Advances in Big Data
Description
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.
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Content
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.
