Abstract:
A vast number of new technologies are emerging day by day in various industries. These technologies are being constructed and updated frequently. Therefore, it creates difficulties for the developers to suggest and predict the technologies which will be trending in the future and overcoming this challenge will lead them to invest their time in a productive manner and get expertized in their working processes. Analyzing the trending technologies regarding their product operations and lifecycles is important to extract the knowledge of prediction. This creates a gap in between the IT individuals and the upcoming technologies in finding which will yield a potential effect in the development of their industries. Since many programming platforms such as Questions and Answers (Q&A) are used to overcome the difficulty, the developers are still struggling to make predictions about the trending patterns of the technologies in a more specific way. This remains as the current challenge for the developers in the IT industry. Based on this challenge, this project introduces a system to make appropriate predictions on the trending technologies in the future by extracting information from Q&A platforms.
The prediction technique involves many key factors such as, neural network prediction and various data analytical techniques. The data are extracted as knowledge and information extraction from predefined documents. To improve the architecture of the system machine learning (supervised learning) and efficient algorithms are used. The system is analyzed and constructed in a way so that the user can productively manage their time in finding the trending patterns of emerging technologies in the future. Once the questions are posted in the Q&A platforms the tags that are created will identify the technology and analyze the trending variations which will be used in the reviewing of trending processes in the following months. This basis of the project is developed by incorporating the above techniques in-order to find a productive solution for this current challenge in the prediction and suggestions of technologies.
