With the popularity of green computing and the huge usage of networks,there is an acute need for expansion of the 5G network.5G is used where energy efficiency is the highest priority,and it can play a pinnacle role i...With the popularity of green computing and the huge usage of networks,there is an acute need for expansion of the 5G network.5G is used where energy efficiency is the highest priority,and it can play a pinnacle role in helping every industry to hit sustainability.While in the 5G network,conventional performance guides,such as network capacity and coverage are still major issues and need improvements.Device to Device communication(D2D)communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques.The issue of energy utilization in the IoT based system is a significant exploration center.Energy optimizationin D2D communication is an important point.We need to resolve this issue for increasing system performance.Green IoT speaks to the issue of lessening energy utilization of IoT gadgets which accomplishes a supportable climate for IoT systems.In this paper,we improve the capacity and coverage of 5G technology using Multiple Inputs Multiple Outputs(MU-MIMO).MUMIMO increases the capacity of 5G in D2D communication.We also present all the problems faced by 5G technology and proposed architecture to enhance system performance.展开更多
The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place i...The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.展开更多
基金The authors extend their heartfelt thanks to the Department of Computer Science,College of Computer Science and Engineering,Taibah University Madinah,Saudi Arabia.
文摘With the popularity of green computing and the huge usage of networks,there is an acute need for expansion of the 5G network.5G is used where energy efficiency is the highest priority,and it can play a pinnacle role in helping every industry to hit sustainability.While in the 5G network,conventional performance guides,such as network capacity and coverage are still major issues and need improvements.Device to Device communication(D2D)communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques.The issue of energy utilization in the IoT based system is a significant exploration center.Energy optimizationin D2D communication is an important point.We need to resolve this issue for increasing system performance.Green IoT speaks to the issue of lessening energy utilization of IoT gadgets which accomplishes a supportable climate for IoT systems.In this paper,we improve the capacity and coverage of 5G technology using Multiple Inputs Multiple Outputs(MU-MIMO).MUMIMO increases the capacity of 5G in D2D communication.We also present all the problems faced by 5G technology and proposed architecture to enhance system performance.
文摘The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.
