Search results for "ML"
showing 10 items of 1465 documents
Langage et Apprentissage en Interaction pour des Assistants Numériques Autonomes - Une Approche Développementale
2021
The rapid development of digital assistants (DA) opens the way to new modes of interaction. Some DA allows users to personalise the way they respond to queries, in particular by teaching them new procedures. This work proposes to use machine learning methods to enrich the linguistic and procedural generalisation capabilities of these systems. The challenge is to reconcile rapid learning skills, necessary for a smooth user experience, with a sufficiently large generalisation capacity. Though this is a natural human ability, it remains out-of-reach for artificial systems and this leads us to approach these issues from the perspective of developmental Artificial Intelligence. This work is thus…
Subglacial bed deformation and dynamics of the Apriķi glacial tongue, W Latvia
2011
Saks, T., Kalvans, A. & Zelcs, V. 2012 (January): Subglacial bed deformation and dynamics of the Apriķi glacial tongue, W Latvia. Boreas, Vol. 41, pp. 124–140. 10.1111/j.1502-3885.2011.00222.x. ISSN 0300-9483. We evaluate the glacial dynamics and subglacial processes of the Apriķi glacial tongue in western Latvia during the Northern Lithuanian (Linkuva) oscillation of the last Scandinavian glaciation. The spatial arrangement of glacial bedforms and deformation structures are used to reconstruct the ice dynamics in the study area. The relationship between geological structures at the glacier bed and the spatial distribution of drumlins and glacigenic diapirs, on the one hand, and the permeab…
An offline/real-time artifact rejection strategy to improve the classification of multi-channel evoked potentials
2008
The primary goal of this paper is to improve the classification of multi-channel evoked potentials (EPs) by introducing a temporal domain artifact detection strategy and using this strategy to (a) evaluate how the performance of classifiers is affected by artifacts and (b) show how the performance can be improved by detecting and rejecting artifacts in offline and real-time classification experiments. Using a pattern recognition approach, an artifact is defined in this study as any signal that may lead to inaccurate classifier parameter estimation and inaccurate testing. The temporal domain artifact detection tests include: a within-channel standard deviation (STD) test that can detect sign…
Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation
2019
Abstract Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect each type of attack separately. Finally, the results of all ELMs are combined using a softmax layer to refine the results and increase the accuracy further. The intuition behind our system is that multi-class classification is quite difficult compared to binary classification. So, we…
Speech Emotion Recognition method using time-stretching in the Preprocessing Phase and Artificial Neural Network Classifiers
2020
Human emotions are playing a significant role in the understanding of human behaviour. There are multiple ways of recognizing human emotions, and one of them is through human speech. This paper aims to present an approach for designing a Speech Emotion Recognition (SER) system for an industrial training station. While assembling a product, the end user emotions can be monitored and used as a parameter for adapting the training station. The proposed method is using a phase vocoder for time-stretching and an Artificial Neural Network (ANN) for classification of five typical different emotions. As input for the ANN classifier, features like Mel Frequency Cepstral Coefficients (MFCCs), short-te…
Integrating genomic binding site predictions using real-valued meta classifiers
2008
Currently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets, support vector machines and the Adaboost algorithm to predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results as the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over-sampling techniques. We find that support vector machines …
The use of artificial intelligence techniques to optimise and control injection moulding processes
1999
In the paper a typical injection moulding process on a single-screw extrusion machine aimed to the production of axisymmetric polypropylene dishes for alimentary use has been investigated. First of all the most important process parameters have been individuated; subsequently a wide testing hyperspace has been investigated, at varying the process parameters in a large range. For each combination both some geometrical characteristics of the obtained component have been measured and the occurrence of defects has been verified. The largest part of the available data have been used to train a neural network aimed to explain the process dynamics. Furthermore an offline control system, based on f…
Cloud screening with combined MERIS and AATSR images
2009
This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more ac…
Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components
2021
This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real…
Ventricular fibrillation detection from ECG surface electrodes using different filtering techniques, window length and artificial neural networks
2017
Medical personnel face many difficulties when diagnosing ventricular fibrillation (VF). Its correct diagnosis allows to decide the right medical treatment and, therefore, it is essential to tell it apart adequately from ventricular tachycardia (VT) and other arrhythmias. If the required therapy is not appropriate, the personnel could cause serious injuries or even induce VF. In this work, a diagnosis automatic system for the detection of VF through feature extraction was developed. To verify the validity of this method, an Artificial Neural Network (ANN) classifier was used. The ECG signals used were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database and AHA (2000 series) d…