Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques

Artificial Intelligence (AI) enables machines to be intelligent, most importantly using Machine Learning (ML) in which machines are trained to be able to make better decisions and predictions. In particular, ML-based chatbot systems have been developed to simulate chats with people using Natural Language Processing (NLP) techniques. The adoption of chatbots has increased rapidly in many sectors, including, Education, Health Care, Cultural Heritage, Supporting Systems and Marketing, and Entertainment. Chatbots have the potential to improve human interaction with machines, and NLP helps them understand human language more clearly and thus create proper and intelligent responses. In addition to classical ML techniques, Deep Learning (DL) has attracted many researchers to develop chatbots using more sophisticated and accurate techniques. However, research has paid chatbots have widely been developed for English, there is relatively less research on Arabic, which is mainly due to its complexity and lack of proper corpora compared to English. Though there have been several survey studies that reviewed the state-of-the-art of chatbot systems, these studies (a) did not give a comprehensive overview of how different the techniques used for Arabic chatbots in comparison with English chatbots; and (b) paid little attention to the application of ANN for developing chatbots. Therefore, in this paper, we conduct a literature survey of chatbot studies to highlight differences between (1) classical and deep ML techniques for chatbots; and (2) techniques employed for Arabic chatbots versus those for other languages. To this end, we propose various comparison criteria of the techniques, extract data from collected studies accordingly, and provide insights on the progress of chatbot development for Arabic and what still needs to be done in the future

Proposing an intelligent information retrieval (IIR) framework for shariah sources retrieval in Islamic financial industry

This research proposes a framework of Intelligent Information Retrieval (IIR) for Shariah sources using Support Vector Machine (SVM) for Shariah decision making in Islamic Financial Industry (IFI). In addition, needs towards an automatic indexing platform for Shariah sources is also discussed in this research. Qualitative research methodology is adopted to review past literatures on IIR, data indexing, and SVM, the popular method of algorithms classification for text categorization. This research would be significant to Islamic banking industry in terms of strengthening the compliancy of the industry towards Shariah (Islamic rules) by proposing an IIR to the industry players. It also would add to the literature on Islamic finance especially in the context of financial technology. This research serves as a cross-field research that integrate the field of technology, Islamic finance, legal, and Shariah for an innovation

Basic Social Math: A Linguistic Upgrade for Decision Analysis and Social Dynamics Research.

There are foundational errors in the mathematical frameworks currently used in Economic and Decision Theories. Recent systemic failures in the interdependent business and educational sectors also show that many practices based on these theories are unsustainable in the changing dynamics of the global economy. A new approach is needed in social science research and systems engineering. This paper examines how the new understandings of complex systems, the role of emotion in cognition, and the core dynamics of decision making can help us correct these errors and to create a general framework for systemic innovation. It argues for the development of more rigorous linguistic tools that can objectively analyze social dynamics from an empirical perspective rather than from subjective cultural frames. In order to upgrade theories and adapt practices in social and educational systems, we need to first correct problems at the fundamental end of the mathematical framework that is used for such analysis. Examples of complex systems are explored within the operational context of cross-cultural language and insurance classrooms at Yamamah University in order to define the methods and illustrate the approach of Basic Social Math to correcting errors and testing theories in the social sciences

Interpreting text and image relations in violent extremist discourse: A mixed methods approach for big data analytics

This article presents a mixed methods approach for analysing text and image relations in violent extremist discourse. The approach involves integrating multimodal discourse analysis with data mining and information visualisation, resulting in theoretically informed empirical techniques for automated analysis of text and image relations in large datasets. The approach is illustrated by a study which aims to analyse how violent extremist groups use language and images to legitimise their views, incite violence, and influence recruits in online propaganda materials, and how the images from these materials are re-used in different media platforms in ways that support and resist violent extremism. The approach developed in this article contributes to what promises to be one of the key areas of research in the coming decades: namely the interdisciplinary study of big (digital) datasets of human discourse, and the implications of this for terrorism analysis and research

School leadership in the United Arab Emirates:A scoping review

Global research identifies school leadership as a critical factor in school success and effectiveness, especially in an educational reform environment with an ever-increasing number of schools working within public–private partnerships, a feature that characterises the United Arab Emirates. To aid leadership development and practices in a fast-moving education context and to underpin future empirical research, this scoping review of the literature from across three databases provides practitioners and policymakers with an understanding of school leadership for the public–private sector in the United Arab Emirates. Our search yielded 38 publications for analysis. Findings indicate that over the last 20 years, school leadership research in the United Arab Emirates has mainly focused on four themes: (1) context: leaders’ roles and school reform; (2) competency: hiring and professional development of school leaders; (3) characteristics: leadership styles and (4) capacity building: teacher leadership. We conclude with recommendations for research, including exploring cultural, relational, and compassionate school leadership through indigenous paradigms. We also provide recommendations for policy and practice, including the need to modify recruitment methods, equip school leaders to lead reform through advanced models of leadership to suit the collectivist United Arab Emirates culture, and align professional development with the professional standards

School leadership in the United Arab Emirates:A scoping review

Global research identifies school leadership as a critical factor in school success and effectiveness, especially in an educational reform environment with an ever-increasing number of schools working within public–private partnerships, a feature that characterises the United Arab Emirates. To aid leadership development and practices in a fast-moving education context and to underpin future empirical research, this scoping review of the literature from across three databases provides practitioners and policymakers with an understanding of school leadership for the public–private sector in the United Arab Emirates. Our search yielded 38 publications for analysis. Findings indicate that over the last 20 years, school leadership research in the United Arab Emirates has mainly focused on four themes: (1) context: leaders’ roles and school reform; (2) competency: hiring and professional development of school leaders; (3) characteristics: leadership styles and (4) capacity building: teacher leadership. We conclude with recommendations for research, including exploring cultural, relational, and compassionate school leadership through indigenous paradigms. We also provide recommendations for policy and practice, including the need to modify recruitment methods, equip school leaders to lead reform through advanced models of leadership to suit the collectivist United Arab Emirates culture, and align professional development with the professional standards

Improving Sentiment Analysis of Short Informal Indonesian Product Reviews using Synonym Based Feature Expansion

Sentiment analysis in short informal texts like product reviews is more challenging. Short texts are sparse, noisy, and lack of context information. Traditional text classification methods may not be suitable for analyzing sentiment of short texts given all those difficulties. A common approach to overcome these problems is to enrich the original texts with additional semantics to make it appear like a large document of text. Then, traditional classification methods can be applied to it. In this study, we developed an automatic sentiment analysis system of short informal Indonesian texts using Naïve Bayes and Synonym Based Feature Expansion. The system consists of three main stages, preprocessing and normalization, features expansion and classification. After preprocessing and normalization, we utilize Kateglo to find some synonyms of every words in original texts and append them. Finally, the text is classified using Naïve Bayes. The experiment shows that the proposed method can improve the performance of sentiment analysis of short informal Indonesian product reviews. The best sentiment classification performance using proposed feature expansion is obtained by accuracy of 98%.The experiment also show that feature expansion will give higher improvement in small number of training data than in the large number of them

Imagining a Future with Intelligent Machines A Middle Eastern and North African Perspective

Narratives about intelligent machines can influence the development, adoption, reception and regulation of artificial intelligence (AI). The Middle East and North Africa (MENA) region is hugely overlooked by the Western mainstream when it comes to discussing the future of artificial intelligence. Recognizing the vital importance of diversity in the global debate on what AI is and should be, this report examines the narratives about technological futures spreading in the Arab world