scholarly journals Classification of Erythemato-squamous diseases using Artificial Neural Network and Genetic algorithm

2020 ◽  
pp. 208-212
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Experimental Result ◽  
Mining System ◽  
Statistical Measures ◽  
Specificity And Sensitivity ◽  
Artificial Neural

This paper introduces a hybrid model using artificial neural network (ANN) and genetic algorithm (GA) to develop an efficient classification technique for classification of different categories of Erythemato-squamous diseases. Neural network has been extensively used in many applications like classification, regression, web mining, system identification and pattern recognition. Weight optimization in neural network has been a matter of concern for researchers in the field of soft computing. In this paper the weights of ANN are optimized with GA. The proposed hybrid model is applied on the Erythemato-squamous dataset taken from UCI machine learning repository. The dataset contains six different categories: psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris of Erythemato-squamous diseases. The main aim of this paper is to determine the type of Eryhemato-Squamous disease using the hybrid model. The performance of the hybrid model is evaluated using statistical measures like accuracy, specificity and sensitivity. The accuracy of the proposed model is found to be 99.34% on test dataset. The experimental result shows the effectiveness of the hybrid model in classification of Erythematosquamous diseases.

scholarly journals Channel selection and classification of electroencephalogram signals: An artificial neural network and genetic algorithm-based approach

2012 ◽  
Vol 55 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Jianhua Yang ◽  
Harsimrat Singh ◽  
Evor L. Hines ◽  
Friederike Schlaghecken ◽  
Daciana D. Iliescu ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Channel Selection ◽  
Artificial Neural

scholarly journals Predictions of Laryngeal Cancer Using Neural Network in Kerman Shafa Hospital

2018 ◽  
Vol 7 ◽  
pp. 4
Author(s):  
Sadegh NejatZadeh ◽  
Fatemeh Rahimi ◽  
Amid Khatibi Bardsiri ◽  
Elham Vahidian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Laryngeal Cancer ◽  
Human Error ◽  
Medical Science ◽  
Specificity And Sensitivity ◽  
The Neural Network ◽  
Functional Features ◽  
Artificial Neural

Introduction: One of the challenges facing medical science is the time and correct diagnosis of diseases. Particularly with regard to certain diseases such as the types of cancer, which are the leading causes of death worldwide, their early diagnosis has a significant impact on the control and treatment of this disease. The use of intelligent decision support systems with high precision can be a good way to reduce human error due to fatigue and lack of experience. Therefore, the present study tries to predict the disease by using data mining techniques and taking into account the variables that influence the prediction of laryngeal cancer. Material and methods: This study is an analytical study. The data from the 249 cases referred to Shafa Hospital in Kerman in 2017 have been obtained. This study is based on the Crisp methodology and in the MATLAB software environment. First, in order to understand the laryngeal cancer, a review of related studies was conducted and interviewed by specialist physicians. Then, according to expert opinion, 24 variables were identified as effective factors in predicting laryngeal cancer. After clearing and preparing data, an artificial neural network model was used to predict the risk of laryngeal cancer. In the following, another model of the combination of the genetic algorithm and the neural network was created. Using genetic algorithm, 9 functional features of prediction of laryngeal cancer were determined from among the 24 selected variables, and artificial neural network was used to predict the risk of laryngeal cancer. Finally, the criteria for accuracy, specificity, and sensitivity were used to evaluate the two models.Results: The genetic algorithm reduced the complexity of the model by reducing the number of features from 24 to 9, but improved the average precision from 80% to 84%. Also, the model made with the characteristics selected by the genetic algorithm, increased the specificity and accuracy criteria by 13% and 8%, respectively.Conclusion: Combining the genetic algorithm with the neural network, in addition to improving the accuracy of prediction of laryngeal cancer, accelerates the diagnosis process, especially at the data collection stage, by reducing the number of characteristics required. Therefore, using this model as a smart decision system is suggested.

scholarly journals Artificial Neural Network for Assembly Line Balancing

2019 ◽  
Vol 2 (2) ◽  
pp. 69-78
Author(s):  
Pius Ucheagwu ◽  
Johnmary Ugochukwu Okeke ◽  
Christian I. Okonta ◽  
Efosa Osamuyimwen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Simulation Techniques ◽  
Artificial Neural

This study examines an assembly line balancing using artificial neural network. An organization that balances the unique workloads must respect the limits and restrictions that hinder the assembly. To optimize the very specific operations, balancing an assembly line may require different methods, including: genetic algorithm, heuristic approach, simulation techniques, the ant colony optimization (ACO), etc., but in this study, artificial neural networks was applied to solving problems of assembly line balancing.  This study also explores the characteristics of the assembly line and the classification of the assembly balancing problems, suggesting as an artificial neural network solve.

scholarly journals A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price

2018 ◽  
Vol 3 (6) ◽  
pp. 10 ◽  
Author(s):  
Azme Bin Khamis ◽  
Phang Hou Yee
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Artificial Neural

The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed.  Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.

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