Image classification is one of the most important tasks of remote sensing information processing used for object recognition. In this paper, a novel scheme is proposed to improve the accuracy of hyperspectral image classification by amalgamating multiple feature vector sets and ensemble methods with different classifiers. Extracting the texture, color and object features of the satellite images...

BACKGROUND The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. NEW METHOD We proposed to use an ensemble of suppor...

Conventional active learning (AL) frameworks aim to reduce the cost of data annotation by actively requesting labeling for most informative points. However, introducing AL hungry deep algorithms has been a challenge. Some proposed approaches include uncertainty-based techniques, geometric methods, implicit combination and approaches, more recently, based on semi/self supervised techniques. In t...

There are so many social networking sites available. Tweets have evolved into a crucial tool for gathering people's thoughts, ideas, behaviours and sentiments surrounding particular entities. One of the most intriguing subjects in this context is analyzing sentiment tweets using natural language processing (NLP). Although several methods been created, accuracy effectiveness those analysis yet t...

Subsampling of received wireless signals is important for relaxing hardware requirements as well the computational cost signal processing algorithms that rely on output samples. We propose a subsampling technique to facilitate use deep learning automatic modulation classification in communication systems. Unlike traditional approaches pre-designed strategies are solely based expert knowledge, p...