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p-Index From 2020 - 2025
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This Author published in this journals
All Journal International Journal of Technology and Modeling
Agyemang, Akosua
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : International Journal of Technology and Modeling

 1 
Revolutionizing Industries: The Role of Technological Innovations in Modern Business Practices Nkrumah, Kwame; Agyemang, Akosua
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.136

Abstract

Technological innovation has emerged as a critical driver in transforming modern business practices across the globe. This study investigates the extent to which technological advancements have reshaped industrial operations and business strategies within the Cameroonian context. Using a mixed-methods approach, we collected data from 150 businesses across various sectors, complemented by in-depth interviews with industry leaders and technology stakeholders. The findings reveal a strong correlation between the adoption of emerging technologies—such as artificial intelligence, cloud computing, and automation—and improvements in productivity, operational efficiency, and market competitiveness. However, the study also highlights persistent challenges, including infrastructure deficits, limited digital literacy, and regulatory constraints that hinder full-scale adoption. Our analysis underscores the need for targeted policy reforms, capacity-building initiatives, and strategic investments to foster a more innovation-friendly ecosystem. This research contributes to the growing body of knowledge on digital transformation in emerging economies and offers actionable insights for business leaders, policymakers, and development practitioners aiming to harness technology for sustainable industrial growth.
Applying AI Models to Analyze Student Learning Interests Through Digital Interaction Patterns Agyemang, Akosua; Mensah, Kofi; Owusu, Esi
International Journal of Technology and Modeling Vol. 2 No. 3 (2023)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v2i3.142

Abstract

In the digital era, students increasingly engage with learning platforms that generate vast amounts of interaction data. This study explores the application of Artificial Intelligence (AI) models to analyze students' learning interests based on their digital interaction patterns. By leveraging machine learning algorithms and behavioral analytics, we identify correlations between user activities—such as clickstreams, time spent on content, and interaction frequencies—and subject preferences. The study utilizes a dataset from an online learning management system and applies classification and clustering techniques to detect interest trends among students. Results show that AI models can effectively predict individual learning preferences and offer insights to personalize educational content. These findings highlight the potential of integrating AI-driven analytics in education to enhance learner engagement and optimize teaching strategies.
Co-Authors Mensah, Kofi Nkrumah, Kwame Owusu, Esi