Published Paper Details:

The ability to notice the initial signs of potential problems can play a key role, as it can ensure effective operation of the process itself, minimize costs and improve the decision making process within the sphere of healthcare, cyberspace, finance, agriculture, IoT devices and much more. In previous approaches the rules needed to be created manually, threshold value to be set up and basic statistical methods were utilized which proved ineffective for large amounts of complex data. The emergence of artificial intelligence revolutionized the industry bringing the idea of deep learning to the new level of popularity and efficiency. Today's computer-based algorithms are capable of analyzing vast amounts of data independently, revealing patterns and anomalies invisible to humans. Moreover, due to the deep learning models the data can be additionally analyzed with the aim of quicker detection of abnormalities. There are numerous concepts that one must be aware of such as CNN, RNN, ANN and LSTMs, not mentioning other deep learning-based models utilized to forecast events, detect problems and analyze features in real-life situations. This research paper aims to explore how artificial intelligence helps detect issues during the very early stages focusing on innovative deep learning techniques. In this paper, there are several parts that discuss neural networks that have been employed by deep learning techniques. The math processes that help train neural networks, the structure and activation function of neural networks, along with ways of improving training processes in terms of efficiency will be examined. In addition, there are some popular data sets used by researchers, software packages and their advantages and disadvantages, along with the issues related to deep learning technology today. Moreover, feature learning via deep learning can bring about better prediction results, while large data management becomes easy with artificial intelligence.