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
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
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
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
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 - 3 | Issue - 2 | june 2021
Published
25 May, 2021
Extreme Learning Machine (ELM) is one of the latest trends in learning algorithm, which can provide a good recognition rate within less computation time. Therefore, the algorithm can sustain for a faster response application by utilizing a feed-forward neural network. In this research article, the ELM method has been designed with the presence of sigmoidal function of biases in the hidden nodes to perform the classification task. The classification task is very challenging with the existing learning algorithm and increased computation time due to the huge amount of dataset. While handling of the stochastic matrix for hidden layer, output provides the lower performance for learning rate and robustness in the determination. To address these issues, the modified version of ELM has been developed to obtain better accuracy and minimize the classification error. This research article includes the mathematical proof of sigmoidal activation function with biases of the hidden nodes present in the networks. The output matrix maintains the column rank in order to improve the speed of the training output weights (β). The proposed improved version of ELM leverages better accuracy and efficacy in classification and regression problems as well. Due to the inclusion of matrix column ranking in mathematical proof, the proposed improved version of ELM solves the slow training speed and over-fitting problems present in the existing learning approach.
KeywordsExtreme Learning Machine
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