ANN Artificial Neural Network-2020


Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules

Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFD
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Sudden expansion of flow in supersonic flow regime has gained relevance in the recent pasts for a wide run of applications. A number of kinematic as well as geometric parameters have been significantly found to impact the base pressure created within the suddenly

Prediction of Adsorptive Capacity of Various Agricultural Wastes in the Removal of Heavy Metals, Dyes, and Antibiotic in Wastewater Using ANN
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Artificial Neural Network model was proposed for the prediction of the adsorptive capacity of various agricultural wastes in the removal of heavy metals, dyes, and antibiotic in water. A total amount of 103 data sets were obtained from different literature and was split into

Prediction of Load-Haul-Dumper (LHD) Machine Performance Characteristics using Feed-Forward-Back-propagation ANN Model
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Faced with intense global competition and to enhance the expected targets of production and productivity, every industry is constantly looking for maintaining the equipment in an efficient and effective manner. Hence, it is more essential to predict the performance of the

Approximation of Fractal Interpolation using Artificial Neural Network
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A new fractal interpolation method called NNA ( Neural Network Algorithm) based on IFS is proposed to interpolate the self-affine signals with the expected interpolation error. Experiments on the theoretical data and real field seismic data show that the proposed

Identifying Earth-impacting asteroids using an artificial neural network
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By means of a fully connected artificial neural network , we identified asteroids with the potential to impact Earth. The resulting instrument, named the Hazardous Object Identifier (HOI), was trained on the basis of an artificial set of known impactors which were generated

Hybrid teaching learning artificial neural network for city-level electrical load prediction
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Dear editor, Rapid economic growth, accompanied by structural changes, strongly affects the global trend of electrical energy consumption. Short-term electrical load forecasting plays a significant role in efficient energy management, fault diagnosis, and power system

Application of Artificial Neural Network and Climate Indices to Drought Forecasting in South-Central Vietnam
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Widespread negative consequences of droughts related to climate indices in Vietnam have motivated many studies integrating those indices to predict the onset of drought in the region. This study aims to examine the capacity of eight climate Pacific Ocean indices as

Artificial neural pseudo- network for production control purposes. LogForum 16 (1), 7-13
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Background: Experience from the implementation of the industry 4.0 concept has proved that the key success factor is the use of techniques and methods of artificial intelligence. One of these techniques is artificial neural networks. The development of artificial neural networks

Developing an Arabic Handwritten Recognition System by Means of Artificial Neural Network
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The matter of handwritten text recognition is as yet a major challenge to mainstream researchers. A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters

Prediction of blasting cost in limestone mines using gene expression programming model and artificial neural network
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The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146

Utilizing an Artificial Neural Network Model to Predict Bearing Capacity of Stone
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Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of

Optimized Functional Link Artificial Neural Network for Multi-label Classification
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Multi-label classification problem is a generalization of the traditional single-label classification, which makes the data more complex in nature. To handle the inherent complexity of multi-label data, a compact and efficient network known as functional link

Feature Selection Using Combined Particle Swarm Optimization and Artificial Neural Network Approach
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This paper deals with identification of the most influencing input attributes related to the accuracy of the prediction model. It is assumed that the prediction model be represented by any machine learning-based models, including artificial neural networks

A Pricing Model for the Nigerian Electricity Transmission Using Artificial Neural Network
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For the restructuring of Nigerias power sector to be effective, efforts must be made to evolve an excellent pricing methodology that will economically be able to accommodate disintegrated units like generating, transmitting and distributing/retailing units. This work is

Performance Analysis of EEG Signals using Conventional and Hybrid Artificial Neural Network
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Brain Computer Interface (BCI) using Electrooencephalogram (EEG) is one of the versatile tools to measure the brain thoughts and convert them to operate the external devices in the deficiency of biological controls. These techniques are used to develop the rehabilitative

Use of Artificial Neural Network for Inquiry Follow Up System in Sales Operations for Two-Wheeler Automotive Dealership.
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Artificial neural network can be the good classifier based on its capabilities of supervised learning and back propagation of the error. We have used this capability of ANN for distinguishing buyers and non-buyers in the automotive industry sales to save the time of the