The World of Computer Science and Information Technology Journal

6

Authors: Farid Bourennani, Mike Bourque

Schema matching (SM) is a fundamental task of data integration and data warehousing. Often SM is performed manually which is time consuming and error prone. Furthermore, existing SM tools do not scale well to large schemas. To alleviate these challenges, a novel tool is proposed for automated schema mapping based on the content by matching data entities exclusively based on the content. The resulting topology is convenient to visually explore the relationship among database entities even in large volume. Also, a post-processing algorithm based on data types is proposed for further enhancement clustering results. We present a case study to demonstrate the efficiency and the practicality of the proposed tool.

5

Authors: Hoda Abdelhafez, Maram Alamri, Riyof Alomari, Bayader Alzoman, Rfeef BinSheeha, Ayah Albawardi, Rehab Alzahrani

The mobile attendance systems are used to reduce the time and effort wasted in taking attendance in the colleges. Our research focuses on the creating mobile attendance system using Android studio and database to avoid lots of paper work and prevent data loss. This system applies QR code for each subject, so each faculty member shows the QR code to the students to scan it and make them self-present. The other option is that the faculty member takes attendance by calling the name of the students in class. If the student attends, the faculty member will pull the button ant it will change to green (the student is presence), if the student absent the button remain the same without pulling it. Faculty members can manage and check absences of students. Student can only make attendance for each subject and check their absence hours. The output will be the number of students’ absences weekly or monthly. The developed system helps faculty members to mange attendance easily and efficiently.

5

Authors: Fatima T. Al-Khawaldeh

There are many reasons behind research on speculation and negation: there is a lot of irrelevant (nonfactual) information, and a huge changing with new discovering information may strengthen or weaken previous knowledge. Speculation and negation values are considered as one of the main factors which play an essential role to predict the factuality of event or sentence. Negation reverses the truth of a statement to give the opposition and speculation increase or decreases the uncertainty of statement. Recently, Deep Neural Networks (DNN) have proven better performance to distinguish factual from nonfactual information. Most previous approaches have been dedicated to the English language. To our knowledge, there is no previous developed research to identify the negative or speculative expression for biomedical texts in the Arabic language. This research will develop DNN-based Speculation and negation detection models that able to check claims (negated or speculated sentences) by considering syntactic paths between speculation or negation cues and the remaining words (candidates) in biomedical texts, using Stanford dependency parser. In this paper, the implemented models are evaluated based on the BIOARABIC corpus. Experiments on BIOARABIC corpus show that DNN models achieve a competitive performance and the Attention based Bidirectional Long Short-Term Memory model achieves the best F-scores of 73.55.

5

Authors: Najah Al-shanableh, Mofleh Al Diabat

This research aims to use data mining to predict health care outcomes. We will investigate patterns of multiple chronic conditions (MCCs), or multimorbidity, among the US elderly population. The multimorbidity prediction model, as a general aspect, was not found in the literature, although some researchers have been exploring the risk of developing further chronic conditions after reporting an index disease. Data mining can provide richer results compared to those produced using a statistical approach and greater depth and breadth. It can also help professionals to identify the best time to intervene. In this research, the primary focus was on building disease knowledge using data mining algorithms for MCCs in the elderly. We identified potential morbidity groups using clustering and tested several prediction models on HCUP real data with high accuracy, where the highest accuracy of 99.05% was achieved by Logistic Regression.

7

Authors: Farid Bourennani

Nowadays, we have access to unprecedented high-performance computing (HPC) resources that can be utilized to solve complex and computationally expensive optimization problem. However, one of the problems with existing metaheuristics algorithms is that they do not scale well. For example, particle swarm optimization (PSO) which is one of the most known metaheuristics performs poorly in terms of accuracy and convergence speed with large dimensional problems. In this paper, we propose a broadcast and distributed PSO using message passing interface (MPI) that showed to be faster and more accurate than the commonly utilized distributed master-slave version of PSO for the studied large-scale optimization problems.