Session Title

How Generative AI is changing Hollywood

Session Overview

Computational techniques are used in many fields of engineering. Artificial intelligence and machine learning (AIML) are gaining increased applications in bioprocess engineering during recent years. Though there are several research reports available in the literature, still AIML techniques are not utilized to its full potential in bioprocess development. Currently experimental planning, and data modeling largely depends on human intervention. AIML techniques combined with statistical methods help in experimental planning with minimum number of experiments, reduced resource utilization, and less human intervention. The use of AIML reduces the overall cost of bioprocess development and increases the bioprocess stability and efficiency. The artificial neural networks (ANNs) have demonstrated their extraordinary potential to model complex experimental data with high nonlinear interactions, and recorded major breakthroughs in bioprocess development. Extreme learning machine (ELM) is a computational modeling technique. ELM uses single-hidden-layer feedforward ANN. In recent years, ELM is reported more advantageous because of its higher learning speed and better generalization capability than conventional ANNs. ANNs modeling combined with Genetic Algorithms (Gas), kernel-based ELM (K-ELM) modeling combined with cuckoo search (CS) are reported for optimization of biodiesel production process using statistical experimental data from response surface methodology. In our research work, the artificial intelligence and machine learning techniques were employed for modeling and optimization of fermentation process conditions for enhanced production of L-asparaginase by submerged fermentation of Aspergillus terreus. The Random Forest algorithms machine learning techniques was found best based on the analysis of regression coefficient of ANN model and metric score values of machine learning algorithms.