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Oxidative stress biomarkers in fish liver tissue, including superoxide dismutase, glutathione peroxidase, and catalase activity, reflect exposure to environmental stressors. While selenium nanoparticles provide a beneficial defence mechanism against oxidative stress within specific limits, toxic concentrations can trigger stress. To monitor these effects, a feedforward neural network trained using the bio-inspired Sine-Cosine algorithm was developed to predict liver enzyme levels in fish reared on nano-selenite diets. The algorithm systematically trains and updates network weights and biases until reaching optimal performance. When evaluated, this approach achieved higher efficiency and superior predictive accuracy compared to existing models, enhancing the ability to assess biomarker responses to varying nanoparticle concentrations.
Accurate monitoring of oxidative stress biomarkers is essential for understanding aquatic ecosystem health and managing dietary additives in fish farming. Computational models capable of predicting liver enzyme responses help identify the threshold between the nutritional benefits of selenium nanoparticles and their toxic side effects, supporting safer feed formulation and environmental assessment.
This model could support aquaculture feed manufacturers and environmental monitoring specialists seeking to predict fish health responses to nano-selenium additives. Given that the work represents early-stage algorithm development and comparative benchmarking, significant field validation across diverse farming conditions would be required before it could be integrated into commercial farm management or feed optimisation software.
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Analytical prediction of oxidative stress biomarkers in ecosystem provides an expressive result for many stressors. These oxidative stress biomarkers including superoxide dismutase, glutathione peroxidase and catalase activity in fish liver tissue were analyzed within feeding different levels of selenium nanoparticles. Se-nanoparticles represent a salient defense mechanism in oxidative stress within certain limits; however, stress can be engendered from toxic levels of these nanoparticles. For instance, prediction of the level of pollution and/or stressors was elucidated to be improved with different levels of selenium nanoparticles using the bio-inspired Sine-Cosine algorithm (SCA). In this paper, we improved the prediction accuracy of liver enzymes of fish fed by nano-selenite by developing a neural network model based on SCA, that can train and update the weights and the biases of the network until reaching the optimum value. The performance of the proposed model is better and achieved more efficient than other models.
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DOI: 10.1109/icenco.2016.7856442
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