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49 results for “regression neural network”
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Research ArticleOpen accessPhrase in the titleGENERALIZED REGRESSION NEURAL NETWORK BASED EXPERT SYSTEM FOR HEPATITIS B DIAGNOSISC. Mahesh, E. Kannan, M. S. SaravananHepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus. The virus interferes with the function of the liver while replicating in hepatocytes. It is a major global health problem and the most serious type of viral hepatitis. Chronic liver disease is caused by viral hepatitis and putting people at high risk of death from cirrhosis … …Journal of Computer Science, Vol. 10, Issue 4 · 3 Dec 2013 · 6,985 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparison of Multiple Linear Regression and Neural Network Models in Bank Performance Prediction in BotswanaHassan Kablay, Victor GumboBank performance is critical to the banking sector and the economy as a whole. In this study, Multiple Linear Regression (MLR) technique and feed forward Neural Network (NN) are used to predict the performance of 11 banks in Botswana. Return on Assets (RoA) is used as the dependent variable, while management quality, credit risk, liquidity, financial leverage and capital adequacy … …Journal of Mathematics and Statistics, Vol. 17, Issue 1 · 10 Jan 2022 · 6,469 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDiagnosis of Hepatitis Disease with Logistic Regression and Artificial Neural NetworksAlaa M. Elsayad, Ahmed M. Nassef, Mujahed Al-DhaifallahHepatitis C refers to the inflammatory state of the liver caused by viruses, bacteria, fungi, and exposure to toxins such as alcohol and self-immunity. The diagnosis requires investigating many laboratory tests and comparing the results to those of the former patients with the same conditions. This study presents the results of our experiments to build a hybrid system that combines … …Journal of Computer Science, Vol. 16, Issue 3 · 27 Mar 2020 · 7,470 accessesView PDFCite
Research ArticleOpen accessEvery word in the titlePerformance of Multiple Linear Regression and Nonlinear Neural Networks and Fuzzy Logic Techniques in Modelling House PricesSiti Amri, Gurudeo Anand TularamHouse price prediction continues to be important for government agencies insurance companies and real estate industry. This study investigates the performance of house sales price models based on linear and non-linear approaches to study the effects of selected variables. Linear stepwise Multiple Regression (MR) and nonlinear models of Neural Network (NN) and Adaptive Neuro-Fuzzy (ANFIS) are developed and compared. The … …Journal of Mathematics and Statistics, Vol. 8, Issue 4 · 18 Dec 2012 · 8,434 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleForecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric ModelsAbdel K.M. Baareh, Alaa F. Sheta, Khaled A. KhnaifesForecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper … …Journal of Computer Science, Vol. 2, Issue 10 · 31 Oct 2006 · 6,968 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleNeural Networks Based Nonlinear Time Series Regression for Water Level Forecasting of Dungun RiverSiti Hajar Arbain, Antoni WibowoThe Department of Irrigation and Drainage (DID) Malaysia and Meteorological Malaysia Department (MMD) has been measured the flood characteristics benchmark which included water level, area inundation, peak inundation, peak discharge, volume of flow and duration of flooding. In terms of water levels, DID have introduced three categories of critical level stages namely normal, alert and danger levels. One of the … …Journal of Computer Science, Vol. 8, Issue 9 · 13 Aug 2012 · 7,800 accessesView PDFCite
Research ArticleOpen accessIn the keywordsLogistic Regression with Multi-Connected WeightsAvazjon Marakhimov Rakhimovich, Kabul Khudaybergenov Kadirbergenovich, Zahriddin Muminov Ishkobilovich, Kudaybergenov Jabbarbergen KadirbergenovichResults of current research in biological and cognitive science reveal that neuronal interactions predominantly depend on neurotransmitters for signaling and sending information between neurons. Furthermore, a specific neuron that is connected to the next neuron transmits or sends multiple neurotransmitters in parallel ways, where each neurotransmitter has specific functional roles. Based on these results, a new type of logistic regression … …Journal of Computer Science, Vol. 20, Issue 9 · 4 Jul 2024 · 5,064 accessesView PDFCite
Research ArticleOpen accessIn the keywordsCardiovascular Disease Prediction using Various Machine Learning AlgorithmsDebabrata Swain, Badal Parmar, Hansal Shah et al.Almostone-third of all deaths caused around the world were caused due tocardiovascular diseases. Even if death was not the result, much cost isincurred during the treatment of such diseases. But much of these deaths andtreatments could have been prevented with prior action. Advance knowledge ofthe symptoms and consequently proper care can lead us to avoid such diseases.Thus, current research proposes … …Journal of Computer Science, Vol. 18, Issue 10 · 13 Oct 2022 · 9,402 accessesView PDFCite
Research ArticleOpen accessIn the keywordsComparison of Linear and Non-Linear Regression Models for Non-Invasive Blood Glucose MeasurementDorsaf Ghozzi, Yassine Manai, Khaled NouriThis paper deals with a new approach of non-invasive glucose monitoring based on near infrared spectroscopy. The proposed approach is coupled with a regression analysis in order to improve the predictive capacity of the designed device. Basic spectral data is a comparison that has been established between linear and non-linear machine learning regression algorithms. The experimental results show that feed … …Journal of Computer Science, Vol. 15, Issue 11 · 13 Nov 2019 · 7,869 accessesView PDFCite
Research ArticleOpen accessIn the keywordsBuilding Energy Modeling Using Artificial Neural NetworksMaya Arida, Nabil Nassif, Rand Talib, Taher Abu-LebdehAccurate modeling of total building energy is now vital to reduce energy consumption. This is especially true for buildings since they are considered as the largest energy consumer in the United States. This paper investigates modeling methods for building energy-systems using non-linear auto-regression artificial neural networks. The proposed model can forecast the whole building energy consumptions given the four input … …Energy Research Journal, Vol. 7, Issue 2 · 6 Jun 2017 · 7,131 accessesView PDFCite