An efficient multi-objective task scheduling in edge computing using adaptive honey badger optimisation
Task scheduling, which is important in cloud computing, is one of the most challenging issues in this area. Hence, an efficient and reliable task scheduling approach is needed to produce more efficient resource employment. So, a multi-objective-based ...
A cultural industry text classification method based on knowledge graph information constraints and knowledge fusion
The study proposes a text classification method for the cultural industry. It uses knowledge graph information constraints and fusion. A knowledge graph is constructed for the cultural industry text, extracting entities and relationships with ...
DLSTMFRNN - a newly developed network-based deep long short-term memory and recurrent neural network for stock market prediction
The stock market (SM) is fundamentally nonlinear in nature and the people invest in SM on the basis of predictions. The SM prediction is a highly challenging and complex process. The classical techniques may not guarantee the prediction reliability. ...
Automatic text summarisation system for scientific papers on the basis of T5 model, on-the-fly constructed corpus and citations
Automatic text summarisation is considered as an important application of automatic natural language processing especially with the unstoppable increasing of information around us. In this paper, we introduce a summarisation prototype based on T5 model ...
A real-time semantic segmentation method for small objects using attention mechanism
Semantic segmentation is an important problem in the field of computer vision, and its goal is to assign a semantic label to each pixel in an image. The effect of the traditional model on the segmentation of large objects such as vehicles and buildings ...
