With the continuous improvement of deep learning algorithms, we can carry out more accurate analysis for more complex spectral data in the future.doi:10.1016/j.ijleo.2021.166423Laixiang XuFuhong CaiYuxin HuZhen LinQian LiuOptik - International Journal for Light and Electron Optics...
(Alpaydin, 2014). In terms ofclassification algorithmsused for HAR, current techniques can be categorized into two types: conventionalclassification algorithmsanddeep learningalgorithms. The conventional classification algorithms attempt to build a complete description of the input with aprobabilistic model...
We realized the problem of satellite image classification as a semantic segmentation problem and built semantic segmentation algorithms in deep learning to tackle this. Algorithms Implemented UNet - GT with RGB channels PSPNet - GT with RGB channels UNet with One Hot Encoded GT PSPNet with One Hot...
In recent years, deep learning has attracted increasing attention [1]. The previous machine learning methods have various limitations. For example, when there are few samples, it is highly difficult to represent complex functions. When using deep learning algorithms to represent complex data ...
We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/.Similar content being viewed by others A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer Article Open access 09 February ...
To improve the classification performance of machine learning algorithms, a common approach is to input extracted features in place of the original signal data. The features provide a representation of the input data that makes it easier for a classification algorithm to discriminate across th...
compared to traditional ML algorithms. This is because the number of parameters that need to be learned is much higher than most other learning algorithms. Second, DL requires significant hyperparameter tuning. Many of these hyperparameters are controlling the training of a DL model, and finding ...
[1]Learning Internal Representations by Error Propagation [2]Deep learning for time series classification: a review [3]The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version [4]HIVE-COTE: The Hierarchical Vote Collective of Transformati...
Conventional classification algorithms are not effective in case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning, in which we formulate the ...
Artificial intelligence (AI) has been applied in medical image classification via deep learning algorithms trained on massive amounts of supervised data1,2. Gulshan et al.3 used deep learning to create an algorithm for the automated detection of two ocular diseases in retinal fundus photographs base...