Alaa Abbas

Tikrit University, College of Petroleum and Minerals Engineering, Dept. of Oil Systems Control Engineering, Faculty Member

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Alaa Kh. Abbas completed his MSc.in Computer Science from University Technology Malaysia, Malaysia, in 2015. Received B.Sc degree in Computer science from Tikrit University, in 2005. From 2015 till now working at Tikrit university as assistant lecturer in the college of Petroleum Processes Engineering, Petroleum Systems Control department. His research interests lie in the area of machine learning, big data analytics, image processing, speech recognition and AI, ranging from theory to design to implementation. In recent years, he has focused on better techniques for big data mining and analyzing. He has collaborated actively with researchers in several other disciplines of computer science. He can be contacted at email: alaa.programmer12@tu.edu.iq
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DIA-English-Arabic neural machine translation domain: sulfur industry

Indonesian Journal of Electrical Engineering and Computer Science

The aim of this paper is the design and development a new English-Arabic neural machine translati... more The aim of this paper is the design and development a new English-Arabic neural machine translation (NMT) called DIA translation system. The main purpose of the designing system is to study translator limited sulfur industry domain as a stand-alone tool in order to improve the translation quality. Machine translation (MT) are very sensitive to the domains they were trained on and can be integrated with general (English-Arabic) MT systems. The proposed system has mainly four directions: supports chemical symbols, terms, phrase, and text and it is evaluated by using (1,200) various English declarative sentences which written by English Language experts. The obtained results indicate that this system is high effective and has an accuracy of 79.33% in comparison with Google translator which has 38.67% for the same test samples.

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An overview of number theory research unit variant development security

Indonesian Journal of Electrical Engineering and Computer Science

Number theory research unit (NTRU) become the most important of securi-ty in recent, with its mod... more Number theory research unit (NTRU) become the most important of securi-ty in recent, with its modification of their variant, this paper search of the literature and A number of studies have examined the in public key variant development and security. In general, prior work is limited to a subset of public key increasing complexity but the benefits of speed up encryption/ decryption have not been fully established. So this paper will be the basis for those who want to develop and find proposed solutions for new studies of the NTRU algorithm. This paper aims to develop a framework to investigate the NTRU development, had been discovered that despite its development over the years and even its acceptance in round three of post quantum cryptograph, then found that limit study in the new scope of quantum facility and the ability of hybrid of new study.

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Image Spam Detection Using Machine Learning and Natural Language Processing

Journal of Southwest Jiaotong University, 2020

Today, images are a part of communication between people. However, images are being used to share... more Today, images are a part of communication between people. However, images are being used to share information by hiding and embedding messages within it, and images that are received through social media or emails can contain harmful content that users are not able to see and therefore not aware of. This paper presents a model for detecting spam on images. The model is a combination of optical character recognition, natural language processing, and the machine learning algorithm. Optical character recognition extracts the text from images, and natural language processing uses linguistics capabilities to detect and classify the language, to distinguish between normal text and slang language. The features for selected images are then extracted using the bag-of-words model, and the machine learning algorithm is run to detect any kind of spam that may be on it. Finally, the model can predict whether or not the image contains any harmful content. The results show that the proposed method...

Twitter Sentiment Analysis Using an Ensemble Majority Vote Classifier

Journal of Southwest Jiaotong University

Twitter social media data generally uses ambiguous text that can cause difficulty in identifying ... more Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques—naive Bayes, decision trees, multilayer perceptron and logistic regression—to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the ...

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Lesion border detection in dermoscopy images using dynamic programming

Automated border detection is an important and challenging task in the computerized analysis of d... more Automated border detection is an important and challenging task in the computerized analysis of dermoscopy images. However, dermoscopic images often contain artifacts such as illumination, dermoscopic gel, and outline (hair, skin lines, ruler markings, and blood vessels). As a result, there is a need for robust methods to remove artifacts and detect lesion borders in dermoscopy images. This automated method consists of three main steps: (1) preprocessing, (2) edge candidate point detection, and (3) tumor outline delineation. First, algorithms to reduce artifacts were used. Second, a least-squares method (LSM) was performed to acquire edge points. Third, dynamic programming (DP) technique was used to find the optimal boundary of the lesion. Statistical measures based on dermatologist-drawn borders were utilized as ground-truth to evaluate the performance of the proposed method. The method is tested on a total of 240 dermoscopic images: 30 benign melanocytic, 50 malignant melanomas, 50 basal cell carcinomas, 20 Merkel cell carcinomas, 60 seborrheic keratosis, and 30 atypical naevi. We obtained mean border detection error of 8.6%, 5.04%, 9.0%, 7.02%, 2.01%, and 3.24%, respectively. The results demonstrate that border detection combined with artifact removal increases sensitivity and specificity for segmentation of lesions in dermoscopy images.

IMAGE SPAM DETECTION USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING 基于机器学习和自然语言处理的图像垃圾邮件检测

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TWITTER SENTIMENT ANALYSIS USING AN ENSEMBLE MAJORITY VOTE CLASSIFIER 基于可投票多数票分类器的推特情感分析

Twitter social media data generally uses ambiguous text that can cause difficulty in identifying ... more Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques-naive Bayes, decision trees, multilayer perceptron and logistic regression-to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the performance of the proposed classifier is better than the independent one.

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TWITTER SENTIMENT ANALYSIS USING AN ENSEMBLE MAJORITY VOTE CLASSIFIER 基于可投票多数票分类器的推特情感分析

Twitter social media data generally uses ambiguous text that can cause difficulty in identifying ... more Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques-naive Bayes, decision trees, multilayer perceptron and logistic regression-to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the performance of the proposed classifier is better than the independent one.

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Alaa Abbas

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