Machine Learning and Artificial Intelligence in Chemical and Biological Sensing 1st Edition

Grab the future of sensing technology with Machine Learning and Artificial Intelligence in Chemical and Biological Sensing, 1st Edition by Jeong-Yeol Yoon and Chenxu Yu. This authoritative volume immediately draws you in with real-world breakthroughs where AI transforms how we detect chemicals, biomarkers, and environmental threats.

Inside, readers discover clear, application-driven explanations of machine learning models, data preprocessing, feature extraction, and model validation tailored to chemical and biological sensors. Case studies span point-of-care diagnostics, environmental monitoring, food safety, and industrial process control—making complex algorithms accessible to researchers, engineers, and graduate students. The book balances theory and practice, showing how convolutional networks, support vector machines, and ensemble methods can improve sensitivity, selectivity, and reliability of biosensors and chemosensors.

Designed to inspire action, this edition highlights reproducible workflows and best practices for sensor data collection, noise reduction, and calibration—essential for anyone developing next-generation diagnostic tools or smart monitoring systems. Whether you’re in academia, healthcare, environmental science, or industry R&D, the practical insights accelerate innovation from laboratory prototypes to deployable solutions.

Perfect for readers in North America, Europe, Asia, and beyond, this 1st Edition is a must-have reference for mastering AI-driven sensing. Equip your library with a resource that bridges data science and sensor technology—click to add Machine Learning and Artificial Intelligence in Chemical and Biological Sensing to your collection and start turning sensor data into actionable intelligence.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.