Data Analytics and Data-Driven Computing: Applications from Internet-of-Things to Classifying Neuropsychiatric Disorders
This talk will describe several approaches to reducing energy consumption in internet-of-things applications and applications of data analytics to neuro-psychiatric disorders. Machine learning and information analytics are important components in all these things. Almost all things should have embedded classifiers to make decisions on data. Thus, reducing energy consumption of features and classifiers is important. First part of the talk will present energy reduction approaches from feature selection, classification and incremental multi-stage classification perspectives. These approaches will be demonstrated using a medical internet-of-thing for monitoring EEG and predicting seizures. Enhancing security and preventing piracy are also of critical concern. In the second part of the talk, I will address hardware security and present approaches to designing circuits that cannot be easily reverse engineered and cannot be pirated. To this end, authentication and obfuscation approaches will be presented. In the third part of the talk, data analytics approaches to classifying psychiatric disorders such as schizophrenia, border line personality disorder and obsessive-compulsive disorder will be discussed.