Interplay of Islam and Economic Growth: Unveiling the Long-run Dynamics in Muslim and Non-muslim Countries

Quadri, Faruq Umar; Olaniyi, Oluwaseun Oladeji; Olaoye, Olugbenga Olaposi

doi:10.9734/ajess/2023/v49i41226

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…Fingerprint authentication, in particular, was favored, with many users indicating a willingness to store more private data on devices equipped with such features. Face and hand recognition were also viewed as comfortable and secure [12,44]. This suggests that while there is acceptance of biometric methods, the specific type of biometric authentication and its perceived security and ease of use can influence user acceptance [11].…”

Section: User Acceptance Of Identity Access and Authenticationmentioning

confidence: 99%

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

Olabanji,

Olaniyi,

Adigwe

et al. 2024

AJRCoS

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This comprehensive study explores the integration and effectiveness of Artificial Intelligence (AI) in Identity and Access Management (IAM) within cloud environments. It primarily focuses on how AI can enhance user authentication, authorization, and access control, addressing the challenges and possibilities in cloud computing. The study adopts a mixed-methods approach, employing both quantitative and qualitative analyses. A survey involving 582 cybersecurity experts provides insights into the current state and potential of AI in IAM, while multiple regression analysis examines the impact of various factors on system effectiveness. Four hypotheses are explored: the impact of hardware and software configurations on system accuracy (H1), the influence of computational environments on reliability (H2), the role of demographic factors in user acceptance (H3), and the effect of technological enhancements on system performance and acceptance (H4). Findings indicate significant correlations between these factors and the effectiveness of AI in IAM. Notably, hardware configurations and security concerns influence system accuracy; computational environment variations affect system reliability; demographic factors impact user acceptance; and enhancements such as user feedback, advancements in AI technology, continuous learning algorithms, and system transparency improve performance and acceptance. These insights underscore the need for advanced hardware, standardized software, user-centric design, and continuous improvement in AI technologies for effective IAM in cloud environments. The study provides actionable recommendations for cloud service providers and developers, emphasizing the importance of involving users in development processes, ensuring transparency, and adopting adaptive algorithms. Future research directions include longitudinal studies on the impact of technological advancements and exploring demographic-specific responses to AI-integrated IAM solutions.

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Section: User Acceptance Of Identity Access and Authenticationmentioning

confidence: 99%

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

Olabanji,

Olaniyi,

Adigwe

et al. 2024

AJRCoS

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“…Moreover, transformational leaders are inherently oriented toward long-term success. Their visionary perspective instills a focus on sustainable growth and stability, even if immediate gains are not always realized [68,75]. Illustrative examples from the leadership of Steve Jobs at Apple and Jeff Bezos at Amazon underscore the profound impact of transformational leadership on organizational outcomes [55].…”

Section: Transformational Leadership and Organizational Outcomesmentioning

confidence: 99%

Transformational Leadership: A Comparative Exploration of the Leadership Prowess of Jeff Bezos and Steve Jobs

Adigwe

2024

AJEBA

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This exploratory study delves into the intricacies of transformational leadership within the technology sector, drawing insights from the leadership styles of Steve Jobs of Apple and Jeff Bezos of Amazon. The research aimed to understand how transformational leadership traits influence organizational outcomes like innovation, employee motivation, engagement, and overall performance. To achieve this, the study employed a mixed-method approach, incorporating both quantitative and qualitative data collection techniques. Primary data was gathered using a structured questionnaire, which was administered to a purposively sampled group of 567 respondents, all holding leadership or managerial roles in various industries. The questionnaire design integrated both closed-ended and open-ended questions to facilitate a comprehensive analysis. The data analysis involved rigorous regression analysis to quantitatively assess the relationships between specific transformational leadership traits and organizational outcomes. Key findings revealed a strong positive correlation between the level of inspirational motivation demonstrated by leaders and the degree of organizational innovation. Additionally, significant relationships were observed between the extent of individualized consideration by leaders and employee motivation and engagement, as well as between strategic vision and risk-taking by leaders and key organizational performance metrics. Drawing from these insights, the study recommends cultivating a leadership style that emphasizes inspirational motivation, individualized consideration, and strategic risk-taking. This approach is crucial for fostering innovation, enhancing employee motivation and engagement, and improving overall organizational performance in the rapidly evolving technology sector.

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“…Machine learning algorithms, particularly those employing unsupervised learning techniques, are adept at processing this voluminous data to detect hidden patterns and anomalies that may signify a potential threat [39,40]. The use of historical data is crucial in training AI models to understand what constitutes normal behavior within a network and what does not.…”

Section: Predictive Capabilities In Ai-driven Security Systemsmentioning

confidence: 99%

AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection

Olabanji,

Marquis,

Adigwe

et al. 2024

AJRCoS

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This study explores the comparative effectiveness of AI-driven user behavior analysis and traditional security measures in cloud computing environments. It specifically examines their accuracy, speed, and predictive capabilities in detecting and responding to cyber threats. As reliance on cloud-based solutions intensifies, the integration of Artificial Intelligence (AI) and machine learning into cloud security has become increasingly vital. The research focuses on how AI-driven security systems, with their advanced pattern recognition and anomaly detection, compare to traditional methods in identifying deviations from standard user behaviors in cloud settings. Employing a quantitative approach, the study utilizes a detailed survey strategy, targeting cybersecurity professionals across multiple industries, including finance, healthcare, information technology, retail, and government sectors. The survey, comprising both closed-ended and Likert-scale questions, is designed to elicit nuanced responses on the perceptions and experiences of these professionals regarding AI-driven versus traditional security methods in cloud environments. The data, collected from a purposive sample of 243 cybersecurity personnel, is analyzed using multiple regression analysis. This analysis facilitates an understanding of the impact of different security systems on the efficacy of threat detection and response in cloud contexts. The results indicate that while both AI-driven and traditional methods significantly improve threat detection accuracy, traditional methods show a slight edge. Conversely, AI-driven systems demonstrate notably superior predictive capabilities and overall enhanced security performance. These findings suggest the necessity of a hybrid security strategy in cloud computing. Such an approach would combine the advanced capabilities of AI, particularly in predictive analytics and adaptability, with the rapid and reliable responses of traditional methods. This integrated strategy is proposed to effectively address the unique challenges posed by the dynamic and complex nature of cloud-based cyber threats. This study provides valuable insights for both businesses and IT professionals on the effective integration of AI-driven security measures in cloud environments. It highlights the evolving role of AI in cloud security and the importance of maintaining a balance between innovative AI approaches and established traditional methods to create a robust, comprehensive cloud security framework.

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Interplay of Islam and Economic Growth: Unveiling the Long-run Dynamics in Muslim and Non-muslim Countries

Cited by 9 publications

References 23 publications

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

AI for Identity and Access Management (IAM) in the Cloud: Exploring the Potential of Artificial Intelligence to Improve User Authentication, Authorization, and Access Control within Cloud-Based Systems

Transformational Leadership: A Comparative Exploration of the Leadership Prowess of Jeff Bezos and Steve Jobs

AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection

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