Characterizing the UAE national Web with a two-step filter

Googling for Health Information

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.

The United Arab Emirates (UAE) has grown rapidly since independence in 1971, but much of this growth has been fuelled by expatriates. The UAE’s workforce now totals c.4.2 million, of which only 225,000 are Emiratis. Of more concern, just 10% of these are employed in the private sector (UAE Ministry of Labour cited by Salama, 2013). With 150,000 Emirati workers entering the job market by 2020 (Salama, 2013), this over reliance on public sector employment is not sustainable (Economist Intelligence Unit, 2012). The private sector employment of Emiratis however, has proved problematic. Private sector employers have traditionally perceived Emiratis as unattractive employees and Nationals are not generally attracted to the sector. Nevertheless, there are Emiratis who have succeeded outside the government. Developing an in-depth understanding of why they engaged with the sector and how they succeeded within their organisation is critical to address the current challenge and attract more to do the same. We currently know little about the motivations and factors that influenced the career choice of Emiratis who joined the private sector and their subsequent experience. This study considers these questions and utilises the concept of employee engagement to frame the Emirati employee experience. The UAE’s banking sector is by far the largest employer of private sector based Emiratis. This research, therefore, explores the situation through the perspectives of Emirati employees that have joined a mid-sized local bank in the last three years. It is the first study of its kind and makes a three-fold contribution to producing actionable knowledge. Firstly, the findings will help the UAE private sector to attract new Emirati employees and strengthen existing engagement efforts to address the status quo and achieve, inter alia, a more effective Emirati workforce. Secondly, it explores the applicability of employee engagement in the UAE’s banking sector. Thirdly, it contributes towards the current public policy debate about how to encourage greater Emirati participation in the private sector. The study was shaped by the social constructionist philosophy and used mixed methods and an emergent approach to study the phenomenon in depth. Data was collected through; semi-structured interviews with 15 Emiratis, 5 formal group discussions in the form of action learning based sets, and an organisation wide quantitative survey. The research specifically adopted the principles of Action Research including iterative cycles of reflection and experiment/action and case study research. Significant issues were found for Emirati employees, including; challenges in building Meaningfulness, finding a trustworthy working environment, developing a career path, learning from colleagues and coping with significant social pressures. These challenges were explored in action learning sets where a collaborative effort was made to address the problems surfaced. The research confirmed the relevance of the employee engagement model but highlights the importance of a contextual perspective in order to surface the priority areas for action. It also moves the debate away from the overwhelming focus on pay and working hours, to much more subtle considerations that will need to be addressed by individual organisations.

The World Wide Web is growing at an uncontrollable rate. Hundreds of thousands of web sites appear every day, with the added challenge of keeping the web directories up-to-date. Further, the uncontrolled nature of web presents difficulties for Web page classification. As the number of Internet users is growing, so is the need for classification of web pages with greater precision in order to present the users with web pages of their desired class. However, web page classification has been accomplished mostly by using textual categorization methods. Herein, we propose a novel approach for web page classification that uses the HTML information present in a web page for its classification. There are many ways of achieving classification of web pages into various domains. This paper proposes an entirely new dimension towards web page classification using Artificial Neural Networks (ANN). Index Terms: World Wide Web, Web page classification, textual categorization, HTML, Artificial Neural Networks, ANN.

