Mohammad Manthouri

TL;DR: An interval probability distribution learning (IPDL) model is proposed based on restricted Boltzmann machines and rough set theory to capture unsupervised temporal features from wind speed data to reveal significant performance improvement in 1-h up to 24-h ahead predictions.

TL;DR: A monolingual BERT for the Persian language (ParsBERT) is proposed, which shows its state-of-the-art performance compared to other architectures and multilingual models and obtains higher scores in all datasets, including existing ones as well as composed ones.

TL;DR: The combination of an Autoencoder neural network and a Long Short-Term Memory (LSTM) neural network is proposed for the estimation of the SOC of a battery with high precision and is observed that the SOC estimation by the proposed method yields to a significantly better accuracy at all three temperatures.

TL;DR: A novel multi-objective optimization algorithm based on ant colony algorithm (ACO) is recommended to solve the community detection problem in complex networks and successfully detects network structures and is competitive with the popular state-of-the-art approaches.

TL;DR: An ensemble learning-based approach is used to overcome the problem of class imbalance at the algorithmic level instead of the data level and an effective model called B2S is proposed by gathering the advantages of each of the ensemble-based methods, including Bagging, Boosting, and Stacking.