Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
The Application of Machine Learning Algorithms to the Analysis of Electromyographic Patterns From Arthritic Patients
2009
The main aim of our study was to investigate the possibility of applying machine learning techniques to the analysis of electromyographic patterns (EMG) collected from arthritic patients during gait. The EMG recordings were collected from the lower limbs of patients with arthritis and compared with those of healthy subjects (CO) with no musculoskeletal disorder. The study involved subjects suffering from two forms of arthritis, viz, rheumatoid arthritis (RA) and hip osteoarthritis (OA). The analysis of the data was plagued by two problems which frequently render the analysis of this type of data extremely difficult. One was the small number of human subjects that could be included in the in…
Quantitative comparison of motion history image variants for video-based depression assessment
2017
Abstract Depression is the most prevalent mood disorder and a leading cause of disability worldwide. Automated video-based analyses may afford objective measures to support clinical judgments. In the present paper, categorical depression assessment is addressed by proposing a novel variant of the Motion History Image (MHI) which considers Gabor-inhibited filtered data instead of the original image. Classification results obtained with this method on the AVEC’14 dataset are compared to those derived using (a) an earlier MHI variant, the Landmark Motion History Image (LMHI), and (b) the original MHI. The different motion representations were tested in several combinations of appearance-based …
Experimental study of electrical FitzHugh-Nagumo neurons with modified excitability
2006
International audience; We present an electronical circuit modelling a FitzHugh-Nagumo neuron with a modified excitability. To characterize this basic cell, the bifurcation curves between stability with excitation threshold, bistability and oscillations are investigated. An electrical circuit is then proposed to realize a unidirectional coupling between two cells, mimicking an inter-neuron synaptic coupling. In such a master-slave configuration, we show experimentally how the coupling strength controls the dynamics of the slave neuron, leading to frequency locking, chaotic behavior and synchronization. These phenomena are then studied by phase map analysis. The architecture of a possible ne…
Learning to Navigate in the Gaussian Mixture Surface
2021
In the last years, deep learning models have achieved remarkable generalization capability on computer vision tasks, obtaining excellent results in fine-grained classification problems. Sophisticated approaches based-on discriminative feature learning via patches have been proposed in the literature, boosting the model performances and achieving the state-of-the-art over well-known datasets. Cross-Entropy (CE) loss function is commonly used to enhance the discriminative power of the deep learned features, encouraging the separability between the classes. However, observing the activation map generated by these models in the hidden layer, we realize that many image regions with low discrimin…
Dissimilarity Application for Medical Imaging Classification
2005
In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, alternative ways can be found by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) die training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discriminative power. Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 col…
To Assess the Validity of the Transfer Function Method: A Neural Model for the Optimal Choice of Conduction Transfer Functions
2010
This paper presents a new mathematical approach applied to the Conduction Transfer Functions (CTFs) of a multilayered wall to predict the reliability of building simula- tions based upon them. Such a procedure can be used to develop a decision support system that identifies the best condition to calculate the best CTFs set. This is a critical point at the core of ASHRAE calculation methodology founded on the Transfer Function Method (TFM). To evaluate the perfor- mance of different CTFs sets, the authors built a large amount of data, subsequently employed to train a Neural Network Classifier (NNC) able to predict the reliability of a simulation without performing it. For this purpose all th…
Influence of ANN parameters on the performance of a refined procedure to solve the load-flow problem
1999
In recent years, interest in the application of Artificial Neural Networks (ANN) to electrical power systems has grown rapidly. In particular the use of ANN in the solution of the load-flow problem in wide electrical networks is an interesting research topic, because it constitutes a good alternative to the classical numerical algorithms. In this paper a refined solution strategy based on statistical methods, on a particular Grouping Genetic Algorithm (GGA) and on Progressive Learning Network (PLN) is presented. Tests on the solution of load-flow equations of the standard IEEE 118 bus network confirm the good potential of this approach; in particular the search for optimal values of the PLN…
Embedded neural network system for microorganisms growth analysis
2020
This study presents autonomous system for microorganisms’ growth analysis in laboratory environment. As shown in previous research, laser speckle analysis allows detecting submicron changes of substrate with growing bacteria. By using neural networks for speckle analysis, it is possible to develop autonomous system, that can evaluate microorganisms’ growth by using cheap optics and electronics elements. System includes embedded processing module, CMOS camera, 670nm laser diode and optionally WiFi module for connecting to external image storage system. Due to small size, system could be fully placed in laboratory incubator with constant humidity and temperature. By using laser diode, Petri d…
Deep Neural Networks for Prediction of Exacerbations of Patients with Chronic Obstructive Pulmonary Disease
2018
Chronic Obstructive Pulmonary Disease (COPD) patients need help in daily life situations as they are burdened with frequent risks of acute exacerbation and loss of control. An automated monitoring system could lead to timely treatments and avoid unnecessary hospital (re-)admissions and home visits by doctors or nurses. Therefore we present a Deep Artificial Neural Networks for approach prediction of exacerbations, particularly Feed-Forward Neural Networks (FFNN) for classification of COPD patients category and Long Short-Term Memory (LSTM), for early prediction of COPD exacerbations and subsequent triage. The FFNN and LSTM models are trained on data collected from remote monitoring of 94 pa…
SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images.
2021
High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), designed to map global context features reflecting the rich tumor morphological heterogeneity. SuperHistopath efficiently combines i) a segmentation approach using the linear iterative clustering (SLIC) superpixels algorithm applied directly on the whole-slide images at low resolution (5x magnification) to adhere to region boundaries and form homogeneous spatial units at tissue-level, followed by ii) classification of superpixels using a convolution neural network (CN…