In this study, three different neural network algorithms (feed forward back propagation, FFBP; radial basis function; generalized regression neural network) and wavelet transformation were used for daily precipitation predictions. Different input combinations were tested for the precipitation estimation. As ...
This, in turn, would require the neural network to "remember" previously encountered information and factor that into future calculations. And the problem of remembering goes beyond videos: For example, manynatural language understandingalgorithms typically deal only with text, but need to recall infor...
Fourth, there are other essential topics in AES applications such as fairness and algorithms’ vulnerability to cheating behavior. Future studies could compare feature-based and embedding-based AES models regarding fairness (Schaller et al.,2024) and cheating behavior in trait assessment (see, e.g....
One of the major findings in this study is that, like EEG features, HRV-derived features based on machine learning algorithms can also distinguish different anaesthesia states. Moreover, Liu et al. used only the similarity and distribution index of HRV based on an artificial neural network to ...
We briefly introduce three state of the art algorithms. FBCSP: FBCSP7is a two-stage method. Firstly, they adopt a group of band-pass filters and CSP algorithm to extract the optimal spatial features from a specific frequency band, and then the classifier is trained to classify the extracted ...
We have tried boosted algorithms (XGBoost, LightGBM, Catboost) and fully connected neural network (FCNN) in this project. There are some technical differences in the application of different algorithms that needs to noticed: Handling missing data: XGBoost, LightGBM, and Catboost can handle missing ...
RNN Recurrent neural network SARIMA Seasonal autoregressive integrated moving average model SN Seasonal naive forecast Keywords Deep learning LSTM SARIMA Temperature forecasts 1. Introduction Short-term temperature forecasts are required in many applications. Demand for such estimates has especially increased in...
algorithms and models for machine vision. One of the key components of these methods is the utilization of the computational principles underlying biological neurons. Additionally, advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual ...
In this paper, we present an experiment to evaluate the performance of deep-learning-based steganalysis methods under different steganographic algorithms and payloads. We designed and implemented a simple and efficient convolutional neural network (CNN) steganalysis model. This model combines SRM [10] ...
Deep learning has been applied to image recognition, speech recognition, video synthesis, and drug discoveries. In addition, it has been applied to complex creations, like self-driving cars, which use deep learning algorithms to identify obstacles and perfectly navigate around them. You must feed l...