The construction of a new Lexicon design for Arabic language

Analyzing Arabic sentences is a difficult task; the difficulties come from several sources. One is that sentences are long and complex, the other difficulties come from the sentence structure. The syntactic structure of sentence parts may be missing, taking into accounts different orders of words and phrases. This paper aims to develop and assess an Arabic Lexicon. The new automatic Lexicon was developed with the purpose of analyzing and extracting the attributes of Arabic words. The lexicon was implemented using two-step process, tokenization and part of speech tagging. The output of the lexicon can be processed by another parser tool which perform an analysis on Arabic sentence to determines if the sentence follows a valid grammatical structure. An evaluation test was conducted to assess the effectiveness and efficiency of the new lexicon design using real sentences taken randomly. The results have shown a minimum accuracy rate of 92% which is considered highly satisfactory. The newly designed lexicon can be widely used for any application that requires Arabic Language analysis and processing

A Printed PAW Image Database of Arabic Language for Document Analysis and Recognition

Document image analysis and recognition are important topics in the field of artificial intelligence. In this context, the availability of a database with good script samples is an important requirement for machine-learning processes. For Latin and Asian languages many suitable databases exist. However, there is a shortage of databases with Arabic samples. In this work, a new database of printed Arabic text is introduced. The new concept of collecting sub-words (PAWs) instead of words or individual character samples was adopted. These PAWs constitute all words in the Arabic language. The collected database consists of 83,056 images of PAWs extracted from approximately 550,000 different words. Each sample is presented in the database in five font types: Thuluth, Naskh, Andalusi, Typing Machine, and Kufi. In total, the database consists of 415,280 images. Moreover, ground truth information is included with each PAW image to describe its occurrence number, occurrence frequency, positions and the shapes of the characters. This paper presents a statistical analysis of the frequency of each PAW in the Arabic language

Image contrast enhancement for preserving entropy and image visual features

Histogram equalization is essential for low-contrast enhancement in image processing. Several methods have been proposed; however, one of the most critical problems encountered by existing methods is their ability to preserve information in the enhanced image as the original. This research proposes an image enhancement method based on a histogram equalization approach that preserves the entropy and fine details similar to those of the original image. This is achieved through proposed probability density functions (PDFs) that preserve the small gray values of the usual PDF. The method consists of several steps. First, occurrences and clipped histograms are extracted according to the proposed thresholding. Then, they are equalized and used by a proposed transferring function to calculate the new pixel values in the enhanced image. The proposed method is compared with widely used methods such as Clahe, CS, HE, and GTSHE. Experiments using benchmark datasets and entropy, contrast, PSNR, and SSIM measurements are conducted to evaluate the performance. The results show that the proposed method is the only one that preserves the entropy of the enhanced image of the original image. In addition, it is efficient and reliable in enhancing image quality. This method preserves fine details and improves image quality, supporting computer vision and pattern recognition fields

A Proposed Model for Measuring Social Responsibility and Its Application in Jordanian Business Environment

Abstract This study examines the extent of corporate social responsibility practices amongst listed Jordanian companies at Amman Stock Exchange (ASE). corporate social responsibility   had been measured in this study using a corporate social responsibility disclosure index  and which encompasses  four groups. 53 Jordanian companies examined during the period 2016-2018. The finding reveals that Jordanian companies adopt corporate social responsibility on different types of information including  employee information, environment information, society information and customer information, although this corporate social responsibility disclosure might be considered low as the means of disclosure were (52.2%). More specifically, the mean disclosure for employee information, environment information, society information and customer information were (50%), (40.9%),(39.1%) and (95.5%), respectively.  In addition, the results show that the highest corporate social responsibility disclosure was for the customer information and the lowest was for the society information

A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images

The increasing use of smartphones and social media apps for communication results in a massive number of screenshot images. These images enrich the written language through text and emojis. In this regard, several studies in the image analysis field have considered text. However, they ignored the use of emojis. In this study, a robust two-stage algorithm for detecting emojis in screenshot images is proposed. The first stage localizes the regions of candidate emojis by using the proposed RGB-channel analysis method followed by a connected component method with a set of proposed rules. In the second verification stage, each of the emojis and non-emojis are classified by using proposed features with a decision tree classifier. Experiments were conducted to evaluate each stage independently and assess the performance of the proposed algorithm completely by using a self-collected dataset. The results showed that the proposed RGB-channel analysis method achieved better performance than the Niblack and Sauvola methods. Moreover, the proposed feature extraction method with decision tree classifier achieved more satisfactory performance than the LBP feature extraction method with all Bayesian network, perceptron neural network, and decision table rules. Overall, the proposed algorithm exhibited high efficiency in detecting emojis in screenshot images

Generating an Arabic Calligraphy Text Blocks for Global Texture Analysis

This paper objective is to improve the current method for generating an Arabic Calligraphy text blocks. We test on seven types of Arabic Calligraphy text. We apply  projection profiles and a proposed filter to discriminate each line of the Arabic Calligraphy scripts. After performing text detection, skew correction, text and line normalization subsequently, we generate Arabic Calligraphy text blocks for global texture analysis purposes. We compare our proposed filter with current method and median filter. The results show that the proposed filter  is outperformed. The proposed method can be further  improved to boost the overall performance

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Measurement of t(t)over-bar normalised multi-differential cross sections in pp collisions at root s=13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions

Peer reviewe

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe