Amir Javadpour

S. Rezaei, J. P. McKean, M. Biehl et al.

We present DECORAS, a deep learning based approach to detect both point and extended sources from Very Long Baseline Interferometry (VLBI) observations. Our approach is based on an encoder-decoder neural network architecture that uses a low number of convolutional layers to provide a scalable solution for source detection. In addition, DECORAS performs source characterization in terms of the position, effective radius and peak brightness of the detected sources. We have trained and tested the network with images that are based on realistic Very Long Baseline Array (VLBA) observations at 20 cm. Also, these images have not gone through any prior de-convolution step and are directly related to the visibility data via a Fourier transform. We find that the source catalog generated by DECORAS has a better overall completeness and purity, when compared to a traditional source detection algorithm. DECORAS is complete at the 7.5$σ$ level, and has an almost factor of two improvement in reliability at 5.5$σ$. We find that DECORAS can recover the position of the detected sources to within 0.61 $\pm$ 0.69 mas, and the effective radius and peak surface brightness are recovered to within 20 per cent for 98 and 94 per cent of the sources, respectively. Overall, we find that DECORAS provides a reliable source detection and characterization solution for future wide-field VLBI surveys.

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, 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