A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

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.

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease(HD)is a serious widespread life-threatening disease.The heart of patients with HD fails to pump sufcient amounts of blood to the entire body.Diagnosing the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment.Classical methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related symptoms.As an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD conditions.However,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with HD.In this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)model.The MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD identication.The CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD features.We evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation criteria.The AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on average.Therefore,the AHDD system proposed herein has the highest capability to identify patients with HD.This system can be used by medical practitioners to diagnose HD efciently。

DeepIoT.IDS:Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points.Recently,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks.However,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks.Therefore,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of attacks.Consequently,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(DeepIoT.IDS)model to detect existing and novel cyberattacks.The HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)classifier.The CICIDS2017 dataset is selected to evaluate the DeepIoT.IDS model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced classes.We have compared the performance of the DeepIoT.IDS model with three recent models.The results show the DeepIoT.IDS model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,respectively.Furthermore,it can detect the occurrence of low-frequency attacks that are undetectable by other models.

Formulating layered adjustable autonomy for unmanned aerial vehicles

Purpose–The purpose of this paper is to propose a layered adjustable autonomy(LAA)as a dynamically adjustable autonomy model for a multi-agent system.It is mainly used to efficiently manage humans’and agents’shared control of autonomous systems and maintain humans’global control over the agents.Design/methodology/approach–The authors apply the LAA model in an agent-based autonomous unmanned aerial vehicle(UAV)system.The UAV system implementation consists of two parts:software and hardware.The software part represents the controller and the cognitive,and the hardware represents the computing machinery and the actuator of the UAV system.The UAV system performs three experimental scenarios of dance,surveillance and search missions.The selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant results.Findings–The results of the UAV system tests prove that segregating the autonomy of a system as multidimensional and adjustable layers enables humans and/or agents to perform actions at convenient autonomy levels.Hence,reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents,increasing humans’workload and exposing the system to disturbances.Originality/value–The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable autonomy.Assessing the autonomy within three phases of agents run cycle(taskselection,actions-selection and actions-execution)is an original idea that aims to direct agents’autonomy toward performance competency.The agents’abilities are well exploited when an incompetent agent switches with a more competent one.