Search results for "NEURAL NETWORK"

2020

Abstract A person-centered approach was used to identify the profiles of symptoms of psychological ill-being among Finnish upper secondary education students (N = 2889); to examine whether gender and educational track (i.e., academic or vocational) are associated with these profiles; and to investigate the role of profiles in school dropout intentions. Using latent profile analysis, one asymptomatic profile (normative, 79.2%) and three symptomatic profiles (internalizing symptoms, 9.1%; externalizing symptoms, 9.1%; and comorbid symptoms, 2.6%) were identified. Boys in the vocational track were overrepresented in the externalizing-symptoms profile, whereas girls in both tracks were overrepr…

Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging

2020

Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we p…

Action Recognition based on Hierarchical Self-Organizing Maps

2014

We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and thus it learns to represent action prototypes independent of how long the activity trajectories last. The third layer of the hierarchy consists of a neural network that le…

Dynamic anomalies at the glass transition of organic van der Waals liquids

1993

Abstract The paper discusses the question of whether there is a characteristic temperature T c above the calorimetric glass transition temperature T g . Mode-coupling theory (MCT) predicts a crossover from liquid- to solid-like dynamics at T c . Neutron scattering and gradient NMR experiments have been carried out to test MCT using the molecular van der Waals liquid ortho -terphenyl as a model system. A significant anomaly of the Debye—Waller factor and a “decoupling” of self-diffusion from viscosity support the MCT predictions. A critical discussion of the relevance of such tests and of the limitations of neutron scattering is presented.

Description of Dynamic Structured Scenes by a SOM/ARSOM Hierarchy

2001

A neural architecture is presented, aimed to describe the dynamic evolution of complex structures inside a video sequence. The proposed system is arranged as a tree of self-organizing maps. Leaf nodes are implemented by ARSOM networks as a way to code dynamic inputs, while classical SOM's are used to implement the upper levels of the hierarchy. Depending on the application domain, inputs are made by suitable low level features extracted frame by frame of the sequence. Theoretical foundations of the architecture are reported along with a detailed outline of its structure, and encouraging experimental results.

Timbre Similarity: Convergence of Neural, Behavioral, and Computational Approaches

1998

The present study compared the degree of similarity of timbre representations as observed with brain recordings, behavioral studies, and computer simulations. To this end, the electrical brain activity of subjects was recorded while they were repetitively presented with five sounds differing in timbre. Subjects read simultaneously so that their attention was not focused on the sounds. The brain activity was quantified in terms of a change-specific mismatch negativity component. Thereafter, the subjects were asked to judge the similarity of all pairs along a five-step scale. A computer simulation was made by first training a Kohonen self-organizing map with a large set of instrumental sounds…

Neural networks for animal science applications: Two case studies

2006

Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…

A New Linear Initialization in SOM for Biomolecular Data

2009

In the past decade, the amount of data in biological field has become larger and larger; Bio-techniques for analysis of biological data have been developed and new tools have been introduced. Several computational methods are based on unsupervised neural network algorithms that are widely used for multiple purposes including clustering and visualization, i.e. the Self Organizing Maps (SOM). Unfortunately, even though this method is unsupervised, the performances in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. In this paper we present a new initialization technique based on a totally connected undirected graph, that report relat…

Recognizing actions with the associative self-organizing map

2013

When artificial agents interact and cooperate with other agents, either human or artificial, they need to recognize others’ actions and infer their hidden intentions from the sole observation of their surface level movements. Indeed, action and intention understanding in humans is believed to facilitate a number of social interactions and is supported by a complex neural substrate (i.e. the mirror neuron system). Implementation of such mechanisms in artificial agents would pave the route to the development of a vast range of advanced cognitive abilities, such as social interaction, adaptation, and learning by imitation, just to name a few. We present a first step towards a fully-fledged int…

A Comparison between Habituation and Conscience mechanism in Self–Organizing Maps

2006

In this letter, a preliminary study of habituation in self-organizing networks is reported. The habituation model implemented allows us to obtain a faster learning process and better clustering performances. The liabituable neuron is a generalization of the typical neuron and can be used in many self-organizing network models. The habituation mechanism is implemented in a SOM and the clustering performances of the network are compared to the conscience learning mechanism that follows roughly the same principle but is less sophisticated.