Professor Khaled Shaalan

Permanent URI for this collection

https://bspace.buid.ac.ae/handle/1234/2424

Khaled is an accomplished full professor of Computer Science with over thirty years of experience in ‎academia. ‎He has a strong background in teaching, mentoring, academic training, community ‎development, and academic ‎administration. Khaled has received numerous honors and awards for ‎academic excellence and outstanding ‎achievements. As the Head of the Computer Science Department, he ‎successfully built the department from ‎scratch and oversaw the development of the academic programs’ ‎curricula. He has mentored academic staff, ‎established a team of researchers, and increased program ‎enrolment. Khaled has collaborated with international ‎institutions, organizations, and corporations and ‎has published extensively in high-quality international journals, ‎book chapters, and conference ‎proceedings. His expertise is widely recognized, and he is frequently invited to ‎evaluate research ‎proposals, academic programs, PhD theses, and academic promotions. Khaled has also taught ‎Computer ‎Science courses at the undergraduate and graduate levels and served as chair of numerous international ‎‎conferences and on the editorial boards of various international journals.‎

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Now showing 1 - 20 of 49

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%).
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.
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.
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.
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.
Artificial Intelligence Chatbots: A Survey of Classical versus Deep Machine Learning Techniques
(2023) A. Alazzam, Bayan; Alkhatib, Manar; Shaalan, Khaled
: 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.
Speech Recognition Using Deep Neural Networks: A Systematic Review
(Speech recognition, deep neural network, systematic review, 2019) BOU NASSIF, ALI; SHAHIN, ISMAIL; ATTILI, IMTINAN; AZZEH, MOHAMMAD; SHAALAN, KHALED
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.
A Multimodal Approach to Improve Performance Evaluation of Call Center Agent
(MDPI, 2021) Ahmed , Abdelrahman; Shaalan, Khaled; Toral, Sergio; Hifny, Yasser
The paper proposes three modeling techniques to improve the performance evaluation of the call center agent. The first technique is speech processing supported by an attention layer for the agent’s recorded calls. The speech comprises 65 features for the ultimate determination of the context of the call using the Open-Smile toolkit. The second technique uses the Max Weights Similarity (MWS) approach instead of the Softmax function in the attention layer to improve the classification accuracy. MWS function replaces the Softmax function for fine-tuning the output of the attention layer for processing text. It is formed by determining the similarity in the distance of input weights of the attention layer to the weights of the max vectors. The third technique combines the agent’s recorded call speech with the corresponding transcribed text for binary classification. The speech modeling and text modeling are based on combinations of the Convolutional Neural Networks (CNNs) and Bi-directional Long-Short Term Memory (BiLSTMs). In this paper, the classification results for each model (text versus speech) are proposed and compared with the multimodal approach’s results. The multimodal classification provided an improvement of (0.22%) compared with acoustic model and (1.7%) compared with text model.
Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks
(MDPI, 2020) Ahmed , Abdelrahman; Toral, Sergio; Shaalan, Khaled; Hifny, Yaser
Measuring the productivity of an agent in a call center domain is a challenging task. Subjective measures are commonly used for evaluation in the current systems. In this paper, we propose an objective framework for modeling agent productivity for real estate call centers based on speech signal processing. The problem is formulated as a binary classification task using deep learning methods. We explore several designs for the classifier based on convolutional neural networks (CNNs), long-short-term memory networks (LSTMs), and an attention layer. The corpus consists of seven hours collected and annotated from three different call centers. The result shows that the speech-based approach can lead to significant improvements (1.57% absolute improvements) over a robust text baseline system.
Arabic Question Answering: A Study on Challenges, Systems, and Techniques
(International Journal of Computer Applications, 2019) Samy, Heba; E. Hassanein, Ehab; Shaalan, Khaled
The enormous increase of the amount of information available on the web creates the need for systems like Question Answering to bridge the gap between general end users and the web with its different data representations. A considerable portion of the available data on the web is written in Arabic for and by Arabic users. This paper provides a review of the Arabic Question Answering Systems building processes and the challenges met by the researchers in this topic due to the Arabic language special characteristics. A general architecture is represented for the Question Answering task on both structured and unstructured data. Then, an overview of the work done in Arabic Question Answering Systems is presented. Finally, a number of tools and linguistic resources are recommended for researchers to develop Arabic question answering systems.
