Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.

Extending the Technology Acceptance Model (TAM) for studying the e-learning acceptance is not a new research topic, and it has been tackled by many scholars. However, the development of a comprehensive TAM that could be able to examine the e-learning acceptance under any circumstances is regarded to be an essential research direction. To identify the most widely used external factors of the TAM concerning the e-learning acceptance, a literature review comprising of 120 significant published studies from the last twelve years was conducted. The review analysis indicated that computer self-efficacy, subjective/social norm, perceived enjoyment, system quality, information quality, content quality, accessibility, and computer playfulness were the most common external factors of TAM. Accordingly, the TAM has been extended by the aforementioned factors to examine the students acceptance of e-learning in five different universities in the United Arab of Emirates (UAE). A total of 435 students participated in the study. The results indicated that system quality, computer self-efficacy, and computer playfulness have a significant impact on perceived ease of use of e-learning system. Furthermore, information quality, perceived enjoyment, and accessibility were found to have a positive influence on perceived ease of use and perceived usefulness of e-learning system.

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible information extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Although many approaches for data extraction from Web pages have been developed, there has been limited effort to compare such tools. Unfortunately, in only a few cases can the results generated by distinct tools be directly compared since the addressed extraction tasks are different. This paper surveys the major Web data extraction approaches and compares them in three dimensions: the task domain, the automation degree, and the techniques used. The criteria of the first dimension explain why an IE system fails to handle some Web sites of particular structures. The criteria of the second dimension classify IE systems based on the techniques used. The criteria of the third dimension measure the degree of automation for IE systems. We believe these criteria provide qualitatively measures to evaluate various IE approaches

The Arabic language presents researchers and developers of natural language processing (NLP) applications for Arabic text and speech with serious challenges. The purpose of this article is to describe some of these challenges and to present some solutions that would guide current and future practitioners in the field of Arabic natural language processing (ANLP). We begin with general features of the Arabic language in Sections 1, 2, and 3 and then we move to more specific properties of the language in the rest of the article. In Section 1 of this article we highlight the significance of the Arabic language today and describe its general properties. Section 2 presents the feature of Arabic Diglossia showing how the sociolinguistic aspects of the Arabic language differ from other languages. The stability of Arabic Diglossia and its implications for ANLP applications are discussed and ways to deal with this problematic property are proposed. Section 3 deals with the properties of the Arabic script and the explosion of ambiguity that results from the absence of short vowel representations and overt case markers in contemporary Arabic texts. We present in Section 4 specific features of the Arabic language such as the nonconcatenative property of Arabic morphology, Arabic as an agglutinative language, Arabic as a pro-drop language, and the challenge these properties pose to ANLP. We also present solutions that have already been adopted by some pioneering researchers in the field. In Section 5 we point out to the lack of formal and explicit grammars of Modern Standard Arabic which impedes the progress of more advanced ANLP systems. In Section 6 we draw our conclusion.

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.