This study addresses a gap in the literature concerning the role of cultural and religious dynamics in the application of security procedures in the international airports in the United Arab Emirates (UAE). The study establishes a reference for further research into the analysis of passenger attitudes towards the experience of using international airports by utilising an empirical research design and a theoretical perspective, the theory of ‘liminality’, to explore passengers’ experience in the international airports in the UAE. The significance of this study lies in the fact that it makes an original contribution to the gradually emerging empirical body of research into airport security and passenger experience in the UAE international airports. The inquiry explores the extent to which airport security measures and legislation recognise religious and cultural sensitivities of the passengers; investigate the impact of religious and cultural dynamics in shaping passengers’ attitudes towards the security measures implemented at the selected international airports and finally, explores the views of the airport security managers and officers concerning the role of religio-cultural factors in the security policy at selected international airports in the UAE. The study utilises a mixed-methods case study research design consisting of a survey (self-completion questionnaire), semi-structured interviews and analysis of some of the relevant policy documents. The study sample consists of two international airports in the UAE i.e. Dubai and Abu Dhabi international airports. The large survey was completed by the two hundred participants and constructed to elicit the attitudes, perception and experience of the passengers using the selected international airports in the UAE. Semi-structured interviews, completed by a selected group managers and officers, explored the views and overall perception of airport managers and staff responsible for the management and oversight of the airport security. The results reveal broad patterns of responses from the study participants on various issues related to their experience of using the selected international airports. The main findings indicate that passengers, overall, were satisfied with use of the international airports and agreed that the application of the security procedures were conducted objectively and the staff appeared to be respectful of personal privacy and religious and cultural sensitivities of the passengers. However, the study also reports that going through the security systems proved to be the most stressful part of passenger experience at the airports. A central finding the study underscores that the use of the full body scanners creating most dissatisfied experiences amongst the passengers. The results further indicate that the security staff’s awareness of intercultural sensitives and passengers’ religious and central values were felt to be highly significant. The study emphasises the need to be aware that legal and security related policies at the international airports should also reflect a deeper ethical values of human engagement unfolding within the context of airports. Based on the empirical findings, the study offers overall conclusions and makes policy and further research related recommendations.

Information filtering and information retrieving applications are based on web page classification methods. Usually, web pages serve different functionalities or develop different topics or subjects. The diversity of web page content increases the need for automatic web page classification, making it a challenging task at the same time. Considering that the main component of the content of a web page is most often represented by the text and the classification of the text is a problem intensively studied in the last years, with researchers reporting state-of-the-art results for various methods, the idea of applying these methods on the text extracted from web pages could lead to important results. In this work, we revisit our experimental study on convolutional neural networks for multi-label multi-language web page classification with a new approach that consists of dividing the classification problem into functional classification and subject classification of web pages. From the experimental evaluation, one may conclude that the separation of the functional and subject classification of web pages leads to an improvement of the overall results.

These days, the Internet is developing at an exponential rate and can cover just about any data required. Nonetheless, the immense measure of web pages makes it more difficult to effectively discover the target data by a user. Therefore, an efficient method, for classifying this huge amount of data is essential if the web pages are to be exploited to its full potential. In the domain of automatic web page classifier many approaches have been tried to solve this problem using different Machine learning-based algorithms including Support Vector Machine (SVM), Naive Bayes, Decision Tree, K-Nearest Neighbor (K-NN) and Neural Networks. However, there is a lack of comparison between these algorithms to find a better framework for the classification and analysis of health related web pages. In this research study, we compare two commonly used supervised Machine Learning algorithms; Support Vector Machines (SVM) and Naive Bayes to classify web pages which provide drugs related information of patients for example side effects, patient action and follow-up information for patients. We use Unified Medical Language System (UMLS) to annotate the health related concepts in Web pages and train SVM and Naive Bayes classifiers in General Architecture for Text Engineering to classify health related and non-health related Web pages. The evaluation was performed using K-fold cross validation using four runs on a data set of fifty Web pages. Results found that SVM performed better to classify health and non-health related pages in terms of precision, recall and F-measure.

World Wide Web (WWW) is a platform that explores a wide range of information used for the development of web applications. Some examples of these applications include social network analysis, personalized item recommendations, and web page classification and ranking. Among these applications, search engines and web page ranking are particularly important as they consistently index and store billions of web pages on the internet. The main objective of this paper is to create an innovative framework for the classification and re-ranking of web pages using intelligent techniques. The framework is structured into two key phases: classification and re-ranking-based retrieval. In the initial classification phase, a series of pre-processing steps are implemented, including the elimination of HTML tags, punctuation, stop words, and the application of stemming. After pre-processing, a word-to-vector conversion is performed, followed by feature extraction utilizing Principal Component Analysis (PCA). This sequence of actions leads to optimal feature selection, which is vital for the precise classification of web pages. Given the multitude of features present in web pages that can compromise classification accuracy, this study employs a novel meta-heuristic algorithm, the Opposition Based-Tunicate Swarm Algorithm (O-TSA), to facilitate optimal feature selection. The refined features are subsequently processed through the Enhanced Convolutional-Recurrent Neural Network (E-CRNN), enhanced by O-TSA, resulting in the effective classification of diverse web page categories. In the second phase, the re-ranking process is executed using O-TSA, which establishes the objective function based on a similarity function (correlation) for URL matching, leading to optimal re-ranking of web.

