Abstract Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its hyper-parameters must be …

Deep neural networks (DNNs) have achieved great success in many applications. The
architectures of DNNs play a crucial role in their performance, which is usually manually …

Machine learning models are used today to solve problems within a broad span of
disciplines. If the proper hyperparameter tuning of a machine learning classifier is …

In this work, we propose hyperparameters optimization using grid search to optimize the
parameters of eight existing models and apply the best parameters to predict the outcomes …

Since deep neural networks were developed, they have made huge contributions to
everyday lives. Machine learning provides more rational advice than humans are capable of …

The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …

The prevention of intrusion is deemed to be a cornerstone of network security. Although
excessive work has been introduced on network intrusion detection in the last decade …

Motion analysis is used in computer vision to understand the behaviour of moving objects in
sequences of images. Optimizing the interpretation of dynamic biological systems requires …