By Foteini Andriopoulou, Dimitrios K. Lymberopoulos (auth.), Lazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos, Kostas Karatzas, Spyros Sioutas (eds.)
This publication constitutes the refereed lawsuits of the Workshops held on the eighth IFIP WG 12.5 foreign convention on man made Intelligence functions and recommendations, AIAI 2012, in Halkidiki, Greece, in September 2012. The e-book features a overall of sixty six attention-grabbing and leading edge learn papers from the subsequent eight workshops: the second one synthetic Intelligence functions in Biomedicine Workshop (AIAB 2012), the 1st AI in schooling Workshop: techniques and purposes (AIeIA 2012), the second one foreign Workshop on Computational Intelligence in software program Engineering (CISE 2012), the 1st Conformal Prediction and Its functions Workshop (COPA 2012), the 1st clever leading edge methods for Video-to-Video Communiccation in sleek clever towns Workshop (IIVC 2012), the 3rd clever platforms for caliber of lifestyles details companies Workshop (ISQL 2012), the 1st Mining Humanistic facts Workshop (MHDW 2012), and the 1st Workshop on Algorithms for facts and textual content Mining in Bioinformatics (WADTMB 2012).
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Extra info for Artificial Intelligence Applications and Innovations: AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part II
The pre-processing was performed through the MATLAB programming environment and the EEGLAB graphic user interface. The EEG signals serve as an input to the Independent Component Analysis (ICA) algorithm. The ICA algorithm computes the source estimations that are hypothesized to produce the input EEG signals. g. eye blinks, muscle artifacts, heart modulation) are recognized and rejected. After the removal of the noisy source components, the whole EEG data were visually inspected by two experts in order to identify data segments that are temporally contaminated with noise.
In  it is propose a supervised variational method that incorporates appearance priors to better disambiguate the tumor from the surrounding deformed brain tissue. Yet, these appearance priors are not suitable for all patients. Nan Zhang et. al  integrate Support Vector Machine classification with a selection of the features in a kernel space to learn the tumor profile from the first MRI examination. Then, they proceed to refine the tumor boundaries using a region growing technique to follow up the brain tumor evolution.
The Mahalanobis Distance (MD) classifier was employed . It is a simple yet robust distance based classifier, which is defined by the following formula: (3) Let assume that we have N features that would be employed for the classification scheme. The average value (centroid) of each feature forms the N-dimensional μ vector. S denotes the covariance matrix and the x variable denotes each instance (one value from each feature corresponding to a single participant) as defined in (4): (4) 3 Results The huge amount of the features extracted from both the rhythmic activity computation and the synchronization evaluation were submitted to statistical analysis through Student t-test.