با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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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 |