Fatima T. Al-Khawaldeh
Department of Computer Science, University of York, York, United Kingdom.
Abstract—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.
Keywords- Arabic NLP; negation; speculation; biomedical (medical and biological); factuality.
Najah Al-shanableh, Mofleh Al Diabat
Department of Computer Science, Al Albayt University, Al Mafraq- Jordan.
Abstract— 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.
Keywords- Multimorbidity; Data mining; Classification; Clustering; Prediction; Chronic Diseases.
Customer Segmentation Based on GRFM: Case Study
Sahar Ghoreishi, Industrial Engineering Department K.N.Toosi University of Technology Tehran, Iran.
Keyvan Khandestani, Information Technology Department, Electronic Branch, Islamic Azad University, Tehran, Iran.
Abstract—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.
Keywords-Component; Data Mining; Constraint Clustering Algorithms; Segmentation; RFM.
Department of Computer Science
Faculty of Computing and IT, University of Jeddah, Saudi Arabia.
Abstract— 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.
Keywords- Particle swarm optimization; distributed computing; large scale optimization; big optimization.
Citation: Farid Bourennani , “Broadcast Distributed PSO for Large Scale Problems “, The World of Computer Science and Information Technology Journal (WSCIT). 2018 Volume 8, Issue 5, pp.43.49.
Farhan M Al Obisat, Zaid T. Alhalhouli, Tamador I. Alrawashdeh, Tamara E. Alshabatat
Computer and Information Technology Department, Tafila Technical University, TTU, Tafila, Jordan.
Abstract— the software development industry considers quality a crucial factor in its development. Applying a certain level of standard to Software Quality (SQ) can help ensure customer satisfaction. This study primarily aims to define the different dimensions of SQ, identify the requirements for enhancing SQ, and present the challenges when SQ is restricted. The study also provides a review on the impact of quality and its measurement in the life cycle of software development. It examines the need for a quality standard to measure the increasing quality requirements and size of software. The findings of this study indicate an increasing need for high-quality software. Moreover, it provides a reference for other scholars regarding SQ testing and SQ in fuzzy logic.
Keywords- quality challenges; Software Development Life Cycle (SDLC); software measurement; software quality.
Citation: Farhan M Al Obisat, Zaid T. Alhalhouli, Tamador I. Alrawashdeh, Tamara E. Alshabatat , “Review of Literature on Software Quality “, The World of Computer Science and Information Technology Journal (WSCIT). 2018 Volume 8, Issue 5, pp.32.42.
Mada’ Abdel Jawad, Saeed Salah, Raid Zaghal
Department of Computer Science, Al-Quds University, Jerusalem, Palestine.
Abstract—A Mobile Ad-hoc Network (MANET) is a dynamic single or multi-hop wireless network where nodes are connected wirelessly, and the network is self-configured. Due to the high mobility of nodes, network topology changes more frequently and thus, routing becomes a challenging task. Several routing protocols have been proposed by the researchers for MANETs like the well-known Destination Sequenced Distance Vector (DSDV) and its variants. It is a table-driven routing protocol that was mainly proposed to solve routing loop problems and it performs very well in sparse and low mobility environments. However, it suffers from several performance issues when implemented on high and dense MANETs. A number of modifications of DSDV have been proposed to make it more adaptive and suitable for different environments. In this paper, the performance of DSDV, E-DSDV, I-DSDV, and O-DSDV routing protocols is compared. The performance metrics that were considered in this analysis are packet delivery ratio, throughput, End-to-End delay, and routing overhead. Several simulation scenarios were carried out using the Network Simulator tool (NS3) by varying the number of nodes, pause time and velocity. The simulation results have shown that I-DSDV outperforms the others in low mobility scenarios, whereas O-DSDV has the best performance in high velocity environments.
Keywords-MANET; DSDV; I-DSDV; E-DSDV; O-DSDV Simulation; Network Performance; NS3.
Citation: Mada’ Abdel Jawad, Saeed Salah, Raid Zaghal, “Performance Comparative Study of DSDV, E-DSDV, I-DSDV and O-DSDV MANET Routing Protocols”, The World of Computer Science and Information Technology Journal (WSCIT). 2018 Volume 8, Issue 4, pp.24.31.
Rufaida M. Shamroukh, Nawal A. Zabin, Muhammed A. Mesleh
Computer Engineering Department, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan.
Abstract—Heart attack is one of the main causes of death nowadays especially for elderly people since it can strike anytime and anywhere. This project aims to build a smart assistant system that can predict the possibility of heart attack occurrence depending on the heartbeat rate before it happens for an elderly person wherever he/she is, and send a message to an elderly’s relative or a specialist informing him/her that the elderly might have a possible heart attack along with the geometric location in order to try to save the elderly’s life on time. In our system, a pulse sensor was attached to an Arduino microcontroller with ESP8266 NodeMCU as a Wi-Fi module to measure the heartbeat rate (BPM), if the heartbeat is either too low or too high, a message will be sent to a specified mobile, warning its owner of a possible heart attack. When the message arrives to the specified mobile, the owner will open an Android application named EPLSA. Through this application, he/she can send a request to the microcontroller asking for the geometric location. The microcontroller replies with the values of the latitude and longitude of the elderly’s location which can be viewed on Google maps too.
Keywords-Heart attack; Heartbeat rate; Beats per Minuit; BPM; Pulse sensor; NodeMCU; ESP8266; Arduino; IoT; Arduino-IDE.
Citation: Rufaida M. Shamroukh, Nawal A. Zabin, Muhammed A. Mesleh, “Elderly People’s Life Saving Assistant (EPLSA) using IoT”, The World of Computer Science and Information Technology Journal (WSCIT). 2018 Volume 8, Issue 3, pp.18.23.