scholarly journals Validation of Neural Network Predictions for the Outcome of Refractive Surgery for Myopia

2020 ◽  
Vol 9 (3) ◽  
pp. 172-178
Author(s):  
Miltos Balidis ◽  
Ioanna Papadopoulou ◽  
Dimitris Malandris ◽  
Zachos Zachariadis ◽  
Dimitrios Sakellaris ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Vector Quantization ◽  
Validation Study ◽  
Refractive Surgery ◽  
Learning Vector Quantization ◽  
Independent Learning ◽  
Good Sensitivity ◽  
Significant Agreement ◽  
The Future

Background: Refractive surgery (RS) for myopia has made a very big progress regarding its safety and predictability of the outcome. Still, a small percentage of operations require retreatment. Therefore, both legally and ethically, patients should be informed that sometimes a corrective RS may be required. We addressed this issue using Neural Networks (NN) in RS for myopia. This was a recently developed validation study of a NN.  Methods: We anonymously searched the Ophthalmica Institute of Ophthalmology and Microsurgery database for patients who underwent RS with PRK, LASEK, Epi-LASIK or LASIK between 2010 and 2018. We used a total of 13 factors related to RS. Data was divided into four sets of successful RS outcomes used for training the NN, successful RS outcomes used for testing the NN performance, RS outcomes that required retreatment used for training the NN and RS outcomes that required retreatment used for testing the NN performance. We created eight independent Learning Vector Quantization (LVQ) networks, each one responding to a specific query with 0 (for the retreat class) or 1 (for the correct class). The results of the 8 LVQs were then averaged so we could obtain a best estimate of the NN performance. Finally, a voting procedure was used to reach to a conclusion. Results: There was a statistically significant agreement (Cohen’s Kappa = 0.7658) between the predicted and the actual results regarding the need for retreatment. Our predictions had good sensitivity (0.8836) and specificity (0.9186). Conclusion: We validated our previously published results and confirmed our expectations for the NN we developed. Our results allow us to be optimistic about the future of NNs in predicting the outcome and, eventually, in planning RS.

scholarly journals Analysis of the experimental positron lifetime spectra by neural networks

2003 ◽  
Vol 18 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Senada Avdic ◽  
Roumiana Chakarova ◽  
Imre Pazsit
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Positron Lifetime ◽  
Learning Vector Quantization ◽  
Polymer Materials ◽  
Analysis Tool ◽  
Spectral Components ◽  
Artificial Neural

This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pzzsitetal, Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposi-tion of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved.

scholarly journals Diagnosa Penyakit Demam Berdarah Dengue (DBD) menggunakan Metode Learning Vector Quantization (LVQ)

2020 ◽  
Vol 4 (3) ◽  
pp. 56
Author(s):  
Firman Tawakal ◽  
Ahmedika Azkiya
Keyword(s):  
Neural Network ◽  
Typhoid Fever ◽  
Vector Quantization ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Learning Vector Quantization ◽  
Mean Square ◽  
Northern Australia ◽  
Neural Network Learning ◽  
Network Learning

Dengue Hemorrhagic Fever is a disease that is carried and transmitted through the mosquito Aedes aegypti and Aedes albopictus which is commonly found in tropical and subtropical regions such as in Indonesia to Northern Australia. in 2013 there are 2.35 million reported cases, which is 37,687 case is heavy cases of DHF. DHF’s symthoms have a similarity with typhoid fever, it often occur wrong handling. Therefore we need a system that is able to diagnose the disease suffered by patients, so that they can recognize whether the patient has DHF or Typhoid. The system will be built using Neural Network Learning Vector Quantization (LVQ) based on the best training results. This research is to diagnose Dengue Hemorrhagic Fever using LVQ with input parameters are hemoglobin, leukocytes, platelets, and heritrocytes. Based on result, the best accuracy is 97,14% with Mean Square Error (MSE) is 0.028571 with 84 train data and 36 test data. Conclution from the research is LVQ method can diagnose DHF Keywords: Dengue Hemorrhagic Fever; Learning Vector Quantization; classification; Neural Network;

scholarly journals Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 745 ◽  
Author(s):  
Malathy Emperuman ◽  
Srimathi Chandrasekaran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Fault Detection ◽  
Vector Quantization ◽  
Probabilistic Neural Network ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Sensor Faults ◽  
Continuous Density

Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.

scholarly journals Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shahenda Sarhan ◽  
Aida A. Nasr ◽  
Mahmoud Y. Shams
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Vector Quantization ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Vector Quantization ◽  
Majority Voting ◽  
Support Vector ◽  
Reliability And Robustness

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.

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