scholarly journals Prediction Methods for the Actual Duration of Greek Highway Projects

2021 ◽  
Vol 18 ◽  
pp. 1389-1396
Author(s):  
Magdalini Titirla ◽  
Walid Larbi ◽  
Georgios Aretoulis
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Models ◽  
Estimation Methods ◽  
Linear Regression Models ◽  
Neural Network Models ◽  
Highway Projects ◽  
Project Duration ◽  
The Neural Network ◽  
Actual Duration

This study presents an overview of estimation methods to predict the actual project duration of Greek highway projects. Emphasis is given to the selection of the appropriate parameters that correlate with the actual project duration and to compare the performances of the main two methods, the linear regression (LR) with the neural network models (NN) based on data available at the bidding stage. In the context of the current research, thirty-seven highway projects were examined, constructed in Greece with similar available data like the extent, the type of work packages and the significance. Selection and ranking variables through correlation analyses using SPSS 25 has been carried on, in order to identify the most significant project variables. These include archeological findings, type of terrain, land expropriation, the existence of bridge, tunnel and embankment. Next step was the use of WEKA application, that highlighted the most efficient subset of variables. After the definition and grouping of the variables for actual duration prediction, these were used as input data for linear regression models (LR) and neural network models (NN). Various models have been created from each investigated method. While their performance and the comparison of linear regression and neural network models to estimate the actual duration of Greek highway projects are presented in this paper. Results’ discussion and conclusions along with limitations and further research are appropriately analyzed.

Neural Network-Based Stock Market Return Forecasting Using Data Mining for Variable Reduction

2008 ◽  
pp. 2476-2493 ◽  
Author(s):  
David Encke
Keyword(s):  
Neural Network ◽  
Information Gain ◽  
Network Models ◽  
Linear Regression Models ◽  
Market Returns ◽  
Neural Network Models ◽  
The Neural Network ◽  
Variable Reduction ◽  
Input Variables ◽  
Using Data

Researchers have known for some time that nonlinearity exists in the financial markets and that neural networks can be used to forecast market returns. Unfortunately, many of these studies fail to consider alternative forecasting techniques, or the relevance of the input variables. The following research utilizes an information-gain technique from machine learning to evaluate the predictive relationships of numerous financial and economic input variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of the models. The results show that the classification models generate higher accuracy in forecasting ability than the buy-and-hold strategy, as well as those guided by the level-estimation-based forecasts of the neural network and benchmark linear regression models.

Neural network models for actual duration of Greek highway projects

2019 ◽  
Vol 17 (6) ◽  
pp. 1323-1339
Author(s):  
Magdalini Titirla ◽  
Georgios Aretoulis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Analysis ◽  
Network Models ◽  
Neural Network Models ◽  
Tender Offer ◽  
Highway Projects ◽  
Content Type ◽  
Initial Cost ◽  
Actual Duration

Purpose This paper aims to examine selected similar Greek highway projects to create artificial neural network-based models to predict their actual construction duration based on data available at the bidding stage. Design/methodology/approach Relevant literature review is presented that highlights similar research approaches. Thirty-seven highway projects, constructed in Greece, with similar type of available data, were examined. Considering each project’s characteristics and the actual construction duration, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most significant project variables toward predicting actual duration. Furthermore, the WEKA application, through its attribute selection function, highlighted the most important subset of variables. The selected variables through correlation analysis and/or WEKA and appropriate combinations of these are used as input neurons for a neural network. Fast Artificial Neural Network (FANN) Tool is used to construct neural network models in an effort to predict projects’ actual duration. Findings Variables that significantly correlate with actual time at completion include initial cost, initial duration, length, lanes, technical projects, bridges, tunnels, geotechnical projects, embankment, landfill, land requirement (expropriation) and tender offer. Neural networks’ models succeeded in predicting actual completion time with significant accuracy. The optimum neural network model produced a mean squared error with a value of 6.96E-06 and was based on initial cost, initial duration, length, lanes, technical projects, tender offer, embankment, existence of bridges, geotechnical projects and landfills. Research limitations/implications The sample size is limited to 37 projects. These are extensive highway projects with similar work packages, constructed in Greece. Practical implications The proposed models could early in the planning stage predict the actual project duration. Originality/value The originality of the current study focuses both on the methodology applied (combination of Correlation Analysis, WEKA, FannTool) and on the resulting models and their potential application for future projects.

