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The ultimate goal of labelling a Quranic verse is to determine its corresponding theme. However, the existing Quranic verse labelling approach is primarily depending on the availability of Quranic scholars who have expertise in Arabic language and Tafseer. In this paper, we propose to automate the labelling task of the Quranic verse using text classification algorithms. We applied three text classification algorithms namely, k-Nearest Neighbour, Support Vector Machine, and Naïve Bayes in automating the labelling procedure. In our experiment with the classification algorithms English translation of the verses are presented as features. The English translation of the verses are then classified as "Shahadah" (the first pillar of Islam) or "Pray" (the second pillar of Islam). It is found that all of the text classification algorithms are capable to achieve more than 70% accuracy in labelling the Quranic verses.

The existence of wireless technology and the emergence of mobile devices enable a simple yet powerful infrastructure for business application. Some early efforts have been made to utilize both technologies in food ordering system implementations. However, the food ordering systems that have been proposed earlier exhibit limitations, primarily in cost effectiveness, allowing customizations and supporting real-time feedback to customers. In this paper, we discuss the design and implementation of a customizable wireless food ordering system with real-time customer feedback for a restaurant (CWOS-RTF). The CWOS-RTF enables restaurant owners to setup the system in wireless environment and update menu presentations easily. Smart phone has been integrated in the CWOS-RTF implementation to facilitate real-time communication between restaurant owners and customers. A preliminary testing suggests that the CWOS-RTF has the potential to eliminate the limitations of existing food ordering systems.

Artificial Neural Network had gained a tremendous attention from researchers particularly because of the architecture of Artificial Neural Network that laid the foundation as a powerful technique in handling problems such as classification, pattern recognition, and data analysis. It is known for its data-driven, self-adaptive, and non-linear capabilities channel that is used in processing at high speed and the ability to learn the solution to a problem from a set of examples. Recently, research in Neural Network training has become a dynamic area of research, with the Multi-Layer Perceptron (MLP) trained with Back-Propagation (BP) was the most popular and been worked on by various researchers. In this study, the performance analysis based on BP training algorithms; gradient descent and gradient descent with momentum, both using the sigmoidal and hyperbolic tangent activation functions, coupled with pre-processing techniques are executed and compared. The Min-Max, Z-Score, and Decimal Scaling preprocessing techniques are analyzed. The simulations results generated from some selected benchmark datasets reveal that preprocessing the data greatly increase the ANN convergence, with Z-Score producing the overall best performance on all datasets.