Artificial Neural Network in Operation Management Regarding Communication Issue

Travelling Salesman Problem ◽

Operation Management ◽

Routing Problem ◽

Important Concern ◽

Management Area ◽

Long Time ◽

Artificial Neural ◽

Communication issue in operation management is important concern in the age of 21st century. In operation, communication can be described based on major three wings- Travelling Salesman Problem (TSP), Vehicle Routing Problem (VRP) and Transportation Problem (TP). Artificial Neural Network (ANN) is an important tool to handle these systems. In this chapter, different ANN based models are discussed in a comprehensive way. This chapter deals with how various approaches of ANN help to design the optimal communication network. This comprehensive study is important to the decision makers for the analytical consideration. Although there is a lot of development in this particular domain from a long time ago; but only the revolutionary contributed models are taken into account. Another motivation of this chapter is understanding the importance of ANN in the operation management area.

Prediction of Resilient Modulus for Hot Mix Asphalt Based on Artificial Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.304.18 ◽

2011 ◽

Vol 304 ◽

pp. 18-23

Author(s):

Chun Hua Hu

Keyword(s):

Neural Network ◽

Test Data ◽

Hot Mix Asphalt ◽

Resilient Modulus ◽

Structure Design ◽

Forecast Precision ◽

Long Time ◽

Artificial Neural ◽

Resilient modulus of material is an important parameter for pavement structure design and analysis. However it is very tedious to get this parameter for hot mixture asphalt in laboratory. Moreover it takes long time to do experiments. In this paper, artificial neural network (ANN) is applied to predict to resilient modulus for hot mixture asphalt. A neural network model is constructed and trained plenty of times with selected test data until precision meets requirement. Then the model is used to predict resilient modulus for hot mix asphalt. Result of contrast prediction with test data shows that forecast precision is high. This provides a new method to predict resilient modulus for hot mixture asphalt.

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

Energies ◽

10.3390/en12183454 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3454 ◽

Cited By ~ 5

Author(s):

Adolfo Crespo Márquez ◽

Antonio de la Fuente Carmona ◽

Sara Antomarioni

Keyword(s):

Neural Network ◽

Association Rule ◽

Performance Monitoring ◽

Predictive Analytics ◽

Practical Implementation ◽

Operational Conditions ◽

Important Concern ◽

Artificial Neural ◽

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.

Application of an Artificial Neural Network to Develop Fracture Toughness Predictor of Ferritic Steels Based on Tensile Test Results

Metals ◽

10.3390/met11111740 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1740

Author(s):

Kenichi Ishihara ◽

Hayato Kitagawa ◽

Yoichi Takagishi ◽

Toshiyuki Meshii

Keyword(s):

Neural Network ◽

Fracture Toughness ◽

Structural Integrity ◽

Steel Structures ◽

Large Temperature ◽

Ferritic Steels ◽

Long Time ◽

Artificial Neural ◽

Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (KJc) of ferritic steels in the ductile-to-brittle transition region. Consequently, predicting KJc from minimal testing has been of interest for a long time. In this study, a Windows-ready KJc predictor based on tensile properties (specifically, yield stress σYSRT and tensile strength σBRT at room temperature (RT) and σYS at KJc prediction temperature) was developed by applying an artificial neural network (ANN) to 531 KJc data points. If the σYS temperature dependence can be adequately described using the Zerilli–Armstrong σYS master curve (MC), the necessary data for KJc prediction are reduced to σYSRT and σBRT. The developed KJc predictor successfully predicted KJc under arbitrary conditions. Compared with the existing ASTM E1921 KJc MC, the developed KJc predictor was especially effective in cases where σB/σYS of the material was larger than that of RPV steel.

How to estimate and predict the expenses incurred by diabetes treatment using Artificial Neural Network (ANN)

Journal of Biology and Today s World ◽

10.15412/j.jbtw.01030101 ◽

2014 ◽

Vol 3 (1) ◽

Author(s):

M. Mahboubi ◽

M. Rahimi ◽

M. Mohebbi ◽

F. Ghahramani

Keyword(s):

Neural Network ◽

Diabetes Treatment ◽

Artificial Neural ◽

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

International Review of Automatic Control (IREACO) ◽

10.15866/ireaco.v12i3.16455 ◽

2019 ◽

Vol 12 (3) ◽

pp. 145 ◽

Cited By ~ 1

Author(s):

Epyk Sunarno ◽

Ramadhan Bilal Assidiq ◽

Syechu Dwitya Nugraha ◽

Indhana Sudiharto ◽

Ony Asrarul Qudsi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural ◽

LOAD SCHEDULING FOR SMART HOME USING DAY-AHEAD PREDICTION FROM ARTIFICIAL NEURAL NETWORK (ANN)

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/9291.42020 ◽

2020 ◽

Vol 9 (1.4) ◽

pp. 658-663

Author(s):

Joharry S. H.

Keyword(s):

Neural Network ◽

Smart Home ◽

Load Scheduling ◽

Artificial Neural ◽

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

Engineering and Technology Journal ◽

10.30684/etj.v38i2a.319 ◽

2020 ◽

Vol 38 (2A) ◽

pp. 255-264

Author(s):

Hanan A. R. Akkar ◽

Sameem A. Salman

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Average Rate ◽

Mean Square ◽

Performance Measurements ◽

Artificial Intelligent ◽

Average Mean Square Error ◽

Artificial Neural ◽

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.