Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction.

In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integrated with GA and PSO were performed to improve the ML models architecture and parameters selection. The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R2 = 0.99, RMSE = 2.33). The partial dependence plots (PDPs) analysis shows the potential effects of each influencing parameter impact on the biochar yield and as well as shows that how these factors will interact during the pyrolysis process. A user-friendly software was developed based on the ELT-PSO model to avoid extensive and expensive experimentations without requiring considerable ML understanding. Difference recorded by GUI was less than 2% with experimental yield.

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review.

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.