International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rapid evolution of Generative Artificial Intelligence (AI) has transformed the technological landscape by enabling automated creation across text, image, audio, video, and code domains. This research paper titled “Generative AI Tools and Platforms Landscape” presents a comprehensive analysis of current generative AI platforms, focusing on their technical capabilities, architecture, and application diversity. The study uses a data-centric and code-based approach, employing Python-based libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn to preprocess, analyze, and visualize real-world data from generative AI tool repositories.

The methodology involves systematic data cleaning, exploratory data analysis (EDA), and predictive modeling using Logistic Regression within a machine learning pipeline. Results indicate that multimodal platforms and open-source models exhibit stronger adaptability and innovation potential. Statistical visualizations, heatmaps, and correlation analyses reveal significant patterns among platform features, release trends, and modality diversity.

This study contributes to understanding the evolving Generative AI ecosystem, offering insights into its current landscape and identifying potential research gaps for future development. The outcomes demonstrate the significance of open innovation, ethical governance, and model transparency in shaping next-generation AI platforms.

Keywords: Generative AI, Machine Learning, AI Platforms, Multimodal Models, Data Analysis, Logistic Regression, Open Source AI, Foundation Models, Artificial Intelligence Tools, Code-based Research.

Downloads: | DOI: 10.17148/IJARCCE.2025.141042