Advanced Analytics and Deep Learning Models

This collection examines how advanced analytics and deep learning are being used in fields such as healthcare (including electronic and remote medicine), transportation, agriculture, and the stock market. Advanced analytics combines machine learning, artificial intelligence, graph methods, text and data mining, and semantic analysis. Rather than stopping at conventional business intelligence, it analyzes data in a partly automated, self-directed way using a range of techniques and tools.

Deep learning and data analysis are both central to data science. Public agencies and private firms alike gather large volumes of domain-specific information, and companies of many sizes mine that data for current and future technology. Deep learning suits unsupervised data, which makes it useful for big data. It can tackle tasks such as organizing unlabeled and unsorted raw data, uncovering intricate patterns in huge datasets, retrieving information quickly, and tagging data.

The book's sixteen chapters cover artificial intelligence, machine learning, and deep learning, along with their applications in stock market prediction, recommender systems for choosing services, e-healthcare, telemedicine, and transportation. Later chapters look at emerging opportunities involving fog and cloud computing alongside artificial intelligence.

The intended readers include researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers working in healthcare, telemedicine, transportation, and finance. Software engineers and advanced students who are new to advanced analytics and deep learning will also find it useful.