Otolith shape and size: The importance of age when determining indices for fish-stock separation

Stock-separation of highly mobile Clupeids (sprat – Sprattus sprattus and herring – Clupea harengus) using otolith morphometrics was explored. Analysis focused on three stock discrimination problems with the aim of reassigning individual otoliths to source populations using experiments undertaken using a machine learning environment known as \{WEKA\} (Waikato Environment for Knowledge Analysis). Six feature sets encoding combinations of size and shape together with nine learning algorithms were explored. To assess saliency of size/shape features half of the feature sets included size indices, the remainder encoded only shape. Otolith sample sets were partitioned by age so that the impact of age on classification accuracy could be assessed for each method. In total we performed 540 experiments, representing a comprehensive evaluation of otolith morphometrics and learning algorithms. Results show that for juveniles, methods encoding only shape performed well, but those that included size indices held more classification potential. However as fish age, shape encoding methods were more robust than those including size information. This study suggests that methods of stock discrimination based on early incremental growth are likely to be effective, and that automated classification techniques will show little benefit in supplementing early growth information with shape indices derived from mature outlines

A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permissionThis work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of themthe ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and RandomForest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing.This work has been supported by the Heineken Endowed Chair in Neuromarketing at the Universitat Politecnica de Valencia in order to research and apply new technologies and neuroscience in communication, distribution and consumption fields.Colomer Granero, A.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Guixeres Provinciale, J.; Ausin-Azofra, JM.; Alcañiz Raya, ML. (2016). A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Frontiers in Computational Neuroscience. 10(74):1-16. doi:10.3389/fncom.2016.00074S116107