Search results for " neural network"
showing 10 items of 1232 documents
Contributions Regarding the Utilization of Neural Networks in SME's Management
2014
Due to the fact that there isnt a clear definition of the terms neural network" and "neuronal network" [1,2], the current paper aims to establish it by a range of comparative research. With the help of some charts, based on the structure of some SMEs (Small and Medium Enterprises), the parts that define the structure of the neuron will be compared with the general structure of an organization, in order to reproduce the neuron in the structuring level of an organization and give a meaning to the term of "organizational neuron. Sometimes it is necessary to take managerial decisions under uncertainty and / or risk, so any method that gives forecasting information to the manager is welcome [3,4…
Employing artificial neural networks to find reaction coordinates and pathways for self-assembly
2021
Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically require to construct accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder architectures based on unsupervised neural networks can be employed to reliably expose transition pathways and to provide a suitable low-dimensional representation. The assembly occurs as a two-step process throug…
An In-Depth Experimental Comparison of RNTNs and CNNs for Sentence Modeling
2017
The goal of modeling sentences is to accurately represent their meaning for different tasks. A variety of deep learning architectures have been proposed to model sentences, however, little is known about their comparative performance on a common ground, across a variety of datasets, and on the same level of optimization. In this paper, we provide such a novel comparison for two popular architectures, Recursive Neural Tensor Networks (RNTNs) and Convolutional Neural Networks (CNNs). Although RNTNs have been shown to work well in many cases, they require intensive manual labeling due to the vanishing gradient problem. To enable an extensive comparison of the two architectures, this paper empl…
Tunable multifunctional topological insulators in ternary Heusler compounds
2010
Recently the Quantum Spin Hall effect (QSH) was theoretically predicted and experimentally realized in a quantum wells based on binary semiconductor HgTe[1-3]. QSH state and topological insulators are the new states of quantum matter interesting both for fundamental condensed matter physics and material science[1-11]. Many of Heusler compounds with C1b structure are ternary semiconductors which are structurally and electronically related to the binary semiconductors. The diversity of Heusler materials opens wide possibilities for tuning the band gap and setting the desired band inversion by choosing compounds with appropriate hybridization strength (by lattice parameter) and the magnitude o…
Electromagnetic behaviour of superconductive amorphous metals
2005
The penetration depth of the magnetic field into an amorphous superconductor is calculated. The ratio of the London penetration depth δL to the electron free path le under zero temperature is above unity for almost all amorphous metals. That is why pure metals, in a superconducting state, change from type I superconductors to type II superconductors during the crystalline–amorphous transition.
Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia
2020
Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are a…
Prediction of banana quality indices from color features using support vector regression
2015
Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acid…
Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity
2020
Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…
Machine Learning Methods for One-Session Ahead Prediction of Accesses to Page Categories
2004
This paper presents a comparison among several well-known machine learning techniques when they are used to carry out a one-session ahead prediction of page categories. We use records belonging to 18 different categories accessed by users on the citizen web portal Infoville XXI. Our first approach is focused on predicting the frequency of accesses (normalized to the unity) corresponding to the user’s next session. We have utilized Associative Memories (AMs), Classification and Regression Trees (CARTs), Multilayer Perceptrons (MLPs), and Support Vector Machines (SVMs). The Success Ratio (SR) averaged over all services is higher than 80% using any of these techniques. Nevertheless, given the …
An AI Walk from Pharmacokinetics to Marketing
2009
This work is intended for providing a review of reallife practical applications of Artificial Intelligence (AI) methods. We focus on the use of Machine Learning (ML) methods applied to rather real problems than synthetic problems with standard and controlled environment. In particular, we will describe the following problems in next sections: • Optimization of Erythropoietin (EPO) dosages in anaemic patients undergoing Chronic Renal Failure (CRF). • Optimization of a recommender system for citizen web portal users. • Optimization of a marketing campaign. The choice of these problems is due to their relevance and their heterogeneity. This heterogeneity shows the capabilities and versatility …