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The purpose of this paper is to speak about the production of biodiesel from waste cooking oil which serves as an alternate fuel in the absence of conventional fuels such as diesel and petrol. Though much research work was carried out using non-edible crops such as Jatropha and Pongamia, cooking oil utilized in bulk quantity is discarded as a waste. This is reused again as it contains more of esters that when combined with an alcohol in presence of an enzyme as a catalyst yields triglycerides (biodiesel).

The lipase producing strain Rhizopus oryzae and pure enzyme lipase is immobilized and treated with waste cooking oil for the production of FAME. Reaction parameters such as temperature, time, oil to acyl acceptor ratio and enzyme concentration were considered for purified lipase and in the case of Rhizopus oryzae, pH, olive oil concentration and rpm were considered for optimization studies. The response generated through each run were evaluated and analyzed through the central composited design of response surface methodology and thus the optimized reaction conditions were determined.

A high conversion (94.01 percent) was obtained for methanol when compared to methyl acetate (91.11 percent) and ethyl acetate (90.06 percent) through lipase catalyzed reaction at oil to solvent ratio of 1:3, enzyme concentration of 10 percent at 30°C after 24 h. Similarly, for methanol a high conversion (83.76 percent) was obtained at an optimum pH of 5.5, olive oil concentration 25 g/L and 150 rpm using Rhizopus oryzae when compared to methyl acetate (81.09 percent) and ethyl acetate (80.49 percent).

This research work implies that the acyl acceptors methyl acetate and ethyl acetate which are novel solvents for biodiesel production can also be used to obtain high yields as compared with methanol under optimized conditions.

The purpose of this paper is to analyze and build a deep learning model that can furnish statistics of COVID-19 and is able to forecast pandemic outbreak using Kaggle open research COVID-19 data set. As COVID-19 has an up-to-date data collection from the government, deep learning techniques can be used to predict future outbreak of coronavirus. The existing long short-term memory (LSTM) model is fine-tuned to forecast the outbreak of COVID-19 with better accuracy, and an empirical data exploration with advanced picturing has been made to comprehend the outbreak of coronavirus.

This research work presents a fine-tuned LSTM deep learning model using three hidden layers, 200 LSTM unit cells, one activation function ReLu, Adam optimizer, loss function is mean square error, the number of epochs 200 and finally one dense layer to predict one value each time.

LSTM is found to be more effective in forecasting future predictions. Hence, fine-tuned LSTM model predicts accurate results when applied to COVID-19 data set.

The fine-tuned LSTM model is developed and tested for the first time on COVID-19 data set to forecast outbreak of pandemic according to the authors’ knowledge.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.