Applications of facial emotion classification is gaining popularity in the world. There are many ways to train a model classify human expressions by use existing technologies. The strategy order and recognize feelings an individual conveyed his expression done contrasting it gathered set labelled experiences feelings. In this paper, we propose making intelligent system that will emotions. A mul...

Area of classifying satellite imagery has become a challenging task in current era where there is tremendous growth in settlement i.e. construction of buildings, roads, bridges, dam etc. This paper suggests an improvised k-means and Artificial Neural Network (ANN) classifier for land-cover mapping of Eastern Himalayan state Sikkim. The improvised k-means algorithm shows satisfactory results com...

Data classification is one of the investment research areas in the field of data mining. Machine learning algorithms such as naive bayes, neural network, and support vector machine are most regularly used for performing the classification task. Supervised learning is one of its kinds where the datasets consist of class labels and the machine learning classifier are trained first using that. It ...

One of the most important topics in the sonar sound data processing is proposing a powerful classifier to detect the sound source. In this paper we propose a classifier with proper accuracy. First, proper features should be extracted from sound data; Features could extract from time or frequency domains. Whenever fastness is important, time features are most effective. Otherwise, frequency doma...

Classifier ensembles have been one of the main topics of interest in the neural networks, machine learning and pattern recognition communities during the past fifteen years [21,28,16,17,26,36,27,23,11]. They are currently one of the state of the art techniques available for the design of classification systems and an effective option to the traditional approach based on the design of a single, ...

In this study, we evaluate the accuracy of classifiers for classification of ultrasonic liver tissues. Two different statistic classifiers ami three various artificial neural networks are included: Bayes classifier k-nearest neighbor classifier. Back-propagation neural networks, probabilistic neural network and modified probabilistic neural network. These Jive different classifiers were investi...