Traditionally, non-terrestrial networks (NTNs) are used for a limited set of applications, such as TV broadcasting and communication support during disaster relief. Nevertheless, due to their technological improvements integration in the 5G 3GPP standards, NTNs have been gaining importance last years will provide further applications services. standardization is integrating low-Earth orbit (LEO...

Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores possibility using generative learning techniques for automatic assessment creativity. The proposed solution does not involve human judgement, it modular and general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden’s definition as compose...

This paper discusses the performance of four popular machine learning techniques for predicting heart failure using a publicly available dataset from kaggle.com, which are Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Logistic Regression (LR). They were selected due to their good in medical-related applications. Heart is common public health problem, there need improve...

Convolutional neural networks (CNNs) are the specific architecture of feed-forward artificial networks. It is de-facto standard for various operations in machine learning and computer vision. To transform this performance towards task network anomaly detection cyber-security, study proposes a model using one-dimensional CNN architecture. The authors' approach divides traffic data into transmiss...

Electroencephalogram (EEG) signals reveal electrical activity of brain in a person. Brain cells interact by impulses even during sleep. Any disruptions to these induce problems the individual. Hence clinicians analyze EEG readings comprehend impulses. and its sub bands depict pattern human brain. data comprises transient components, spikes, other sorts artifacts due eye blinking, movement indiv...