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Found 12 papers, 5 papers with code
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors
After extracting features from smartphone-embedded sensors, various machine learning methods can be used to identify the driver.
Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives
The evaluation results on the network-based process show the best results for the artificial neural network model with an F1 score of 88. 68%.
Low-rank Dictionary Learning for Unsupervised Feature Selection
Due to the hardship of labeling on these datasets, there are a variety of approaches on feature selection process in an unsupervised setting by considering some important characteristics of data.
Adaptive Low-Rank Regularization with Damping Sequences to Restrict Lazy Weights in Deep Networks
The experimental results show that ALR regularizes the deep networks well with high training speed and low resource usage.
Similarity Preserving Unsupervised Feature Selection based on Sparse Learning
Various feature selection methods have been recently proposed on different applications to reduce the computational burden of machine learning algorithms as well as the complexity of learned models.
Diverse and Styled Image Captioning Using SVD-Based Mixture of Recurrent Experts
We show that the proposed captioning model can generate a diverse and stylized image captions without the necessity of extra-labeling.
Adaptive Low-Rank Factorization to regularize shallow and deep neural networks
However, this scheme similar to [1] probably decreases the training accuracy when it tries to decrease the number of operations.
Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering
Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms.
Regularized Deep Networks in Intelligent Transportation Systems: A Taxonomy and a Case Study
Both of these paradigms are utilized in the recent intelligent transportation systems (ITS) to support decision-making by the aid of different operations such as frequent patterns mining, regression, clustering, and classification.
Modified SMOTE Using Mutual Information and Different Sorts of Entropies
These four pre-processing methods are combined with 1NN and J48 classifiers and their performance are compared with the previous methods on 11 imbalanced datasets from KEEL repository.
A real-time warning system for rear-end collision based on random forest classifier
Rear-end collision warning system has a great role to enhance the driving safety.
A real-time decision support system for bridge management based on the rules generalized by CART decision tree and SMO algorithms
Under dynamic conditions on bridges, we need a real-time management.
