E. Gangadevi – författare

COMPUTATIONAL INTELLIGENCE IN BIOPRINTING The book provides a comprehensive exploration of the evolving field of bioprinting in regenerative medicine and is an essential guide for professionals seeking a thorough understanding of the field. Computational Intelligence in Bioprinting provides a comprehensive overview of the evolving field of bioprinting in reformative medicine, defining the process of printing structures using viable cells, biomaterials, and living molecules. The primary goal is to provide substitutes for tissue implants, which might lead to eliminating the requirement for organ donors, as well as to transform animal testing for the learning and analysis of disease and the growth of treatments. The book offers a comprehensive overview of bioprinting technologies and their applications, emphasizing the integration of computation intelligence, artificial intelligence, and other computer science advancements in the field. By harnessing the power of computational intelligence techniques such as AI, machine learning, optimization algorithms, and data analytics, existing hurdles can be overcome and the full potential of bioprinting can be unlocked. The book covers an extensive range of topics, including bio-ink formulation and characterization, bioprinter hardware and software design, tissue and organ modeling, image analysis, process optimization, and quality control. Audience The book is aimed at professionals, practitioners and researchers in the fields of bioprinting, tissue engineering, and computational intelligence in medicine.

The book provides a comprehensive understanding of Automated Machine Learning’s transformative potential across various industries, empowering users to seamlessly implement advanced machine learning solutions without needing extensive expertise. Automated Machine Learning (AutoML) is a process to automate the responsibilities of machine learning concepts for real-world problems. The AutoML process is comprised of all steps, beginning with a raw dataset and concluding with the construction of a machine learning model for deployment. The purpose of AutoML is to allow non-experts to work with machine learning models and techniques without requiring much knowledge in machine learning. This advancement enables data scientists to produce the easiest solutions and most accurate results within a short timeframe, allowing them to outperform normal machine learning models. Meta-learning, neural network architecture, and hyperparameter optimization, are applied based on AutoML. Automated Machine Learning and Industrial Applications offers an overview of the basic architecture, evolution, and applications of AutoML. Potential applications in healthcare, banking, agriculture, aerospace, and security are discussed in terms of their frameworks, implementation, and evaluation. This book also explores the AutoML ecosystem, its integration with blockchain, and various open-source tools available on the AutoML platform. It serves as a practical guide for engineers and data scientists, offering valuable insights for decision-makers looking to integrate machine learning into their workflows. Readers will find the book: Aims to explore current trends such as augmented reality, virtual reality, blockchain, open-source platforms, and Industry 4.0;Serves as an effective guide for professionals, researchers, industrialists, data scientists, and application developers; Explores technologies such as IoT, blockchain, artificial intelligence, and robotics, serving as a core guide for undergraduate and postgraduate students.Audience Data and computer scientists, research scholars, professionals, and industrialists interested in technology for Industry 4.0 applications.