Text clustering is one of the most commonly used methods for detecting themes or types of documents. Text clustering is used in many fields, but its effectiveness is still not sufficient to be used for the understanding of Arabic text, especially with respect to terms extraction, unsupervised feature selection, and clustering algorithms. In most cases, terms extraction focuses on nouns. Clustering simplifies the understanding of an Arabic text like the text of the Quran; it is important not only for Muslims but for all people who want to know more about Islam. This paper discusses the complexity and limitations of Arabic text clustering in the Quran based on their themes. Unsupervised feature selection does not consider the relationships between the selected features. One weakness of clustering algorithms is that the selection of the optimal initial centroid still depends on chances and manual settings. Consequently, this paper reviews literature about the three major stages of Arabic clustering: terms extraction, unsupervised feature selection, and clustering. Six experiments were conducted to demonstrate previously un-discussed problems related to the metrics used for feature selection and clustering. Suggestions to improve clustering of the Quran based on themes are presented and discussed.

This paper presents a Dynamic Gradient Pattern (DGP) based on Kalman filtering technique for urban road users tracking. DGP technique is proposed to enhance rigid object descriptive ability for improved verification. DGP descriptor along with weighted centroid was integrated with a Kalman filtering framework to enhance data association robustness and tracking accuracy. To handle multiple objects tracking, a DGP verification approach is addressed based on normalized Bhattacharyya distance. The proposed technique achieves a closer trajectory for rigid body movement. The DGP descriptor can discriminate the objects correctly, and it overcomes the partial occlusion and misdetection by verifying object location using the normalized Bhattacharyya distance between DGP features. Experimental evaluation is performed on urban videos that include a slow-motion temporary stop and partial occlusion. The experimental results demonstrate that the detecting and tracking accuracy are above 98.08% and 97.70% respectively.

Motion segmentation is a fundamental step in urban traffic surveillance systems, since it provides necessary information for further processing. Background subtraction techniques are widely used to identify foreground moving vehicles from static background scene. Conventional techniques utilize single background model or Gaussian mixture model, which involves either poor adaptation or high computation.The complexity of urban traffic scenarios lies in pose and orientation variations, slow or temporarily stopped vehicles and sudden illumination variations. To address these problems Sigma-Delta Mixture Model (SDMM)is proposed. Mixed distributions are updated dynamically based on matching and contribution in the two order temporal statistics. The constant amplification factor is replaced byweightedfactor to update the variance rate over its temporal activity. The proposed technique achieve robust and accurate performance,which improves adaptation capability with balanced sensitivity and reliability, moreover, integerlinear operations enables the real-time capability.

Low quality digital images are still a common problem to all of us. It happens under low lighting condition, interference of atmospheric veil and other conditions. Automated enhancement on a mobile device is still a challenge. This research introduced and implemented an enhancement tool using contrast stretching method. Pixels are stretched within the non-outlier range from the input image. Higher quality images with less noise were produced. This tool is embedded into a mobile device, namely Beagleboard-XM on a Linux environment.

ABSTRACT: There seems to be lack of academic papers trying to set out a concept of sama’(hearing) verses ontology. Hence this study wishes to fill in the gap by describing a new concept of Quran ontology for the sama’verses from an existing data. This study also aims to highlight the unique features of sama’verses by using data extraction based on ontology processes. In doing so, this study analyzes every single feature and gives it an appropriate interpretation. In addition to Quran, this study uses all sources of both classic Islamic and modern science literatures as part of references to enhance existing methodology and findings. The result from this study is applicable to be used for ear, nose and throat (ENT) medical health and important to encapsulate the benefit of ontology in representing an authentic information from Quran. Keywords:Interpreting; Sama’verses; Data Extraction; Quran ontology