducted by following the recommendations of the ISO 8529 standard.The measured total neutron ambient dose equivalent rates,denoted as H*(10)tot,were analyzed to obtain direct components,denoted as H*(10)dir,using a reduced fitting method.The CF was then calculated as the ratio between the conventional true value of the neutron ambient dose equivalent rate in a free field,denoted as H*(10)FF,and the value of H*(10)dir.In contrast,in the simulated workplace neutron fields,the calibration of the neutron dose rate meter was conducted by following the ISO 12789 standard.The CF was calculated as the ratio between the values of H*(10)totmeasured by a standard instrument(i.e.,Bonner sphere spectrometer)and the neutron dose rate meter.The CF values were obtained in the range of 0.88–1.0.The standard uncertainties(k=1)of the CFs were determined to be in the range of approximately 6.6–13.1%.展开更多
We describe a unique new species and genus of agamid lizard from the karstic massifs of Khammouan Province,central Laos.Laodracon carsticola Gen.et sp.nov.is an elusive medium-sized lizard(maximum snout-vent length101...We describe a unique new species and genus of agamid lizard from the karstic massifs of Khammouan Province,central Laos.Laodracon carsticola Gen.et sp.nov.is an elusive medium-sized lizard(maximum snout-vent length101 mm)specifically adapted to life on limestone rocks and pinnacles.To assess the phylogenetic position of the new genus amongst other agamids,we generated DNA sequences from two mitochondrial gene fragments(16S rRNA and ND2)and three nuclear loci(BDNF,RAG1 and c-mos),with a final alignment comprising 7418 base pairs for 64 agamid species.Phylogenetic analyses unambiguously place the new genus in the mainland Asia subfamily Draconinae,where it forms a clade sister to the genus Diploderma from East Asia and the northern part of Southeast Asia.Morphologically,the new genus is distinguished from all other genera in Draconinae by possessing a notably swollen tail base with enlarged scales on its dorsal and ventral surfaces.Our work provides further evidence that limestone regions of Indochina represent unique“arks of biodiversity”and harbor numerous relict lineages.To date,Laodracon carsticola Gen.et sp.nov.is known from only two adult male specimens and its distribution seems to be restricted to a narrow limestone massif on the border of Khammouan and Bolikhamxai provinces of Laos.Additional studies are required to understand its life history,distribution,and conservation status.展开更多
DEAR EDITOR,We report on a new species, Zhangixalus melanoleucus sp.nov., from Phou Samsoum Mountain(PSM) in Xiengkhouang Province, northeastern Laos, based on an integrative taxonomic approach, including morphologica...DEAR EDITOR,We report on a new species, Zhangixalus melanoleucus sp.nov., from Phou Samsoum Mountain(PSM) in Xiengkhouang Province, northeastern Laos, based on an integrative taxonomic approach, including morphological, molecular, and bioacoustic lines of evidence. Morphologically, the new species can be distinguished from its congeners by a combination of the following diagnostic characters: medium body size(SVL 34.4–36.3 mm in males, 53.7 mm in a single female);dorsum smooth and green;chest and belly lacking spots;flanks, axillae, ventral surfaces of forearms, inguinal.展开更多
Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in flight.In this study,a nonlinear finite element(FE)model is developed to investigate the dynamic resp...Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in flight.In this study,a nonlinear finite element(FE)model is developed to investigate the dynamic response and damage behavior of hybrid corrugated sandwich structures subjected to high velocity hail ice impact.The impact and breaking behavior of hail are described using the FE-smoothed particle hydrodynamics(FE-SPH)method.A rate-dependent progressive damage model is employed to capture the intra-laminar damage response;cohesive element and surface-based cohesive contact are implemented to predict the inter-laminar delamination and sheet/core debonding phenomena respectively.The transient processes of sandwich structure under different hail ice impact conditions are analyzed.Comparative analysis is conducted to address the influences of core shape and impact position on the impact performance of sandwich structures and the corresponding energy absorption characteristics are also revealed.展开更多
In today’s information technology(IT)world,the multi-hop wireless sensor networks(MHWSNs)are considered the building block for the Internet of Things(IoT)enabled communication systems for controlling everyday tasks o...In today’s information technology(IT)world,the multi-hop wireless sensor networks(MHWSNs)are considered the building block for the Internet of Things(IoT)enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service(QoS)in a stipulated time slot to end-user over the Internet.Smart city(SC)is an example of one such application which can automate a group of civil services like automatic control of traffic lights,weather prediction,surveillance,etc.,in our daily life.These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput,energy efficiency,and end-to-end delay,wherein low latency is considered a challenging issue in next-generation networks(NGN).This paper introduces a single and parallels stable server queuing model with amulti-class of packets and native and coded packet flowto illustrate the simple chain topology and complexmultiway relay(MWR)node with specific neighbor topology.Further,for improving data transmission capacity inMHWSNs,an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node.Finally,the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets.The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results.展开更多
Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Mac...Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.展开更多
We present one-loop contributions for h→ℓℓγ with ℓ=νe,μ,τ,e,μ and e−e+→hγ in the U(1)_(B−L) extension of the standard model. In the phenomenological results, the signal strengths for h→ℓℓγ at the Large Hadro...We present one-loop contributions for h→ℓℓγ with ℓ=νe,μ,τ,e,μ and e−e+→hγ in the U(1)_(B−L) extension of the standard model. In the phenomenological results, the signal strengths for h→ℓℓγ at the Large Hadron Collider and for e−e+→hγ at future lepton colliders are analyzed in the physical parameter space for both the vector and chiral B−L models. We found that the contributions from the neutral gauge boson Z′ to the signal strengths are rather small. Consequently, the effects will be difficult to probe at future colliders. However, the impacts of charged Higgs and CP-odd Higgs in the chiral B−L model on the signal strengths are significant and can be measured with the help of the initial polarization beams at future lepton colliders.展开更多
A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six ...A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.展开更多
The secosteroid hormone vitamin D has, in addition to its effects in bone metabolism also functions in the modulation of immune responses against infectious agents and in inhibiting tumorigenesis. Thus, deficiency of ...The secosteroid hormone vitamin D has, in addition to its effects in bone metabolism also functions in the modulation of immune responses against infectious agents and in inhibiting tumorigenesis. Thus, deficiency of vitamin D is associated with several malignancies, but also with a plethora of infectious diseases. Among other communicable diseases, vitamin D deficiency is involved in the pathogenesis of chronic liver diseases caused by hepatitis B and C viruses(HBV, HCV) and high prevalence of vitamin D deficiency with serum levels below 20 mg/mL in patients with HBV and HCV infection are found worldwide. Several studies have assessed the effects of vitamin D supplementation on the sustained virological response(SVR) to interferon(IFN) plus ribavirin(RBV) therapy in HBV and HCV infection. In these studies, inconsistent results were reported. This review addresses general aspects of vitamin D deficiency and, in particular, the significance of vitamin D hypovitaminosis in the outcome of HBVand HCV-related chronic liver diseases. Furthermore,current literature was reviewed in order to understand the effects of vitamin D supplementation in combination with IFN-based therapy on the virological response in HBV and HCV infected patients.展开更多
Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport systems.The network topology in IoV paradigm is highly d...Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport systems.The network topology in IoV paradigm is highly dynamic.Clustering is one of the promising solutions to maintain the route stability in the dynamic network.However,existing algorithms consume a considerable amount of time in the cluster head(CH)selection process.Thus,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of vehicles.The MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean distance.The CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%.展开更多
In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic informatio...In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.展开更多
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neur...The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.展开更多
The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,whi...The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.展开更多
基金supported by Duy Tan University,Da Nang,Vietnam.
文摘The representative collective digital signature,which was suggested by us,is built based on combining the advantages of group digital signature and collective digital signature.This collective digital signature schema helps to create a unique digital signature that deputizes a collective of people representing different groups of signers and may also include personal signers.The advantage of the proposed collective signature is that it can be built based on most of the well-known difficult problems such as the factor analysis,the discrete logarithm and finding modulo roots of large prime numbers and the current digital signature standards of the United States and Russian Federation.In this paper,we use the discrete logarithmic problem on prime finite fields,which has been implemented in the GOST R34.10-1994 digital signature standard,to build the proposed collective signature protocols.These protocols help to create collective signatures:Guaranteed internal integrity and fixed size,independent of the number of members involved in forming the signature.The signature built in this study,consisting of 3 components(U,R,S),stores the information of all relevant signers in the U components,thus tracking the signer and against the“disclaim of liability”of the signer later is possible.The idea of hiding the signer’s public key is also applied in the proposed protocols.This makes it easy for the signing group representative to specify which members are authorized to participate in the signature creation process.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)(No.NRF-2021R1A2C2004109)the Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(No.P0020612,2022 The Competency Development Program for Industry Specialist).
