A novel dataset for quranic words identification and authentication

Quran is the holy book for Muslims around the world. For the past fourteen centuries after its revelation, ithas been preserved in all possible ways from any distortions. The huge increase in Internet usage and the spread of digital media lead to the development of many websites, services, and applications related to Quran. These efforts include the conversion of Quranic verses, translations, explanations,tafseer and other Quranic sciences into digital formats. Some of these efforts are foundless authentic. The authentication dependson correct identification of Quranic words in the text. In this paper, we introduce a novel dataset for Quranic words identification and authentication. The proposed dataset contains more than 93,000 samples with64 features for each extracted in numerical form.The validation tests of the proposed dataset resulted high accuracy average

Website speed testing analysis using speedtesting model

Page load speed reflects the website´s performance. It has a significant influence on user experience and satisfaction. In this work we study and analyze the reasons cause the slowness of webpages load speed. By the deep investigation of the related literature and the review of websites speed testing services from different perspectives such as functions, interface and additional settings. We present a detailed recommendations that can be followed to optimize site's performance. Our hypotheses about reliability of website speed testing has been tested and conformed experimentally. 378 individual speed tests with various combinations of settings experiments have been performed to confirm out hypotheses, and recommendations have been provided based on our results. We believe that following these rules would ensure significantly more reliable website speed testing in comparison with a common practic

Schema matching quality: thesaurus as the matcher

Thesaurus is used in many Information Retrieval (IR) applications such as data integration, data warehousing, semantic query processing and classifiers. It was also utilized to solve the problem of schema matching. Considering the fact of existence of many thesauri for a certain area of knowledge, the quality of schema matching results when using different thesauri in the same field is not predictable. In this paper, we propose a methodology to study the performance of the thesaurus in solving schema matching. The paper also presents results of experiments using different thesauri. Precision, recall, F-measure, and similarity average were calculated to show that the quality of matching changed according to the used thesaurus

Sensorial Network Framework Embedded in Ubiquitous Mobile Devices

Today’s digital society is interconnected and networked, with modern smart devices ubiquitously built into and embedded within smart environments and other environments, where people (their users) typically live. It is very important to mention that sensorial awareness of an environment depends on one’s current location and equipment, as well as the equipment’s real-time capabilities. Personal sensorial information is considered to be the key factor for progress in the improvement of the productivity of everyday life and creation of a smart surrounding environment. This paper describes the design, implementation, and testing process of a new sensorial framework based on the current possibilities created by ubiquitous smart mobile devices with sensors, which involves computing power and battery power issues. The two parts of the proposed framework have been designed, implemented, and tested. The client part is represented by a front-end mobile application, and the back-end part is represented by a server-side application. The analysis of the data, captured during the testing phase, involves the analysis of the processing time, battery consumption, and transmitted data amount. This analysis reveals that Transmission Control Protocol (TCP) and user datagram protocol (UDP) protocols have a comparable performance, although TCP is preferable for use in local networks. In comparison to other solutions such as MobiSense or Feel the World framework, the final solution of the proposed and developed sensorial framework has two main capabilities, which are the security support and social networking possibilities. The advantage of the MobiSense platform is the existence of several real-world applications, whereas the proposed sensorial framework needs to be verified in the massive context of many users in real time

Coverage Enhancement Algorithms for Distributed Mobile Sensors Deployment in Wireless Sensor Networks

Sensor nodes in wireless sensor networks are deployed to observe the surroundings for some phenomenon of interest. The fundamental issue in observing such environments is the area coverage which reflects how well the region is monitored. The nonuniform sensor nodes distribution in a certain region caused by random deployment might lead to coverage holes/gaps in the network. One of the solutions to improve area coverage after initial deployment is by sensor nodes mobility. However, the main challenge in this approach is how to increase area coverage with the least energy consumption. This research work aims to improve area coverage with minimal energy consumption and faster convergence rate. The Edge Based Centroid (EBC) algorithm is presented to improve the area coverage with faster convergence rate in a distributed network. The simulation based performance evaluations of the proposed algorithms are carried out in terms of area coverage, convergence rate, and energy efficiency. Compared to the existing works, EBC improved area coverage with faster convergence. It is concluded that the proposed algorithm has improved area coverage with faster convergence and minimal energy consumption

Hybridized term-weighting method for dark web classification

The role of intelligence and security informatics based on statistical computations is becoming more significant in detecting terrorism activities proactively as the extremist groups are misusing many of the obtainable facilities on the Internet to incite violence and hatred. However, the performance of statistical methods is limited due to the inadequate accuracy produced by the inability of these methods to comprehend the texts created by humans. In this paper, we propose a hybridized feature selection method based on the basic term-weighting techniques for accurate terrorism activities detection in textual contexts. The proposed method combines the feature sets selected based on different individual feature selection methods into one feature space for effective web pages classification. UNION and Symmetric Difference combination functions are proposed for dimensionality reduction of the combined feature space. The method is tested on a selected dataset from the Dark Web Forum Portal and benchmarked using various famous text classifiers. Experimental results show that the hybridized method efficiently identifies the terrorist activities content and outperforms the individual methods. Furthermore, the results revealed that the classification performance achieved by hybridizing few feature sets is relatively competitive in the number of features used for classification with higher hybridization levels. Moreover, the experiments of hybridizing functions show that the dimensionality of the feature sets is significantly reduced by applying the Symmetric Difference function for feature sets combination

Hybridized term-weighting method for web contents classification using SVM

The role of intelligence and security informatics based on statistical computations is becoming more significant in detecting terrorism activities proactively as the extremist groups are misusing many of the obtainable facilities on the Internet to incite violence and hatred. However, the performance of statistical methods is reported to be limited due to the inadequate accuracy produced by the inability of these methods to comprehend the meaning of texts created by humans. Miss classification of the actual terrorism web content as non-terrorism or vice versa reduces the usefulness of intelligent techniques to support the efforts against potential threats, and limits the opportunities for the effective use of intelligence and security informatics in the early detection of terrorist activities. In this paper, we propose a hybridized method based on the basic term-weighting techniques for accurate terrorism activities detection in textual contexts. The proposed method combines the feature sets generated by different individual term-weighting techniques such as Term Frequency (TF), Document Frequency (DF), Term Frequency-Inverse Document Frequency (TF-IDF), Glasgow, and Entropy into one feature set for effective classification. Moreover, two combination functions are proposed to reduce the dimensionality of combined feature set. The method is tested on a selected dataset from the Dark Web Portal Forum (DWPF) and benchmarked using Support Vector Machine (SVM), and other famous text classifiers such as K-Nearest Neighbor (KNN), Decision Trees (DT), Naïve Bayes (NB), and Extreme Learning Machine (ELM) classifiers. Experimental results show that the hybridized method efficiently identifies the terrorist activities content and outperforms the individual methods. Moreover, the results further revealed that the classification performance achieved by hybridizing few feature sets is relatively competitive in the number of features used for classification with higher hybridization levels. Moreover, the experiments of hybridizing functions show that the dimensionality of the feature sets is significantly reduced by applying the symmetric difference function for feature sets combination