A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences.

In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.

Optimal energy management of distributed generation in micro-grids using artificial bee colony algorithm.

The use of renewable energy sources in energy distribution networks as distributed generation sources for dispersed and low consumption loads in remote areas such as remote villages and islands with low population can be a proper solution for reducing economic costs, reducing environmental pollutions and increasing energy efficiency. The purpose of this paper is optimal operation management of micro-grids by considering the existing capacities in the electricity market. In fact the microgrid operator, which is responsible for the safe operation of the network, should consider a process for planning in the network that takes into account all benefits of micro-grid's components.

Real-time detection of microgrid islanding considering sources of uncertainty using type-2 fuzzy logic and PSO algorithm.

In this paper, a new innovative type-2 fuzzy-based for microgrid (MG) islanding detection is proposed in the condition of uncertainties. Load and generation uncertainties are two main sources of uncertainties in microgrids (MGs). Regardless of the uncertainties, the results cannot be confirmed.