Amir Javadpour

Amir Javadpour, Samira Rezaei, Kuan-Ching Li et al.

Due to the fast-growing volume of text documents and reviews in recent years, current analyzing techniques are not competent enough to meet the users' needs. Using feature selection techniques not only support to understand data better but also lead to higher speed and also accuracy. In this article, the Whale Optimization algorithm is considered and applied to the search for the optimum subset of features. As known, F-measure is a metric based on precision and recall that is very popular in comparing classifiers. For the evaluation and comparison of the experimental results, PART, random tree, random forest, and RBF network classification algorithms have been applied to the different number of features. Experimental results show that the random forest has the best accuracy on 500 features. Keywords: Feature selection, Whale Optimization algorithm, Selecting optimal, Classification algorithm

Amir Javadpour, Sanaz Kazemi Abharian, Guojun Wang

Characteristics and way of behavior of attacks and infiltrators on computer networks are usually very difficult and need an expert In addition; the advancement of computer networks, the number of attacks and infiltrations are also increasing. In fact, the knowledge coming from an expert will lose its value over time and must be updated and made available to the system and this makes the need for the expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such as statistical models, safe system approach, neural networks, etc., all weaken the fact that it uses all the features of an information packet rotating in the network for intrusion detection. Also, the huge volume of information and the unthinkable state space is also an important issue in the detection of intrusion. Therefore, the need for automatic identification of new and suspicious patterns in an attempt for intrusion with the use of more efficient methods Lower cost and higher performance is needed more than before. The purpose of this study is to provide a new method based on intrusion detection systems and its various architectures aimed at increasing the accuracy of intrusion detection in cloud computing. Keywords : intrusion detection, feature Selection, classification Algorithm, machine learning, neural network.

Amir Javadpour, HamidrezaMemarzadeh-Tehran

Thispaper presents the design and realization of a context-aware wireless health monitoring system for recording the heartbeat (HR) and respiration (RR) rate based on an indirect measurement approach. The system consists of a contact-less medical sensor as well as a communication infrastructure for handling the transmission and reception of the measured results. The contact-less sensor includes a highly sensitive tri-axial accelerometer, an accurate temperature and air pressure sensor that enable one to inspect patients' health condition by continuously monitoring of two critical signs related to the cardiorespiratory system. The developed system can also be utilized in performing a number of long-term inspection on the heart and lungs while measuring the HR and RR values in addition to calculating the HR and RR ratio, which is denoted by HRR. The obtained results show the potential of the developed system for versatile monitoring applications applied to telemedicine