The World of Computer Science and Information Technology Journal

6

Authors: Sahar Ghoreishi, Keyvan Khandestani

in the last decades' firms which have directly or indirectly contact with a customer migrate from product-oriented to be a customer-oriented, hence, some products and customers are not profitable in the same way and some of them bring detriment to the firm. In this regard, firms should recognize loyal, profitable and potential customers with a glance of impressive product which brings added value for them. In order to distinguish profitable customers, they supposed to cluster customers and study their behavior’s group for the sake of having the best investment in the best segment. In this paper, we utilize customize GRFM (Group RFM) to cluster customers based on proposed APC (account-pattern constraint clustering) algorithm. Hence, we calculate the cluster RFM value which could aid the bank to explore both profitable accounts and customers.

6

Authors: Hoda A. Abdelhafez, Abeer A. Amer

Visual Analytics plays an important role in discovering hidden information from massive, heterogenous and streaming data. Visual analytics using visual representations and interactive techniques that are combined with statistical and machine learning methods for analysis process. Big data visual analytics faces many challenges related to technological issues and human cognition. This paper’s aim is to focus on the challenges of big data visual analytics and how the recent platforms including Knime, SAS visual analytics, Arcadia enterprise and TensorFlow could deal with these challenges. It also provides comparison between these platforms. The results show that these platforms can overcome most of the general challenges. TensorFlow is a promising platform for handling challenges related to interaction and user interfaces. Other specific challenges like uncertainty and human cognitive bottlenecks still need more efforts.

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.