S. A. Edalatpanah

Ramin Safa, S. A. Edalatpanah, Ali Sorourkhah

Mental disorders such as depression and suicidal ideation are hazardous, affecting more than 300 million people over the world. However, on social media, mental disorder symptoms can be observed, and automated approaches are increasingly capable of detecting them. The considerable number of social media users and the tremendous quantity of user-generated data on social platforms provide a unique opportunity for researchers to distinguish patterns that correlate with mental status. This research offers a roadmap for analysis, where mental state detection can be based on machine learning techniques. We describe the common approaches for predicting and identifying the disorder using user-generated content. This research is organized according to the data collection, feature extraction, and prediction algorithms. Furthermore, we review several recent studies conducted to explore different features of candidate profiles and their analytical methods. Following, we debate various aspects of the development of experimental auto-detection frameworks for identifying users who suffer from disorders, and we conclude with a discussion of future trends. The introduced methods can help complement screening procedures, identify at-risk people through social media monitoring on a large scale, and make disorders easier to treat in the future.

Zeinab Khodaverdian, Hossein Sadr, Seyed Ahmad Edalatpanah et al.

Cloud computing service models have experienced rapid growth and inefficient resource usage is known as one of the greatest causes of high energy consumption in cloud data centers. Resource allocation in cloud data centers aiming to reduce energy consumption has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into sensitive or insensitive classes to latency, and thereafter suitable VMs can be selected for migration. In this paper, the combination of Convolution Neural Network (CNN) and Gated Recurrent Unit (GRU) is utilized for the classification of VMs in the Microsoft Azure dataset. Due to the fact the majority of VMs in this dataset are labeled as insensitive to latency, migration of more VMs in this group not only reduces energy consumption but also decreases the violation of Service Level Agreements (SLA). Based on the empirical results, the proposed model obtained an accuracy of 95.18which clearly demonstrates the superiority of our proposed model compared to other existing models.