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PubMed2001

Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems.

PURPOSE: A genetic neural network (GNN) model was developed to predict the phase behavior of microemulsion (ME), lamellar liquid crystal (LC), and coarse emulsion forming systems (W/O EM and O/W EM) depending on the content of separate components in the system and cosurfactant nature. METHOD: Eight pseudoternary phase triangles, containing ethyl oleate as the oil component and a mixture of two nonionic surfactants and n-alcohol or 1,2-alkanediol as a cosurfactant, were constructed and used for training, testing, and validation purposes. A total of 21 molecular descriptors were calculated for each cosurfactant. A genetic algorithm was used to select important molecular descriptors, and a supervised artificial neural network with two hidden layers was used to correlate selected descriptors and the weight ratio of components in the system with the observed phase behavior. RESULTS: The results proved the dominant role of the chemical composition, hydrophile-lipophile balance, length of hydrocarbon chain, molecular volume, and hydrocarbon volume of cosurfactant. The best GNN model, with 14 inputs and two hidden layers with 14 and 9 neurons, predicted the phase behavior for a new set of cosurfactants with 82.2% accuracy for ME, 87.5% for LC, 83.3% for the O/W EM, and 91.5% for the W/O EM region. CONCLUSIONS: This type of methodology can be applied in the evaluation of the cosurfactants for pharmaceutical formulations to minimize experimental effort.

Algorithms↗
PubMed2005

The comparison of different feed forward neural network architectures for ECG signal diagnosis.

The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. A number of neural network architectures are designed and compared for their ability to classify six different heart conditions. Two network architectures based on one stage and two stage feed forward neural networks are chosen for this investigation. The training and testing ECG signals are obtained from MIT-BIH database. The network inputs are comprised of 12 ECG features and 13 compressed components of each heart beat signal. The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NET_BST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.

Algorithms↗
PubMed2004

Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression.

The blood flow hemodynamics of carotid arteries were obtained from carotid arteries of 168 individuals with diabetes using the 7.5 MHz ultrasound Doppler M-unit. Fast Fourier Transform (FFT) methods were used for feature extraction from the Doppler signals on the time-frequency domain. The parameters, obtained from the Doppler sonograms, were applied to the mathematical models that were constituted to analyze the effect of diabetes on internal carotid artery (ICA) stenosis. In this study, two different mathematical models such as the traditional statistical method based on logistic regression and a Multi-Layer Perceptron (MLP) neural network were used to classify the Doppler parameters. The correct classification of these data was performed by an expert radiologist using angiograpy before they were executed by logistic regression and MLP neural networks. We classified the carotid artery stenosis into two categories such as non-stenosis and stenosis and we achieved similar results (correctly classified (CC) = 92.8%) in both mathematical models. But, as the degree of stenosis had been increased to 4 (0-39%, 40-59%, 60-79% and 80-99% diameter stenosis), it was found that the neural network (CC = 73.9%) became more efficient than the logistic regression analysis (CC = 67.7%). These outcomes indicate that the Doppler sonograms taken from the carotid arteries may be classified successfully by neural network.

Carotid Stenosis↗
PubMed2006

On global-local artificial neural networks for function approximation.

We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate identification of aspects of a relationship that are expressed universally from those that vary only within particular regions of the input space. We test the effectiveness of our method using five regression tasks; four use synthetic datasets while the last problem uses real-world data on the wave overtopping of seawalls. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower mean square errors are often achievable using fewer hidden neurons and with less need for regularization. Our global-local artificial neural network (GL-ANN) is also seen to compare favorably with both perceptron radial basis net and regression tree derived RBFs. A number of issues concerning the training of GL-ANNs are discussed: the use of regularization, the inclusion of a gradient descent optimization step, the choice of RBF spreads, model selection, and the development of appropriate stopping criteria.

Algorithms↗
PubMed2004

The processing of verbs and nouns in neural networks: insights from synthetic brain imaging.

