حسين عقيل حسين الاعسم

Internet of Things (IoT) has connected millions of devices. IoT has enabled the use of smart services in all parts of daily life. Determining the position of objects in their internal environments is a challenge. Locating indoor environments is one of the concerns in using IoT. Several methods have been provided for locating in indoor environments. Methods based on deep learning are among the successful methods in determining the location of objects on the internet in indoor locations. The methods based on deep learning with advantages such as the ability to generalize and make decisions and high accuracy in determining the location of the object are desired. The main goal of this paper is to introduce deep learning methods in determining the internal location of objects.

Abstract. This article reviews and discusses feature extraction techniques used for text classification as well as natural language processing in biomedical applications.This researchaims to analyze the similarities of techniques used as technology and algorithms that have become more sophisticated to optimize feature extraction. In feature extraction, a specific of words is taken out from text data. After that, they transform into a feature set to be usable by a classifier. Several algorithms have been identified for classification,including but not limited to SVM, deep neural networks as well as Naïve Bayes algorithms. Next, the natural language is processed and achieves better performance results as indicated by certain metrics like execution time, specificity, accuracy, specificity,and sensitivity.