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Abeer Alsadoon

Bio: Abeer Alsadoon is an academic researcher from Charles Sturt University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 16, co-authored 188 publications receiving 1082 citations. Previous affiliations of Abeer Alsadoon include University of Western Sydney & Hodges University.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: This article aims to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.
Abstract: The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.

388 citations

Journal ArticleDOI
TL;DR: The main aim of this research is to evaluate the various computer-aided techniques, analyzing the current best technique and finding out their limitation and drawbacks and finally proposing the new model with improvements in the currently best model.

169 citations

Journal ArticleDOI
TL;DR: It is demonstrated that emotion recognition based on facial expressions is feasible in distance education, permitting identification of a student’s learning status in real time, and can help teachers to change teaching strategies in virtual learning environments according to the student's emotions.

135 citations

Journal ArticleDOI
TL;DR: A taxonomy that accounts for components required to develop an effective recommendation system was developed and it was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components.
Abstract: The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.

112 citations

Journal ArticleDOI
TL;DR: Experimental results show that the segmented images have a high accuracy while substantially reducing the computation time and can be used to diagnose brain tumor in patients with a high success rate.

97 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.
Abstract: In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

1,084 citations

Proceedings Article
22 Aug 1999
TL;DR: The accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device is addressed.
Abstract: ▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

752 citations

Journal ArticleDOI
TL;DR: This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis and presents a taxonomy of sentiment analysis, which highlights the power of deep learning architectures for solving sentiment analysis problems.
Abstract: Social media is a powerful source of communication among people to share their sentiments in the form of opinions and views about any topic or article, which results in an enormous amount of unstructured information. Business organizations need to process and study these sentiments to investigate data and to gain business insights. Hence, to analyze these sentiments, various machine learning, and natural language processing-based approaches have been used in the past. However, deep learning-based methods are becoming very popular due to their high performance in recent times. This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis. We present a taxonomy of sentiment analysis and discuss the implications of popular deep learning architectures. The key contributions of various researchers are highlighted with the prime focus on deep learning approaches. The crucial sentiment analysis tasks are presented, and multiple languages are identified on which sentiment analysis is done. The survey also summarizes the popular datasets, key features of the datasets, deep learning model applied on them, accuracy obtained from them, and the comparison of various deep learning models. The primary purpose of this survey is to highlight the power of deep learning architectures for solving sentiment analysis problems.

385 citations