Advances in Big Data Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece - Angelov, Plamen; Manolopoulos, Yannis; Iliadis, Lazaros; Roy, Asim.; Vellasco, Marley; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2017)
The book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on different computing platforms (e.g. neuromorphic, graphics processing units (GPUs), clouds, clusters); and big data analytics applications to solve real-world problems (e.g. weather prediction, transportation, energy management). The book, which reports on the second edition of the INNS Conference on Big Data, held on October 23-25, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies.
Predicting human behavior based on web search activity: Greek referendum of 2015 -- Compact Video Description and Representation for Automated Summarization of Human Activities -- Attribute Learning for Network Intrusion Detection -- A Fast Deep Convolutional Neural Network for face detection in Big Visual Data -- Learning Symbols by Neural Network -- Designing HMMs models in the age of Big Data -- Extended Formulations for Online Action Selection on Big Action Sets -- Multi-Task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports -- An infrastructure and approach for infering knowledge over Big Data in the Vehicle Insurance Industry -- Unified Retrieval Model of Big Data -- Adaptive Elitist Differential Evolution Extreme Learning Machines on Big Data: Intelligent Recognition of Invasive Species.
Keywords: Computational intelligence; Data mining; Artificial intelligence; Computational Intelligence; Data Mining and Knowledge Discovery; Artificial Intelligence