A review on big data based on deep neural network approaches

Big data analytics has become a significant trend for many businesses as a result of the daily acquisition of enormous volumes of data. This information has been gathered because it may be important for dealing with complicated problems including fraud, marketing, healthcare, and cyber security. Analytics are used by big businesses like Facebook and Google to analyse and make decisions about their massive volumes of collected data. Such analyses and decisions impact both the present and the future of technology. The inherent non-linear properties in huge data may be captured by deep learning (DL) algorithms using automated feature extraction techniques. In order to estimate renewable energy, energy consumption, demand, and supply, among other things, over the short, medium, and long term, a complete and in-depth investigation of generative, hybrid, and discriminative DL models is being conducted. This article examines the benefits and drawbacks of DL that depends on a variety of deep neural networks, including recurrent neural networks, multilayer neural networks, auto encoders and long short-term memory.