Algal Green Chemistry: Recent Progress in Biotechnology presents emerging information on green algal technology for the production of diverse chemicals, metabolites, and other products of commercial value. This book describes and emphasizes the emerging information on green algal technology, with a special emphasis on the production of diverse chemicals, metabolites, and products from algae and cyanobacteria. Topics featured in the book are exceedingly valuable for researchers and scientists in the field of algal green chemistry, with many not covered in current academic studies. It is a unique source of information for scientists, researchers, and biotechnologists who are looking for the development of new technologies in bioremediation, eco-friendly and alternative biofuels, biofertilizers, biogenic biocides, bioplastics, cosmeceuticals, sunscreens, antibiotics, anti-aging, and an array of other biotechnologically important chemicals for human life and their contiguous environment. This book is a great asset for students, researchers, and biotechnologists.

Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.