Comparative analysis of text classification algorithms for automated labelling of quranic verses

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

Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks

We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function. In our approach we obtain a shallow network by utilizing a greedy algorithm with the prescribed dictionary provided by the available training data and a set of possible inner weights. To facilitate the greedy selection process we employ an integral representation of the network, based on the ridgelet transform, that significantly reduces the cardinality of the dictionary and hence promotes feasibility of the greedy selection. Our approach allows for the construction of efficient architectures which can be treated either as improved initializations to be used in place of random-based alternatives, or as fully-trained networks in certain cases, thus potentially nullifying the need for backpropagation training. Numerical experiments demonstrate the tenability of the proposed concept and its advantages compared to the conventional techniques for selecting architectures and initializations for neural networks

Nearly Exponential Neural Networks Approximation in Lp Spaces

يدخل التقريب باستخدام الشبكات العصبية في الكثير من التطبيقات المهمة. حيث انه يحل الكثير من المشاكل في مجالات علوم الحاسوب و الهندسة و الفيزياء, الخ. ان سبب نجاح التقريب باستخدام الشبكات العصبية هو امكانيته من تقريب اية دالة مهما كان نوعها. في الثلاثين سنة الماضية نشرت الكثير من البحوث جميع تلك البحوث بينت ان كل دالة معرفة على مجموعة مرصوصة محدبة و جزئية من الفضاء الاقليدي    يمكن تقريبها بانتظام باستخدام الشبكة العصبية ذان الطبقة المخفية الواحدة. في هذا البحث قمنا بتعميم الحقائق التي قدمها رانجيتا في و برهنا أن لأية داله تنتمي الى (   و معرفه على مجموعه محدبة ومرصوصة  في  يمكن تقريبها باستخدام شبكه عصبيه ذات طبقه مخفيه واحده من نوع الاس القريب وهذا ما نسميه بالتقريب باستخدام الشبكات العصبية من نوع الاس القريب.In different applications, we can widely use the neural network approximation. They are being applied to solve many problems in computer science, engineering, physics, etc. The reason for successful application of neural network approximation is the neural network ability to approximate arbitrary function. In the last 30 years, many papers have been published showing that we can approximate any continuous function defined on a compact subset of the Euclidean spaces of dimensions greater than 1, uniformly using a neural network with one hidden layer. Here we prove that any real function in L_P (C) defined on a compact and convex subset  of can be approximated by a sigmoidal neural network with one hidden layer, that we call nearly exponential approximation

The Essential Order of (L_p,p

درسنا في هذا البحث درجة التقريب الاساسي بأستخدام الشبكة العصبية المنتظمة ، وكيف يمكن تقريب الدوال  المتعددة المتغيرات في فضاء  عندما  بأستخدام الشبكة العصبية الامامية المنتظمة ، وكذلك بامكاننا الحصول على مبرهنات مباشرة وعكسية ونظرية تكافؤ للتقريب المتعددة المتغيرات في فضاء  عندما  بأستخدام الشبكة العصبية الامامية المنتظمة .This paper is concerning with essential degree of approximation using regular neural networks and how a multivariate function in  spaces for  can be approximated using a forward regular neural network. So, we can have the essential approximation ability of a multivariate function in  spaces for  using regular FFN

Model following using multilayer perceptrons

A model following controller is proposed for discrete nonlinear systems using a recursive multilayer perceptron (MLP). The MLP network contains dynamics and is able to minimize the error between the plant and a desired model in many cases. An example is given