Research Articles
Business AI Automation
Authors: Mahabub Sultan Adnan Ghaffar
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
This article explores how artificial intelligence (AI) technologies like machine learning, NLP, RPA, and cognitive computing streamline business processes, reduce errors, and enhance operational efficiency. It highlights real-world use cases in customer service, marketing, HR, and supply chain management.
View on Google Scholar →Infrastructural and Pedagogical Barriers to Integrating AI Tools in CLT-Oriented Classrooms in Rural vs. Urban Higher Secondary Schools
Authors: Yusuf Adebayo, Adnan Ghaffar
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
The integration of Artificial Intelligence (AI) tools into Communicative Language Teaching (CLT) classrooms presents promising opportunities to enhance language learning outcomes, particularly at the higher secondary level
View on Google Scholar →Bridging the Gap: How AI Can Support Teacher Training for Effective CLT Implementation at the Higher Secondary Level
Authors: Adnan Ghaffar, Bruce Williams
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
The successful implementation of Communicative Language Teaching (CLT) at the higher secondary level remains a persistent challenge in many educational contexts, particularly in developing countries where teacher preparedness, resource availability, and pedagogical support are limited.
View on Google Scholar →AI in the CLT Classroom: Examining Student Engagement and Motivation in Technology-Assisted Communicative Language Learning
Authors: Adnan Ghaffar, Chidiebere Joshua
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
The integration of Artificial Intelligence (AI) in Communicative Language Teaching (CLT) classrooms represents a transformative approach to language learning, emphasizing student engagement and motivation through technology-assisted environments.
View on Google Scholar →AI Into Business Automation: Practical Frameworks For Streamlining Operations
Authors: Adnan Ghaffar, Abimbola Oyeronke
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
This critique evaluates the paper “AI into Business Automation: Practical Frameworks for Streamlining Operations” through the lens of international research standards, SaaS business practices, and 2025 market trends.
View on Google Scholar →Sustainable AI: Integrating Carbon Capture and Energy-Efficient Algorithms for Green Business Automation
Authors: Bruce William, Adnan Ghaffar, Hart Charles
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
As artificial intelligence (AI) becomes increasingly integral to business automation, concerns about its environmental impact have grown due to the high energy consumption of AI models and data centers.
View on Google Scholar →AI-Driven Software Development and Business Automation in 2025: A Comprehensive Framework for Enhanced Efficiency, Transparency, and Decision Support
Authors: Adnan Ghaffar
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
This paper introduces an end-to-end framework combining state-of-the-art AI models such as GPT-5 and AlphaCode 2.0 to enable software development and business process automation.
View on Google Scholar →AI-Driven Forecasting Models for Green Tech Stocks: Linking Carbon Capture Innovation to Stock Market Trends in 2025
Authors: Yusuf Adebayo, Syed Sadique Basha, Adnan Ghaffar
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
The increasing urgency to combat climate change has propelled green technologies—particularly carbon capture and sequestration (CCS)—to the forefront of environmental and economic discourse.
View on Google Scholar →Integrating AI into Business Automation: Practical Frameworks for Streamlining Operation
Authors: Adnan Ghaffar, Chirag Gajiwala, Syed Basha, Bruce William
Credentials: MS in Data Science and Analytics, CEO of CodeAutomation.ai
In today’s digital era, businesses are under pressure to deliver faster, smarter, and more cost-effective services. Traditional rule-based automation is no longer sufficient to meet these demands.
View on Google Scholar →