Factors affecting the E-learning acceptance: A case study from UAE
(E-learning . Technology acceptance . Knowledge sharing . Technology innovativeness. System quality. Trust, 2018) A. Salloum, Said; Al-Emran, Mostafa; Shaalan, Khaled; Tarhini, Ali
The main objective of this article is to study the factors that affect university students’ acceptance of E-learning systems. To achieve this objective, we have proposed a new model that aims to investigate the impact of innovativeness, quality, trust, and knowledge sharing on E-learning acceptance. Data collection has taken place through an online questionnaire survey, which was carried out at The British University in Dubai (BUiD) and University of Fujairah (UOF) in the UAE. There were 251 students participated in this study. Data were analyzed using SmartPLS and SPSS. The Structural Equation Modelling (SEM) has been used to validate the proposed model. The outcomes revealed that knowledge sharing and quality in the universities have a positive influence on E-learning acceptance among the students. Innovativeness and trust were found not to significantly affect the E-learning system acceptance. By identifying the factors that influence the E-learning acceptance, it will be more useful to provide better services for E-learning. Other implications are also presented in the study.
Understanding the Quality Determinants that Influence the Intention to Use the Mobile Learning Platforms: A Practical Study
(Elsevier, 2019) Alshurideh, Muhammad; A. Salloum, Said; Al Kurdi, Barween; Abdel Monem, Azza; Shaalan, Khaled
There is a widespread use of Internet technology in the present times, because of which universities are making investments in Mobile learning to augment their position in the face of extensive competition and also to en hance their students’ learning experience and efficiency. Nonetheless, Mobile Learning Platform are only going to be successful when students show ac ceptance and adoption of this technology. Our literature review indicates that very few studies have been carried out to show how university students accept and employ Mobile Learning Platform. In addition, it is asserted that behavioral models of technology acceptance are not equally applied in different cultures. The purpose of this study is to develop an extension of Technology Acceptance Model (TAM) by including four more constructs: namely, content quality, service quality, information quality and quality of the system. This is proposed to make it more relevant for the developing countries, like the United Arab Emirates (UAE). An online survey was carried out to obtain the data. A total of 221 students from the UAE took part in this survey. Structural equation modeling was used to determine and test the measurement and structural model. Data analysis was carried out, which showed that ten out of a total of 12 hypotheses are supported. This shows that there is support for the applicability of the extended TAM in the UAE. These outcomes suggest that Mobile Learning Platform should be considered by the policymakers and education developers as being not only a technological solution but also as being new e-learning platform especially for distance learning students.
An Innovative Study of E-Payment Systems Adoption in Higher Education: Theoretical Constructs and Empirical Analysis
(International Journal of Interactive Mobile Technologies, 2019) A. Salloum, Said; Al-Emran, Mostafa; Khalaf, Rifat; Habes, Mohammed; Shaalan, Khaled
Examining the adoption of e-payment systems is not a new research topic. Nevertheless, studying the factors affecting the adoption of epayment systems in higher educational institutions is a new research trend. Thus, this study is considered one of the few that attempts to investigate the fac tors affecting the e-payment systems adoption in six different universities in the United Arab of Emirates (UAE). A total number of 289 students took part in the study. This study proposed a new research model in which the students’ intention to use the e-payment systems are affected by five different factors including perceived benefit, performance expectancy, perceived risk, perceived security/privacy, and trust. The partial least squares-structural equation modeling (PLS-SEM) approach was used to validate the research model. The empirical results suggested that perceived benefit and performance expectancy have a significant positive relationship with the students’ intention to use e-payment systems, whereas perceived security/privacy and perceived risk exhibited a significant negative relationship. However, the results triggered out that trust has an insignificant relationship with the students’ intention to use e-payment systems. The results acquired from this research provide a fresh and an up-to-date information on the e-payment systems adoption in the higher educational institutions.
