Learning from Negative News by Substituting Negative Images with Word2vec – A new technique called negative image enhancement (NNE) has been proposed to exploit image attributes such as background, background color and foreground in a way that can increase the quality of a visual scene. However, only a limited amount of training data is available for the NNE approach. This paper proposes a novel approach based on the use of the image dimensionality score to enhance the quality of the image in a deep learning framework. We show that our proposed technique can effectively enhance the image in the same way as the image dimensionality score would enhance. The evaluation on several popular image enhancement benchmarks shows that our proposed method significantly improves performance compared to other similar approaches.
Many recent studies have demonstrated that human EEG data is noisy given the presence of noise and its interaction with the EEG signal. These noisy studies also have applications such as monitoring traffic in cities and monitoring weather conditions. We propose a novel approach for analyzing and estimating the presence of noise in a human EEG signal. The approach is based on a novel unsupervised approach which focuses on the presence of noise in the human EEG signal to estimate the noise and the interference in the data signal. Our proposed analysis is based on the use of the noise-weighted metric in the classification of the EEG signals. The accuracy of the estimated noise in the human EEG signal is calculated using multiple noisy data points and the input signal is ranked according to its interference level and the interference level in the noisy input. A weighted average signal is used in the estimation. The final outcome of the estimation algorithm is a weighted prediction value that is an unbiased estimate from the noisy input. Experiments on human EEG data obtained using real and noisy EEG measurements show that the proposed approach produces a good estimate of the noise and the interference of the human EEG signal.
The Statistical Analysis Unit for Random Forests
Morphon: a collection of morphological and semantic words
Learning from Negative News by Substituting Negative Images with Word2vec
Matching Strategies for Multi-Object Tracking with Variational Autoencoders
Flexible and Adaptive Approach to Noise Removal in Distributed Data ProtectionMany recent studies have demonstrated that human EEG data is noisy given the presence of noise and its interaction with the EEG signal. These noisy studies also have applications such as monitoring traffic in cities and monitoring weather conditions. We propose a novel approach for analyzing and estimating the presence of noise in a human EEG signal. The approach is based on a novel unsupervised approach which focuses on the presence of noise in the human EEG signal to estimate the noise and the interference in the data signal. Our proposed analysis is based on the use of the noise-weighted metric in the classification of the EEG signals. The accuracy of the estimated noise in the human EEG signal is calculated using multiple noisy data points and the input signal is ranked according to its interference level and the interference level in the noisy input. A weighted average signal is used in the estimation. The final outcome of the estimation algorithm is a weighted prediction value that is an unbiased estimate from the noisy input. Experiments on human EEG data obtained using real and noisy EEG measurements show that the proposed approach produces a good estimate of the noise and the interference of the human EEG signal.