The paper presents a computational model of language in which linguistic abilities evolve in organisms that interact with an environment. Each individual's behavior is controlled by a neural network and we study the consequences in the network's internal functional organization of learning to process different classes of words. Agents are selected for reproduction according to their ability to manipulate objects and to understand nouns (objects' names) and verbs (manipulation tasks). The weights of the agents' neural networks are evolved using a genetic algorithm. Synthetic brain imaging techniques are then used to examine the functional organization of the neural networks. Results show that nouns produce more integrated neural activity in the sensory-processing hidden layer, while verbs produce more integrated synaptic activity in the layer where sensory information is integrated with proprioceptive input. Such findings are qualitatively compared with human brain imaging data that indicate that nouns activate more the posterior areas of the brain related to sensory and associative processing, while verbs activate more the anterior motor areas.

Algorithms↗
PubMed2003

Estimating respiratory system compliance during mechanical ventilation using artificial neural networks.

UNLABELLED: In this study we evaluated whether a technology based on artificial neural networks (ANN) could estimate the static compliance (C(RS)) of the respiratory system, even in the absence of an end-inspiratory pause, during continuous mechanical ventilation. A porcine model of acute lung injury was used to provide recordings of different respiratory mechanics conditions. Each recording consisted of 10 or more consecutive breaths in volume-controlled mechanical ventilation, followed by a breath having an end-inspiratory pause used to calculate C(RS) according to the interrupter technique (IT). The volume-pressure loop of the breath immediately preceding the one with pause was given to the ANN for the training, together with the C(RS) separately calculated by the IT. The prospective phase consisted of giving only the loops to the trained ANN and comparing the results yielded by it to the compliance separately calculated by the investigators. Determination of measurement agreement between ANN and IT methods showed an error of -0.67 +/- 1.52 mL/cm H(2)O (bias +/- SD). We could conclude that ANN, during volume-controlled mechanical ventilation, can extract C(RS) without needing to stop inspiratory flow. IMPLICATIONS: We studied the application of artificial neural networks (ANN) to the estimation of respiratory compliance during mechanical ventilation. The study was performed on an animal model of acute lung injury, testing the performance of ANN in both healthy and diseased conditions of the lung.

Air Pressure↗
PubMed1997

A neural network based artificial vision system for licence plate recognition.

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

Algorithms↗
PubMed1998

[Leukocyte differentiation by MICRO21 system using neural networking as a type of artificial intelligence].

NeuralVision is a type of artificial intelligence that has the ability to identify specific microscopic objects. The MICRO21 system uses neural networking to analyze the features of blood cells; color, shape, size, and density, just as a technologist would. The basic nucleated blood cell processing procedure of the MICRO21 is divided into three general categories; location, segmentation, and classification. Classification takes place using the Neural Network and is fine tuned during post-processing. We evaluated the performance of the MICRO21 automated differential vs. manual differential method. The intra-assay of leukocyte differentiation by MICRO21 was fairly good in normal samples with normal leukocyte counts. The neutrophils, lymphocytes and eosinophils count correlated well between MICRO21 and visual count. Post-processing was performed after the Neural Network has initially classified the cell, in which the confidence levels of MICRO21 to visual count was 87%. By post-processing, coincidence ratio of cell-by-cell by MICRO21 and visual count has been improved to 97%. Based on the above, MICRO21 is an effective and efficient system for locating, storing and displaying images of white blood cells, in which the technologist could determine specific characteristics of cells in each laboratory. Furthermore, the system may be applied to any blood cell and would be important from the perspective of cytological and morphological analysis, such as blood cells in bone marrow and body fluid, in the future.

Artificial Intelligence↗
PubMed1993

Neural network in the clinical diagnosis of acute pulmonary embolism.

The purpose of this investigation was to test the hypothesis that computer-based pattern recognition can accurately assess the likelihood of acute pulmonary embolism (PE) based on readily obtainable clinical characteristics. Data were obtained from 1,213 patients who participated in the collaborative study of the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED). Characteristics of the history, physical examination, electrocardiograph, chest radiograph, and arterial blood gases of patients with suspected acute PE were presented to a back propagation neural network. The 1,213 patients were divided into training set A (n = 606) and test set B (n = 607). These groups were then reversed into training set B (n = 607) and test set A (n = 606). A receiver operating characteristic (ROC) curve was constructed from PIOPED clinical assessment, and from neural network clinical assessment in groups A and B. Areas under the respective ROC curves were 0.7450, 0.7477, and 0.7324. All differences were not significant. Areas under ROC curves for PIOPED clinical assessment combined with ventilation/perfusion (V/Q) scan results were compared with neural network clinical assessment combined with V/Q scan results in groups A and B. The respective ROC areas were 0.8324, 0.8203, 0.8496 (all differences not significant). These data show that neural networks were able to predict the clinical likelihood of PE with an accuracy comparable to experienced clinicians.

