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All Journal International Journal of Electrical and Computer Engineering
Rami Mustafa A. Mohammad
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

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The adoption of bitcoins technology: The difference between perceived future expectation and intention to use bitcoins: Does social influence matter? Ibrahim Almarashdeh; Kamal Eldin Eldaw; Mutasem Alsmadi; Fahad Alghamdi; Ghaith Jaradat; Ahmad Althunibat; Malek Alzaqebah; Rami Mustafa A. Mohammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5351-5366

Abstract

Bitcoin is a decentralized system that tries to become a solution to the shortcomings of fiat and gold-based currencies. Considering its newness, the adoption level of bitcoin is yet understood. Hence, several variables are proposed in this work in examining user perceptions regarding performance expectancy, effort expectancy, trust, adoption risk, decentralization and social influence interplay, with the context of user’s future expectation and behavioral intentions to use bitcoins. Data were gathered from 293 completed questionnaire and analised using AMOS 18. The outcomes prove the sound predictability of the proposed model regarding user’s future expectations and intentions toward bitcoins. All hypotheses were supported, they were significantly affecting the dependent variables. Social influence was found as the highest predictor of behavioral intention to negatively utilize bitcoins. The significant impact of social influence, adoption risk and effort expectancy which affect behavioral intention to use bitcoins the most, are demonstrated in this study. Bitcoins should thus, present an effective, feasible and personalized program which will assist efficient usage among users. Additionally, the impacts of social influence, adoption risk and perceived trust on behavioral intention to utilize new technology were compared, and their direct path was tested together, for the first time in this context.
Hybrid feature selection method based on particle swarm optimization and adaptive local search method Malek Alzaqebah; Sana Jawarneh; Rami Mustafa A. Mohammad; Mutasem K. Alsmadi; Ibrahim Al-marashdeh; Eman A. E. Ahmed; Nashat Alrefai; Fahad A. Alghamdi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2414-2422

Abstract

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.
Co-Authors Ahmad Althunibat Eman A. E. Ahmed Fahad A. Alghamdi Fahad Alghamdi Ghaith Jaradat Ibrahim Al-Marashdeh Ibrahim Almarashdeh Kamal Eldin Eldaw Malek Alzaqebah Mutasem Alsmadi Mutasem K. Alsmadi Nashat Alrefai Sana Jawarneh