CITIZENS AS CONSUMERS: PROFILING E-GOVERNMENT SERVICES’ USERS IN EGYPT VIA DATA MINING TECHNIQUES

This study uses data mining techniques to examine the effect of various demographic, cognitive and psychographic factors on Egyptian citizens’ use of e-government services. Multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), classification and regression trees (CART), and multivariate adaptive regression splines (MARS) are compared to a standard statistical method (linear discriminant analysis (LDA). The variable sets considered are sex, age, educational level, e-government services perceived usefulness, ease of use, compatibility, subjective norms, trust, civic mindedness, and attitudes. The study shows how it is possible to identify various dimensions of e-government services usage behavior by uncovering complex patterns in the dataset, and also shows the classification abilities of data mining techniques

Sentiment Analysis of Spanish Words of Arabic Origin Related to Islam: A Social Network Analysis

With the arrival of Muslims in 711 till their expulsion in the 1600s, Arabic language was present in Spain for more than eight centuries. Although social networks have become a valuable resource for mining sentiments, there is no previous research investigating the layman’s sentiment towards Spanish words of Arabic etymology related to Islamic terminology. This study aim at analyzing Spanish words of Arabic origin related to Islam. A random sample of 4586 out of 45860 tweets was used to evaluate general sentiment towards some Spanish words of Arabic origin related to Islam. An expert-predefined Spanish lexicon of around 6800 seed adjectives was used to conduct the analysis. Results indicate a generally positive sentiment towards several Spanish words of Arabic etymology related to Islam. By implementing both a qualitative and quantitative methodology to analyze tweets’ sentiments towards Spanish words of Arabic etymology, this research adds breadth and depth to the debate over Arabic linguistic influence on Spanish vocabulary

Antibacterial Effect of Nano-based Intra-canal Medicaments against Enterococcus Faecalis

Intracanal medicaments have been thought as an important step in killing the bacteria in root canals. The application of nanoparticles in the medication applied between visits was supposed to improve its antibacterial effect. With the introduction of nanotechnology in dentistry, intracanal medicaments could become more effective against bacteria. Objectives: The purpose of this study was to evaluate the antibacterial efficacy of silver nanoparticles incorporated with Calcium hydroxide and Nano Chitosan intracanal medicaments against Enterococcus faecalis biofilms formed on root dentin. Materials and methods: 40 extracted human single rooted permanent teeth were selected; samples were randomly divided into three equal experimental groups eight samples each according to the intracanal medicament used. Group 1 (n=8) Calcium hydroxide with nano silver intracanal medicament was used, group 2 (n=8) Nano chitosan intracanal medicament was used, group 3 (n=8) Nano conventional Calcium hydroxide intracanal medicament was used and two control groups eight samples each (n=8). After inoculation with Enterococcus Faecalis, teeth were injected different intracanal medicaments for 7 days then evaluation of dead and live bacteria percentage was done using Confocal LASER Microscope. Results: Reduction of Enterococcus faecalis mean percentages were significantly higher in groups 1 and 2 than group 3 regardless the root level and different root regions except the coronal region. Conclusion: Nano silver and nano Chitosan provide promising antibacterial effect which was proved by Confocal LASER Microscop

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate

Visualizing the influence of geography, oil and geopolitics on civil wars in the Arab world: A novel application of self-organizing maps and duration models

The aim of this paper is to investigate why some internal conflicts are terminated quickly, while others linger for several decades without a looming resolution in the horizon. In an attempt to achieve this objective, the role played by geopolitical factors in the Arab world's internal conflicts was investigated. More specifically, we used Kohonen self-organizing maps, an artificial intelligence-based neural network technique, along with event duration models to investigate the role played by distance from the capital, access to international borders, terrain, valuable natural resources such as oil, and rebels fighting capability in civil wars in the Arab world. Using recently validated data spanning more than 50 years of Arab civil wars (1948–2003), our findings indicate that previously ignored geopolitical factors seem to play an important role in the duration of internal conflicts in the Arab World