Access to the internet inside moving vehicles with the proper speed and reliability creates several challenges to avail the service to user's satisfaction, unmanned car operation, traffic control, etc. A Vehicular ad-hoc Network (VANET) is familiar as the appropriate model for broadcasting data among running objects in a certain topographical range and behaves as an intelligent system for road safety and traffic management. A Greedy routing scheme is widely preferred to set a dedicated dynamical path from the source to the destination with the moving bodies that result in several unwanted intermediate nodes as the obstacle to the signal frequencies. The greater number of forwarding nodes in a certain range creates several issues such as process delay, packet loss, etc. leading to a slower and resource-consuming process that is not suitable as the next-generation data dissemination model for ad-hoc network. Hence, a novel Neuro-fuzzy forwarder scheme is proposed for the elimination of undesirable nodes for the achievement of better signal strength with improved performance. Furthermore, the comparative study demonstrates expected performance in path reliability with less latency in end-to-end connection after the node optimization with the proposed system.

Summary In the process of modern software development, an important role is played by software effort estimation. The failure or success of the projects is mostly based on the schedule outcomes and effort estimation accuracy. The effort estimation problem still remains as a challenging issue because of the limitations of various standard measures for forecasting the efforts in the plan‐driven software development. This article emphasizes on novel software effort estimation framework using heuristically improved hybrid learning model. This article proposes heuristically improved hybrid learning (HI‐HL) with deep belief network and artificial neural network for software effort estimation. The weight optimization strategy of DBN and ANN to attain highly accurate estimation. Here, the weight optimization is performed by integrating two standard optimization algorithms such as forest optimization algorithm and moth‐flame optimization, so called as solution index‐based forest moth flame optimization with the purpose of solving the fitness function concerning the “Magnitude of Relative Error and Mean Absolute Error (MAE).” From the table results, for dataset 1, the SMAPE of SI‐FMFO‐HI‐HL is correspondingly secured 60.75%, 34.26%, 56.77%, and 61.44% improved than ELM, LSTM, DNN, and fuzzy. The simulation findings indicated that the recommended estimation model outperformed the baseline approaches on the three publically available datasets.