Acute Disease↗
PubMed2006

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

Climate↗
PubMed1998

Artificial neural networks for drug vulnerability recognition and dynamic scenarios simulation.

Semeion researchers have developed and used different kinds of Artificial Neural Networks (ANN) in order to process selected, "standard" data coming from drug users and from people who never used drugs before. In the first step a collection of 112 general variables, not traditionally connected to drug user's behavior, were collected from a sample of 545 people (223 heroin addicted and 322 non-users). Different types of ANNs were used to test the capability of the system to classify the drug users and the non-drug users correctly. A special ANN tool, created by Semeion, was also used to prune the number of the independent variables. The ANN selected for this first experiment was a Supervised Feed Forward Network, whose equations were enhanced by Semeion researchers. For the validation of the capability of generalization of the ANN, the Training-Testing protocol was used. This ANN was able, in the Testing phase, to classify approximately 95% of the sample with accuracy. A special sensitivity tool selected only 47 among the 112 independent variables as necessary to train the ANN. In the second step, different types of ANN were tested on the new 47 variables to decide which kind of ANN was better able to classify the sample. This benchmark included the following ANNs: a) Back Propagation with Soft Max; b) Learning Vector Quantization; c) Logicon Projection; d) Radial Basis Function; e) Squash (Semeion Network); f) Fuzzy Art Map; g) Modular Neural Network. In the third step a Constraint Satisfaction Network, specifically created by Semeion, was used to simulate a dynamic fuzzy map of the drug user's world; that is, which fuzzy, or approximate, variables are critical to decide the fuzzy membership of a subject from the fuzzy membership of the drug users to the fuzzy membership of non-users and vice versa.

Artificial Intelligence↗
PubMed2003

Functional nodes in dynamic neural networks for bioprocess modelling.

This contribution presents a novel method for the direct integration of a-priori knowledge in a neural network and its application for the online determination of a secondary metabolite during industrial yeast fermentation. Hereby, existing system knowledge is integrated in an artificial neural network (ANN) by means of 'functional nodes'. A generalized backpropagation algorithm is presented. For illustration, a set of ordinary differential equations describing the diacetyl formation and degradation during the cultivation is incorporated in a functional node and integrated in a dynamic feedforward neural network in a hybrid manner. The results show that a hybrid modelling approach exploiting available a-priori knowledge and experimental data can considerably outperform a pure data-based modelling approach with respect to robustness, generalization and necessary amount of training data. The number of training sets were decreased by 50%, obtaining the same accuracy as in a conventional approach. All incorrect decisions, according to defined cost criteria obtained with the conventional ANN, were avoided.

Journal Article↗
PubMed2006

Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach.

There is much interest in rule extraction from neural networks and a plethora of different methods have been proposed for this purpose. We discuss the merits of pedagogical and decompositional approaches to rule extraction from trained neural networks, and show that some currently used methods for binary data comply with a theoretical formalism for extraction of Boolean rules from continuously valued logic. This formalism is extended into a generic methodology for rule extraction from smooth decision surfaces fitted to discrete or quantized continuous variables independently of the analytical structure of the underlying model, and in a manner that is efficient even for high input dimensions. This methodology is then tested with Monks' data, for which exact rules are obtained and to Wisconsin's breast cancer data, where a small number of high-order rules are identified whose discriminatory performance can be directly visualized.

Algorithms↗
PubMed1997

Neural network and linear regression models in residency selection.

