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Access to news in our time has become very easy, for a person only has to browse social media and will be in direct contact with everything that is going on in the world. Still, despite these positives, many news and information spread are fake and unreal, aiming to mislead public opinion. In this paper, we study fake news detection with machine learning. Right now, we live in a world of misinformation and fake news. The goal of this study is to detect fake news using recurrent neural networks. AI (Artificial Intelligence) and ML (Machine Learning)-based counterfeit news detectors are crucial for companies and media to predict whether circulating news is fake or not automatically. In this study, we analyze thousands of news texts to detect if they are fake or not. Our model is a type of artificial neural network known as LSTM or long short-term memory network which is type of recurrent neural networks. The primary purpose is to classify the news and achieve good results at the end of the project. [ABSTRACT FROM AUTHOR]

In this paper, we study a class of nonlocal functional evolution inclusions in the form of multivalued perturbations of m–dissipative operators. We prove some new existence results in case of dissipative type conditions on the right-hand side. Illustrative example is provided. [ABSTRACT FROM AUTHOR]

In this paper we study nonlocal semilinear fractional evolution inclusions involving Riemann-Liouville derivative with right-hand side depending on casual operator. The existence of solutions is proved under compactness type conditions on the multivalued term. [ABSTRACT FROM AUTHOR]