It is obvious that the results of an ensemble of CNNs are better than just one single CNNs. Also, the proposed method introduces a new simple type of multi-focus images dataset. It simply changes the arranging of the patches of the multi-focus datasets, which is very useful for obtaining...
Taherkhani, Cosma, and McGinnity (2020) presented AdaBoost-CNN for multi-class imbalanced datasets using transfer learning. Kumar, Biswas, and Devi (2019) presented Tomek link undersampling-based boosting (TLUSBoost), which combines Tomek link and redundancy-based undersampling (TLRUS) (Devi, ...
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Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance of early detection in mitigating its impact. Addressing this challenge head-on, this study introduces an innovative deep learning fr
Deep neural networks have shown promising results in the classification of skin lesion images, particularly when they focus on the most significant regions of an image. However, the identification of melanoma continues to pose a significant challenge, primarily because of the substantial variability both...
for Intrusion Detection (HAEnID), an innovative and powerful method to enhance intrusion detection, different from the conventional techniques. HAEnID is composed of a string of multi-layered ensemble, which consists of a Stacking Ensemble (SEM), a Bayesian Model Averaging (BMA), and a ...
Anomaly detection is a highly important task in the field of data analysis. Traditional anomaly detection approaches often strongly depend on data size, structure and features, while introducing the idea of ensemble into anomaly detection can greatly imp
Recently, Wang et al. [72] proposed a fault estimation technique in conjunction with a fault tolerant control methodology for multi-input multi-output systems using the Q-learning algorithm. The proposed algorithm is validated using a robot numerical simulation, revealing improvements both in convergen...
Network (CNN) for optimal movie frame results. Genre determination is achieved through a bi-LSTM attention model. Studies [41,42] presents a probabilistic method, considering the importance of each background scene within different video categories, and recommends measuring shot length for varying ...
In this study, we present AtheroPoint’s GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep ...