In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Drought is a natural phenomenon in many arid, semi-arid, or wet regions. This showed that no region worldwide is excluded from the occurrence of drought. Extreme droughts were caused by global weather warming and climate change. Therefore, it is essential to review the studies conducted on drought to use the recommendations made by the researchers on drought.  The drought was classified into meteorological, agricultural, hydrological, and economic-social. In addition, researchers described the severity of the drought by using various indices which required different input data.  The indices used by various researchers were the Joint Deficit Index (JDI), Effective Drought Index (EDI), Streamflow Drought Index (SDI), Sta

Mining association rules is a popular and well-studied method of data mining tasks whose primary aim is the discovers of the correlation among sets of items in the transactional databases. However, generating high- quality association rules in a reasonable time from a given database has been considered as an important and challenging problem, especially with the fast increasing in database's size. Many algorithms for association rules mining have been already proposed with promosing results. In this paper, a new association rules mining algorithm based on Bees Swarm Optimization metaheuristic named Modified Bees Swarm Optimization for Association Rules Mining (MBSO-ARM) algorithm is proposed. Results show that the proposed algorithm can

It has increasingly been recognised that the future developments in geospatial data handling will centre on geospatial data on the web: Volunteered Geographic Information (VGI). The evaluation of VGI data quality, including positional and shape similarity, has become a recurrent subject in the scientific literature in the last ten years. The OpenStreetMap (OSM) project is the most popular one of the leading platforms of VGI datasets. It is an online geospatial database to produce and supply free editable geospatial datasets for a worldwide. The goal of this paper is to present a comprehensive overview of the quality assurance of OSM data. In addition, the credibility of open source geospatial data is discussed, highlight

     The rapid evolution of wireless networking technologies opens the door to the evolution of the Wireless Sensor Networks (WSNs) and their applications in different fields. The WSN consists of small energy sensor nodes used in a harsh environment. The energy needed to communicate between the sensors networks can be identified as one of the major challenges. It is essential to avoid massive loss, or loss of packets, as well as rapid energy depletion and grid injustice, which lead to lower node efficiency and higher packet delivery delays. For this purpose, it was very important to track the usage of energy by nodes in order to improve general network efficiency by the use of intelligent methods to reduce the energy

     Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicate

Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. T

— In light of the pandemic that has swept the world, the use of e-learning in educational institutions has become an urgent necessity for continued knowledge communication with students. Educational institutions can benefit from the free tools that Google provide and from these applications, Google classroom which is characterized by ease of use, but the efficiency of using Google classroom is affected by several variables not studied in previous studies Clearly, this study aimed to identify the use of Google classroom as a system for managing e-learning and the factors affecting the performance of students and lecturer. The data of this study were collected from 219 members of the faculty and students at the College of Administra