An Intelligent and Optimal Deep-Learning Approach in Sensor-Based Networks for Detecting Microbes

Microbes constitute a substantial portion of Earth’s biodiversity and play a critical role in maintaining the equilibrium of ecosystems. Their significance extends to various domains such as industry, agriculture, and medicine. While most microorganisms pose threats and can cause severe illnesses, a few offer substantial benefits to human life. Traditional methodologies employed in the identification and classification of microorganisms involve time-consuming processes, including staining, biochemical testing, and motility assays. These procedures are indispensable for conducting microbiology experiments. Thus, there is a growing need to integrate deep-learning techniques to automate the process of microbial detection. In this study, we propose a novel deep-learning model that leverages biosensors to capture real-time data. Extensive analysis is conducted on four diverse datasets collected for this purpose. The empirical results demonstrate that our proposed model outperforms existing methods, achieving an average accuracy exceeding 98% for predicting each microbial class. Notably, this approach also offers practical advantages such as improved feasibility, speed, and cost-effectiveness in microbial detection. By harnessing this state-of-the-art technology, microbiologists can efficiently address complex challenges and obtain optimal results.