An Artificial Intelligence and Genetic Algorithm Optimized Biosensor System for Real-Time Detection of Environmental Toxins and Their Biomedical Impact: A Natural Science Driven Approach to Sustainable Public Health Monitoring

The environmental pollution is turning environment-unfriendly, and food safety, and human health are endangered threateningly, and most of the established monitoring systems are not sensitive, adaptive, and capable of real-time analysis to offer such level of protection to the people. This paper describes a biosensor based on Artificial Intelligence (AI) and Genetic Algorithm (GA)-based optimization to monitor major environmental poisons in real time and evaluate their possible biomedical effects. An electrochemical and optical transduction-based multimodal biosensing platform was developed to detect heavy metals (Pb2+, Cd2+), organophosphate pesticides and volatile organic compounds (VOCs). With the aim of increasing analytical accuracy, a hybrid AI system, which includes convolutional neural networks (CNNs), along with feature extraction, and Long Short-Term Memory (LSTM) networks, which retrieve temporal prediction of toxins, was trained on 8,450 datasets of biosensor signals. The optimization of the model hyperparameters, calibration curves and receptor -analyte affinity thresholds was refined using GA. The optimized system was able to accomplish a 97.8 percent toxin classification efficiency, 94.6 percent prediction accuracy and 21 percent better sensitivity sight as compared with conventional biosensor systems. Simultaneously, a biomedical impact module was created that calculated the biomarkers of oxidative stress and inflammatory cytokine probabilities and toxin toxicity pathways. The field tests showed strong real-time functionality with less than 1.6 Second latency, which proved field-testing with the example that permits continuous monitoring coverage of the environment. On the whole, the findings indicate that the hybrid of AI analytics and GA optimization can be used to boost the performance of biosensors to a considerable extent and allow observing environmental toxins on a large scale and in the long-run. The suggested system will present an effective instrument of environmental risks preventive, food quality, and timely anticipation of health effects, toxins.

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