A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques
(IEEE, 2022) SALLOUM, SAID; GABER, TAREK; VADERA, SUNIL; SHAALAN, KHALED
Every year, phishing results in losses of billions of dollars and is a major threat to the Internet economy. Phishing attacks are now most often carried out by email. To better comprehend the existing research trend of phishing email detection, several review studies have been performed. However, it is important to assess this issue from different perspectives. None of the surveys have ever comprehensively studied the use of Natural Language Processing (NLP) techniques for detection of phishing except one that shed light on the use of NLP techniques for classification and training purposes, while exploring a few alternatives. To bridge the gap, this study aims to systematically review and synthesise research on the use of NLP for detecting phishing emails. Based on specific predefined criteria, a total of 100 research articles published between 2006 and 2022 were identified and analysed. We study the key research areas in phishing email detection using NLP, machine learning algorithms used in phishing detection email, text features in phishing emails, datasets and resources that have been used in phishing emails, and the evaluation criteria. The findings include that the main research area in phishing detection studies is feature extraction and selection, followed by methods for classifying and optimizing the detection of phishing emails. Amongst the range of classification algorithms, support vector machines (SVMs) are heavily utilised for detecting phishing emails. The most frequently used NLP techniques are found to be TF-IDF and word embeddings. Furthermore, the most commonly used datasets for benchmarking phishing email detection methods is the Nazario phishing corpus. Also, Python is the most commonly used one for phishing email detection. It is expected that the findings of this paper can be helpful for the scientific community, especially in the field of NLP application in cybersecurity problems. This survey also is unique in the sense that it relates works to their openly available tools and resources. The analysis of the presented works revealed that not much work had been performed on Arabic language phishing emails using NLP techniques. Therefore, many open issues are associated with Arabic phishing email detection.
Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic
(Taylor and Francis Journals, 2020) Saeed Al-Maroof, Rana; A. Salloum, Said; Ella Hassanien, Aboul; Shaalan, Khaled
ABSTRACT This study seeks to explore the effect of fear emotion on students’ and teachers’ technology adoption during COVID-19 pandemic. The study has made use of Google Meet© as an educational social platform in private higher education institutes. The data obtained from the study were analyzed by using the partial least squares structural equation modeling (PLS-SEM) and machine learning algorithms. The main hypotheses of this study are related to the effect of COVID-19 on the adoption of Google Meet as COVID-19 rise s various types of fear. During the Coronavirus pandemic, fear due to family lockdown situation, fear of education failure and fear of losing social relationships are the most common types of threat that may face students and teachers/educators. These types of fears are connected with two important factors within TAM theory, which are: perceived ease of use (PEOU) and perceived usefulness (PU), and with another external factor of TAM, which is subjective norm (SN). The results revealed that both data analysis techniques have successfully provided support to all the hypothesized relationships of the research model. More interesting, the J48 classifier has performed better than the other classifiers in predicting the dependent variable in most cases.
Factors Affecting Autonomous Vehicles Adoption: A Systematic Review, Proposed Framework, and Future Roadmap
(Taylor & Francis online, 2023) Al Mansoori, Saeed; Al-Emran, Mostafa; Shaalan, Khaled
Autonomous vehicles (AVs) offer several benefits, such as improving road safety, mitigating traffic congestion, and reducing fuel consumption and gas emissions. Despite these benefits, their adop tion rate remains limited due to various factors influencing users’ decisions. While previous studies have identified numerous factors influencing AV adoption using various adoption frameworks, the factors have not been comprehensively analyzed and synthesized. Thus, this systematic review aims to bridge this gap by identifying and classifying the factors influencing the adoption of AVs. Out of 3,532 collected research papers, 71 empirical studies were analyzed thoroughly. The find ings demonstrated that the technology acceptance model (TAM) was the most widely used model for investigating AV adoption. The identified factors in the analyzed studies were classified into distinct categories: psychological and behavioral factors, technological factors, social factors, envir onmental factors, security and privacy factors, AV-related factors, risky and negative factors, condi tional factors, and monetary factors. We have proposed an AV adoption framework grounded in this taxonomy to direct subsequent empirical research. We have also highlighted numerous agen das to serve as a blueprint for future AV adoption studies. This review offers various theoretical insights and actionable recommendations for multiple AV research, development, and implemen tation stakeholders.
