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Microbial fuel cells have been constantly explored as robust technol. for complex industrial wastewater treatment.The partial treatment of wastewater in anode chamber of MFC discourages its ability to thrive as stand-alone treatment process and further necessitates secondary treatment, prior to disposal.In our study, we have evaluated sequential treatment of ghee industry wastewater in the anode (primary) and later in microalgae-based biocathode chamber (secondary) to improve the treatment efficiency of bioelectrochem. system.The wastewater collected from ghee manufacturing unit was biochem. treated in the anode chamber after primary sedimentation and at the end of each batch, the anode effluent was transferred to a transient beaker and was used as a source for microalgal growth in cathode chamber without any modification.On comparison with conventional anaerobic treatment, microbial fuel cell could render higher COD removal with lower batch time.The highest power d. and c.d. of 13.7 mW m^-2 and 53.5 mA m^-2 was produced with abiotic cathode.Though the power production was low in photo-bioelectrochem. system (3.33 mW m^-2, 13.45 mA m^-2) compared to abiotic cathode the treatment efficiency improved to 96%.Further, the photo-bioelectrochem. reactors enabled eco-friendly and efficient wastewater treatment with considerable power production

01 Jun 2025·Computers in Biology and Medicine

Optimized heart disease prediction model using a meta-heuristic feature selection with improved binary salp swarm algorithm and stacking classifier

Article

Author: Malar, E ; Sowmiya, M ; Banu Rekha, B

Despite technological advancements, heart disease continues to be a major global health challenge, emphasizing the importance of developing accurate predictive models for early detection and timely intervention. This study proposes a heart disease prediction model integrating a stacking classifier with a nature-inspired meta-heuristic algorithm. It employs an improved Binary Salp Swarm Algorithm (BSSA) by incorporating a wolf optimizer and opposition-based learning for optimal feature selection. The proposed Stacking Classifier (SC) architecture features a two-tier ensemble: heterogeneous base classifiers at level 0 and a meta-learner at level 1. The BSSA is used to identify optimal features, which are then utilized to construct the stacking classifier. Experimental results demonstrate superior performance, achieving 95 % accuracy, 0.92 sensitivity, 0.97 specificity, 0.96 precision, and an F1 score of 0.95, with notably low false positive and false negative rates. Further, validation on larger datasets yielded an accuracy of 87.46 %. The feature selection process adopts a multi-objective strategy which enhances the classification accuracy and outperforms conventional techniques. The proposed method demonstrates significant potential for improving the predictive modelling in clinical settings for diagnosing heart diseases.

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