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MOON: Multimodal Omniscient Operational Network

Dr. P. Sumalatha
Dept. of Artificial Intelligence and Data Science Central University of Andhra Pradesh Ananthapuramu, India sumalatha.psl@gmail.com

Kanundla Nithin
Dept. of Artificial Intelligence and Data Science Central University of Andhra Pradesh Ananthapuramu, India kanundlanithinkumar@gmail.com

Abstract—The advancement of artificial intelligence (AI) has significantly accelerated the development of multimodal virtual assistants that integrate diverse sensory modalities to enrich human-computer interaction. This paper introduces MOON (Multimodal Omniscient Operational Network), an AI assistant designed to seamlessly combine voice recognition, computer vision, gesture control, and environmental analysis within an adaptive and intuitive interface. Built upon frameworks such as MediaPipe for gesture recognition, YOLOv3 for real-time object detection, and spaCy for natural language processing, MOON performs a wide range of tasks, including application control, sentiment analysis, and facial recognition-based user identification. The system incorporates a dynamic memory model to facilitate context-aware responses and personalization.

Experimental evaluations examining accuracy, latency, and user satisfaction indicate that MOON significantly outperforms unimodal assistants. However, its use of facial recognition tech- nology raises ethical concerns related to privacy and surveillance. This research proposes a scalable and modular multimodal AI framework with implications for smart environments, ambient intelligence, and accessibility technologies.

Keywords— Multimodal AI, Virtual Assistant, Computer Vision, Natural Language Processing, Human-Computer Inter- action.