scholarly journals Machine learning-based structure-property predictions in silica aerogels

Soft Matter ◽  
2021 ◽  
Author(s):  
Rasul Abdusalamov ◽  
Prakul Pandit ◽  
Barbara Milow ◽  
Mikhail Itskov ◽  
Ameya Rege
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Silica Aerogels ◽  
Structural Features ◽  
Structure Property ◽  
Cluster Aggregation ◽  
Diffusion Limited ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The structural features in silica aerogels are known to be modelled effectively by the diffusion-limited cluster-cluster aggregation (DLCA) approach. In this paper, an artificial neural network (ANN) is developed for...

Fundamental Categories of Artificial Neural Networks

2021 ◽  
pp. 30-64
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

Fundamental Categories of Artificial Neural Networks

2022 ◽  
pp. 1-30
Author(s):  
Arunaben Prahladbhai Gurjar ◽  
Shitalben Bhagubhai Patel
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Mathematical Operation ◽  
Artificial Neural ◽  
Classification Prediction ◽  
Artificial Neural Network Ann ◽  
Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

scholarly journals Artificial Neural Network Algorithms for 3D Printing

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 163
Author(s):  
Muhammad Arif Mahmood ◽  
Anita Ioana Visan ◽  
Carmen Ristoscu ◽  
Ion N. Mihailescu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
3D Printing ◽  
Process Parameters ◽  
Optimization Methods ◽  
Physical Models ◽  
Network Algorithms ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

scholarly journals Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marwah Sattar Hanoon ◽  
Ali Najah Ahmed ◽  
Nur’atiah Zaini ◽  
Arif Razzaq ◽  
Pavitra Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Unseen Data ◽  
Temperature And Humidity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractAccurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

Artificial Neural Network and Its Application in Steel Industry

2016 ◽  
pp. 267-300 ◽  
Author(s):  
Itishree Mohanty ◽  
Dabashish Bhattacherjee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Computational Intelligence ◽  
Steel Industry ◽  
Empirical Modelling ◽  
Precise Knowledge ◽  
Recent Developments ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent developments in computational intelligence has enhances the applicability of empirical modelling in different areas particularly in the area of machine learning. These new approaches are based on analysing the data about a system, in particular finding connections between the system state variables (input, internal and output variables) without having precise knowledge about the physical behaviour of the system. These data driven methods explain advances on conventional empirical modelling and include contributions from many overlapping fields like Artificial Intelligence (AI), Computational Intelligence (CI), Soft Computing (SC), Machine Learning (ML), Intelligent Data Analysis (IDA), and Data Mining (DM). The most popular computational intelligence techniques used in process modelling of steel industry includes neural networks, fuzzy rule-based systems, genetic algorithms as well as approaches to model integration. This chapter describes mainly the application of Artificial Neural Network (ANN) in steel industry. ANN has extensively used in improving and controlling different processes of steel industry like steel making, casting and rolling which lead to indirect energy savings through reduced product rejects, improved productivity and reduced down time. The efficiency of artificial neural network tool in handling steel plant processes has been discussed in detail. ANN based models are found to be very potential to handle very complex, dynamic and non-linear problems.

Definition of Artificial Neural Network

2018 ◽  
pp. 32-46
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Learning Tools ◽  
Activation Functions ◽  
Mathematical Definition ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Definition Of

This chapter is an explanation of artificial neural network (ANN), which is one of the machine learning tools applied for medical purposes. The biological and mathematical definition of neural network is provided and the activation functions effective for processing are listed. Some figures are collected for better understanding.

scholarly journals Perbandingan Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel

2021 ◽  
Vol 6 (1) ◽  
pp. 29
Author(s):  
Moch Shandy Tsalasa Putra ◽  
Yufis Azhar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Management Systems ◽  
Complex Data ◽  
Hotel Performance ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Modern Era

Prediction for canceled booking hotels is an important part of hotel revenue management systems in the modern era. Because the predicted result can be used for the optimization of hotel performance. The application of machine learning will be very helpful for predicting canceled booking hotels because machine learning can process complex data. In this research, the proposed methods are Artificial Neural Network (ANN) and Logistic Regression. Later it will be done five times experiments with hyperparameter tuning to see which method is the most optimal to do prediction canceled booking hotel. From five times experiments, experiments number five (logistic regression with GridSearchCV) is the most optimal for predicting canceled booking hotels, with 79.77% accuracy, 85.86% precision, and 55.07% recall.

scholarly journals An Overview of Convolutional Neural Network: Its Architecture and Applications

2018 ◽  
Author(s):  
Shadman Sakib ◽  
Nazib Ahmed ◽  
Ahmed Jawad Kabir ◽  
Hridon Ahmed
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Strategies ◽  
Learning Tasks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

Artificial Neural Network and Its Application in Steel Industry

2016 ◽  
pp. 138-171 ◽  
Author(s):  
Itishree Mohanty ◽  
Dabashish Bhattacherjee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Computational Intelligence ◽  
Steel Industry ◽  
Empirical Modelling ◽  
Precise Knowledge ◽  
Recent Developments ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent developments in computational intelligence has enhances the applicability of empirical modelling in different areas particularly in the area of machine learning. These new approaches are based on analysing the data about a system, in particular finding connections between the system state variables (input, internal and output variables) without having precise knowledge about the physical behaviour of the system. These data driven methods explain advances on conventional empirical modelling and include contributions from many overlapping fields like Artificial Intelligence (AI), Computational Intelligence (CI), Soft Computing (SC), Machine Learning (ML), Intelligent Data Analysis (IDA), and Data Mining (DM). The most popular computational intelligence techniques used in process modelling of steel industry includes neural networks, fuzzy rule-based systems, genetic algorithms as well as approaches to model integration. This chapter describes mainly the application of Artificial Neural Network (ANN) in steel industry. ANN has extensively used in improving and controlling different processes of steel industry like steel making, casting and rolling which lead to indirect energy savings through reduced product rejects, improved productivity and reduced down time. The efficiency of artificial neural network tool in handling steel plant processes has been discussed in detail. ANN based models are found to be very potential to handle very complex, dynamic and non-linear problems.

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