NEXT-GEN ALGORITHMS FOR REAL-TIME GENDER DETECTION IN ECOLOGICAL RESEARCH

Abstract

One of the essential activities of the study of the reproductive behavior of a population, its dynamics, and the preservation of the species is gender identification in the wildlife and ecological study. Conventional procedures such manual inspection and genetic transfer are cumbersome, labor-intensive and are usually not practical in real-time applications. As the artificial intelligence (AI) and the field of machine learning (ML) progress forward at a rapid rate, more possibilities are becoming available to create real-time, automated gender classification in ecological studies. The presented paper discusses the use of next-generation machine learning algorithms to detect the gender in real time in the ecological environment. The proposed research is set to explore the future of Deep Learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and their possible application with other types of data, such as visual, acoustic and biometric signals. Our results prove that these models are much better than conventional approaches, which offer precise and effective gender recognition in real-life, active settings. It points to scale and difficulties of integrating multi-modal data and issues of quality in the large-scale data. In summary, the study creates an impression on how next-gen algorithms may transform real-time gender detection in ecological studies and valuable information on conservation and managing wild animals.

Keywords: Gender Detection (Real time), Ecological studies, Artificial intelligence, Machine learning, convolutional neural networks, Multi-modal data, Conservation.