Exploring Students Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model
(IEEE, 2019) A. SALLOUM , SAID; QASIM MOHAMMAD ALHAMAD, AHMAD; AL-EMRAN, MOSTAFA; ABDEL MONEM, AZZA; SHAALAN , KHALED
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 signi cant published studies from the last twelve years was conducted. The review analysis indicated that computer self ef cacy, 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 TAMhasbeenextended bythe aforementioned factors to examine the students acceptance of e-learning in ve 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-ef cacy, and computer playfulness have a signi cant impact on perceived ease of use ofe-learning system. Furthermore, information quality, perceived enjoyment, and accessibility were found to have a positive in uence on perceived ease of use and perceived usefulness of e-learning system.
PREDICTING THE INTENTION TO USE GOOGLE GLASS IN THE EDUCATIONAL PROJECTS: A HYBRID SEM-ML APPROACH
(Academy of Strategic Management Journal, 2022) Alfaisal, Raghad; Idris Khadija Alhumaid, Sultan; Alnazzawi, Noha; Abou Samra, Rasha; Aburayya, Ahmad; Salloum, Said; Shaalan, Khaled; Al Khasoneh, Osama; Abdel Monem, Azza
The emergence of newer technology and rapid global changes has led to the development of technology-based education environments, wherein teachers and students interact via technological interfaces such as Google Glass. Very few educational institutions have, however, opted to use this interface. The reason for this tendency is not very well understood or adequately researched. Therefore, this study aims to understand the factors influencing the adoption of Google Glass in the UAE. Our hypothesis is that providing information about the salient features and practical applications of Google Glass to teachers and learners would result in a higher percentage of educational institutions using this technology. The findings of this study will be based on the interrelation between the Technology Acceptance Model (TAM) and other influential factors. It will evaluate the integration of TAM with the well-known influential features of the device such as enhancement of teaching, facilitation of learning, functionality of motivating learning, and assurance of trust and information privacy. These features play a key role in facilitating communication between teachers and students in the classroom environment. Our approach will make use of hybrid analysis techniques involving Structural Equation Modeling (SEM) and Machine Learning (ML). This work of research thus proposes to offer practical inputs that can help decision-makers and other practitioners focus particularly on creating conducive environments for the use of Google Glass as well as further adopt strategies for meeting their specific needs.
The Impact of Project Management Office's Role on Knowledge Management: A Systematic Review Study
(Computer Integrated Manufacturing Systems, 2021) Salameh , Mohammad; Taamneh, Abdallah; Kitana, Abdelkarim; Aburayya, Ahmad; Shwedeh, Fanar; Salloum, Said; Shaalan, Khaled; Varshney, Deepanjana
The purpose of this study is to review the research on the function and impact of project management offices (PMOs) in knowledge management (KM) and to establish the current body of knowledge by addressing the following questions: What function does the PMO play in the knowledge management process, and what types of knowledge does the PMO deal with? The study includes a systematic literature review for six papers on project management office (PMO) role and impact on knowledge management. PRISMA guidelines were used to select the papers and the research focused on the most important six papers to investigate the research questions. The study findings revealed that PMO plays a vital role in boosting information usefulness by sharing the correct knowledge with the right people at the right time. Furthermore, PMO is important for maintaining knowledge repositories, which comprise knowledge generated from projects in the form of lessons learned, updated project management standards, and individual learning that becomes organizational learning.
The effect of social media usage on students’ e learning acceptance in higher education: A case study from the United Arab Emirates
(E-learning; technology acceptance model; social media; motivation; knowledge sharing; YouTube; Twitter; Facebook., 2019) Alghizzawi, Mahmoud; Habes, Mohammed; A. Salloum, Said; =Abd. Ghani, Mazuri; =Mhamdi, Chaker; Shaalan, Khaled
This study investigates the influence of student social media usage on the acceptance of e learning platforms at the British University in Dubai. A modified Technology Acceptance Model was developed and validated for the quantitative study, which comprised data collected from 410 graduate and postgraduate students via an electronic questionnaire. The findings showed that knowledge sharing, social media features and motivation to use social media systems, including Facebook YouTube and Twitter, positively affected the perceived usefulness and perceived ease-of-use of e learning platforms, which, in turn, led to increased e-learning platform acceptance by students. The research model can be adapted to similar studies to assist in further research regarding how higher education institutions in the UAE can maximize the benefits and uptake of e-learning platforms.

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