unsupervised machine learning (UML), in an effort to model the impact of extreme air pollutants on cardiovascular and respiratory …

in the range [0.01-0.5] on a GPU-enabled TensorFlow and experiments of traditional machine learning algorithms are done … traditional machine learning classifiers. The primary reason is that RNN architectures are able to store information for long …

recognition accuracy by applying elementary image processing with rough tuning and supplementing with methods of machine learning …. The major contributions of this work are the architecture of machine learning specified for this task, the dataset, and … the evaluation metrics, which indicate the performance of OMR system, provide objective function for machine learning and …

of machine learning, with emphasis on models of deep learning. The results showed trained CNNs can overcome the … performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves …

article describes a customized machine-learning method for remote fault detection and a computer vision framework for cloud …

obfuscation features and then adjust the selection of meaningful features to improve corresponding machine learning analysis. The …