Predicting Question Quality on StackOverflow with Neural Networks

The wealth of information available through the Internet and social media is unprecedented. Within computing fields, websites such as Stack Overflow are considered important sources for users seeking solutions to their computing and programming issues. However, like other social media platforms, Stack Overflow contains a mixture of relevant and irrelevant information. In this paper, we evaluated neural network models to predict the quality of questions on Stack Overflow, as an example of Question Answering (QA) communities. Our results demonstrate the effectiveness of neural network models compared to baseline machine learning models, achieving an accuracy of 80%. Furthermore, our findings indicate that the number of layers in the neural network model can significantly impact its performance.

Vehicle to Vehicle (V2V) Communication Protocol: Components, Benefits, Challenges, Safety and Machine Learning Applications

Vehicle to vehicle communication is a new technology that enables vehicles on roads to communicate with each other to reduce traffic, accidents and ensure the safety of people. The main objective of vehicle-to-vehicle communication protocol is to create an effective communication system for intelligent transport systems. The advancement in technology made vehicle industries to develop automatic vehicles that can share real-time information and protect each other from accidents. This research paper gives an explanation about the vehicle-to-vehicle communication process, benefits, and the challenges in enabling vehicle-to-vehicle communication as well as safety and machine learning applications.