scholarly journals Process-Aware Enterprise Social Network Prediction and Experiment Using LSTM Neural Network Models

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Dinh-Lam Pham ◽  
Hyun Ahn ◽  
Kyoung-Sook Kim ◽  
Kwanghoon Pio Kim
Keyword(s):  
Neural Network ◽  
Social Network ◽  
Network Models ◽  
Neural Network Models ◽  
Network Prediction ◽  
Enterprise Social Network

Merger Premium Predictions Using a Neural Network Approach

2005 ◽  
Vol 2 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Tara J. Shawver
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Network Approach ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Bank Merger ◽  
Financial Variables ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Merger Premiums

Over 80 percent of mergers fail to achieve projected financial, strategic, and operational synergies (Marks and Mirvis 2001). It is critical for management to find accurate models to price merger premiums. Management has an interest to protect stakeholders by acquiring companies that can add value to their investments at the most favorable price. Published studies in the area of pricing mergers have not attempted to use expert systems in the decision-making process. This paper is the first of its kind that describes the development and testing of neural network models for predicting bank merger premiums accurately. A neural network prediction model provides a tool that can filter through noise and recognize patterns in complicated financial relationships. The results confirm that a neural network approach provides more explanation between the dependent and independent financial variables in the model than a traditional regression model. The higher level of accuracy provided by a neural network approach can provide practitioners with a competitive advantage in pricing merger offers.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

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

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

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