An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
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Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
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Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
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Review on Data Securing Techniques for Internet of Medical Things
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Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
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Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
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Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
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Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 4 | Issue - 3 | september 2022
Published
16 September, 2022
The prediction of earthquakes, which can be devastating calamities, has proven to be a challenging research area. Because it involves filtering data to disturbed day changes, the contribution from multi-route effects and typical day-to-day fluctuations even on quiet days, the extraction of earthquake-induced features from this parameter requires intricate processing. Nevertheless, many researchers have successfully used several seismological concepts for computing the seismic features, employing the maximum Relevance and Minimum Redundancy (mRMR) criteria to extract the relevant features. The Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are the primary soft computing tools that can be collaborated to detect and estimate earthquakes positively. The model in ANFIS is developed using subtractive clustering and grid partitioning procedures. The outcome shows that compared to ANFIS, ANN is more effective at predicting earthquake magnitude. Furthermore, it has been discovered that using this method to estimate earthquake magnitude is highly quick and cost-effective. Compared to earlier prediction studies, the acquired numerical findings show enhanced prediction performance for all the regions considered.
KeywordsEarthquake prediction neural network machine learning neuro-fuzzy interference performance enhancement
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