Applications of Mining Arabic Text: A Review

Since the appearance of text mining, the Arabic language gained some interest in applying several text mining tasks over a text written in the Arabic language. There are several challenges faced by the researchers. These tasks include Arabic text summarization, which is one of the challenging open areas for research in natural language processing (NLP) and text mining fields, Arabic text categorization, and Arabic sentiment analysis. This chapter reviews some of the past and current researches and trends in these areas and some future challenges that need to be tackled. It also presents some case studies for two of the reviewed approaches

Attribute Set Weighting and Decomposition Approaches for Reduct Computation

This research is mainly in the Rough Set theory based knowledge reduction for data classification within the data mining framework. To facilitate the Rough Set based classification, two main knowledge reduction models are proposed. The first model is an approximate approach for object reducts computation used particularly for the data classification purposes. This approach emphasizes on assigning weights for each attribute in the attributes set. The weights give indication for the importance of an attribute to be considered in the reduct. This proposed approach is named Object Reduct by Attribute Weighting (ORAW). A variation of this approach is proposed to compute full reduct and named Full Reduct by Attribute Weighting (FRAW).The second proposed approach deals with large datasets particularly with large number of attributes. This approach utilizes the principle of incremental attribute set decomposition to generate an approximate reduct to represent the entire dataset. This proposed approach is termed for Reduct by Attribute Set Decomposition (RASD).The proposed reduct computation approaches are extensively experimented and evaluated. The evaluation is mainly in two folds: first is to evaluate the proposed approaches as Rough Set based methods where the classification accuracy is used as an evaluation measure. The well known IO-fold cross validation method is used to estimate the classification accuracy. The second fold is to evaluate the approaches as knowledge reduction methods where the size of the reduct is used as a reduction measure. The approaches are compared to other reduct computation methods and to other none Rough Set based classification methods. The proposed approaches are applied to various standard domains datasets from the UCI repository. The results of the experiments showed a very good performance for the proposed approaches as classification methods and as knowledge reduction methods. The accuracy of the ORAW approach outperformed the Johnson approach over all the datasets. It also produces better accuracy over the Exhaustive and the Standard Integer Programming (SIP) approaches for the majority of the datasets used in the experiments. For the RASD approach, it is compared to other classification methods and it shows very competitive results in term of classification accuracy and reducts size. As a conclusion, the proposed approaches have shown competitive and even better accuracy in most tested domains. The experiment results indicate that the proposed approaches as Rough classifiers give good performance across different classification problems and they can be promising methods in solving classification problems. Moreover, the experiments proved that the incremental vertical decomposition framework is an appealing method for knowledge reduction over large datasets within the framework of Rough Set based classification

The application of social responsibility in Jordanian banks and its impact on the competitive feature from the point of view of banks’ employees

ABSTRACTThe study aims to identify the reality of the application of social responsibility in Jordanian banks, and its impact on competitive advantage. It also seeks to determine the most important pillars of the Jordanian banks that facilitate the success of social responsibility with a view to achieve its objectives in favor of the related parties. This happens through trying to identify the extent of the application of social responsibility within the nine following dimensions: community, environment, customers, employees, shareholders, government, suppliers, competitors, minorities and people with special needs. In order to achieve that, the researchers selected a random sample of 170 employees of the study population and the bank employees in the scope of various aspects of their work. Questionnaires were also distributed to managers of banks directorates and branches; they included 45 paragraphs about social responsibility and 16 paragraphs about competitive advantage. Data were integrated into the computer and processed using SPSS statistical program. The study concluded that social responsibility is a subject of interest, along with competitive advantage for banks. It also found out that there is a relationship between social responsibility and competitive advantage among directorates and branches under study

Dewaterability of sludge digested in extended aeration plants using conventional sand drying beds

Dewaterability of unconditioned sludge digested in full scale and lab scale experiments using either extended aeration (EA) or anaerobic digestion were compared on full and lab scale sand drying beds. Sludge digested in EA plants resulted in improvement in sludge dewaterability compared to  sludge digested anaerobically. This was demonstrated by comparing capillary suction time, time to filter a specific amount of water, the sludge volume index and the dry solids content. In addition, sieve analysis results from both types of sludge after drying in sand drying beds clearly shows that the grain portions in the fine range in case of anaerobically digested sludge are more than that in case of EA sludge. This was also clear in microscopic photos of samples. The microscopic photos of EA stabilized sludge are characterized by larger colonies of flocs and more open structure than anaerobically digested sludge

Bayes model for assessing the reading difficulty of English text for English education in Jordan

