31P MRS Data Analysis of Liver Cancer Based on Neural Networks

2010 ◽  
Vol 20-23 ◽  
pp. 630-635
Author(s):  
Qiang Liu ◽  
Ning Wang ◽  
Yi Hui Liu ◽  
Shao Qing Wang ◽  
Jin Yong Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Liver Cancer ◽  
Back Propagation ◽  
Network Models ◽  
Resonance Spectroscopy ◽  
Self Organizing Map ◽  
Neural Network Models ◽  
31P Mrs ◽  
Hepatic Diseases

31P MRS(31Phosphorus Magnetic Resonance Spectroscopy) is a non invasive protocol for analyzing the energetic metabolism and biomedical changes in cellular level. Evaluation of 31P MRS is important in diagnosis and treatment of many hepatic diseases. In this paper, we apply back-propagation neural network (BP) and self-organizing map (SOM) neural network to analyze 31P MRS data to distinguish three diagnostic classes of cancer, normal and cirrhosis tissue. 66 samples of 31P MRS data are selected including cancer, normal and cirrhosis tissue. Four experiments are carried out. Good performance is achieved with limited samples. Experimental results prove that neural network models based on 31P MRS data offer an alternative and promising technique for diagnostic prediction of liver cancer in vivo.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models

Materials ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 3708 ◽  
Author(s):  
In-Ji Han ◽  
Tian-Feng Yuan ◽  
Jin-Young Lee ◽  
Young-Soo Yoon ◽  
Joong-Hoon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Experimental Studies ◽  
Back Propagation ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Models

A new hybrid intelligent model was developed for estimating the compressive strength (CS) of ground granulated blast furnace slag (GGBFS) concrete, and the synergistic benefits of the hybrid algorithm as compared with a single algorithm were verified. While using the collected 269 data from previous experimental studies, artificial neural network (ANN) models with three different learning algorithms namely back-propagation (BP), particle swarm optimization (PSO), and new hybrid PSO-BP algorithms, were constructed and the performance of the models was evaluated with regard to the prediction accuracy, efficiency, and stability through a threefold procedure. It was found that the PSO-BP neural network model was superior to the simple ANNs that were trained by a single algorithm and it is suitable for predicting the CS of GGBFS concrete.

scholarly journals Prediction of EV arrivals at Battery Swapping Station using Hybrid Neural Network models

2021 ◽  
Vol 2062 (1) ◽  
pp. 012021
Author(s):  
Manikanta Suri ◽  
Neha Raj ◽  
K Sireesha
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Pearson Correlation ◽  
Back Propagation ◽  
Network Models ◽  
Absolute Error ◽  
Hybrid Models ◽  
Time Data ◽  
Neural Network Models ◽  
Local Optima

Abstract There is an enormous increase in demand for Electric Vehicles (EV) in the present era, as they are environment-friendly when compared to conventional vehicles. Battery Swapping Stations (BSS) are gaining a lot of attention from the EV sector as it is like the gasoline stations. Forecasting of EV arrivals at BSS helps in optimally scheduling the depleted batteries to different charging piles without affecting the State of Health of the battery. Back Propagation Neural Network (BPNN) is widely used in the prediction of real-time data. Training of BPNN using metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) helps to overcome the local optima problem in BPNN. Thus, in the present work forecasting on the EV arrivals is carried out using GA-BPNN and PSO-BPNN hybrid models. Finally, a comparative study is carried out among BPNN, GA-BPNN, and PSO-BPNN models using the performance metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE) and Pearson Correlation Coefficient (PCC). From the results, it was obtained that GA-BPNN model is preferred in forecasting the EV arrivals at BSS as the model has less overfitting. The hybrid models have been simulated in MATLAB/Simulink software.

scholarly journals Prediction of weld quality using intelligent decision making tools

2012 ◽  
Vol 1 (2) ◽  
pp. 131 ◽  
Author(s):  
Edwin Raja Dhas ◽  
Somasundaram Kumanan ◽  
C.P. Jesuthanam
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Regression Model ◽  
Back Propagation ◽  
Network Models ◽  
Multiple Regression Model ◽  
Decision Making Process ◽  
Weld Quality ◽  
Neural Network Models ◽  
Intelligent Decision Making

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design anddemand of quality products. To make decision making process online, effective and efficient artificial intelligent tools likeneural networks are being attempted. This paper proposes the development of neural network models for prediction ofweld quality in Submerged Arc Welding (SAW). Experiments are designed according to Taguchi’s principles andmathematical equations are developed using multiple regression model. Proposed neural network models are developedusing experimental data, supported with the data generated by regression model. The performances of the developedmodels are compared in terms of computational speed and prediction accuracy. It is found that Neural Network trainedwith Particle Swarm Optimization (NNPSO) performs better than Neural Network trained with Back Propagation (BPNN)algorithm, Radial Basis Functional Neural Network (RBFNN) and Neural Network trained with Genetic Algorithm(NNGA). The developed scheme for weld quality prediction is flexible, competent, and accurate than existing models andit scopes better online monitoring system. Finally the developed models are validated. The proposed and developedtechnique finds a good scope and a better future in the relevant field where human can avoid unwanted risks duringoperations with the deployment of robots.

Sign in / Sign up

Export Citation Format

Share Document