Apparently, it seems that our knowledge of computers will improve gradually. Certainly, it can be said that the topic of machine learning play a highly significant role in the field of computer science and game technology. This paper describes machine learning algorithms, feature selection methods,...
CONFLICT OF INTEREST: None Reported READ MORE July 6, 2019 Error in Text, Table 1, Figure 2 and Supplement Jenny Lo-Ciganic, PhD | University of Florida, College of Pharmacy In the Original Investigation titled “Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose among Medic...
Land uses (LU) in a region are discerned through the classification of spatial data based on supervised and unsupervised algorithms. The unsupervised classification is based on clusters of pixels without prior knowledge of common spectral signatures and characteristics. Commonly used techniques include K...
In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k...
The recent popularisation and expansion of high-throughput sequencing and bioinformatics tools have facilitated large-scale genomic-phenomic investigations and comparisons between or among species [20,21]. In particular, machine-learning (ML) algorithms are enhancing essentiality predictions and comparative...
In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k...
In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms ...
49it is feasible to consider how clinicians of the future will have access to symptom profiles linked to biomarkers through machine learning algorithms. A first-use case might be for detection of high-risk patients; for example, those with symptoms such as loss of touch with reality (loss of...
It is often desirable to assess the properties of a learning algorithm. Frequently such evaluation take the form of comparing the relative suitability of a set of algorithms for a specific task or class of tasks. Learning algorithm evaluation is the process of performing such assessment of a lear...
Scikit-learn is a Python module that integrates a wide range of machine learning algorithms for both supervised and unsupervised problems (Pedregosa et al., 2011). This case study implements the NB classifier and uses “SVC”, “RandomForestClassifier”, “MLPClassifier”, “GradientBoostingClassifie...