TY - CHAP A1 - W. David Pan A2 - Yuhang Dong A3 - Dongsheng Wu ED1 - Hamed Farhadi Y1 - 2018-09-19 PY - 2018 T1 - Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks N2 - The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. BT - Machine Learning SP - Ch. 8 UR - https://doi.org/10.5772/intechopen.72426 DO - 10.5772/intechopen.72426 SN - 978-1-78923-753-5 PB - IntechOpen CY - Rijeka Y2 - 2020-04-07 ER -