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نتیجه جستجو - Probabilistic neural network

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 Data mining and application of ship impact spectrum acceleration based on PNN neural network
داده کاوی و کاربرد شتاب طیف تأثیر کشتی بر اساس شبکه عصبی PNN-2020
The selection of the smoothing coefficient of the probabilistic neural network directly affects the performance of the network. Traditionally, all the mode layer neurons use a uniform smoothing coefficient, and then the optimal smoothing parameters suitable for this problem are searched by the optimization algorithm. In this study, the smoothing coefficients of the mode layer neurons connected by the same summation layer are set to the same value, which not only reflects the relationship between the training samples of the same pattern, but also highlights the difference between the training samples of different modes. Two probabilistic neural network models are applied to the ship impact environment prediction respectively. The results show that the classification effect of multiple smoothing factors is further improved than the single smoothing factor network.
Keywords: Ship impact environment prediction | Probabilistic neural network | Smoothing coefficient | Optimization algorithm
مقاله انگلیسی
2 Prediction of kidney disease stages using data mining algorithms
پیش بینی مراحل بیماری کلیه با استفاده از الگوریتم های داده کاوی-2019
Early detection and characterization are considered to be critical factors in the management and control of chronic kidney disease. Herein, use of efficient data mining techniques is shown to reveal and extract hidden information from clinical and laboratory patient data, which can be helpful to assist physicians in maximizing accuracy for identification of disease severity stage. The results of applying Probabilistic Neural Networks (PNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Radial Basis Function (RBF) algorithms have been compared, and our findings show that the PNN algorithm provides better classification and prediction performance for determining severity stage in chronic kidney disease.
Keywords: Prediction of kidney disease stages | Data mining techniques | Probabilistic neural networks | Multilayer perceptron | Support vector machine | Radial basis function
مقاله انگلیسی
3 A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier
استخراج ویژگی منحصر به فرد با استفاده از MRDWT برای طبقه بندی خودکارضربان قلب غیر طبیعی از داده های بزرگ ECG با چند لایه طبقه بندی احتمالی شبکه عصبی-2018
This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is pro posed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.
Keywords: Signal processing ، Artificial intelligence ، Pattern recognition ، Soft computing ، Wavelet transform
مقاله انگلیسی
4 A Novel Adaptive Feature Extraction for Detection of Cardiac Arrhythmias Using Hybrid Technique MRDWT & MPNN Classifier from ECG Big Data
رویکرد استخراج ویژگی تطبیقی برای تشخیص آریتمی های قلب با استفاده از تکنیک ترکیبی MRDWT و MPNN طبقه بندی از داده بزرگ ECG-2018
The efficient automatic detection of cardiac arrhythmia using a hybrid technique from ECG big data has been proposed with novel feature extraction technique using Multiresolution Discrete Wavelet Transform (MRDWT) and Multilayer Probabilistic Neural Network (MPNN) classifier. Big Data of ECG signals have been selected from MIT–BIH arrhythmia database for detection of two types of arrhythmias LBBB (Left Bundle Branch Block) and RBBB (Right Bundle Branch Block). The proposed technique can accurately detect and classify LBBB and RBBB along with normal heartbeat. A novel and hybrid method of detection of cardiac arrhythmia have four main stages: denoising of raw ECG, baseline wander removal, proposed feature extraction, and detection of abnormal heartbeats using MPNN neural classifier. 8600 ECG beats were selected, including 4200 normal and 4400 abnormal beats (2200 LBBB and 2200 RBBB) were utilized for testing the proposed technique. The detection outcome using MPNN was compared with other two neural classifiers: Feed Forward Neural Network (FFNN) and Back Propagation Neural Network (BPNN) classifiers. The accuracy and efficiency of classifiers performance were attained in terms of CER (Classification Error Rate), SP (Specificity), Se (Sensitivity), Pr (Precision), PPr (Positive Predictivity) and F Score. The system performance is achieved with 96.22%, 97.15% and 99.07% overall accuracy using FFNN, BPNN and MPNN. The average percentage of classification error rate (CER) using MPNN classifier is lowest 0.62% whereas FFNN and BPNN show 2.2% and 1. 90% average CER.
Keywords: Big data ، Cardiac arrhythmias ،Biomedical signal processing ، Artificial intelligence ، Machine learning
مقاله انگلیسی
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