scholarly journals Prediction of Heart Disease using Artificial Neural Network

2018 ◽  
pp. 102-112 ◽  
Author(s):  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Accuracy Rate ◽  
Real World Application ◽  
Attribute Extraction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Matching Techniques

Heart disease is increasing rapidly due to number of reasons. If we predict cardiac arrest (dangerous conditions of heart) in the early stages, it will be very helpful to cured this disease. Although doctors and health centres collect data daily, but mostly are not using machine learning and pattern matching techniques to extract the knowledge that can be very useful in prediction. Bioinformatics is the real world application of machine learning to extract patterns from the datasets using several data mining techniques. In this research paper, data and attributes are taken from the UCI repository. Attribute extraction is very effective in mining information for the prediction. By utilizing this, various patterns can be derived to predict the heart disease earlier. In this paper, we enlighten the number of techniques in Artificial Neural Network (ANN). The accuracy is calculated and visualized such as ANN gives 94.7% but with Principle Component Analysis (PCA) accuracy rate improve to 97.7%.

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 The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

2017 ◽  
Vol 7 (4) ◽  
pp. 2183
Author(s):  
Wiharto Wiharto ◽  
Harianto Herianto ◽  
Hari Kusnanto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Assessment Model ◽  
Model Assessment ◽  
Marquardt Algorithm ◽  
One Step ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level. </p>

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 A Conceptual Artificial Neural Network Model in Warehouse Receiving Management

2021 ◽  
Vol 11 (2) ◽  
pp. 130-136
Author(s):  
Judy X Yang ◽  
◽  
Lily D Li ◽  
Mohammad G. Rasul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mechanical Design ◽  
Joint Probability ◽  
Initial Result ◽  
Ann Model ◽  
Accuracy Rate ◽  
Proposed Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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.

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