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Item An Architectural Model for Integrating Big Data in Educational Information Systems(Springer Science and Business Media Deutschland GmbH, 2024) Redouane, AbdesselamWe live in an era of big data that has overwhelmed most of our society and organizations. Companies would benefit from this big data, but information systems that support companies hamper its use. In this paper, we present an architectural model for integrating big data into educational information systems. The model is based on the urbanization paradigm that city planners use when creating new cities or renovating existing ones. The model is illustrated at two levels of architecture, highlighting the refinement from one level to the next using the urbanization paradigm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item Analysis of Issues Affecting IoT, AI, and Blockchain Convergence(Springer Science and Business Media Deutschland GmbH, 2023) Taleb, Nasser; Al-Dmour, Nidal A.; Issa, Ghassan F.; Abdellatif, Tamer Mohamed; Alzoubi, Haitham M.; Alshurideh, Muhammad; Salahat, MohammedThe purpose of this project was to appraise the integration or convergence issues influencing the mutual functioning of blockchain, AI, and IoT. The study argued that the recent developments in the field of IoT and blockchain prediction have involved the integration of innumerable classification schemes to establish a hybrid model. The introduction of the hybrid technique relies on the prediction performance that strives to override the limitations of any available architectural scheme. This study offers a comprehensive exploratory appraisal of the issues influencing the successful integration of IoT and blockchain in regards to functionality and effectiveness of security, trust, and flawless communication issues. The exploratory research methodology was used in analyzing the issues affecting the integration of blockchain, artificial intelligence (AI), and the internet of things (IoT). The findings indicated that the integration challenges influencing the effective operations of blockchain, AI, and IoT as a single system involve security, scalability, accountability, and trust of communications. The study recommends that successful and effective integration will enhance the development of new business models as well as the digital transformation of market corporations. Accordingly, new approaches to convergence should ensure that executives address the new technology demands to obtain significant gains in efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item An Integrated Cloud and Blockchain Enabled Platforms for Biomedical Research(Springer Science and Business Media Deutschland GmbH, 2023) Ghazal, Taher M.; Hasan, Mohammad Kamrul; Abdullah, Siti Norul Huda Sheikh; Bakar, Khairul Azmi Abu; Taleb, Nasser; Al-Dmour, Nidal A.; Yafi, Eiad; Chauhan, Ritu; Alzoubi, Haitham M.; Alshurideh, MuhammadIn the current pandemic scenario, healthcare data tends to be an important asset among organizations. The major challenge is to handle the data effectively while maintaining the privacy and security of the data. In a real-world, context healthcare data proves to be heterogeneous. Hence, managing such significance to big data has ardently laid numerous challenges among researchers and scientists around the globe. Cloud environment and blockchain technology can be discussed as usable platforms which can deliver a comprehensive centralized data privacy system. In the current approach study, we have integrated both technologies to provide usability in medical systems. Further, we have also proposed and implemented a blockchain application with an integrated cloud-based environment regarding heterogeneous medical databases. The study is proposed in 2 phases to maintain the privacy and the accessibility of the data. The double-spending problem is also presented, as mentioned above, using Blockchain’s consensus process. Each network node independently verifies the validity of individual transactions and entire blocks. As a result, there is no need to put faith in a single entity or other nodes. As a result, third parties are no longer required for network actions or blockchain management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item A Comprehensive Review on Big Data Challenges(Institute of Electrical and Electronics Engineers Inc., 2023) Bharany, Salil; Taleb, Nasser; Sadiq, M. Tariq; Kanwal, Nayab; Abdelhakim, Mohamed; Ghazal, Taher M.; Pradhan, Manas; Rehman, Ateeq UrItem Post-Covid-19 Pandemic IT Project Management Skills and Challenges(Institute of Electrical and Electronics Engineers Inc., 2023) Ali, Liaqat; Taleb, Nasser; Ali, Atif; Abu-Alsondos, Ibrahim A.; Naseem, Hina; Yousaf, Farhan; Abdelhakim, MohamedItem Reducing cognitive dissonance in health care: Design of a new Positive psychology intervention tool to regulate professional stress among nurses(Institute of Electrical and Electronics Engineers Inc., 2022) Alami, Rachid; Elrehail, Hamzah; Alzghoul, AmroItem Customization of Bookkeeping system for Blockchain System Analysis: A Review(Institute of Electrical and Electronics Engineers Inc., 2022) Joshi, Kapil; Pandey, Richa; Bharany, Salil; Rehman, Ateeq Ur; Taleb, Nasser; Kalra, DeepakItem Linking hotel environmental management initiatives and sustainable hotel performance through employees’ eco-friendly behaviour and environmental strategies: a moderated-mediated model(Emerald