Search

The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. [ABSTRACT FROM AUTHOR]

This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function. [ABSTRACT FROM AUTHOR]

In many real applications, data are not all available at the same time, or it is not affordable to process them all in a batch process, but rather, instances arrive sequentially in a stream. The scenario of streaming data introduces new challenges to the machine learning community, since difficult decisions have to be made. The problem addressed in this paper is that of classifying incoming instances for which one attribute arrives only after a given delay. In this formulation, many open issues arise, such as how to classify the incomplete instance, whether to wait for the delayed attribute before performing any classification, or when and how to update a reference set. Three different strategies are proposed which address these issues differently. Orthogonally to these strategies, three classifiers of different characteristics are used. Keeping on-line learning strategies independent of the classifiers facilitates system design and contrasts with the common alternative of carefully crafting an ad hoc classifier. To assess how good learning is under these different strategies and classifiers, they are compared using learning curves and final classification errors for fifteen data sets. Results indicate that learning in this stringent context of streaming data and delayed attributes can successfully take place even with simple on-line strategies. Furthermore, active strategies behave generally better than more conservative passive ones. Regarding the classifiers, it was found that simple instance-based classifiers such as the well-known nearest neighbor may outperform more elaborate classifiers such as the support vector machines, especially if some measure of classification confidence is considered in the process. [ABSTRACT FROM AUTHOR]

Ensemble of classifiers constitutes one of the main current directions in machine learning and data mining. It is accepted that the ensemble methods can be divided into static and dynamic ones. Dynamic ensemble methods explore the use of different classifiers for different samples and therefore may get better generalization ability than static ensemble methods. However, for most of dynamic approaches based on KNN rule, additional part of training samples should be taken out for estimating 'local classification performance' of each base classifier. When the number of training samples is not sufficient enough, it would lead to the lower accuracy of the training model and the unreliableness for estimating local performances of base classifiers, so further hurt the integrated performance. This paper presents a new dynamic ensemble model that introduces cross-validation technique in the process of local performances' evaluation and then dynamically assigns a weight to each component classifier. Experimental results with 10 UCI data sets demonstrate that when the size of training set is not large enough, the proposed method can achieve better performances compared with some dynamic ensemble methods as well as some classical static ensemble approaches. [ABSTRACT FROM AUTHOR]