ORCID Profile
0000-0001-9875-4369
Current Organisations
University of Jordan
,
Curtin University
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Publisher: MDPI AG
Date: 11-09-2023
Publisher: ACM
Date: 24-02-2018
Publisher: IGI Global
Date: 2020
Abstract: The pervasiveness of social media and user-generated content has triggered an exponential increase in global data. However, due to collection and extraction challenges, data in embedded comments, reviews and testimonials are largely inaccessible to a knowledge management system. This article describes a KM framework for the end-to-end knowledge management and value extraction from such content. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source. Three contributions are described in this article. First, a method for automatically navigating webpages to expose UGC for collection is presented. This is evaluated using browser emulation integrated with automated collection. Second, a method for collecting data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing techniques, providing evidence of the increased amount of UGC data extracted.
Publisher: Informa UK Limited
Date: 02-10-2018
Publisher: Emerald
Date: 27-04-2023
DOI: 10.1108/GKMC-07-2022-0161
Abstract: The purpose of this study is to investigate the determinants that likely influence the intention of using digital payment systems such as the Jordan Mobile Payment (JoMoPay) system among public sector employees in Jordan. To achieve the purpose of the current study, the authors developed a new research model based on the extended unified theory of acceptance and use of technology (UTAUT2), with one of Hofstede’s cross-cultural dimension scales [uncertainty avoidance (UA)] to provide a further understanding of the JoMoPay system acceptance in Jordan. A partial least squares-structural equation modeling approach was used to analyze the data collected by self-administration from the 270 employees working in the Jordanian public sector located in Amman city, the capital city of Jordan. Because most main public sectors are located in Amman and because of the cost and time considerations, the current study applied a non-probability s ling with the purposive s ling technique. The empirical results reveal that the evident drivers of behavioral intention to use the JoMoPay system are significantly and positively influenced by social influence, UA, performance expectancy, price value and effort expectancy therefore, the H1 , H2 , H3 , H5 and H6 were supported. Conversely, the results show no significant relationship between facilitating conditions and the behavioral intention to use the JoMoPay system, and hence, the related hypothesis ( H4 ) was not supported. The results of this study provide beneficial information to the Central Bank of Jordan and other service providers in Jordan about employee intentions to adopt JoMoPay system and increase decision-makers’ knowledge on factors that have an important impact in UTAUT2 model. The results of this study enable policymakers to understand the important factors that will enhance savings, investments and living standards, create job opportunities as well as reduce the poverty, the paper money printing cost, risks of money transportation cost and the risk of human errors. The outcomes obtained will help both practitioners and researchers elucidate and understand the situation of digital payment systems acceptance among Jordanian public sector employees, as well as help them formulate plans to expedite the adoption process of digital payment systems in the case of UA.
Publisher: Elsevier BV
Date: 09-2023
Publisher: SAGE Publications
Date: 15-09-2022
DOI: 10.1177/01655515221124062
Abstract: The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users’ behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules.
Publisher: Emerald
Date: 06-03-2018
Abstract: This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs. This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets. Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics. This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data. This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc. This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.
Publisher: MDPI AG
Date: 14-07-2023
DOI: 10.3390/COMPUTATION11070141
Abstract: Time-series analysis is a widely used method for studying past data to make future predictions. This paper focuses on utilizing time-series analysis techniques to forecast the resource needs of logistics delivery companies, enabling them to meet their objectives and ensure sustained growth. The study aims to build a model that optimizes the prediction of order volume during specific time periods and determines the staffing requirements for the company. The prediction of order volume in logistics companies involves analyzing trend and seasonality components in the data. Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) are well-established and effective in capturing these patterns, providing interpretable results. Deep-learning algorithms require more data for training, which may be limited in certain logistics scenarios. In such cases, traditional models like SARIMAX, ARIMA, and AR can still deliver reliable predictions with fewer data points. Deep-learning models like LSTM can capture complex patterns but lack interpretability, which is crucial in the logistics industry. Balancing performance and practicality, our study combined SARIMAX, ARIMA, AR, and Long Short-Term Memory (LSTM) models to provide a comprehensive analysis and insights into predicting order volume in logistics companies. A real dataset from an international shipping company, consisting of the number of orders during specific time periods, was used to generate a comprehensive time-series dataset. Additionally, new features such as holidays, off days, and sales seasons were incorporated into the dataset to assess their impact on order forecasting and workforce demands. The paper compares the performance of the four different time-series analysis methods in predicting order trends for three countries: United Arab Emirates (UAE), Kingdom of Saudi Arabia (KSA), and Kuwait (KWT), as well as across all countries. By analyzing the data and applying the SARIMAX, ARIMA, LSTM, and AR models to predict future order volume and trends, it was found that the SARIMAX model outperformed the other methods. The SARIMAX model demonstrated superior accuracy in predicting order volumes and trends in the UAE (MAPE: 0.097, RMSE: 0.134), KSA (MAPE: 0.158, RMSE: 0.199), and KWT (MAPE: 0.137, RMSE: 0.215).
Publisher: MDPI AG
Date: 23-03-2023
DOI: 10.3390/SU15075669
Abstract: Climate change is contributing to extreme weather conditions, which transform the scale and degree of flood events. Therefore, it is important for relevant government agencies to effectively respond to both extreme climate conditions and their impacts by providing more efficient asset management strategies. Although international research projects on water-sensitive urban design and rural drainage design have provided partial solutions to this problem, road networks commonly serve unique combinations of urban-rural residential and undeveloped areas these areas often have erse hydrology, geology, and climates. Resultantly, applying a one-size-fits-all solution to asset management is ineffective. This paper focuses on data-driven flood modelling that can be used to mitigate or prevent floodwater-related damage in Western Australia. In particular, a holistic and coherent view of data-driven asset management is presented and multi-criteria analysis (MCA) is used to define the high-risk hotspots for asset damage in Western Australia. These state-wide hotspots are validated using road closure data obtained from the relevant government agency. The proposed approach offers important insights with regard to factors influencing the risk of damage in the stormwater management system.
