Artificial Intelligence Assessment of Primate Social Behaviour Under Habitat Disturbance

Abstract

Primates' social behavior is fundamental in studying animal adaptation, group behavior, communication, and reaction to the environmental changes. Habitat disturbances due to deforestation, urbanization, human intervention, and climatic changes greatly affect primate movements, behaviors, feeding habits, and group formation. Nevertheless, conventional methods for analyzing primate social behavior are based on observation and manual annotation, which are tedious, laborious, and limited by the environmental conditions and human bias. In this case, there is a need for intelligent monitoring methods for constant behavioral analysis and assessment in natural environments. This research paper outlines an AI-based framework for the assessment of primate social behavior under various habitat disturbance conditions. The framework uses wildlife monitoring data in order to automatically recognize and classify various social behaviors, including grooming, communication, aggression, resting, and group interactions. This AI-based approach comprises of data preprocessing, behavioral features extraction, AI-based classification and evaluation processes. Deep learning and machine learning algorithms are used to analyze various behavioral patterns and the variations in primate social behavior in disturbed and undisturbed habitats. This framework analyzes the various behavioral indices, which include interaction frequency, group activity, movements and social association. Experimental analysis reveals that the developed AI model can reliably detect primate social behavior patterns, thereby improve the efficiency of the wildlife behavior analysis process when compared with traditional observation-based approaches. This automatic system facilitates largescale, non-intrusive, and continuous monitoring of primate species, thus promoting ecological studies and biodiversity conservation programs. The results point to the usefulness of AI technologies in investigating behavioral adaptation patterns in primate species under conditions of habitat disturbances. The future studies will concentrate on the use of multimodal sensory data and deep learning techniques for real-world wildlife monitoring applications