This paper proposed the principles of data analysis in order to present the prototype of
mushroom classification based on physical characteristics. We created a model of …

Nowadays, electricity theft has been a major problem worldwide. Although many single-
classification algorithms or an ensemble of single learners (ie, homogeneous ensemble …

The k NN machine learning method is widely used as a classifier in Human Activity
Recognition (HAR) systems. Although the k NN algorithm works similarly both online and in …

Nearest Neighbors (k-NN) is a well-established algorithm for classification widely used in
various machine learning applications. Although-NN has many advantages, it suffers …

Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile
communication because of the spectrum scarcity in actual mobile technologies such as 4G …

K-Nearest Neighbor (kNN) is an efficient algorithm used in many applications, eg, text
categorization, data mining, and predictive analysis. Despite having a high computational …

Mainstream satellite cloud masking algorithms are prone to mis-masking in haze-polluted
areas, which may cause errors in aerosol radiative effect calculations and attribution of …

This study focuses on meeting the challenges of big data visualization by using of data
reduction methods based the feature selection methods. To reduce the volume of big data …

k-NN, as one of the well-employed classification algorithms, severely suffers from a
computationally intensive nature. This paper exploits the parallelism and digit level …

This work aimed to enhance a previous neural network hardware implementation based on
an efficient combination of Stochastic Computing (SC) and Morphological Neural Networks …