Publisher: IEEE
Date: 05-2018
Publisher: Springer Science and Business Media LLC
Date: 12-05-2021
Publisher: SAGE Publications
Date: 26-07-2019
Abstract: The widespread use of big social data has influenced the research community in several significant ways. In particular, the notion of social trust has attracted a great deal of attention from information processors and computer scientists as well as information consumers and formal organisations. This attention is embodied in the various shapes social trust has taken, such as its use in recommendation systems, viral marketing and expertise retrieval. Hence, it is essential to implement frameworks that are able to temporally measure a user’s credibility in all categories of big social data. To this end, this article suggests the CredSaT (Credibility incorporating Semantic analysis and Temporal factor), which is a fine-grained credibility analysis framework for use in big social data. A novel metric that includes both new and current features, as well as the temporal factor, is harnessed to establish the credibility ranking of users. Experiments on real-world datasets demonstrate the efficacy and applicability of our model in determining highly domain-based trustworthy users. Furthermore, CredSaT may also be used to identify spammers and other anomalous users.
Publisher: Pensoft Publishers
Date: 28-02-2021
DOI: 10.3897/JUCS.65096
Abstract: Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world& #39 s software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users& rsquo query and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user& rsquo s query with the minimum mismatches.
Publisher: Springer Singapore
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 27-02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 02-06-2023
DOI: 10.3390/BDCC7020109
Abstract: The lack of regulations and oversight on Online Social Networks (OSNs) has resulted in the rise of social spam, which is the dissemination of unsolicited and low-quality content that aims to deceive and manipulate users. Social spam can cause a range of negative consequences for in iduals and businesses, such as the spread of malware, phishing scams, and reputational damage. While machine learning techniques can be used to detect social spammers by analysing patterns in data, they have limitations such as the potential for false positives and false negatives. In contrast, ontologies allow for the explicit modelling and representation of domain knowledge, which can be used to create a set of rules for identifying social spammers. However, the literature exposes a deficiency of ontologies that conceptualize domain-based social spam. This paper aims to address this gap by designing a domain-specific ontology called DSpamOnto to detect social spammers in microblogging that targes a specific domain. DSpamOnto can identify social spammers based on their domain-specific behaviour, such as posting repetitive or irrelevant content and using misleading information. The proposed model is compared and benchmarked against well-proven ML models using various evaluation metrics to verify and validate its utility in capturing social spammers.
Publisher: Frontiers Media SA
Date: 08-09-2022
DOI: 10.3389/FDATA.2022.998648
Abstract: Metaverse is now perceived as a celebrated future version of the internet. In this new anticipated virtual universe, interconnected digital platforms leveraged by augmented, extended, and virtual realities will elevate users' immersive experiences through multidimensional interactions. In particular, users will be offered a broad spectrum of digital activities within a newly immersive setting mediated by technology. This study aims to design a domain ontology (MetaOntology) for the metaverse to provide an explicit specification of relevant state-of-the-art technologies and infrastructure. A four-step methodological approach is followed to construct the designated ontology. Due to the immaturity of the metaverse, MetaOntology is not intended to furnish a complete outlook on the domain, rather it aims to establish a cornerstone so as to facilitate future efforts in building extant versions of this ontology considering the evolvement of relevant technologies.
Publisher: MDPI AG
Date: 28-01-2023
DOI: 10.3390/BDCC7010024
Abstract: In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running ex les of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content.
Publisher: Elsevier BV
Date: 07-2021
Publisher: Elsevier BV
Date: 05-2023
Publisher: Springer Science and Business Media LLC
Date: 10-02-2020
DOI: 10.1186/S40537-020-0283-3
Abstract: Online social networks have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, it is vital to have an accurate understanding of the contextual content of social users, thus establishing grounds for measuring their social influence accordingly. In particular, there is the need for a better understanding of domain-based social trust to improve and expand the analysis process and determining the credibility of Social Big Data. The aim of this paper is to determine domain-based social influencers by means of a framework that incorporates semantic analysis and machine learning modules to measure and predict users’ credibility in numerous domains at different time periods. The evaluation of the experiment conducted herein validates the applicability of semantic analysis and machine learning techniques in detecting highly trustworthy domain-based influencers.
Publisher: Informa UK Limited
Date: 04-10-2018
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 04-2021
Publisher: Springer Science and Business Media LLC
Date: 05-02-2021
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 09-2021
Publisher: IEEE
Date: 07-2015
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 05-2018
Publisher: MDPI AG
Date: 10-01-2023
DOI: 10.3390/ELECTRONICS12020358
Abstract: Social distancing is one of the most important ways to prevent many diseases, especially the respiratory system, where the latest internationally spread is coronavirus disease, and it will not be the last. The spreading of this pandemic has become a major threat to human life, especially to the elderly and people suffering from chronic diseases. During the Corona pandemic, medical authorities were keen to control the spread through social distancing and monitoring it in markets, universities, and schools. This monitoring was mostly used to estimate the distance with the naked eye and interfere with estimating the distance on the observer only. In this study, a computer application was designed to monitor social distancing in closed areas, especially in schools and kindergartens, using a fast, effective and unobtrusive technique for children. In addition to this system, we use augmented reality to help to determine the location of violation of social distancing. This system was tested, and the results were accurate exceeding 98.5%.
No related grants have been discovered for Bilal Abu-Salih.