Mukiibi Moses • Artificial Intelligence • Machine Learning • Data Science
Investigates emotion recognition capabilities in conversational AI systems.
Explores efficient fine-tuning strategies for compact transformer models.
Artificial Intelligence (AI) has rapidly evolved into a transformative technology influencing numerous aspects of modern society. Advances in machine learning, deep learning, and generative models have enabled machines to perform complex tasks such as natural language understanding, image recognition, and predictive analytics. This review article synthesizes current research on the foundations of artificial intelligence and its major technological components, including machine learning, deep learning, natural language processing, and computer vision. The article also examines major applications of AI in sectors such as healthcare, finance, and transportation. In addition, key challenges such as algorithmic bias, interpretability, and computational requirements are discussed. By reviewing existing literature, this article highlights the progress made in AI research and identifies future directions for developing more reliable, transparent, and human-centered AI systems.
This project aims to restore old or damaged photographs using advanced image processing techniques. The methodologies include inpainting for region filling, noise reduction using Gaussian blur, contrast enhancement through histogram equalization, bilateral filtering for preserving details, and Roberts edge detection for edge highlighting. The application of these techniques is demonstrated through a structured approach, with results visualized using Matplotlib to illustrate the effectiveness of each step in revitalizing aged images.
Improves diffusion model performance under memory constraints.
Analysis of modern AI developments and future trends.
Overview of robotics systems and automation technologies.
The paper proposes an Emotional State Engine (ESE) that models user emotions dynamically using a Valence–Arousal–Dominance (VAD) framework instead of static emotion labels. It integrates emotion detection with a dialogue model to maintain emotional continuity and generate more contextually appropriate, empathetic responses.
This paper introduces MozeAI, an intelligent assistant built with Streamlit and Groq LLM that supports real-time web search, document analysis, file comparison, memory, and image generation. It handles multiple file formats and can generate documents (PowerPoint, Excel, Word), demonstrating practical AI applications for education and professional tasks.