文摘Wearable biosensors have received great interest as patient-friendly diagnostic technologies because of their high flexibility and conformability.The growing research and utilization of novel materials in designing wearable biosensors have accelerated the development of point-of-care sensing platforms and implantable biomedical devices in human health care.Among numerous potential materials,conjugated polymers(CPs)are emerging as ideal choices for constructing high-performance wearable biosensors because of their outstanding conductive and mechanical properties.Recently,CPs have been extensively incorporated into various wearable biosensors to monitor a range of target biomolecules.However,fabricating highly reliable CP-based wearable biosensors for practical applications remains a significant challenge,necessitating novel developmental strategies for enhancing the viability of such biosensors.Accordingly,this review aims to provide consolidated scientific evidence by summarizing and evaluating recent studies focused on designing and fabricating CP-based wearable biosensors,thereby facilitating future research.Emphasizing the superior properties and benefits of CPs,this review aims to clarify their potential applicability within this field.Furthermore,the fundamentals and main components of CP-based wearable biosensors and their sensing mechanisms are discussed in detail.The recent advancements in CP nanostructures and hybridizations for improved sensing performance,along with recent innovations in next-generation wearable biosensors are highlighted.CPbased wearable biosensors have been—and will continue to be—an ideal platform for developing effective and user-friendly diagnostic technologies for human health monitoring.
文摘Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.
基金the National Natural Science Foundation of China(NSFC)under Grant Nos.12272124 and 11972146.
文摘Isogeometric analysis (IGA) is known to showadvanced features compared to traditional finite element approaches.Using IGA one may accurately obtain the geometrically nonlinear bending behavior of plates with functionalgrading (FG). However, the procedure is usually complex and often is time-consuming. We thus put forward adeep learning method to model the geometrically nonlinear bending behavior of FG plates, bypassing the complexIGA simulation process. A long bidirectional short-term memory (BLSTM) recurrent neural network is trainedusing the load and gradient index as inputs and the displacement responses as outputs. The nonlinear relationshipbetween the outputs and the inputs is constructed usingmachine learning so that the displacements can be directlyestimated by the deep learning network. To provide enough training data, we use S-FSDT Von-Karman IGA andobtain the displacement responses for different loads and gradient indexes. Results show that the recognition erroris low, and demonstrate the feasibility of deep learning technique as a fast and accurate alternative to IGA formodeling the geometrically nonlinear bending behavior of FG plates.
文摘Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.
基金funded through Researchers Supporting Project Number(RSPD2024R996)King Saud University,Riyadh,Saudi Arabia。
文摘Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.
文摘A numerical analysis is performed to analyze the bioconvective double diffusive micropolar non-Newtonian nanofluid flow caused by stationary porous disks.The consequences of the current flow problem are further extended by incorporating the Brownian and thermophoresis aspects.The energy and mass species equations are developed by utilizing the Cattaneo and Christov model of heat-mass fluxes.The flow equations are converted into an ordinary differential model by employing the appropriate variables.The numerical solution is reported by using the MATLAB builtin bvp4c method.The consequences of engineering parameters on the flow velocity,the concentration,the microorganisms,and the temperature profiles are evaluated graphically.The numerical data for fascinating physical quantities,namely,the motile density number,the local Sherwood number,and the local Nusselt number,are calculated and executed against various parametric values.The microrotation magnitude reduces for increasing magnetic parameters.The intensity of the applied magnetic field may be utilized to reduce the angular rotation which occurs in the lubrication processes,especially in the suspension of flows.On the account of industrial applications,the constituted output can be useful to enhance the energy transport efficacy and microbial fuel cells.
