25 天前 - … evaluation for future Model ensemble improvements. The … One of the main objectives
of this activity is detection of … (CNNs) are noteworthy for their superior image processing …

63 天前 - … and artificial neural networks (ANN) for classification purposes. Venation … effectiveness
of leveraging pre-trained convolutional neural networks for complex image classification …

82 天前 - … ensemble model that uses Long ShortTerm Memory (LSTM) and Convolutional
Neural Network (… So, our method gives a good tool for farmers and experts in plant growth. It …

83 天前 - … We investigate the reasons for adversarial vulnerability, … We use ensemble models
of varying inherent … to convolutional neural networks used in image classification tasks, and …

88 天前 - … neural networks (CNNs) in image segmentation and … challenging for a number of
reasons, such as excessive high … results in the tasks of image classification. It has a learnable …

97 天前 - … The neural network design in Table 3 combines CNN … As a result, the purpose of
this research is to compile and … of Convolutional Neural NetworNs on image classification is …

104 天前 - … neural networks (CNN) and random forest (RF) for the … The model uses the CNN
deep learning capabilities for … CNN whose role in image recognition and classification tasks is …

112 天前 - … (AI) have proven effective for this purpose. The entire … detection through a
Convolutional Neural Network (CNN). … for defect image classification within ductile cast iron pipe (…

113 天前 - … for this purpose. Our approach is distinguished by a two-… image segmentation and
Convolutional Neural Networks (… it an efficient and effective solution for image classification …

154 天前 - … data on fauna uses motion-sensing cameras … Neural Networks (DNNs). I go onto
compare the performance, when applied to CT data, of DNNs designed for image classification …