Search results for "Neural"
showing 10 items of 2783 documents
The Asian Taenia and the possibility of cysticercosis
2000
In certain Asian countries, a third form of human Taenia, also known as the Asian Taenia, has been discovered. This Asian Taenia seems to be an intermediate between Taenia solium and T. saginata since in morphological terms it is similar to T. saginata, yet biologically, as it uses the same intermediate host (pigs), it is more akin to T. solium. Taenia solium causes human cysticercosis, while T. saginata does not. It is not known whether the Asian taeniid is able to develop to the larval stage in humans or not. The arguments proposed by those authors who consider it unlikely that the Asian Taenia causes human cysticercosis are: (a) its molecular similarities with T. saginata; (b) the absenc…
Integrated System for Monitoring the Tool State Using Temperature Measuring by Natural Thermocouple Method
2014
The intensive developments of intelligent manufacturing systems in the last decades open the large possibilities of more accurate monitoring of the metal cutting process. One of the most important factors of the process is the tool state given by the rate of the tool wear, which is the result of a lot of influences of almost all cutting parameters. The modern tool monitoring systems relieved that the accuracy of the results increases when using a combination of surveyed signals such as: vibrations, power consumption, acoustic emission, forces or tool temperature. Combining the output signals in a monitoring function using the neural network method gives the best results when using on-line m…
Neural network-based models for a vibration suppression system equipped with MR brake
2012
This paper is devoted to the modeling and simulation of a full-scale commercially available magnetorheological (MR) brake installed in a semi-active suspension (SAS) system. The analysis of the Bouc-Wen and Dahl mathematical models of MR damper is presented. Influence of their parameters on the response is explored. Subsequently, by using the neural networks, the parameters characterizing each model are estimated. This makes it possible to perform the comparative analysis of the suggested damper models responses with the measured experimental results. The novelty of the presented methodology is the application of artificial intelligence methods to estimate model parameters of a MR brake uti…
Efficacy and tolerability of a fixed dose combination of cortex phospholipid liposomes and cyanocobalamin for intramuscular use in peripheral neuropa…
2019
Peripheral neuropathies are frequently encourtered in clinical practice and are assiociated with a major impairment in quality of life . Howewvwr, their management reamans poor, and current therapies are often burdened with major side effects and can present poor efficacy on pain and functionality. Therefore, it has been suggested that the combination of two or more different drugs may improve analgesis efficacy and reduce side effects. Tricortin 1000 is formulated with 12 mg of Brain cortex phospholipid liposomes + 1000 microgrammi of Cyanocobalamin injectable (PL+ CNCb1) for intramuscolar use and is indicated in the tratment of poly-algo-neuropathic syndromes. This combination exerts a ma…
A Comparative Study of Nonlinear Machine Learning for the "In Silico" Depiction of Tyrosinase Inhibitory Activity from Molecular Structure.
2011
In the preset report, for the first time, support vector machine (SVM), artificial neural network (ANN), Baye- sian networks (BNs), k-nearest neighbor (k-NN) are applied and compared on two "in-house" datasets to describe the tyrosinase inhibitory activity from the molecular structure. The data set Data I is used for the identification of tyrosi- nase inhibitors (TIs) including 701 active and 728 inactive compounds. Data II consists of active chemicals for potency estimation of TIs. The 2D TOMOCOMD-CARDD atom-based quadratic indices are used as molecular descriptors. The de- rived models show rather encouraging results with the areas under the Receiver Operating Characteristic (AURC) curve …
Modeling anti-allergic natural compounds by molecular topology.
2013
Molecular topology has been applied to the search of QSAR models able to identify the anti-allergic activity of a wide group of heterogeneous compounds. Through the linear discriminant analysis and artificial neural networks, correct classification percentages above 85% for both the training set and the test set have been obtained. After carrying out a virtual screening with a natural product library, about thirty compounds with theoretical anti-allergic activity have been selected. Among them, hesperidin, naringin, salinomycin, sorbitol, curcumol, myricitrin, diosmin and kinetin stand out. Some of these compounds have already been referenced as having anti-allergic activity.
Channel Capacity in Psychovisual Deep-Nets: Gaussianization Versus Kozachenko-Leonenko
2020
In this work, we quantify how neural networks designed from biology using no statistical training have a remarkable performance in information theoretic terms. Specifically, we address the question of the amount of information that can be extracted about the images from the different layers of psychophysically tuned deep networks. We show that analytical approaches are not possible, and we propose the use of two empirical estimators of capacity: the classical Kozachenko-Lonenko estimator and a recent estimator based on Gaussianization. Results show that networks purely based on visual psychophysics are extremely efficient in two aspects: (1) the internal representation of these networks dup…
Heart Failure Occurrence: Mining Significant Patterns and 10 Days Early Prediction
2021
Electronic health records containing patient’s medical history, drug prescription, vital signs measurements, and many more parameters, are being frequently extracted and stored as unused raw data. On the other hand, machine learning and data mining techniques are becoming popular in the medical field, providing the ability to extract knowledge and valuable information from electronic health records along with accurately predicting future disease occurrence. This chapter presents a study on medical data containing vital signs recorded over the course of some years, for real patients suffering from heart failure. The first significant patterns that come along with heart failure occurrence are…
Experimental studies on continuous speech recognition using neural architectures with “adaptive” hidden activation functions
2010
The choice of hidden non-linearity in a feed-forward multi-layer perceptron (MLP) architecture is crucial to obtain good generalization capability and better performance. Nonetheless, little attention has been paid to this aspect in the ASR field. In this work, we present some initial, yet promising, studies toward improving ASR performance by adopting hidden activation functions that can be automatically learned from the data and change shape during training. This adaptive capability is achieved through the use of orthonormal Hermite polynomials. The “adaptive” MLP is used in two neural architectures that generate phone posterior estimates, namely, a standalone configuration and a hierarch…
Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography
2019
Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal d…