The role of artificial neural network and machine learning in utilizing spatial information

Published in

Spatial Information Research(2022)

External Links:

TL;DR

The study presents that the Recurrent neural network and convolutional neural network are the best method in spatial information processing, healthcare, and weather forecasting with greater than 90% accuracy.

Abstract

In this age of the fourth industrial revolution 4.0, the digital world has a plethora of data, including the internet of things, mobile, cybersecurity, social media, forecasts, health data, and so on. The expertise of machine learning and artificial intelligence (AI) is required to soundly evaluate the data and develop related smart and automated applications, These fields use a variety of machine learning techniques including supervised, unsupervised, and reinforcement learning. The objective of the study is to present the role of artificial neural networks and machine learning in utilizing spatial information. Machine learning and AI play an increasingly important role in disaster risk reduction from hazard mapping and forecasting severe occurrences to real-time event detection, situational awareness, and decision assistance. Some of the applications employed in the study to analyze the various ANN domains included weather forecasting, medical diagnosis, aerospace, facial recognition, stock market, social media, signature verification, forensics, robotics, electronics hardware, defense, and seismic data gathering. Machine learning determines the many prediction models for problems involving classification, regression, and clustering using known variables and locations from the training dataset, spatial data that is based on tabular data creates different observations that are geographically related to one another for unknown factors and places. The study presents that the Recurrent neural network and convolutional neural network are the best method in spatial information processing, healthcare, and weather forecasting with greater than 90% accuracy.

Authors

A. Goel

1 papers

A. Goel

1 papers

Adesh Kumar

1 papers

References55 items

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A Deep Learning-Based Comparative Study to Track Mental Depression from EEG Data

Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review

Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

The role of artificial neural network and machine learning in utilizing spatial information

Published in

Spatial Information Research(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

A. Goel

1 papers

A. Goel

1 papers

Adesh Kumar

1 papers

References55 items

Forensic bone age estimation of adolescent pelvis X-rays based on two-stage convolutional neural network

Methodology for Interactive Labeling of Patched Asphalt Pavement Images Based on U-Net Convolutional Neural Network

A Deep Learning-Based Comparative Study to Track Mental Depression from EEG Data

Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review

Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Spatial machine learning: new opportunities for regional science

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