Search results for " classification"
showing 10 items of 1043 documents
Acoustic Scene Classification with Squeeze-Excitation Residual Networks
2020
Acoustic scene classification (ASC) is a problem related to the field of machine listening whose objective is to classify/tag an audio clip in a predefined label describing a scene location (e. g. park, airport, etc.). Many state-of-the-art solutions to ASC incorporate data augmentation techniques and model ensembles. However, considerable improvements can also be achieved only by modifying the architecture of convolutional neural networks (CNNs). In this work we propose two novel squeeze-excitation blocks to improve the accuracy of a CNN-based ASC framework based on residual learning. The main idea of squeeze-excitation blocks is to learn spatial and channel-wise feature maps independently…
La Comprensión Asíncrona de las Emociones Básicas: un Estudio Longitudinal con Niños de 3 a 5 Años
2021
El presente estudio tiene como finalidad explorar la trayectoria evolutiva de la comprensión de cuatro emociones en niños de tres a cinco años. Sabemos que los niños identifican las expresiones faciales y después entienden la causa de las emociones, pero ¿las emociones se comprenden a la vez, en el mismo momento evolutivo? Para llevar a cabo este estudio se evaluó de forma longitudinal a un grupo de 103 niños y niñas entre los 3 y los 5 años. A través del Test de Comprensión Emocional se midieron los componentes de identificación de la expresión emocional y el conocimiento de la causa de cuatro emociones –tristeza, alegría, enfado y miedo– a lo largo de los tres años. Los resultados confirm…
Improved FMECA for effective risk management decision making by failure modes classification under uncertainty
2022
Failure Mode, Effects, and Criticality Analysis (FMECA) is a proactive reliability and risk management technique extensively used in practice to ensure high system performance by prioritising failure modes. Owing to the limitations of traditional FMECA, multi-criteria decision-making methods have been employed over the past two decades to enhance its effectiveness. To consider the vagueness and uncertainty of the FMECA evaluation process, an interval-based extension of the Elimination et Choice Translating Reality (ELECTRE) TRI method is proposed in the present paper for the classification of failure modes into risk categories. Therefore, ratings of failure modes against risk parameters are…
Classification of Heart Sounds Using Convolutional Neural Network
2020
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…
Combining feature extraction and expansion to improve classification based similarity learning
2017
Abstract Metric learning has been shown to outperform standard classification based similarity learning in a number of different contexts. In this paper, we show that the performance of classification similarity learning strongly depends on the data format used to learn the model. We then present an Enriched Classification Similarity Learning method that follows a hybrid approach that combines both feature extraction and feature expansion. In particular, we propose a data transformation and the use of a set of standard distances to supplement the information provided by the feature vectors of the training samples. The method is compared to state-of-the-art feature extraction and metric lear…
A multi-layer method to study genome-scale positions of nucleosomes
2009
AbstractThe basic unit of eukaryotic chromatin is the nucleosome, consisting of about 150 bp of DNA wrapped around a protein core made of histone proteins. Nucleosomes position is modulated in vivo to regulate fundamental nuclear processes. To measure nucleosome positions on a genomic scale both theoretical and experimental approaches have been recently reported. We have developed a new method, Multi-Layer Model (MLM), for the analysis of nucleosome position data obtained with microarray-based approach. The MLM is a feature extraction method in which the input data is processed by a classifier to distinguish between several kinds of patterns. We applied our method to simulated-synthetic and…
Feedback Classification and Optimal Control with Applications to the Controlled Lotka-Volterra Model
2023
Let M be a σ-compact C^∞ manifold of dimension n ≥ 2 and consider a single-input control system: ẋ(t) = X (x(t)) + u(t) Y (x(t)), where X , Y are C^∞ vector fields on M. We prove that there exist an open set of pairs (X , Y ) for the C^∞ –Whitney topology such that they admit singular abnormal rays so that the spectrum of the projective singular Hamiltonian dynamics is feedback invariant. It is applied to controlled Lotka–Volterra dynamics where such rays are related to shifted equilibria of the free dynamics.
Soil Moisture Effect on Thermal Infrared (8–13-μm) Emissivity
2010
Thermal infrared (TIR) emissivities of soils with different textures were measured for several soil moisture (SM) contents under controlled conditions using the Box method and a high-precision multichannel TIR radiometer. The results showed a common increase of emissivity with SM at water contents lower than the field capacity. However, this dependence is negligible for higher water contents. The highest emissivity variations were observed in sandy soils, particularly in the 8-9-μm range due to water adhering to soil grains and decreasing the reflectance in the 8-9-μm quartz doublet region. Thus, in order to model the emissivity dependence on soil water content, different approaches were st…
Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
2020
The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep lea…
Resource-efficient hardware implementation of a neural-based node for automatic fingerprint classification
2017
Modern mobile communication networks and Internet of Things are paving the way to ubiquitous and mobile computing. On the other hand, several new computing paradigms, such as edge computing, demand for high computational capabilities on specific network nodes. Ubiquitous environments require a large number of distributed user identification nodes enabling a secure platform for resources, services and information management. Biometric systems represent a useful option to the typical identification systems. An accurate automatic fingerprint classification module provides a valuable indexing scheme that allows for effective matching in large fingerprint databases. In this work, an efficient em…