For many years, multiple linear regression models have been used at a residency program to generate preliminary rank lists of residency applicants. These lists are then used by the admissions committee as an aid in developing a final ranking to submit to the National Residency Match Program (NRMP). A study was undertaken to compare predictions made using linear regression with those generated by a newer technique, an artificial neural network. A prospective cohort design was used. Seventy-four applicants to an emergency medicine program were evaluated by faculty and resident interviewers with regard to medical school grades, autobiography, interviews, letters of recommendation, and National Board scores. Normalization of these scores (by linear transformation of interviewer means) was used to correct for differences among interviewers. Multivariate linear regression and neural network models were developed using data from the previous 5 years' applicants. These models were used to forecast provisional rank orderings of the candidates. These rankings were combined into a single hybrid list that was used by the admissions committee as the starting point for development of the final rank list by consensus. Each model's predictions were tested for goodness of fit against the final NRMP rank using Wilks' test. Using the final submitted NRMP rank order as the dependent variable, the neural network yielded a correlation coefficient of 0.77 and an R2 of 59.4%. The linear regression model exhibited a correlation coefficient of 0.74 and an R2 of 54.0%. No significant difference was found (chi 2 = 1.08, P = .7). A neural network performs as well as a linear regression model when used for forecasting the rank order of residency applicants.

Cohort Studies↗
PubMed2006

Neural networks for longitudinal studies in Alzheimer's disease.

OBJECTIVE: Alzheimer's disease affects a growing population of elderly people today. The predictions about the course of the disease is a key component of health care decision making for patients with Alzheimer's. The physician's prognosis and predicted trajectory of cognitive decline often form the basis of treatment and health care decisions taken by patients and their families. These predictions are difficult to make because of the high variability and non-linearity exhibited by individual patterns of cognitive decline. This paper presents a new method of predicting the course of a disease using longitudinal data collected through multiple clinic visits. Longitudinal databases are similar to temporal databases, with some important differences--data is collected at irregular time intervals that are patient specific and also a varying number of observations are made for each patient, depending upon the number of times the patient visited the clinic. We propose a new type of neural network called the mixed effects neural network (MENN) model that can incorporate this type of longitudinal information. MATERIAL AND METHODS: We have used longitudinal data on 704 subjects enrolled at the Layton aging and research center (LAARC) at Oregon Health and Science University. A back-propagation algorithm, modified for longitudinal data is used to obtain the weight parameters of the MENN. The modified back-propagation algorithm is further embedded in an iterative procedure that estimates the noise variance and the parameters that capture the longitudinal (temporal) correlation structure. RESULTS: We have compared the performance of the MENN with linear mixed effects models and standard neural networks (NN). MENN show better performance (misclassification rate = 0.13 and relative MSE = 0.35) as compared to standard NN (misclassification rate = 0.34 and relative MSE = 2.74) and linear mixed effects models (misclassification rate = 0.14 and relative MSE = 0.4). CONCLUSION: The results show that this method can be a useful tool for predicting non-linear disease trajectories and uncovering significant prognostic factors in longitudinal databases.

Aged↗
PubMed2000

Design and testing of a genetic algorithm neural network in the assessment of gait patterns.

It is important to be able to quantify changes in gait pattern accurately in order to understand the clinical implications of surgery or rehabilitation. Although supervised feed-forward backpropagation neural networks are very efficient in many pattern-recognition tasks, the genetic algorithm neural network (GANN), which can search in some appropriate space, has not been used previously for gait-pattern recognition. This study discusses how to use the GANN approach in gait-pattern recognition, and evaluates the complexity and training strategy of the particular classification problem. Both the GANN and a traditional artificial neural network (ANN) were used to classify the gait patterns of patients with ankle arthrodesis and normal subjects. The GANN model was able to classify subjects with recognition rates of up to 98.7%. In contrast, the ANN trained by using all possible predictor variables was only able to classify the subjects with recognition rates of 89.7%. It is suggested that the GANN model is more suitable to exploit the patient's gait pattern. The value of the neuron output can be used as an index of the difference from normal. By this means, all pathological gait patterns may be presented quantitatively.

Algorithms↗
PubMed1992

A study of the formation of the neural network in the lungs of Perdicula asiatica, as revealed by the cholinesterase technique.

The investigation was undertaken to investigate the formation and association of neural network and their fibres with nerve cells and ganglia in the parenchyma. The staining of the neural network was done by the cholinesterase technique under the maintained pH 5.2, temperature 40 degrees C and incubation period 30 hrs. The neural network was formed by a good number of tortuous thick nerves and was related either with nerve cells or ganglia. At times, it was formed by a good number of thick nerves and a few thin nerves in the connective tissue space.

Acetylcholinesterase↗
PubMed2002

NETASA: neural network based prediction of solvent accessibility.

MOTIVATION: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction. RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed. AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.

Algorithms↗