Avais Jan

1. Introduction The need for reliable and autonomous vehicles is increasing with time. A vehicular ad hoc network (VANET) is crucial because it provides seamless and convenient communication between the infrastructure and the vehicle [1]. However, to ensure safety and efficiency between vehicles, real-time decision-making is very essential [2].

Abstract The accurate identification of B-cell epitopes is critical in antibody design, diagnostics, and immunotherapies. Many \textit{in silico} approaches have recently been proposed to predict epitopes, but these approaches struggle primarily because of the variational and conformational nature of epitopes. However, deep learning-based approaches have recently shown great promise in achieving better performance at the epitope prediction task.

Abstract A major challenge in computational antibody design is the accurate identification of antigen binding site, i.e., epitope. The current approaches to epitope prediction struggle because of the variational nature of epitopes and the lack of availability of experimental datasets. However, deep learning-based approaches have shown great promise in achieving better results for the epitope prediction task in recent years.