How do you choose the right CNN architecture for a specific task?

Jan 30, 2023 ... Abstract. In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural ... Mar 25, 2023 ... In this paper, we compared and explored the two methodologies on the DEMoS dataset consisting of 8869 audio files of 58 speakers in different emotional states. Current approaches to object recognition make essential use of machine learning methods. To im- prove their performance, we can collect larger datasets, learn ... Machine Learning in Clinical Neuroimaging - 5th International Workshop, MLCN 2022, Held in Conjunction with MICCAI 2022, Proceedings. Editors, Ahmed ... Nov 26, 2015 ... The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These ... Mar 25, 2025 ... We introduce CNN-TumorNet, an innovative method for brain tumor classification utilizing a deep convolutional neural network (CNN) for the ... Feb 10, 2020 ... The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models ... Feb 18, 2021 ... The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as ... ... machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation. Contact information for Technical Support and Student ... The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current ...