基金by the National Foundation for Science and Technology Development of Vietnam(No.103.04-2017.37)。
文摘This paper presents the calibration of a neutron dose rate meter and the evaluation of its calibration factors(CFs)in several neutron standard fields(i.e.,two standard fields with bare sources of252Cf and241Am-Be,and five simulated workplace fields with241Am-Be moderated sources).The calibration in standard fields with bare sources was conducted by following the recommendations of the ISO 8529 standard.The measured total neutron ambient dose equivalent rates,denoted as H*(10)tot,were analyzed to obtain direct components,denoted as H*(10)dir,using a reduced fitting method.The CF was then calculated as the ratio between the conventional true value of the neutron ambient dose equivalent rate in a free field,denoted as H*(10)FF,and the value of H*(10)dir.In contrast,in the simulated workplace neutron fields,the calibration of the neutron dose rate meter was conducted by following the ISO 12789 standard.The CF was calculated as the ratio between the values of H*(10)totmeasured by a standard instrument(i.e.,Bonner sphere spectrometer)and the neutron dose rate meter.The CF values were obtained in the range of 0.88–1.0.The standard uncertainties(k=1)of the CFs were determined to be in the range of approximately 6.6–13.1%.
基金supported by the Russian Science Foundation(22-14-00037)to N.A.P.(phylogenetic analyses)National Natural Science Foundation of China(32130015)to K.W.(data collection)partially by Rufford Foundation(39897-1) to N.T.V.(data collection)。
文摘We describe a unique new species and genus of agamid lizard from the karstic massifs of Khammouan Province,central Laos.Laodracon carsticola Gen.et sp.nov.is an elusive medium-sized lizard(maximum snout-vent length101 mm)specifically adapted to life on limestone rocks and pinnacles.To assess the phylogenetic position of the new genus amongst other agamids,we generated DNA sequences from two mitochondrial gene fragments(16S rRNA and ND2)and three nuclear loci(BDNF,RAG1 and c-mos),with a final alignment comprising 7418 base pairs for 64 agamid species.Phylogenetic analyses unambiguously place the new genus in the mainland Asia subfamily Draconinae,where it forms a clade sister to the genus Diploderma from East Asia and the northern part of Southeast Asia.Morphologically,the new genus is distinguished from all other genera in Draconinae by possessing a notably swollen tail base with enlarged scales on its dorsal and ventral surfaces.Our work provides further evidence that limestone regions of Indochina represent unique“arks of biodiversity”and harbor numerous relict lineages.To date,Laodracon carsticola Gen.et sp.nov.is known from only two adult male specimens and its distribution seems to be restricted to a narrow limestone massif on the border of Khammouan and Bolikhamxai provinces of Laos.Additional studies are required to understand its life history,distribution,and conservation status.
基金supported by Thailand Research Fund2019 (MRG6280203)the Unit of Excellence 2023 on Biodiversity and Natural Resources Management,University of Phayao (FF66-Uo E003,specimen collection) to C.S.partially by the Russian Science Foundation (22-14-00037, molecular phylogenetic analyses) to N.A.P。
文摘DEAR EDITOR,We report on a new species, Zhangixalus melanoleucus sp.nov., from Phou Samsoum Mountain(PSM) in Xiengkhouang Province, northeastern Laos, based on an integrative taxonomic approach, including morphological, molecular, and bioacoustic lines of evidence. Morphologically, the new species can be distinguished from its congeners by a combination of the following diagnostic characters: medium body size(SVL 34.4–36.3 mm in males, 53.7 mm in a single female);dorsum smooth and green;chest and belly lacking spots;flanks, axillae, ventral surfaces of forearms, inguinal.
基金supported by the Natural Science Foundation of Jiangsu Province(Grant No.BK20180855)Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures(Grant No.MCMS-E-0219Y01)Research and Practice Innovation Program of postgraduates in Jiangsu Province(Grant No.KYCX20-3076)。
文摘Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in flight.In this study,a nonlinear finite element(FE)model is developed to investigate the dynamic response and damage behavior of hybrid corrugated sandwich structures subjected to high velocity hail ice impact.The impact and breaking behavior of hail are described using the FE-smoothed particle hydrodynamics(FE-SPH)method.A rate-dependent progressive damage model is employed to capture the intra-laminar damage response;cohesive element and surface-based cohesive contact are implemented to predict the inter-laminar delamination and sheet/core debonding phenomena respectively.The transient processes of sandwich structure under different hail ice impact conditions are analyzed.Comparative analysis is conducted to address the influences of core shape and impact position on the impact performance of sandwich structures and the corresponding energy absorption characteristics are also revealed.
