scholarly journals The Use of Fuzzy BackPropagation Neural Networks for the Early Diagnosis of Hypoxic Ischemic Encephalopathy in Newborns

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Liu Li ◽  
Huo Liqing ◽  
Lu Hongru ◽  
Zhang Feng ◽  
Zheng Chongxun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Diagnosis ◽  
Recognition Rate ◽  
Hypoxic Ischemic Encephalopathy ◽  
Correct Recognition ◽  
Backpropagation Neural Networks ◽  
Artificial Neural ◽  
Correct Recognition Rate ◽  
Ischemic Encephalopathy

Objective. To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility.Methods. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks.Results. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%.Conclusion. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.

PREDIKSI PERTUMBUHAN PENDUDUK PADA PROVINSI SUMATERA BARAT DENGAN MENGGUNAKAN METODE BACKPROPAGATION

2020 ◽  
Vol 7 (4) ◽  
pp. 216
Author(s):  
Hijrah Yanti Sitanggang ◽  
Vera Irma Delianti
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Artificial Neural Networks ◽  
Population Growth ◽  
Population Growth Rate ◽  
Backpropagation Neural Networks ◽  
West Sumatra ◽  
Artificial Neural ◽  
The City

The problem of population is one of the problems in the Province of West Sumatra, especially in the City of Padang, Kota Bukitinggi, and the City of Payakumbuh which has a very fast population growth rate, this occurs due to several factors such as births, deaths, residents who come, and residents who leave. The highest population growth occurred in Padang City in 2018 amounting to 939,112 residents and the smallest population growth occurred in the City of Bukitinggi in 2014 amounting to 120,491 residents. The purpose of this study is to predict population growth that will occur in 2019 in the cities of Padang, Bukittinggi and Payakumbuh. The method used in this research is descriptive correlational by applying backpropagation neural networks. The application used is Matlab. Based on the problems and methods obtained, the predicted results in 2019 in Padang City amounted to 124,7150, Bukittinggi numbered 126,8040 and Payakumbuh totaled 128.7830.  Keywords: Artificial Neural Networks, Backpropagation, Matlab.

scholarly journals Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

2016 ◽  
Vol 67 (1) ◽  
pp. 117-134 ◽  
Author(s):  
Pavol Marák ◽  
Alexander Hambalík
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Image Quality ◽  
Positive Impact ◽  
Recognition Rate ◽  
Fingerprint Recognition ◽  
Artificial Neural ◽  
Level 2

Abstract Performance of modern automated fingerprint recognition systems is heavily influenced by accuracy of their feature extraction algorithm. Nowadays, there are more approaches to fingerprint feature extraction with acceptable results. Problems start to arise in low quality conditions where majority of the traditional methods based on analyzing texture of fingerprint cannot tackle this problem so effectively as artificial neural networks. Many papers have demonstrated uses of neural networks in fingerprint recognition, but there is a little work on using them as Level-2 feature extractors. Our goal was to contribute to this field and develop a novel algorithm employing neural networks as extractors of discriminative Level-2 features commonly used to match fingerprints. In this work, we investigated possibilities of incorporating artificial neural networks into fingerprint recognition process, implemented and documented our own software solution for fingerprint identification based on neural networks whose impact on feature extraction accuracy and overall recognition rate was evaluated. The result of this research is a fully functional software system for fingerprint recognition that consists of fingerprint sensing module using high resolution sensor, image enhancement module responsible for image quality restoration, Level-1 and Level-2 feature extraction module based on neural network, and finally fingerprint matching module using the industry standard BOZORTH3 matching algorithm. For purposes of evaluation we used more fingerprint databases with varying image quality, and the performance of our system was evaluated using FMR/FNMR and ROC indicators. From the obtained results, we may draw conclusions about a very positive impact of neural networks on overall recognition rate, specifically in low quality.

scholarly journals Identifying Olive (Olea europaea) Cultivars Using Artificial Neural Networks

1999 ◽  
Vol 124 (5) ◽  
pp. 527-531 ◽  
Author(s):  
S. Mancuso ◽  
F.P. Nicese
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Olea Europaea ◽  
Mediterranean Basin ◽  
Training Phase ◽  
Backpropagation Neural Networks ◽  
Artificial Neural ◽  
The Mediterranean Basin ◽  
Olea Europaéa

Backpropagation neural networks (BPNNs) were used to distinguish among 10 olive (Olea europaea L.) cultivars, originating throughout the Mediterranean basin. Identification was performed on the basis of 17 phyllometric parameters resulting from image analysis. Different BPNN architectures were attempted and best performance was achieved using a 17 × 20 × 10 BPNN. Networks were tested with sets of phyllometric parameters not involved in the training phase. Results enabled identification with certainty all cultivars tested.

scholarly journals D2 shape distribution and artificial neural networks for 3D objects recognition

2018 ◽  
Vol 7 (2.13) ◽  
pp. 103
Author(s):  
Safae Elhoufi ◽  
Aicha Majda ◽  
Khalid Abbad
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Recognition Rate ◽  
Back Propagation ◽  
Support Vector ◽  
3D Object ◽  
Shape Distribution ◽  
Triangulated Surface ◽  
3D Objects ◽  
Artificial Neural

In this paper, we propose a 3D object recognition approach, based on the shape distribution D2 and artificial neural networks. The challenge is to discriminate between similar and dissimilar shapes by finding a shape signature that can be constructed and classified quickly. We propose a connectionist system to recognize 3D objects in VRML (Virtual Reality Modeling Language) format. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The proposed strategy is the following: from a polygon object to be recognized, a triangulation is performed. Then, distances are calculated between two random points of the triangulated surface of the 3D object. The frequency of these distances will be represented by a normalized histogram. The values of these histograms feed a multi-layer neural network with back- propagation training. We demonstrate the potential of this approach in a set of experiments, which proved our system could achieve above 91.7% recognition rate. In addition, to evaluate the efficiency of our method, we compare our classifier with Support vector machine and k- nearest neighbours. The simulation results highlight the performance of the proposed approach.  

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