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Research

Our Research

Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations.

The rise of big data and AI has increased information complexity, challenging human understanding. The Adaptive Cognitive Fit (ACF) framework shows how AI-enhanced representations improve performance in such environments.

The Rise of Artificial Intelligence Phobia! Unveiling News-Driven Spread of AI Fear Sentiment using ML, NLP and LLMs

Analyzing 70,000 AI-related news headlines, this study finds persistent fear-based language driving public anxiety and misconceptions. It urges responsible media coverage and stronger AI education.

Unlocking Business Value with Generative AI! Economic Value Assessment for Chatbots and Gen AI ROI Discovery

This study examines the real economic value of generative AI and chatbots by analyzing research, industry reports, and pricing data. It offers insights into productivity, cost savings, and ROI to guide policymakers, businesses, and researchers.

Would You Please Like My Tweet?! An Artificially Intelligent, Generative Probabilistic, and Econometric Based System Design for Popularity-Driven Tweet Content Generation

This study develops an automated decision support system for social media managers that uses econometrics, ML, and Bayesian models to predict engagement and generate high-impact Tweet content, addressing the “blank screen” problem.

Cultivation of Human Centered Artificial Intelligence: Culturally Adaptive Thinking in Education (CATE) for AI

This study introduces the CATE-AI framework to make AI education culturally adaptive and inclusive. By integrating human behavior theories and human-centered AI principles, it enhances understanding while reducing confusion, AI-phobia, and resistance among diverse learners.

Artificially Intelligent Readers: An Adaptive Framework for Original Handwritten Numerical Digits Recognition with OCR Methods

This study presents an adaptive AI-based OCR framework using CNNs to accurately recognize handwritten digits, handling personalized handwriting and limited datasets through custom augmentation and model improvements.

MOMCare with AI: A Dual Embedding based RAG-LLM Chatbot for Postpartum Depression

MOMCare is a chatbot using AI and medical-specific mechanisms to provide empathetic, accurate support for mothers with postpartum depression, demonstrating safe and effective mental health interventions.

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

This study analyzes COVID-19 Twitter sentiment, tracking fear trends and comparing ML methods, with Naïve Bayes achieving 91% accuracy. It offers insights into pandemic fear and guidance for textual sentiment analysis.

ESHRO: An Innovative Evaluation Framework for AI Driven Mental Health Chatbots

This study presents ESHRO, a framework for evaluating mental health chatbots on empathy, safety, and effectiveness, demonstrated using the ELY Chatbot to enhance AI-driven mental health support.

When Machines Create! Envisioning Our Future With the Transformative Power of Generative AI

Modern generative AI, widely accessible and practical, drives innovation and value creation when combined with frameworks like ACF, while emphasizing human-centric use and managing associated risks.

Emoji Augmented AI Chatbot (EACh): Improving NLU and NLG for Social Communications in RAG-LLMs with Emoji Awareness

This study presents EACh, an emoji-augmented AI chatbot using RAG architecture to interpret and generate emojis accurately, enhancing conversational relevance and emotional alignment in digital communication.

Wildfire Generative AI Chatbot Track: Artificial Intelligence (AI)

This study presents an AI-powered wildfire chatbot that uses big data and machine learning to assess risks, provide real-time guidance, and support disaster preparedness for residents and first responders.