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All Journal Journal of Multidisciplinary Science: MIKAILALSYS
Rilwan Abdulyekeen
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Environmental Science Physics Social Sciences Other

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Digital Transformation in West African SMEs: The Role of Cloud Computing and Artificial Intelligence in Enhancing Operational Efficiency Abdullahi Lawal; Rilwan Abdulyekeen
Journal of Multidisciplinary Science: MIKAILALSYS Vol 3 No 3 (2025): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v3i3.7489

Abstract

Small and Medium-sized Enterprises (SMEs) are the backbone of West Africa’s economy, contributing substantially to employment and GDP, yet they face persistent constraints such as limited access to finance, inadequate infrastructure, and intensifying global competition. This paper examines how SMEs in West Africa are adopting digital technologies, specifically cloud computing and artificial intelligence (AI), the impact of these technologies on their operations, and the barriers that hinder wider uptake. Using a mixed-method approach that combines literature review, surveys, and interviews with SMEs in Nigeria, Ghana, and Senegal, the study analyzes patterns of technology adoption and associated operational changes. The findings indicate that the adoption of cloud computing and AI enhances scalability, supports process automation, and improves decision-making; however, overall adoption remains limited due to digital literacy gaps, unreliable internet connectivity, and the perceived high cost of digital solutions. The study concludes that digital transformation offers a critical pathway for strengthening the competitiveness and operational efficiency of West African SMEs but that structural and capability-related barriers must be addressed for its potential to be fully realized. The paper contributes to the digital transformation and SME literature by providing empirical insights from a regional context that is underrepresented in existing research and by informing strategies for SMEs, policymakers, and technology providers to promote more sustainable and inclusive adoption of cloud and AI solutions.
Cross-Lingual Sentiment Analysis with Natural Language Processing: Insights from Selected Nigeria Languages (Yoruba, Hausa, Igbo, and Nigerian Pidgin) Rilwan Abdulyekeen; Bashir Abbayaro Abdullahi
Journal of Multidisciplinary Science: MIKAILALSYS Vol 4 No 1 (2026): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v4i1.8596

Abstract

Natural Language Processing (NLP) plays a pivotal role in sentiment analysis, particularly in multilingual societies such as Nigeria, where languages like Igbo, Hausa, Yoruba, and Pidgin English coexist yet remain largely underrepresented in existing NLP tools and resources. This study aims to develop a sentiment analysis framework specifically tailored to Nigeria’s four major languages, addressing key challenges including code-mixing, tonal variations, and the scarcity of annotated datasets. The research leverages existing linguistic studies on these languages while constructing customized annotated datasets and designing models optimized for their structural and phonological properties. By systematically integrating linguistic insights with task-specific model development for low-resource settings, the proposed framework is designed to handle multilingual and code-mixed inputs more effectively than generic NLP systems. The study concludes that a targeted, language-aware approach is essential for improving sentiment analysis performance in underrepresented African languages and for ensuring that NLP technologies reflect the linguistic realities of multilingual societies. The contributions of this research lie in advancing multilingual sentiment analysis for low-resource African languages, providing methodological guidance for handling code-mixing and tonal features, and supporting practical applications in business intelligence, governance, public opinion mining, and social media analytics.
Co-Authors Abdullahi Lawal Bashir Abbayaro Abdullahi