文摘In today’s information technology(IT)world,the multi-hop wireless sensor networks(MHWSNs)are considered the building block for the Internet of Things(IoT)enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service(QoS)in a stipulated time slot to end-user over the Internet.Smart city(SC)is an example of one such application which can automate a group of civil services like automatic control of traffic lights,weather prediction,surveillance,etc.,in our daily life.These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput,energy efficiency,and end-to-end delay,wherein low latency is considered a challenging issue in next-generation networks(NGN).This paper introduces a single and parallels stable server queuing model with amulti-class of packets and native and coded packet flowto illustrate the simple chain topology and complexmultiway relay(MWR)node with specific neighbor topology.Further,for improving data transmission capacity inMHWSNs,an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node.Finally,the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets.The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results.
基金This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government(MSIT)-NRF-2020R1A2B5B02002478.
文摘Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.
基金Supported by Vietnam National Foundation for Science and Technology Development(NAFOSTED,103.01-2023.16)。
文摘We present one-loop contributions for h→ℓℓγ with ℓ=νe,μ,τ,e,μ and e−e+→hγ in the U(1)_(B−L) extension of the standard model. In the phenomenological results, the signal strengths for h→ℓℓγ at the Large Hadron Collider and for e−e+→hγ at future lepton colliders are analyzed in the physical parameter space for both the vector and chiral B−L models. We found that the contributions from the neutral gauge boson Z′ to the signal strengths are rather small. Consequently, the effects will be difficult to probe at future colliders. However, the impacts of charged Higgs and CP-odd Higgs in the chiral B−L model on the signal strengths are significant and can be measured with the help of the initial polarization beams at future lepton colliders.
基金funded by the National Science Foundation of China(41807259)the Innovation-Driven Project of Central South University(No.2020CX040)the Shenghua Lieying Program of Central South University(Principle Investigator:Dr.Jian Zhou)。
文摘A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.
基金financial support from Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number 108.02-2017.15Thirumalaisamy P Velavan acknowledges the support from Federal Ministry of Edu-cation and Research,Germany(BMBF01DP17047)
文摘The secosteroid hormone vitamin D has, in addition to its effects in bone metabolism also functions in the modulation of immune responses against infectious agents and in inhibiting tumorigenesis. Thus, deficiency of vitamin D is associated with several malignancies, but also with a plethora of infectious diseases. Among other communicable diseases, vitamin D deficiency is involved in the pathogenesis of chronic liver diseases caused by hepatitis B and C viruses(HBV, HCV) and high prevalence of vitamin D deficiency with serum levels below 20 mg/mL in patients with HBV and HCV infection are found worldwide. Several studies have assessed the effects of vitamin D supplementation on the sustained virological response(SVR) to interferon(IFN) plus ribavirin(RBV) therapy in HBV and HCV infection. In these studies, inconsistent results were reported. This review addresses general aspects of vitamin D deficiency and, in particular, the significance of vitamin D hypovitaminosis in the outcome of HBVand HCV-related chronic liver diseases. Furthermore,current literature was reviewed in order to understand the effects of vitamin D supplementation in combination with IFN-based therapy on the virological response in HBV and HCV infected patients.
基金This work was supported by National Natural Science Foundation of China(No.61821001)Science and Tech-nology Key Project of Guangdong Province,China(2019B010157001).
文摘Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport systems.The network topology in IoV paradigm is highly dynamic.Clustering is one of the promising solutions to maintain the route stability in the dynamic network.However,existing algorithms consume a considerable amount of time in the cluster head(CH)selection process.Thus,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of vehicles.The MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean distance.The CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%.
基金supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)Project of Environmental Business Big Data Platform and Center Construction funded by the Ministry of Science and ICT。
文摘In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.
基金the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)Project of Environmental Business Big Data Platform and Center Construction funded by the Ministry of Science and ICT.
文摘The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.
文摘The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.