Predicting the reading difficulty level of English texts is a critical process for second language education and assessment. Reading difficulty level is concerned with the problem of matching a reader’s proficiency and the appropriate text. The reading difficulty level or readability assessment is the process for predicting the reading grade level required from an input text or document, which corresponds to the reader and to the materials. Students in Jordan at their academic levels find obstacles in finding relevant readable data for any subject at their levels. This paper is intended to introduce a model that foretells the reading difficulty level of a given text in terms of a student's ability to read and understand English as a non-native English speaker in Jordanian schools. In this paper, Jordanian students were classified into four categories according to their knowledge of English. The prediction of the reading difficulty level is achieved by using a modern statistical model that is situated on the Bayes model. The model compares the given text with some standard predefined text that strongly reflects the ability to read and understand English text. The accuracy of the proposed model was tested using the hold-out method. The overall prediction accuracy was 75.9%

Input current control of boost converters using current-mode controller integrated with linear quadratic regulator

The application of power electronic converter in the renewable energy systems significantly increases their efficiencies by maintaining the operation of these systems at the optimal operating points, therefore, absorbing the maximum available power from the renewable sources all the time. In this paper, the small-signal models of the open-loop, current-mode controlled boost converter are derived. In addition, both the Current Mode Control (CMC) and the Linear Quadratic Regulator (LQR) methods are combined to design a controller that forces the input current of the converter to follow accurately a reference current, which could be generated using maximum power point tracking (MPPT) algorithms. The controller performance is tested under transient conditions and with disturbance signals using MATLAB/Simulink simulation package. The simulation results indicate that both a good response and disturbance rejection are achieved in tested conditions

Chapter 12 Groundwater scarcity in the Middle East

The lack of natural water supplies in the Middle East and North Africa region is fundamentally a crucial constraint on socioeconomic growth, development, and stability. This is the most water-scarce region in the world. The present chapter provides insights into the problem and evaluates the drivers of this challenge and the multi impacts on the regional water scarcity. It also discusses the current use and future trends in water resources, including water-quality issues. It explores selected mitigation measures with potentially high applicability in the region. These measures include a range of sustainable water productions such as desalination, treated wastewater reuse, rainwater harvesting, and artificial aquifer recharge. Case studies of shared river basins, groundwater from the region, and its associated challenges and opportunities were also presented

Seven-Tesla magnetization transfer imaging to detect multiple sclerosis white matter lesions

BACKGROUND AND PURPOSE: Fluid-attenuated inversion recovery (FLAIR) imaging at 3 Tesla (T) field strength is the most sensitive modality for detecting white matter lesions in multiple sclerosis. While 7T FLAIR is effective in detecting cortical lesions, it has not been fully optimized for visualization of white matter lesions and thus has not been used for delineating lesions in quantitative magnetic resonance imaging (MRI) studies of the normal appearing white matter in multiple sclerosis. Therefore, we aimed to evaluate the sensitivity of 7T magnetization-transfer-weighted (MTw) images in the detection of white matter lesions compared with 3T-FLAIR. METHODS:Fifteen patients with clinically isolated syndrome, 6 with multiple sclerosis, and 10 healthy participants were scanned with 7T 3-dimensional (D) MTw and 3T-2D-FLAIR sequences on the same day. White matter lesions visible on either sequence were delineated. RESULTS: Of 662 lesions identified on 3T-2D-FLAIR images, 652 were detected on 7T-3D-MTw images (sensitivity, 98%; 95% confidence interval, 97% to 99%). The Spearman correlation coefficient between lesion loads estimated by the two sequences was .910. The intrarater and interrater reliability for 7T-3D-MTw images was good with an intraclass correlation coefficient (ICC) of 98.4% and 81.8%, which is similar to that for 3T-2D-FLAIR images (ICC 96.1% and 96.7%). CONCLUSION: Seven-Tesla MTw sequences detected most of the white matter lesions identified by FLAIR at 3T. This suggests that 7T-MTw imaging is a robust alternative for detecting demyelinating lesions in addition to 3T-FLAIR. Future studies need to compare the roles of optimized 7T-FLAIR and of 7T-MTw imaging

A rare event modelling approach to assess injury severity risk of vulnerable road users

Vulnerable road users (VRUs) represent a large portion of fatalities and injuries occurring on European Union roads. It is therefore important to address the safety of VRUs, particularly in urban areas, by identifying which factors may affect the injury severity level that can be used to develop countermeasures. This paper aims to identify the risk factors that affect the severity of a VRU injured when involved in a motor vehicle crash. For that purpose, a comparative evaluation of two machine learning classifiers—decision tree and logistic regression—considering three different resampling techniques (under-, over- and synthetic oversampling) is presented, comparing both imbalanced and balanced datasets. Crash data records were analyzed involving VRUs from three different cities in Portugal and six years (2012–2017). The main conclusion that can be drawn from this study is that oversampling techniques improve the ability of the classifiers to identify risk factors. On the one hand, this analysis revealed that road markings, road conditions and luminosity affect the injury severity of a pedestrian. On the other hand, age group and temporal variables (month, weekday and time period) showed to be relevant to predict the severity of a cyclist injury when involved in a crash.publishe