Hybrid journal (It can contain Open Access articles) ISSN (Print) 1756-7017 - ISSN (Online) 1756-7025 Published by Inderscience Publishers[439 journals]
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Authors:Jaya Sinha, Shri Kant, Megha Saini Pages: 27 - 45 Abstract: Big data environment in current scenario is dealing with challenges in handling inherent complexity residing in the massive heterogeneous, multivariate and continuously evolving real-time data along with offline statistics. The role of big data analytics to analyse such a highly diverse data also plays a significant role in estimating predictive performance of a system. This paper thus aims at proposing an intelligent agent-based architecture that coordinates with big data analytics framework to model a system with an objective to improve the predictive performance of system by handling such diverse data. The paper also includes implementing predictive algorithm to predict crop yield in the agricultural domain. Various machine learning analytical tools have been used for data analysis to produce comprehensive and more accurate prediction using the proposed architecture. Keywords: multi-agent system; MAS; big data; data acquisition; data analysis; data storage; machine learning; intelligent agents Citation: International Journal of Information and Decision Sciences, Vol. 15, No. 1 (2023) pp. 27 - 45 PubDate: 2023-03-20T23:20:50-05:00 DOI: 10.1504/IJIDS.2023.129657 Issue No:Vol. 15, No. 1 (2023)
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Authors:Ameneh Khadivar, Hamideh Nazarian, Sanaz Bodaghi Pages: 46 - 72 Abstract: Core banking system (CBS) implementation is a time-consuming, cost-intensive, and complex task. As a result, plenty of CBS projects have failed. This research proposes a model for accepting the CBS using the fuzzy DEMATEL technique at Parsian Bank, Iran. Identified from the users' standpoint, factors affecting the acceptance of the CBS have been prioritised through the questionnaire. Moreover, the involved criteria of CBS are separated into the cause and effect groups to help decision-makers focus on those criteria providing great influence. The findings indicate that three of the influencing factors are identified as critical ones; within the cause group, the criterion of 'the output quality of the CBS' is the most important factor for accepting the CBS, whereas 'the quality of the CBS' has the best effect on the other criteria. By contrast, 'adequate training for employees to use the CBS' is the most easily improved of the effect group criteria. Keywords: core banking system; CBS; technology acceptance; accepting the CBS; fuzzy DEMATEL technique; Iran Citation: International Journal of Information and Decision Sciences, Vol. 15, No. 1 (2023) pp. 46 - 72 PubDate: 2023-03-20T23:20:50-05:00 DOI: 10.1504/IJIDS.2023.129653 Issue No:Vol. 15, No. 1 (2023)
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Authors:Yazan Alshboul, Nareman Al.Hamouri Pages: 73 - 93 Abstract: Online services such as online banking, particularly, the online payment system (OPS), plays an important role in modern life. In developing countries, there is a kind of resistance to adopting OPSs. Therefore, more focus is needed to understand the behaviour toward OPS, especially in developing countries. This paper integrates the trust model and the theory of planned behaviour and addresses the antecedents of the trust factor in the context of OPSs. Particularly, it focuses on the cybersecurity factors as antecedents to the trust model. We tested our model empirically using data gathered from 200 participants who use eFawateercom system, an online payment system used in Jordan. The results showed that cybersecurity factors like systems security, privacy, and reliability play an essential role in affecting users' trust, which has a crucial impact on the attitude toward OPS adoption. This article concluded with implications for academia and practitioners. Keywords: online payment system; OPS; cybersecurity factors; trust; privacy; security; reliability Citation: International Journal of Information and Decision Sciences, Vol. 15, No. 1 (2023) pp. 73 - 93 PubDate: 2023-03-20T23:20:50-05:00 DOI: 10.1504/IJIDS.2023.129654 Issue No:Vol. 15, No. 1 (2023)
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Authors:Annapurna P. Patil, Lalitha Chinmayee M. Hurali Pages: 94 - 115 Abstract: With the growing need for anonymity and privacy on the internet, anonymous communication networks (ACNs) such as Tor, I2P, JonDonym, and Freenet have risen to fame. Such anonymous networks aim to provide freedom of expression and protection against tracking to its users. Simultaneously, there is also a class of users involved in the illegal usage of these ACNs. An emerging research topic in the field of ACNs is network traffic classification, as it can improve the network security against illegal users as well as improve the quality of service for its legal users. In this study, we review the research works available in the literature relevant to traffic classification in ACNs based on machine learning (ML) and also present to the researchers the general concepts and techniques in this area. A discussion on future trends in this area is also provided to bring out the future enhancements required in ML-based network traffic classification in ACNs. Keywords: anonymous communication networks; ACNs; machine learning; traffic classification; Tor; network security Citation: International Journal of Information and Decision Sciences, Vol. 15, No. 1 (2023) pp. 94 - 115 PubDate: 2023-03-20T23:20:50-05:00 DOI: 10.1504/IJIDS.2023.129656 Issue No:Vol. 15, No. 1 (2023)
Hybrid journal (It can contain Open Access articles)
[439 journals]
