Performance of Multi-Layer Feedforward Neural Networks to Predict Liver Transplantation Outcome

1996 ◽  
Vol 35 (01) ◽  
pp. 12-18 ◽  
Author(s):  
M. Subotin ◽  
W. Marsh ◽  
J. McMichael ◽  
J. J. Fung ◽  
I. Dvorchik
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Liver Transplantation ◽  
Missing Data ◽  
Network Performance ◽  
Missing Values ◽  
Feedforward Neural Networks ◽  
Linear Scaling ◽  
Data Set ◽  
New Approach

AbstractA novel multisolutional clustering and quantization (MCO) algorithm has been developed that provides a flexible way to preprocess data. It was tested whether it would impact the neural network’s performance favorably and whether the employment of the proposed algorithm would enable neural networks to handle missing data. This was assessed by comparing the performance of neural networks using a well-documented data set to predict outcome following liver transplantation. This new approach to data preprocessing leads to a statistically significant improvement in network performance when compared to simple linear scaling. The obtained results also showed that coding missing data as zeroes in combination with the MCO algorithm, leads to a significant improvement in neural network performance on a data set containing missing values in 59.4% of cases when compared to replacement of missing values with either series means or medians.

scholarly journals Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Buildings ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 204 ◽  
Author(s):  
Yang ◽  
Tan ◽  
Santamouris ◽  
Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Missing Data ◽  
Recurrent Neural Networks ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Data Set ◽  
Raw Data ◽  
Deep Recurrent Neural Network

With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

scholarly journals Book spine recognition with the use of deep neural networks

2020 ◽  
Vol 44 (6) ◽  
pp. 968-977
Author(s):  
M.O. Kalinina ◽  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Performance ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Original Data ◽  
Network Architectures ◽  
Data Set ◽  
The Neural Network ◽  
Processing Information

Nowadays deep neural networks play a significant part in various fields of human activity. Especially they benefit spheres dealing with large amounts of data and lengthy operations on obtaining and processing information from the visual environment. This article deals with the development of a convolutional neural network based on the YOLO architecture, intended for real-time book recognition. The creation of an original data set and the training of the deep neural network are described. The structure of the neural network obtained is presented and the most frequently used metrics for estimating the quality of the network performance are considered. A brief review of the existing types of neural network architectures is also made. YOLO architecture possesses a number of advantages that allow it to successfully compete with other models and make it the most suitable variant for creating an object detection network since it enables some of the common disadvantages of such networks to be significantly mitigated (such as recognition of similarly looking, same-color book coves or slanted books). The results obtained in the course of training the deep neural network allow us to use it as a basis for the development of the software for book spine recognition.

Constructing Multilayer Feedforward Neural Networks to Approximate Nonlinear Functions in Engineering Mechanics Applications

2008 ◽  
Vol 75 (6) ◽  
Author(s):  
Jin-Song Pei ◽  
Eric C. Mai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Feedforward Neural Networks ◽  
Nonlinear Functions ◽  
Neural Network Architecture ◽  
New Approach ◽  
Restoring Forces ◽  
Multilayer Feedforward Neural Network ◽  
Engineering Mechanics

