Neutrosophic set, proposed by Smarandache considers a truth membership function, an indeterminacy membership function and a falsity membership function. Soft set, proposed by Molodtsov is a mathematical framework which has the ability of independency of parameterizations inadequacy, syndrome of fuzzy set, rough set, probability. Those concepts have been utilized successfully to model uncertainty in several areas of application such as control, reasoning, game theory, pattern recognition, and computer vision. Nonetheless, there are many problems in real-world applications containing indeterminate and inconsistent information that cannot be effectively handled by the neutrosophic set and soft set. In this paper, we propose the notation of bipolar neutrosophic soft sets that combines soft sets and bipolar neutrosophic sets. Some algebraic operations of the bipolar neutrosophic set such as the complement, union, intersection are examined. We then propose an aggregation bipolar neutrosophic soft operator of a bipolar neutrosophic soft set and develop a decision making algorithm based on bipolar neutrosophic soft sets. Numerical examples are given to show the feasibility and effectiveness of the developed approach.

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Electricity load forecasting plays an important role in the operation of power systems. Inaccurate forecast would reduce the safety of power supply and affect the economic and social activities as well as national defense and security. In addition, the forecast results also support decision-making on electricity generation and market transactions. Traditional methods such as AR, ARIMA, SARIMA have been widely used to forecast short term electricity load. Recently, load forecasting based on artificial and deep neural networks have shown significant accuracy improvement over traditional statistical models. In this research, a novel recurrent neural network named temporal fusion transformer (TFT) is used to forecast short-term electricity load of Hanoi city. The TFT is a newly developed model and it combines the advantages of several other RNN models such as LSTM and the self-attention mechanism. In addition to historical load data, we use temperature and humidity features, and time features such as calendar month, lunar month, days of the week, hours of the day and holidays. The forecast results of TFT are compared with traditional statistical models as well as well-known RNN models. The compared results show that the proposed method is better than other methods in both MAE and MAPE criteria.INDEX TERMS power systems, load forecasting, artificial intelligence, recurrent neural network, temporal fusion transformer.

Abstract-RDF (Resource Description Framework) ontologies has been playing an important role for many knowledge applications because they support a source of precisely defined terms. However, the wide-spread of RDF ontologies creates a demand for automatic way of assessing their similarity. In this paper, we present a novel method to measure the semantic similarity between elements in different RDF ontologies. This measure is designed so as to enable extraction of information encoded in RDF element descriptions and to take into account the element relationships with its ancestors and children. We evaluate the proposed measures in the context of matching two RDF ontologies to determine the number of matches between them and then compare with human estimation and the related methods. The experimental results show that our similarity values are better than other approaches with regard to the accuracy of semantics and structure similarities.