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IEEE8mon
Carabids hierarchical classification with ensemble model architecture
However, advances in machine learning and computer vision technologies have transformed ... It was trained from scratch to enable accurate classification of Carabids. The image dataset used for ...
GitHub3y
Induction-Motor-Faults-Detection-with-Stacking-Ensemble-Method-and-Deep-Learning
We use Stacking Ensembles method (with Random Forest, Support Vector Machine, Deep Neural Network and Logistic Regression) and Machinery Fault Dataset dataset ... performance than any single model in ...
Frontiers1y
Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization
In the past, many research works have been carried out by applying the machine learning techniques to ... diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, ...
IEEE19d
A Hybrid Dependable Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection
Deep learning (DL) and machine ... image-based publicly available Invasive Ductal Carcinoma (IDC) dataset comprising samples of different sizes. The results obtained from our rigorous experiments ...
Frontiers2y
EmergeNet: A novel deep-learning based ensemble segmentation model for emergence timing detection of coleoptile
Therefore, we have developed a publicly available benchmark dataset called the University of Nebraska-Lincoln Maize Emergence Dataset (UNL-MED) consisting of time-lapse image ... ensemble ...