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.

Quran is the holy book for Muslims around the world. Since it was revealed to the Prophet Muhammad (PBUH) before about 14 hundreds years, Quran is preserved in all imaginable ways from distortion. The rapid and huge growth of digital media and internet usage, cause a wide spread of the Quranic knowledge as well as Quranic Verses, scripts, Translations, and many other Quranic sciences in its digital formats. Some of the online sources, websites, services and social network users are introducing a less authentic Quranic content, services and applications. The ordinary user of such online resources could not detect and authenticate the provided Quranic verses. In this paper, we propose a machine learning approach to detect Quranic words in a text extracted from online sources. The proposed approach of detection utilizes Support Vector Machine to generate a learning model of Quranic words by training the learner on the Quranic words dataset. The generated classification model is used later to classify the words from online content. Experiments based on different features categories such as the Diacritics, and Statistical features are performed and a prototype is developed, Results show that the accuracy and other evaluation measurements achieved by the proposed approach are higher than the previous measurement in the domain. The Future works will focus on incorporating more machine learning and optimization techniques for achieving higher evaluation measurements.

Quran is the holy book for all Muslims around the world. Since more than 1400 years, it was preserved in all possible ways from distortion. The huge increment and spread of digital media and internet usage, leaded to many organizational and individual websites, services, and applications are being introduced to spread the knowledge related to Quran as well as Quranic Verses, Translations, Explanations with the Tafseer and other Quranic sciences in its digital formats, some of these services are less authentic. In this paper we introduce a framework to detect and authenticate Quranic verses in a text extracted from online source especially forums posts. The proposed methodology of detection is based on the assumption that Quranic Verses are the parts of the text that contain more diacritics (Harakat). Other assumptions were also established to increase the accuracy of detection in case of less diacritic text. Authentication methodology is based on computing numerical Identifiers of words in the detected text then comparing these identifiers with Identifiers of original Quranic manuscript. Experiments show acceptable results on the detections rate of the highly and less diacritic text. The accuracy was 62% in average while the precision and recall were 75% and 78%, respectively. Future works will focus on authentication side as well as incorporating computational intelligence methods, that involved the sound of the words pronounce during the reading of Quranic verses, image processing and others, to improve the detection.

Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyper-boxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, the key part of the spam and non-spam classifiers’ structure is captured by applying the interval analysis through the high homogeneity of the patterns. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules’ performance. The size of the hyperboxes is expanded away from the center of the granules by PSO. There are some patterns that do not placed in any of the created clusters and known as noise points. In the third step, the membership function in fuzzy sets is applied to solve the noise points’ problem by allocating the noise points through the membership grades. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.