Development of Sustainable Wireless Resource model through Shrewd Neural Network

Due to an increasing number of wireless spectrums,the network components are tangling with multiple frequencies and the result create hindrance in resource management process.During resource management process,data leakage is one of the sensitive enigma that requires an astute consideration.Considering all these issues,a sustainable wireless resource management proposal(DSWR-SNN)has been developed by incorporating a shrewd Neural Network.The resources are managed by testing performance of each network component connected wirelessly through dataset testing which matches the results from the dataset corpus.The performance of the proposed DSWR-SNN method has been compared with state of the art studies Hopfield Neural Network(HNN),Radio Resource Management(RRM),and Deep Q-Network(DQN),and results are evaluated by conducting simulation using Python with TensorFlow based on Bandwidth Utilization,Duplicate Packet Handling,Data Leakage,and Energy Consumption.The result illustrates the marvelous performance of the proposed method and effective in addressing the challenges of resource allocation in wireless communication systems.

Uncertainties of landslide susceptibility prediction:Influences of different spatial resolutions,machine learning models and proportions of training and testing dataset

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.