Neural Network-Based Stock Market Return Forecasting Using Data Mining for Variable Reduction

2006 ◽  
pp. 43-63
Author(s):  
David Encke
Keyword(s):  
Neural Network ◽  
Information Gain ◽  
Network Models ◽  
Linear Regression Models ◽  
Market Returns ◽  
Neural Network Models ◽  
The Neural Network ◽  
Variable Reduction ◽  
Input Variables ◽  
Using Data

Researchers have known for some time that nonlinearity exists in the financial markets and that neural networks can be used to forecast market returns. Unfortunately, many of these studies fail to consider alternative forecasting techniques, or the relevance of the input variables. The following research utilizes an information-gain technique from machine learning to evaluate the predictive relationships of numerous financial and economic input variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of the models. The results show that the classification models generate higher accuracy in forecasting ability than the buy-and-hold strategy, as well as those guided by the level-estimation-based forecasts of the neural network and benchmark linear regression models.

scholarly journals Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei-Bo Chen ◽  
Wen-Cheng Liu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Linear Regression ◽  
Network Models ◽  
Water Quality Modeling ◽  
Neural Network Models ◽  
Anfis Model ◽  
Inference System ◽  
The Neural Network ◽  
Study Results

In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient, computed from the measured and the model-simulated DO, TP, Chl a, and SD values. The results indicate that the performance of the ANFIS model is superior to those of the MLR and RBFN models. The study results show that the neural network using the ANFIS model is suitable for simulating the water quality variables with reasonable accuracy, suggesting that the ANFIS model can be used as a valuable tool for reservoir management in Taiwan.

Estimating Uncertainty of Work Zone Capacity using Neural Network Models

2019 ◽  
Vol 2673 (2) ◽  
pp. 49-59
Author(s):  
Zilin Bian ◽  
Kaan Ozbay
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Prediction Intervals ◽  
Work Zone ◽  
Estimation Methods ◽  
Work Intensity ◽  
Neural Network Models ◽  
The Neural Network

This study aims to develop a neural network model to predict work zone capacity including various uncertainties stemming from traffic and operational conditions. The neural network model is formulated in terms of the number of total lanes, number of open lanes, heavy vehicle percentage, work intensity, and work duration. The data used in this paper are obtained from previous studies published in open literature. To capture the uncertainty of work zone capacity, this paper provides two recent methods that enable neural network models to generate prediction intervals which are determined by mean work zone capacity and prediction standard error. The research first builds a Bayesian neural network model with the application of black-box variational inference (BBVI) technique. The second model is based on a regular artificial neural network with an application of the recently proposed Monte-Carlo dropout technique. Both of the neural network models construct prediction intervals under various confidence levels and provide the coverage rates of the actual work zone capacities. The statistical accuracy (MAPE, MAE, MSE, and RMSE) of the models is then compared with traditional estimation methods in predicted mean work zone capacity. BBVI produces better statistical results than the other three models. Both of the models provide predicted work zone capacity distribution and prediction intervals, whereas traditional models only provide a single estimate.

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach

2021 ◽  
Vol 12 (6) ◽  
pp. 1-21
Author(s):  
Jayant Gupta ◽  
Carl Molnar ◽  
Yiqun Xie ◽  
Joe Knight ◽  
Shashi Shekhar
Keyword(s):  
Neural Network ◽  
Spatial Variability ◽  
Network Architecture ◽  
Network Models ◽  
Neural Network Architecture ◽  
Neural Network Models ◽  
Climatic Zones ◽  
The Neural Network ◽  
Plant Hardiness ◽  
Interpretation Model

Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all (OSFA) approach to train single deep neural network models that do not account for spatial variability. Quantification of spatial variability can be challenging due to the influence of many geophysical factors. In preliminary work, we proposed a spatial variability aware neural network (SVANN-I, formerly called SVANN ) approach where weights are a function of location but the neural network architecture is location independent. In this work, we explore a more flexible SVANN-E approach where neural network architecture varies across geographic locations. In addition, we provide a taxonomy of SVANN types and a physics inspired interpretation model. Experiments with aerial imagery based wetland mapping show that SVANN-I outperforms OSFA and SVANN-E performs the best of all.

scholarly journals Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design

2018 ◽  
Vol 8 (8) ◽  
pp. 1290 ◽  
Author(s):  
Beata Mrugalska
Keyword(s):  
Neural Network ◽  
Experimental Design ◽  
Fault Detection ◽  
Network Models ◽  
Neural Model ◽  
Neural Network Models ◽  
Optimum Experimental Design ◽  
The Neural Network ◽  
Adaptive Thresholds ◽  
Linear Neural Network

Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed.

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 Constructive Learning of Deep Neural Networks for Bigdata Analysis

2020 ◽  
Vol 9 (12) ◽  
pp. 311-322
Author(s):  
Soha Abd Mohamed El-Moamen ◽  
Marghany Hassan Mohamed ◽  
Mohammed F. Farghally
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Binary Classification ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Constructive Learning ◽  
The Neural Network ◽  
Rapid Pace ◽  
Better Than

The need for tracking and evaluation of patients in real-time has contributed to an increase in knowing people’s actions to enhance care facilities. Deep learning is good at both a rapid pace in collecting frameworks of big data healthcare and good predictions for detection the lung cancer early. In this paper, we proposed a constructive deep neural network with Apache Spark to classify images and levels of lung cancer. We developed a binary classification model using threshold technique classifying nodules to benign or malignant. At the proposed framework, the neural network models training, defined using the Keras API, is performed using BigDL in a distributed Spark clusters. The proposed algorithm has metrics AUC-0.9810, a misclassifying rate from which it has been shown that our suggested classifiers perform better than other classifiers.

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