This paper presents a major step in the development and validation of a systematic prototype-based methodology for designing multilayer feedforward neural networks to model nonlinearities common in engineering mechanics. The applications of this work include (but are not limited to) system identification of nonlinear dynamic systems and neural-network-based damage detection. In this and previous studies (Pei, J. S., 2001, “Parametric and Nonparametric Identification of Nonlinear Systems,” Ph.D. thesis, Columbia University; Pei, J. S., and Smyth, A. W., 2006, “A New Approach to Design Multilayer Feedforward Neural Network Architecture in Modeling Nonlinear Restoring Forces. Part I: Formulation,” J. Eng. Mech., 132(12), pp. 1290–1300; Pei, J. S., and Smyth, A. W., 2006, “A New Approach to Design Multilayer Feedforward Neural Network Architecture in Modeling Nonlinear Restoring Forces. Part II: Applications,” J. Eng. Mech., 132(12), pp. 1301–1312; Pei, J. S., Wright, J. P., and Smyth, A. W., 2005, “Mapping Polynomial Fitting Into Feedforward Neural Networks for Modeling Nonlinear Dynamic Systems and Beyond,” Comput. Methods Appl. Mech. Eng., 194(42–44), pp. 4481–4505), the authors do not presume to provide a universal method to approximate any arbitrary function. Rather the focus is given to the development of a procedure which will consistently lead to successful approximations of nonlinear functions within the specified field. This is done by examining the dominant features of the function to be approximated and exploiting the strength of the sigmoidal basis function. As a result, a greater efficiency and understanding of both neural network architecture (e.g., the number of hidden nodes) as well as weight and bias values is achieved. Through the use of illuminating mathematical insights and a large number of training examples, this study demonstrates the simplicity, power, and versatility of the proposed prototype-based initialization methodology. A clear procedure for initializing neural networks to model various nonlinear functions commonly seen in engineering mechanics is provided. The proposed methodology is compared with the widely used Nguyen–Widrow initialization to demonstrate its robustness and efficiency in the specified applications. Future work is also identified.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

scholarly journals Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

2020 ◽  
Vol 5 (9) ◽  
pp. 1124-1130
Author(s):  
Ledisi Giok Kabari ◽  
Young Claudius Mazi
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Neural Networks ◽  
Niger Delta ◽  
Feedforward Neural Networks ◽  
Flood Prediction ◽  
The Neural Network ◽  
Significant Damage ◽  
Weather Underground ◽  
Short Term Prediction

Climate change generates so many direct and indirect effects on the environment.  Some of those effects have serious consequences. Rain-induced flooding is one of the direct effects of climate change and its impact on the environment is usually devastating and worrisome. Floods are one of the most commonly occurring disasters and have caused significant damage to life, including agriculture and economy. They are usually caused in areas where there is excessive downpour and poor drainage systems. The study uses Feedforward Multilayer Neural Network to perform short-term prediction of the amount of rainfall flood for the Niger Delta   sub region of Nigeria given previous rainfall data for a specified period of time. The data for training and testing of the Neural Network was sourced from Weather Underground official web site https://www.wunderground.com.  An iterative Methodology was used and implemented in MATLAB. We adopted multi-layer Feedforward Neural Networks. The study accurately predicts the rain-induced flood for the Niger Delta   sub region of Nigeria.

scholarly journals Adaptation of the Diagnostic Artificial Neural Network Structure When New Training Examples Appear

2020 ◽  
Vol 6 (4) ◽  
pp. 120-126
Author(s):  
A. Malikov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Information Security ◽  
Classification Problem ◽  
Data Set ◽  
Neural Network Structure ◽  
Artificial Neural ◽  
Training Examples ◽  
Information Image

In this paper we can see that identified computer incidents are subject for diagnostics, during which the characteristics of information security violations are clarified (purpose, causes, consequences, etc.). To diagnose computer incidents, we can use methods of automation while collection and processing the events that occur as a result of the implementation of scenarios for information security violations. Artificial neural networks can be used to solve the classification problem of assigning diagnostic data set (information image of a computer incident) to one of the possible values of the violation characteristic. The purpose of this work is to adapt the structure of an artificial neural network that allows the accuracy diagnostics of computer incidents when new training examples appear.

scholarly journals NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION

2019 ◽  
Vol 2019 (02) ◽  
pp. 89-98
Author(s):  
Vijayakumar T
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Early Stage ◽  
Original Data ◽  
Data Set ◽  
Cancer Prediction ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
The Neural Networks ◽  
Human Nervous System

Predicting the category of tumors and the types of the cancer in its early stage remains as a very essential process to identify depth of the disease and treatment available for it. The neural network that functions similar to the human nervous system is widely utilized in the tumor investigation and the cancer prediction. The paper presents the analysis of the performance of the neural networks such as the, FNN (Feed Forward Neural Networks), RNN (Recurrent Neural Networks) and the CNN (Convolutional Neural Network) investigating the tumors and predicting the cancer. The results obtained by evaluating the neural networks on the breast cancer Wisconsin original data set shows that the CNN provides 43 % better prediction than the FNN and 25% better prediction than the RNN.

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