Publishing, 2023-02-13) Rehman, Shafique Ur; Elrehail, Hamzah; Alshwayat, Dana; Ibrahim, Blend; Alami, RachidPurpose: The purpose and current research objective is to determine sustainable hotel performance through hotel environmental management initiatives (HEMI) with the mediating influence of employee’s’ eco-friendly behaviour (EEB), and to determine the moderating role of environmental strategies (ES) in the relationship between HEMI and EEB. Design/methodology/approach: A total of 95 five-star hotels were contacted, with data collected from only 30 of them. The study used only 433 questionnaires for the final analysis with SPSS 25.0 and SmartPLS 3.2.8. Findings: The results revealed that HEMI is positively associated with sustainable hotel performance and with EEB. EEB is positively associated with sustainable hotel performance. ES significantly influence EEB, and significantly strengthen the relationship between HMEI and EEB. EEB significantly mediates the relationship between HEMI and sustainable hotel performance. Practical implications: The current research highlights a significant issue: how the management of the hotel industry uses HEMI, ES and EEB to improve sustainable performance. The study fills the gap in the literature and enables hotel management to concentrate on studying exogenous variables to increase sustainable performance. Originality/value: The current research contributes to the body of knowledge by concentrating on factors that influence sustainable hotel performance. It examines HEMI influence on sustainable performance with moderating (ES) and mediating (EEB) effects, from the leans of natural resource-based view (RBV) theory. © 2022, Emerald Publishing Limited.Item Proposed Model of Work Ethics in Artificial Intelligence and Emerging Digital Technologies(Institute of Electrical and Electronics Engineers Inc., 2022) Aldulaimi, Saeed Hameed; Abdeldayem, Marwan M.; Abo Keir, Mohammed Yousif; Abdelhakim, MohamedItem Information Technology Adoption Barriers in Public Sector(Institute of Electrical and Electronics Engineers Inc., 2022) Abdelhakim, Mohamed; Abdeldayem, Marwan M.; Aldulaimi, Saeed HameedItem Feature optimization and identification of ovarian cancer using internet of medical things(John Wiley and Sons Inc, 2022-11) Ghazal, Taher M.; Taleb, NasserOvarian cancer (OC) is one kind of tumour that impacts women's ovaries and is hard to diagnose in the initial phase as a primary cause of cancer death. The ovarian cancer information generated by the Clinical Network has been used, and the Self Organizing Map (SOM) and Optimized Neural Networks have suggested a new method for the distinction between ovarian cancer and remaining cancer. Feature optimization and identification of the ovarian cancer (FOI-OV) framework are proposed in this research. The SOM algorithm has also been used separately to improve the functional subset, with understandable and intriguing information from participants' health information steps. The SOM-based collection appears to be tolerable in guided learning strategies due to the lack of different classifiers, which would direct the quest for knowledge specific to the classification algorithm. The classification technique will classify data from ovarian cancer as benign/malignant. By optimizing Neural Network configuration, Advanced Harmony Searching Optimization (AHSO) can enhance the ovarian cancer detection method compared with other methods. This research's suggested model can also diagnose cancer with high precision, and low root means square error (RMSE) early. With 94% precision and 0.029%, RMSE, SOM, and NN techniques have shown identification and precision in ovarian cancer. Optimization (AHSO) has provided an efficient classification approach with a better failure rate. © 2022 John Wiley & Sons Ltd.Item Ovary Cancer Diagnosing Empowered with Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2022) Taleb, Nasser; Mehmood, Shahid; Zubair, Muhammad; Naseer, Iftikhar; Mago, Beenu Skyline University Colleg; Nasir, Muhammad UmarItem A Proposed Architecture for Traffic Monitoring Control System via LiFi Technology in Smart Homes(Institute of Electrical and Electronics Engineers Inc., 2022) Asif, Muhammad; Khan, Tahir Abbas; Taleb, Nasser; Said, Raed A.; Siddiqui, Shahan Yamin; Batool, GhanwaItem Machine Learning Models for the Classification of Skin Cancer(Institute of Electrical and Electronics Engineers Inc., 2022) Arooj, Sahar; Khan, Muhammad Farhan; Khan, Muhammad Adnan; Khan, Muhammad Saleem; Taleb, NasserItem Using blockchain to ensure trust between donor agencies and ngos in under-developed countries(MDPI AG, 2021-08) Rehman, Ehsan; Khan, Muhammad Asghar; Soomro, Tariq Rahim; Taleb, Nasser; Afifi, Mohammad A.; Ghazal, Taher M.Non-governmental organizations (NGOs) in under-developed countries are receiving funds from donor agencies for various purposes, including relief from natural disasters and other emergencies, promoting education, women empowerment, economic development, and many more. Some donor agencies have lost their trust in NGOs in under-developed countries, as some NGOs have been involved in the misuse of funds. This is evident from irregularities in the records. For instance, in education funds, on some occasions, the same student has appeared in the records of multiple NGOs as a beneficiary, when in fact, a maximum of one NGO could be paying for a particular beneficiary. Therefore, the number of actual beneficiaries