Parabolic trough Photovoltaic/Thermal hybrid system: Thermal modeling, case studies, and economic and environmental analyzes

Selecting the right materials supplier is of paramount importance to the success of the construction project and the organization as a whole. The objective of this paper is to identify and assess the key criteria for selecting suppliers for oil and gas projects in the United Arab Emirates (UAE). A survey of 23 factors is developed and distributed to experts in oil and gas EPC companies in the UAE. The top 10 criteria are then clustered into two main groups, each consisting of five selection factors. Group one includes technical and commercial aspects, which include quality, price, delivery, service and warranties and claims. Group two is company-related aspects, which include technical capability, production facility and capability, financial position, performance history and geographical location. The Analytic Hierarchy Process (AHP) and Delphi methods are used in evaluating and determining the weights and orders for the 10 selected criteria. The results indicate that quality and price are the most important, with a weight of 0.376 and 0.228, respectively. Additionally, the results show that the technical and commercial criteria are more important than the company-specific criteria.

The lack of sufficient labeled Web pages in many languages, especially for those uncommonly used ones, presents a great challenge to traditional supervised classification methods to achieve satisfactory Web page classification performance. To address this, we propose a novel Nonnegative Matrix Tri-factorization (NMTF) based Dual Knowledge Transfer (DKT) approach for cross-language Web page classification, which is based on the following two important observations. First, we observe that Web pages for a same topic from different languages usually share some common semantic patterns, though in different representation forms. Second, we also observe that the associations between word clusters and Web page classes are a more reliable carrier than raw words to transfer knowledge across languages. With these recognitions, we attempt to transfer knowledge from the auxiliary language, in which abundant labeled Web pages are available, to target languages, in which we want classify Web pages, through two different paths: word cluster approximations and the associations between word clusters and Web page classes. Due to the reinforcement between these two different knowledge transfer paths, our approach can achieve better classification accuracy. We evaluate the proposed approach in extensive experiments using a real world cross-language Web page data set. Promising results demonstrate the effectiveness of our approach that is consistent with our theoretical analyses.

This paper seeks to determine the motivators that affect the adoption of information technology (IT), specifically in modern healthcare systems, based on a case study in the United Arab Emirates (UAE). The aim is to identify, examine, and place in a hierarchical structure, based on a two-step exploratory methodology, the motivators for IT adoption of four main categories of healthcare stakeholders (employees, patients, UAE citizens and residents, and accredited foresight experts) via an analytic hierarchy process (AHP) model. AHP revealed the relative importance weights of the seven IT adoption motivators and their 41 sub-motivators. Government support was found to be the most important group of motivators, followed by knowledge sharing, infrastructure, green management, lean management, internal/external environment, and social sustainability. The paper facilitates improving technology adoption generally, specifically in the UAE’s healthcare system. A Pareto chart visually presents the ranking of all sub-factors to help supply-chain practitioners better allocate resources for IT implementation. Little research has examined the combined perspectives of various stakeholders and no study has hitherto investigated the significant motivators of IT adoption in the UAEs’ healthcare industry. Including foresight experts is new in this domain.

ALBAIK is one of the leading fast-food brands that has been known for its high-quality food offered at affordable prices in a courtesy and inviting environment. The story of ALBAIK restaurant started in 1974 in the city of Jeddah in Saudi Arabia, and recently, it has been able to achieve international expansion in other countries, including the United Arab Emirates (UAE) and Bahrain. This case study presents about the success story of ALBAIK restaurant in relation to its expansion in the UAE. Specifically, it focuses on the entry mode used by ALBAIK as well as the benefits from expansion to UAE market. Also, comprehensive analysis about the political, economic, cultural, technological and legal environment of UAE, which would have significant implications on doing business in the country are presented. Finally, the case study aims to enrich our understanding about the fast-food industry in UAE as well as the contributions of ALBAIK to CSR, which have enabled it to position itself as a market leader in the industry.

