AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning
Ensemble models achieve high accuracy by combining a number of base estimators and
can increase the reliability of machine learning compared to a single estimator. Additionally …
can increase the reliability of machine learning compared to a single estimator. Additionally …
Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries
The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in
different countries. To determine the policies and plans, the study of the relations between …
different countries. To determine the policies and plans, the study of the relations between …
Class imbalance ensemble learning based on the margin theory
The proportion of instances belonging to each class in a data-set plays an important role in
machine learning. However, the real world data often suffer from class imbalance. Dealing …
machine learning. However, the real world data often suffer from class imbalance. Dealing …
[HTML][HTML] Simultaneous design of fuzzy PSS and fuzzy STATCOM controllers for power system stability enhancement
The low frequency oscillations have always been the main problem of power system and
can lead to power angle instability, limiting the maximum power to be transmitted on tie-lines …
can lead to power angle instability, limiting the maximum power to be transmitted on tie-lines …
[HTML][HTML] A hybrid deep neural net learning model for predicting Coronary Heart Disease using Randomized Search Cross-Validation Optimization
Abstract Coronary Heart Disease (CHD) is a life-threatening public health problem. Many
chronic CHDs and health risks can be avoided, reversed, and reduced with proper risk …
chronic CHDs and health risks can be avoided, reversed, and reduced with proper risk …
Spatial distribution-based imbalanced undersampling
Undersampling is one of the most popular techniques for dealing with class-imbalance
problems. Various undersampling methods have emerged over the past few decades. Each …
problems. Various undersampling methods have emerged over the past few decades. Each …
Adaptive clustering-based malicious traffic classification at the network edge
The rapid uptake of digital services and Internet of Things (IoT) technology gives rise to
unprecedented numbers and diversification of cyber attacks, with which commonly-used rule …
unprecedented numbers and diversification of cyber attacks, with which commonly-used rule …
Tomato disease and pest diagnosis method based on the Stacking of prescription data
C Xu, J Ding, Y Qiao, L Zhang - Computers and Electronics in Agriculture, 2022 - Elsevier
Crop prescription data contains an extensive amount of information on crops, environment
and pests, and has notable diagnostic capabilities. At present, there is lack of feasible …
and pests, and has notable diagnostic capabilities. At present, there is lack of feasible …
Adaptive subspace optimization ensemble method for high-dimensional imbalanced data classification
It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which
the performance of classifiers is seriously affected and becomes poor. Although many …
the performance of classifiers is seriously affected and becomes poor. Although many …
A comprehensive study of clustering ensemble weighting based on cluster quality and diversity
A Nazari, A Dehghan, S Nejatian, V Rezaie… - Pattern Analysis and …, 2019 - Springer
Clustering as a major task in data mining is responsible for discovering hidden patterns in
unlabeled datasets. Finding the best clustering is also considered as one of the most …
unlabeled datasets. Finding the best clustering is also considered as one of the most …