Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language

Text classification (TC) is a crucial subject. The number of digital files available on the internet is enormous. The goal of TC is to categorize texts into a series of predetermined groups. The number of studies conducted on the English database is significantly higher than the number of studies conducted on the Arabic database. Therefore, this research analyzes the performance of automatic TC of the Arabic language using Machine Learning (ML) approaches. Further, Single-label Arabic News Articles Datasets (SANAD) are introduced, which contain three different datasets, namely Akhbarona, Khaleej, and Arabiya. Initially, the collected texts are pre-processed in which tokenization and stemming occur. In this research, three kinds of stemming are employed, namely light stemming, Khoja stemming, and no- stemming, to evaluate the effect of the pre-processing technique on Arabic TC performance. Moreover, feature extraction and feature weighting are performed; in feature weighting, the term weighting process is completed by the term frequency- inverse document frequency (tf-idf) method. In addition, this research selects C4.5, Support Vector Machine (SVM), and Naïve Bayes (NB) as a classification algorithm. The results indicated that the SVM and NB methods had attained higher accuracy than the C4.5 method. NB achieved the maximum accuracy with a performance of 99.9%

An Enhanced AdaBoost Classifier for Smart City Big Data Analytics

The targeted goal regarding the smart cities is improving the goodness of their people and to raise the economic improvement in maintaining certain rate or level. Smart cities would increase all set of utilities, which involves healthcare, education, transportation and agriculture among other utilities. Smart cities are depended on the ICT framework, which includes the Internet of Things methodology. These methodologies make bulk of diverse in data, which referred to as big data. Moreover, these data have no purpose by themselves. Modules needed to improve as new to explain the large amount of data collected and one of the good methods to solve is to use the methods of big data analytics. It shall be maintained and designed through the methods of analytics to get good understanding and in order to increase the utilities of smart city

Synthesis and Cyclopolymerzation of N-Aryl Di ‎AllylAmins As Antioxidants for Lubricant's Oil

في هذا البحث حضرت العديد من مونومرات N-أريل ثنائي أليل امين المعوضة [A1-A2] والتي تم بلمرتها بالجذور الحرة باستخدام الأمونيوم بير سلفات كباديء في 70درجة مئوية، شخصت البوليمرات الحلقية الأمينية المعوضة   و المعوقة الجديدة  بواسطة طيف الاشعة تحت الحمراء وطيف الرنين النووي المغناطيسي ، تم قياس الخواص الفيزيائية والكيميائية. وقد درست .TGA و DSC حيث درس الاستقرار الحراري للبوليمرات المحضرة. تم استخدام هذه البوليمرات[A3 - A4] كمضادات أكسدة لزيوت التشحيم. والتي أعطت الاستقرار الحراري العالي  بالمقارنة مع مضادات الأكسدة القياسية. تم فحص ثبات الأكسدة للزيت الاساس المضاف ب (1٪) وفقا لطريقة الاختبار للمنشآت النفطية IP280In this research many new N-aryl substituted di allylAmins were  synthesized [ A1-A2 ] which were Polymerized by free radically mechanism by using Ammonium per sulfate  as an initiator at 700C .The new hindered   amines   as  cyclo  polymers  were  characterized  by FTIR  and  H-NMR Spectroscopies ,Physical   and   chemical properties were measured  using TGA and DSC to   study   the thermal stability  of   prepared  cyclopolymers  . These   prepared  N-aromatic  substituted di allylamins cyclopolymers [ A3 - A4 ]  were  used  as  antioxidants  for  lubricant  oil  .   which   give    high stability   when  comparing  with standard  antioxidant . The oxidation stability of base  oil with (1% ) additive  were  examined according to Institute of Petroleum testing method IP280&nbsp

Detecting Arabic Offensive Language in Microblogs Using Domain-Specific Word Embeddings and Deep Learning

In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution. However, users often take advantage of perceived anonymity on social media platforms to share offensive or hateful content. Thus, offensive language has grown as a significant issue with the increase in online communication and the popularity of social media platforms. This problem has attracted significant attention for devising methods for detecting offensive content and preventing its spread on online social networks. Therefore, this paper aims to develop an effective Arabic offensive language detection model by employing deep learning and semantic and contextual features. This paper proposes a deep learning approach that utilizes the bidirectional long short-term memory (BiLSTM) model and domain-specific word embeddings extracted from an Arabic offensive dataset. The detection approach was evaluated on an Arabic dataset collected from Twitter. The results showed the highest performance accuracy of 0.93% with the BiLSTM model trained using a combination of domain-specific and agnostic-domain word embeddings

A Secure Cloud Computing Model based on Data Classification

AbstractIn cloud computing systems, the data is stored on remote servers accessed through the internet. The increasing volume of personal and vital data, brings up more focus on storing the data securely. Data can include financial transactions, important documents, and multimedia contents. Implementing cloud computing services may reduce local storage reliance in addition to reducing operational and maintenance costs. However, users still have major security and privacy concerns about their outsourced data because of possible unauthorized access within the service providers. The existing solutions encrypt all data using the same key size without taking into consideration the confidentiality level of data which in turn will increase the cost and processing time. In this research, we propose a secure cloud computing model based on data classification. The proposed cloud model minimizes the overhead and processing time needed to secure data through using different security mechanisms with variable key sizes to provide the appropriate confidentiality level required for the data. The proposed model was tested with different encryption algorithms, and the simulation results showed the reliability and efficiency of the proposed framework

Fine-grained Arabic named entity recognition

This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and short vowels, the complex morphology, and the highly in infection process. Instead of classifying the detected NE phrases into small sets of classes, we target a broader range (i.e. 50 fine-grained classes 'hierarchal-based of two levels') to increase the depth of the semantic knowledge extracted. This has increased the number of classes, complicating the task, when compared with traditional (coarse-grained) NER, because of the increase in the number of semantic classes and the decrease in semantic differences between fine-grained classes. Our approach to developing fine-grained NER relies on two different supervised Machine Learning (ML) technologies (i.e. Maximum Entropy 'ME' and Conditional Random Fields 'CRF'), which require annotated training data in order to learn by extracting informative features. We develop a methodology which exploit the richness of Arabic Wikipedia (A W) in order to create a scalable fine-grained lexical resource and a corpus automatically. Moreover, two gold-standard created corpora from different genres were also developed to perform comparable evaluation. The thesis also developed a new approach to feature representation by relying on the dependency structure of the sentence to overcome the limitation of traditional window-based (i.e. n-gram) representation. Furthermore, by exploiting the richness of unannotated textual data to extract global informative features using word-level clustering technique was also achieved. Each contribution was evaluated via controlled experiment and reported using three commonly applied metrics, i.e. precision, recall and harmonic F-measure

High-fidelity adiabatic inversion of a 31P^{31}\mathrm{P} electron spin qubit in natural silicon

The main limitation to the high-fidelity quantum control of spins in semiconductors is the presence of strongly fluctuating fields arising from the nuclear spin bath of the host material. We demonstrate here a substantial improvement in single-qubit gate fidelities for an electron spin qubit bound to a $^{31}$P atom in natural silicon, by applying adiabatic inversion instead of narrow-band pulses. We achieve an inversion fidelity of 97%, and we observe signatures in the spin resonance spectra and the spin coherence time that are consistent with the presence of an additional exchange-coupled donor. This work highlights the effectiveness of adiabatic inversion techniques for spin control in fluctuating environments.Comment: 4 pages, 2 figure