scholarly journals Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models

2018 ◽  
Vol 10 (6) ◽  
pp. 1-9 ◽  
Author(s):  
Abimbola R. Iyanda ◽  
◽  
Olufemi D. Ninan ◽  
Anuoluwapo O. Ajayi ◽  
Ogochukwu G. Anyabolu
Keyword(s):  
Neural Network ◽  
Academic Performance ◽  
Computer Science ◽  
Network Models ◽  
Neural Network Models ◽  
Science Courses ◽  
Computer Science Courses ◽  
Student Academic Performance

ANALYSIS AND PREDICTION OF TRENDS IN THE USE OF TERMS IN COMPUTER SCIENCE BASED ON NEURAL NETWORK MODELS

2021 ◽  
pp. 24-38
Author(s):  
T. V. Nguyen ◽  
Q. H. T. Duong ◽  
A. G. Kravets
Keyword(s):  
Neural Network ◽  
Computer Science ◽  
Information And Communication Technologies ◽  
Digital Libraries ◽  
Network Models ◽  
Parameters Optimization ◽  
Research Trend ◽  
Sudden Increase ◽  
Neural Network Models ◽  
Usage Frequency

The widespread use of information and communication technologies, database technologies and the Internet has led to the development of specialized digital libraries. These digital libraries serve a huge number of different users and play an important role as repositories and providers of information and knowledge. Therefore, the automatic extraction of useful information from texts stored in digital libraries is becoming an increasingly important research topic in the field of data mining. The article discusses the statistical analysis of texts in the digital library arXiv.org to identify the most common terms, bigrams and trigrams. After the hyper-parameters optimization process of neural network models, the trend prediction results in the use of terms in the field of computer sciences are presented. By analyzing statistics and predicting usage frequency of bigram and trigram terms our findings provide evidence that papers concerned with machine learning, reinforcement learning, generative adversarial network, convolutional neural network and recurrent neural network can be seen as main future research trend in Computer science in the next 3 years. Moreover, topics related to will experience a sudden increase in usage frequency. Being able to predict scientific trends in advance could potentially revolutionize the way science is done, for instance, by enabling funding agencies to optimize allocation of resources towards promising research areas.

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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