Publication

Abstract : Microorganisms, which include a wide range of living forms such as fungi and algae, are essential to human health, biotechnological uses, and ecological equilibrium. Accurate taxonomic categorization of these minuscule organisms is crucial in comprehending their ecological importance and capitalizing on their possibilities in several scientific fields. Our research explores the nexus between microbiology and AI, thoroughly examining our novel method for classifying microorganisms. Our approach is the precise training of sophisticated neural networks on large data sets selected to capture the complex genetic and morphological characteristics unique to every microbe. This work highlights the potential revolutionary effect of deep learning in microbiology and demonstrates the high classification accuracy reached. Our research produced an astounding accuracy of 99%, highlighting the resilience of our method in distinguishing the complex morphological and genetic subtleties unique to every bacterium. Our method’s resilience provides possibilities for automated taxonomic categorization, which may have a wide range of applications, from biotechnological advancements to medical diagnostics and environmental monitoring.