Multi-directional temporal convolutional artificial neural network for PM2.5 forecasting with missing values: A deep learning approach

Urban Climate ◽  
2021 ◽  
Vol 36 ◽  
pp. 100800
Author(s):  
K. Krishna Rani Samal ◽  
Korra Sathya Babu ◽  
Santos Kumar Das
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Missing Values ◽  
Learning Approach ◽  
Artificial Neural

Neural networks and Keras

2019 ◽  
pp. 66-90
Author(s):  
Thomas P. Trappenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Basic Operation ◽  
Neuron Models ◽  
Recent Success ◽  
Artificial Neurons ◽  
Artificial Neural ◽  
The Brain

This chapter discusses the basic operation of an artificial neural network which is the major paradigm of deep learning. The name derives from an analogy to a biological brain. The discussion begins by outlining the basic operations of neurons in the brain and how these operations are abstracted by simple neuron models. It then builds networks of artificial neurons that constitute much of the recent success of AI. The focus of this chapter is on using such techniques, with subsequent consideration of their theoretical embedding.

scholarly journals Deep Extreme Learning Machine and Its Application in EEG Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shifei Ding ◽  
Nan Zhang ◽  
Xinzheng Xu ◽  
Lili Guo ◽  
Jian Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Eeg Classification ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Learning Machine

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

scholarly journals Modeling the Influence of the Composition of Refractory Elements on the Heat Resistance of Nickel Alloys by a Deep Learning Artificial Neural Network

2020 ◽  
Author(s):  
Dmitry TARASOV ◽  
Oleg Milder ◽  
Andrei Tiagunov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
High Temperature ◽  
Nickel Alloys ◽  
Rupture Strength ◽  
Chemical Degradation ◽  
High Temperature Strength ◽  
Refractory Elements ◽  
Artificial Neural

Nickel alloys are widely used in the production of gas turbine parts. The alloys show resistance to mechanical and chemical degradation under severe long-term stress and high temperatures. One of the major mechanical properties of the alloys is the high-temperature rupture strength, which is measured after a specimen is heated to a certain temperature and held for a certain time considering deformation. Determining the influence of certain elements on the properties of an alloy is a complex scientific and engineering problem that affects the time and cost of developing new materials. Simulation is a great chance to cut costs. In this paper, we predict a high-temperature strength based on the composition of refractory elements in alloys using a deep learning artificial neural network. We build the model based on prior knowledge of the composition of the alloys, information on the role of alloying elements, type of crystallization, test temperature and time, and the tensile strength. Successful simulation results show the applicability of this method in practice.

scholarly journals PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING

2020 ◽  
Vol 25 (1) ◽  
pp. 40
Author(s):  
Stefanus Santosa ◽  
Suroso Suroso ◽  
Marchus Budi Utomo ◽  
Martono Martono ◽  
Mawardi Mawardi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Deep Learning ◽  
Information Needs ◽  
Chemical Properties ◽  
Stochastic Gradient Descent ◽  
Design Data ◽  
Artificial Neural

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.

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