Introduction The transition from a resource-based economy to alternative economic systems represents one of the most pressing challenges in the current global context. The environmental aspect significantly influences energy policy, serving as a foundation for transforming the economies of leading energy-exporting countries. The United Arab Emirates (UAE) presents a notable example of a successful transition from a resource-based economy to technology-driven development, as evidenced by its energy policy and diplomatic strategies. The aim of this study was to examine the global external energy policy and diplomacy of the UAE and other Persian Gulf countries energy policy and diplomacy incorporated a methodological approach combining qualitative and quantitative methods energy policy and diplomacy, as well as strategies employed by other countries, vividly demonstrates the global significance of these processes., focusing on its role in the profound transformation of the energy market. Methods Methodologically, the study involved comparative analysis, as well as an assessment of global energy transition trends and their relationship with international diplomacy, using the UAE as a case study. Results The share of revenue from the UAE’s oil sector in total exports declined from 93.8% in 2000 to 68.2% in 2023. Investments in the economy nearly tripled between 2005 and 2023, reaching $30.7 billion. Findings from the model analysis indicate that an abrupt reduction in resource dependence could result in economic difficulties in the short term. However, in the medium and long term, this path is the most favorable, contingent upon the rapid expansion of high-technology exports. Based on the VECM results for the UAE, Saudi Arabia, Kuwait, and Oman, targeted recommendations for energy and economic policy can be formulated. Given that oil production acts as the main long-term determinant but remains largely unadjusted in the short term. In contrast, global oil prices and energy intensity exert strong short-term effects on GDP and exports, the energy policies of these countries should aim to diversify long-term development strategies in extraction and strengthen mechanisms for responding to external shocks. Discussion The practical application of these research results lies in their potential use for studying other oil-producing countries and devising recommendations for an effective transition from resource dependence to innovation-driven progress.

"This working paper presents the results of an intensive case study of education reform to support the development of a post-petroleum, knowledge-based economy in the United Arab Emirates. The case study is part of a larger research project exploring the ways in which state leaders may cultivate engaged citizens who are willing and able to contribute to the development of their countries, particularly in the contemporary era of heightened globalization and intense economic competition. Building on existing theory and empirical work, the case study identifies and measures attitudes that are believed to facilitate knowledge-intensive growth within one country, such as achievement motivation, risk-taking propensity, civic duty, willingness to invest in a promising business idea, and trust and social capital. For both policymakers and researchers, the case study offers a rich portrait of one strategy that state leaders may use to help foster knowledge-intensive economic development. The case study uses a quasi-experimental research methodology that compares UAE students’ attitudes in a new type of school (“treatment” schools) with UAE students’ attitudes at regular government schools (“control” schools). The new type of school has been developed as part of the country’s larger movement of education reform to support a post-petroleum, knowledge-based economy. The working paper presents findings on the major differences between students’ attitudes at the two types of schools and uses a difference-in-differences (DD) approach to estimate the causal effect of the treatment schools on students’ attitudes in these areas. Key findings include: • Students in the treatment schools reported significantly higher levels of achievement motivation, willingness to take risks, willingness to invest in a promising business idea, and social capital, compared with students in regular government schools. • Students in the treatment schools reported significantly higher levels of civic-mindedness relative to students in regular government schools. • Positive, significant DD estimates of the causal effect of treatment schools on students’ levels of nationalism and pride in the UAE were also found, suggesting that treatment schools are successfully promoting these types of attitudes in UAE youth. • Respect from friends and personal interest, or “a job I like,” were students’ highest priorities in selecting a job across both types of schools; treated students consider “contributing to UAE society” almost as important. • The data suggest that treatment schools are increasing the degree to which UAE youth value determination and perseverance, based on positive and significant DD treatment effect estimates for these variables." تعرض ورقة العمل هذه نتائج دراسة حالة مكثفة لإصلاح التعليم من أجل دعم تطوير اقتصاد قائم على المعرفة لمرحلة ما بعد النفط في دولة الإمارات العربية المتحدة .