Titlebook: Machine Learning for Advanced Functional Materials; Nirav Joshi,Vinod Kushvaha,Priyanka Madhushri Book 2023 The Editor(s) (if applicable)

MAPLE

A Review of the High-Performance Gas Sensors Using Machine Learning,, the possible challenges/prospects are emphasized and discussed as well. Our review further indicated that machine-learning techniques are effective strategies to successfully improve the gas sensing behavior of a single gas sensor or sensor array.

phytochemicals

PANEL

Contemplation of Photocatalysis Through Machine Learning,rovides basic PC research knowledge that could potentially be useful for machine learning methods. Additionally, we also describe the pre-existing ML practices in PC are for quick identification of novel photocatalysts. Finally, the available conceptualized strategies for complementing data-driven M

使饥饿

Discovery of Novel Photocatalysts Using Machine Learning Approach,ional research along withmaterials informatics can offer a way forward. We note here that to screen photocatalyst basedon their efficiencies, ML technique would require accurate and adequate descriptors. Formationenergy, cohesive energy, binding energy, energy band gap, conduction band minimum (CBM)

SPURN

Machine Learning in Impedance-Based Sensors,trochemical system. Machine learning (ML) tools help us to train the systems to process the data and obtain the perfect matching equivalent circuit but several challenges remain as EIS database creation is the biggest challenge.

menopause

灵敏

orce for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods..978-981-99-0395-5978-981-99-0393-1

赏钱

Book 2023troduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning an

苦笑

A Machine Learning Approach in Wearable Technologies,machine learning algorithms to wearable technologies. After introducing the algorithms more commonly used for analyzing data from wearable devices, we review contributions to the field within the last 5 years. Special emphasis is placed on the application of this approach to health monitoring, sports analytics, and smart agriculture.

赏钱

ell as data analyses on material characteristics.Provides a .This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as