Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.
Spherical fuzzy set (SFS) is one of the most important and extensive concept to accommodate more uncertainties than existing fuzzy set structures. In this article, we will describe a novel enhanced TOPSIS-based procedure for tackling multi attribute group decision making (MAGDM) issues under spherical fuzzy setting, in which the weights of both decision-makers (DMs) and criteria are totally unknown. First, we study the notion of SFSs, the score and accuracy functions of SFSs and their basic operating laws. In addition, defined the generalized distance measure for SFSs based on spherical fuzzy entropy measure to compute the unknown weights information. Secondly, the spherical fuzzy information-based decision-making technique for MAGDM is presented. Lastly, an illustrative example is delivered with robot selection to reveal the efficiency of the proposed spherical fuzzy decision support approach, along with the discussion of comparative results, to prove that their results are feasible and credible.
Background:
The amino acid residues, in protein, undergo post-translation modification
(PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn
alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification
which is known to be associated with regulation of various biological functions and pathological processes.
Thus its identification is necessary to understand its mechanism. Experimental determination through
site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process,
thus, the reliable computational model is required for identification of sulfotyrosine sites.
Methodology:
In this paper, we present a computational model for the prediction of the sulfotyrosine
sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of
protein amino acid sequences and various position/composition relative features. These features are
incorporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and
independent testing.
Results:
Accuracy determined through validation was 93.93% for jackknife test, 95.16% for crossvalidation,
94.3% for self-consistency and 94.3% for independent testing.
Conclusion:
The proposed model has better performance as compared to the existing predictors, however,
the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.
Abstract:Mobile phones have become an essential part of our lives because we depend on them to perform many tasks, and they contain personal and important information. The continuous growth in the number of Android mobile applications resulted in an increase in the number of malware applications, which are real threats and can cause great losses. There is an urgent need for efficient and effective Android malware detection techniques. In this paper, we present an adaptive neuro-fuzzy inference system with fuzzy c-means clustering (FCM-ANFIS) for Android malware classification. The proposed approach utilizes the FCM clustering method to determine the optimum number of clusters and cluster centers, which improves the classification accuracy of the ANFIS. The most significant permissions used in the Android application selected by the information gain algorithm are used as input to the proposed approach (FCM-ANFIS) to classify applications as either malware or benign applications. The experimental results show that the proposed approach (FCM-ANFIS) achieves the highest classification accuracy of 91%, with lowest false positive and false negative rates of 0.5% and 0.4%, respectively.
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