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Rockfall detection is a crucial procedure in the field ofgeology, which helps to reduce the associated risks.Currently, geologists identify rockfall events almostmanually utilizing point cloud and imagery data ob-tained from different caption devices such as TerrestrialLaser Scanner (TLS) or digital cameras. Multitemporalcomparison of the point clouds obtained with thesetechniques requires a tedious visual inspection to iden-tify rockfall events which implies inaccuracies that de-pend on several factors such as human expertize and thesensibility of the sensors. This paper addresses this issueand provides an intelligent framework for rockfall eventdetection for any individual working in the intersectionof the geology domain and decision support systems.The development of such an analysis framework pre-sents major research challenges and justifies exhaustiveexperimental analysis. In particular, we propose an in-telligent system that utilizes multiple machine learningalgorithms to detect rockfall clusters of point cloud data.Due to the extremely imbalanced nature of the problem,aplethoraofstateoftheart resampling techniques ac-companied by multiple models and feature selectionprocedures are being investigated. Various machine learning pipeline combinations have been examinedand benchmarked applying wellknown metrics to beincorporated into our system. Specifically, we developedmachine learning techniques and applied them to ana-lyze point cloud data extracted from TLS in two distinctcase studies, involving different geological contexts: thebasaltic cliff of Castellfollit de la Roca and the con-glomerate Montserrat Massif, both located in Spain. Ourexperimental results indicate that some of the abovementioned machine learning pipelines can be utilized todetect rockfall incidents on mountain walls, with ex-perimentally validated accuracy.

This paper attempts to provide a data analysis of cryptocurrency markets. Such markets have been developed rapidly and their volatility poses significant research challenges and justifies intensive behavior analysis. For this, we develop statistical and machine learning techniques and apply them to analyze their price variations and to generate inferences. In particular, we utilize deep learning algorithms to predict the closing price of the Ethereum cryptocurrency in a short period. The price data is accumulated from Poloniex exchange and analyzed through a Convolutional Neural Network and four types of Recurrent Neural Network including the Long Short Term Memory network, the Stacked Long Short Term Memory network, the Bidirectional Long Short Term Memory network, and the Gated Recurrent Unit network. These deep learning models are benchmarked and compared under various metrics. Our experimental data suggest that certain of the above models can be utilized to predict the Ethereum closing price in real time with promising accuracy and experimentally proven profitability.