scholarly journals Product image classification using Eigen Colour feature with ensemble machine learning

2018 ◽  
Vol 19 (2) ◽  
pp. 83-100 ◽  
Author(s):  
S.A. Oyewole ◽  
O.O. Olugbara
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Ensemble Machine Learning ◽  
Product Image ◽  
Colour Feature

Lung Nodule Image Classification Based on Ensemble Machine Learning

2016 ◽  
Vol 6 (7) ◽  
pp. 1679-1685 ◽  
Author(s):  
Mao Keming ◽  
Deng Zhuofu
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Lung Nodule ◽  
Ensemble Machine Learning

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Structure-Aware Deep Learning for Product Image Classification

2019 ◽  
Vol 15 (1s) ◽  
pp. 1-20 ◽  
Author(s):  
Zhineng Chen ◽  
Shanshan Ai ◽  
Caiyan Jia
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Product Image

A Novel Ensemble Machine Learning Method to Detect Phishing Attack

2020 ◽  
Author(s):  
Abdul Basit ◽  
Maham Zafar ◽  
Abdul Rehman Javed ◽  
Zunera Jalil
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Ensemble Machine Learning

scholarly journals Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

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