would be smaller than the number of claimed beneficiaries. This research proposes a blockchain-based solution to ensure trust between donor agencies from all over the world, and NGOs in under-developed countries. The list of National IDs along with other keys would be available publicly on a blockchain. The distributed software would ensure that the same set of keys are not entered twice in this blockchain, preventing the problem highlighted above. The details of the fund provided to the student would also be available on the blockchain and would be encrypted and digitally signed by the NGOs. In the case that a record inserted into this blockchain is discovered to be fake, this research provides a way to cancel that record. A cancellation record is inserted, only if it is digitally signed by the relevant donor agency. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Item Software defect prediction using ensemble learning: A systematic literature review(Institute of Electrical and Electronics Engineers Inc., 2021) Matloob, Faseeha; Ghazal, Taher M.; Taleb, Nasser; Aftab, Shabib; Ahmad, Munir; Khan, Muhammad AdnanRecent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. This technique was introduced to improve the prediction performance by overcoming the limitations of any single classification technique. This research provides a systematic literature review on the use of the ensemble learning approach for software defect prediction. The review is conducted after critically analyzing research papers published since 2012 in four well-known online libraries: ACM, IEEE, Springer Link, and Science Direct. In this study, five research questions covering the different aspects of research progress on the use of ensemble learning for software defect prediction are addressed. To extract the answers to identified questions, 46 most relevant papers are shortlisted after a thorough systematic research process. This study will provide compact information regarding the latest trends and advances in ensemble learning for software defect prediction and provide a baseline for future innovations and further reviews. Through our study, we discovered that frequently employed ensemble methods by researchers are the random forest, boosting, and bagging. Less frequently employed methods include stacking, voting and Extra Trees. Researchers proposed many promising frameworks, such as EMKCA, SMOTE-Ensemble, MKEL, SDAEsTSE, TLEL, and LRCR, using ensemble learning methods. The AUC, accuracy, F-measure, Recall, Precision, and MCC were mostly utilized to measure the prediction performance of models. WEKA was widely adopted as a platform for machine learning. Many researchers showed through empirical analysis that features selection, and data sampling was necessary pre-processing steps that improve the performance of ensemble classifiers. © 2013 IEEE.Item Impacts of Big-Data Technologies in Enhancing CRM Performance(Institute of Electrical and Electronics Engineers Inc., 2020) Taleb, Nasser; Salahat, Mohammad; Ali, Liaqatbig data is a hot business topic today. In business organizations, customer-relationship management (CRM) is an important pillar to achieve competitive advantages. Big data refers to practices of integrating big data into an organizational CRM process to achieve the objectives of improving and sustaining customer service. The alternative goal of big data is to combine internal CRM data with customer behavior and buying patterns from the environment external to the organization. Several tools exist that can integrate big data with other CRM data to improve customer analysis and understand buying behavior and patterns. This paper reports research evaluating the role of big-data technology in enhancing the effective use of CRM. Research proves that data's predictive model is enhanced by analyzing customer buying patterns. Issues and challenges related to the implementation of big data are discussed and benefits of appropriate implementation of big-data technologies are highlighted. The research further demonstrates some tangible benefits of implementing big-data technologies in CRM. © 2020 IEEE.Item Cloud computing trends: A literature review(Richtmann Publishing Ltd, 2020-01) Taleb, Nasser; Mohamed, Elfadil A.This study is a literature review on cloud computing cloud computing trends as one the fastest growing technologies in the computer industry and their benefits and opportunities for all types of organizations. In addition, it addresses the challenges and problems that contribute to increasing the number of customers willing to adopt and use the technology. A mixed research study approach was adopted for the study, that is, by collecting and analyzing both quantitative and qualitative information within the same literature review and summarizing the findings of previous (related) studies. Results highlights the current and future trends of cloud computing and exposes readers to the challenges and problems associated with cloud computing. The reviewed literature showed that the technology is promising and is expected to grow in the future. Researchers have proposed many techniques to address the problems and challenges of cloud computing, such as security and privacy risks, through mobile cloud computing and cloud-computing governance. © 2020 Nasser Taleb and Elfadil A. Mohamed. This is an open access article licensed under the Creative Commons Attribution-NonCommercial 4.0 International License