Titlebook: Recent Advances in Big Data and Deep Learning; Proceedings of the I Luca Oneto,Nicolò Navarin,Davide Anguita Conference proceedings 2020 Sp

捐助

Restoration Time Prediction in Large Scale Railway Networks: Big Data and Interpretability,available exogenous data such as the weather information, and the experience of the operators. Results on real world data coming from the Italian railway network will show the effectiveness and potentiality of our proposal.

Basal-Ganglia

CLAM

Fast Transfer Learning for Image Polarity Detection,ering that polarity predictors -in the era of social network and custom profiles- might need to be updated within a short time interval (i.e., hours or even minutes). Thus, the paper proposes a design of experiment that supports a fair comparison between predictors that rely on different architectures.

消滅

Psychiatric Disorders Classification with 3D Convolutional Neural Networks,ture..This work aims to analyze the behavior of classical machine learning techniques against 2D and novel 3D Convolutional Neural Network models. An exhaustive empirical assessment has been performed to evaluate these methods on 4 real-world neuroimaging tasks, including Schizophrenia and Bipolar Disorder classification.

Diuretic

dagger

Train Overtaking Prediction in Railway Networks: A Big Data Perspective,ld data coming from the Italian railway network will show that the proposed solution outperforms the fully data-driven approach and could help the operators in timely identify and schedule the best train overtaking solution.

反抗者

Innovation Capability of Firms: A Big Data Approach with Patents, Deep Neural Networks (DNN), and Decision Trees (DT), are employed for this investigation. Results show that the most important patent’s features useful to predict IC refer to the specific technological areas, the backward citations, the technological domains and the family size. These findings are confirmed by all the three algorithms used.

起來了

難管

Presumable Applications of Deep Learning for Cellular Automata Identification,ple of deep learning for classical CA is described. Some possibilities of deep learning application for identification problems for CA with memory and anticipation are proposed. The case of of deep learning for the systems with multivalued behavior had been proposed.

雪崩

Cavitation Noise Spectra Prediction with Hybrid Models,for the prediction of the ship propeller cavitating vortex noise, adopting real data collected during extensive model scale tests in a cavitation tunnel. Results will show the effectiveness of the proposal.