BSpace

The British University in Dubai (BUiD) Digital Repository

Welcome to BSpace, the online institutional repository of the British University in Dubai. BSpace provides access to the Dissertations, Thesis, Research projects, Faculty publications and archives of BUiD.

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Now showing 1 - 7 of 7

Recent Submissions

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GenDE: A CRF-Based Data Extractor
(River Publishers, 2020) Kayed, Mohammed; Shaalan, Khaled
Web site schema detection and data extraction from the Deep Web have been studied a lot. Although, few researches have focused on the more challenging jobs: wrapper verification or extractor generation. A wrapper verifier would check whether a new page from a site complies with the detected schema, and so the extractor will use the wrapper to get instances of the schema types. If the wrapper failed to work with the new page, a new wrapper/schema would be re-generated by calling an unsupervised wrapper induction system. In this paper, a new data extractor called GenDE is proposed. It verifies the site schema and extracts data from the Web pages using Conditional Random Fields (CRFs). The problem is solved by breaking down an observation sequence (a Web page) into simpler subsequences that will be labeled using CRF. Moreover, the system solves the problem of automatic data extraction from modern JavaScript sites in which data/schema are attached (on the client side) in a JSON format. The experiments show an encouraging result as it outperforms the CSP-based extractor algorithm (95% and 96% of recall and precision, respectively). Moreover, it gives a high performance result when tested on the SWDE benchmark dataset (84.91%).
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Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media
(IEEE, 2021) ALATTAR , FUAD; SHAALAN, KHALED
Sentiment Analysis tools allow decision-makers to monitor changes of opinions on social media towards entities, events, products, solutions, and services. These tools provide dashboards for tracking positive, negative, and neutral sentiments for platforms like Twitter where millions of users express their opinions on various topics. However, so far, these tools do not automatically extract reasons for sentiment variations, and that makes it difficult to conclude necessary actions by decision-makers. In this paper, we first compare performance of various Sentiment Analysis classifiers for short texts to select the top performer. Then we present a Filtered-LDA framework that significantly outperformed existing methods of interpreting sentiment variations on Twitter. The framework utilizes cascaded LDA Models with multiple settings of hyperparameters to capture candidate reasons that cause sentiment changes. Then it applies a filter to remove tweets that discuss old topics, followed by a Topic Model with a high Coherence Score to extract Emerging Topics that are interpretable by a human. Finally, a novel Twitter’s sentiment reasoning dashboard is introduced to display the most representative tweet for each candidate reason.
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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique
(Tech science press, 2022) AlZaabi, Hanadi; Shaalan, Khaled; M. Ghazal, Taher; A. Khan, Muhammad; Abbas, Sagheer; Mago, Beenu; A. A. Tomh, Mohsen; Ahmad, Munir
Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer’s energy requirements. In many regions, energy utilization in the housing area is 30%–40%. The growth of smart homes has raised the requirement for intelligence in applications such as asset management, energy-efficient automation, security, and healthcare monitoring to learn about residents’ actions and forecast their future demands. To overcome the challenges of energy consumption optimization, in this study, we apply an energy management technique. Data fusion has recently attracted much energy efficiency in buildings, where numerous types of information are processed. The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate. The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%, which is higher than the previously published approaches.
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Toward an Integrated Model for Examining the Factors Affecting the Acceptance of Queue Management Solutions in Healthcare
(IEEE, 2022) Ahmad AlQudah, Adi; Al-Emran, Mostafa; U. Daim, Tugrul; Shaalan, Khaled
Despite the previous article on technology adoption and the importance of users’ intention to use various technologies in healthcare, users’ acceptance of queue management solutions (QMS) has rarely been measured. The key driver for this article is to evaluate the constructs that have an influence on the acceptance of QMS in the healthcare domain. To achieve this purpose, this article proposes an integrated model based on the integration of various constructs extracted from different theoretical models, including the unified theory of acceptance and use of technology (UTAUT), technology acceptance model (TAM), and social cognitive theory (SCT) along with trust and innovativeness as external factors. The data were collected using an online questionnaire survey from 242 healthcare professionals. The structural equation modeling technique has been employed to validate the model. In general, the results exposed that the suggested model has explained 66.5% of the total variance in the behavioral intention to use QMS. The proposed model is believed to be helpful in exploring the acceptance of other information technologies in the healthcare domain, and the results can provide valuable knowledge to managers and decision-makers in healthcare organizations.
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Suspicious Activity Detection of Twitter and Facebook using Sentimental Analysis
(TEM journal, 2020) Al Mansoori , Saeed; Almansoori, Afrah; Alshamsi, Mohammed; A. Salloum, Said; Shaalan, Khaled
The purpose of this study is to evaluate the criminal behavior on the social media platforms and to classify the gathered data effectively as negative, positive, or neutral in order to identify a suspect. In this study, data was collected from two platforms, Twitter and Facebook, resulting in the creation of two datasets. The following findings have been pointed out from this study: Initially, VADER twitter sentimental analysis showed that out of 5000 tweets 50.8% people shared a neutral opinion, 39.2% shared negative opinion and only 9.9% showed positive opinion. Secondly, on Facebook, the majority of people showed a neutral response which is 55.6%, 38.9% shared positive response and only 5.6% shared negative opinion. Thirdly, the score of sentiments and engagement in every post affects the intensities of sentiments.