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In this paper, a radial basis neural network (RBFN) for lung cancer screening algorithm is presented. Because of the learning characteristics of the radial basis neural network (RBFN), it has been selected to train the samples, which are the lung cancer examples, and then extracts the internal relations between the pathogenic factors and inducing lung cancer, and eventually it generates empirical...
Classification is a major problem of study that involves formulation of decision boundaries based on the training data samples. The limitations of the single neural network approaches motivate the use of multiple neural networks for solving the problem in the form of ensembles and modular neural networks. While the ensembles solve the problem redundantly, the modular neural networks divide the computation...
This article presents two classifiers based on machine learning methods, aiming to detect physiologic anomalies considering Poincaré plots of heart rate variability. It was developed a preprocessing procedure to encoding the plots, based on the Cellular Features Extraction Method. Simulation of different classifiers, artificial neural networks and support vector machine, has been performed and the...
A tool for discovery of gait anomalies of elderly from motion sensor data is proposed. The gait of the user is captured with the motion capture system, which consists of tags attached to the body and sensors situated in the apartment. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with dynamic time warping and machine learning algorithms...
Feature selection continues to grow in importance in many areas of science and engineering, as large datasets become increasingly common. In particular, bioscience and medical datasets routinely contain several thousands of features. For effective data mining in such datasets, tools are required that can reliably distinguish the most relevant features. The latter is a useful goal in itself (e.g. such...
In traditional Chinese medicine (TCM) field, medical cases are viewed as semi-structured text, which is between free text and structured text. They lack of grammar, have no strict formats, and even don't have complete sentences. Most of them consist of phrases having the characteristics of TCM field. Presently, the information in TCM medical cases is extracted based on structured templates. This process...
In this paper, we propose an artificial neural network approach to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARI). In order to accurately describe the structural properties of ARIs, besides the popularly used 2-dimensional (2D) descriptors, we have used 3-dimensional (3D) molecular descriptors which are obtained through the DRAGON software...
Feed-Forward Neural Network (FFNN) has recently been utilized to estimate blood pressure (BP) from the oscillometric measurements. However, there has been no study till now that consolidated the role played by the different neural network (NN) training algorithms in affecting the BP estimates. This paper compares the estimation errors in the BP due to ten different training algorithms belonging to...
Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) post-dialysis blood urea or...
This paper presents a study of the dynamic (recurrent) quadratic neural unit (QNU) -a class of higher order network or a class of polynomial neural network- as applied to the prediction of lung respiration dynamics. Human lung motion during respiration features nonlinear dynamics and displays quasiperiodical or even chaotic behavior. An attractive approximation capability of the recurrent QNU are...
This paper aims to conduct supervised learning of the cigarette-smoking signatures from the placental gene expression data sets under the neural network framework and build classifiers to identify the cigarette-smoking moms during pregnancy. First, a unified model for gene selection is proposed to single out a set of informative gene sets (up-or down-regulated genes). The selected signature gene sets...
Although protien classification for Drug design is one of the most widely studied area in the past few years, it is difficult to obtain high accuracy. We used a feature weighting algorithm in order to represent the whole needed feature set. Because of scarce labeled data and high computational complexity of supervised learning methods, a new semi-supervised learning algorithm extended from Gaussian...
Studying complex systems including biological systems is a multi-disciplinary research area. It must be derived by the recent explosion of ICT including high-performance computing, high-throughput experiments, the Internet, knowledge discovery and Artificial Intelligence (AI). The goal of this research is to establish a computational architecture and tools to deal with complex systems based on such...
In this paper we introduce a set of adaptive signal procedure techniques which could be used. Firstly, we introduce discrete wavelet transform and extract the characteristics of Electrocardiogram (ECG) optimization. Then, we make use of Radial Basis Function (RBF) neural network to achieve the classification of ECG and to compare the performance of their respectively. Among which two types of ECG...
Electrograms (EGM) stored in Implantable Cardioverter Defibrillator (ICD) during ventricular tachycardia episodes have recently been shown to convey valuable information for the identification of the anatomical origin of the arrhythmia and subsequent ablation therapy. We developed an automatic procedure for estimating the focal origin of the arrhythmia by analyzing the EGM waveforms. A clinical protocol...
One of the most popular machine learning algorithms, ANN (Artificial Neural Network) has been extensively used for Data Mining, which extracts hidden patterns and valuable information from large databases. Data mining has extensive and significant applications in a large variety of areas. This paper introduces a new adaptive Higher Order Neural Network (HONN) model and applies it in data mining tasks...
This paper shows the effectiveness of a classifier ensemble composed of weak classifiers trained with a boosting algorithm implemented in a multiprocessor system on chip. The network is applied on the classification on thyroid disease diagnosis. The objective is to show that, even an FPGA with hardware restrictions, can be used to implement a complex problem, when parallel processing is used. To improve...
The objective of study is to develop intelligent decision support system to aid radiologist in diagnosis using pattern recognition techniques to estimate diagnostic function. In this study 3 approaches investigated namely statistical, neural networks and optimization techniques which were applied on the Wisconsin dataset. Trained neural networks, with the data set used as input, improve on the independent...
Although there are many studies on computer-aided drug design in recent years, determination of proteins for drug candidates is a remarkable area for research. The first major shortcoming of this kind of problems is the feature selection representing the protein structure best, the former one is the computational complexity. We use three datasets with different sizes such as Cherkasov dataset with...
Breast cancer is a life threatening disease affecting one of every eight women, with the risk increasing significantly with age. Non-invasive diagnostic modalities are preferable over biopsy. However, these non-invasive diagnostic techniques have lower diagnostic accuracy when compared to biopsy. Currently, CAD (Computer-Aided Diagnosis) techniques have demonstrated strong potential to increase the...
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