Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons a...Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector.Computer aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical images.The recently developed deep learning(DL)models find a way for accurate lung nodule classification process.Therefore,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)model.The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal.Besides,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model.In addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule classification.Finally,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance.The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects.The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.展开更多
The fiber nonlinearity and phase noise(PN)are the focused impairments in the optical communication system,induced by high-capacity transmission and high laser input power.The channels include high-capacity transmissio...The fiber nonlinearity and phase noise(PN)are the focused impairments in the optical communication system,induced by high-capacity transmission and high laser input power.The channels include high-capacity transmissions that cannot be achieved at the end side without aliasing because of fiber nonlinearity and PN impairments.Thus,addressing of these distortions is the basic objective for the 5G mobile network.In this paper,the fiber nonlinearity and PN are investigated using the assembled methodology of millimeter-wave and radio over fiber(mmWave-RoF).The analytical model is designed in terms of outage probability for the proposed mmWave-RoF system.The performance of mmWave-RoF against fiber nonlinearity and PN is studied for input power,output power and length using peak to average power ratio(PAPR)and bit error rate(BER)measuring parameters.The simulation outcomes present that the impacts of fiber nonlinearity and PNcan be balanced for a huge capacity mmWave-RoF model applying input power carefully.展开更多
基金This work was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,Under Grant No.(D-782-980-1443).
文摘Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages.Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector.Computer aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical images.The recently developed deep learning(DL)models find a way for accurate lung nodule classification process.Therefore,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)model.The proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise removal.Besides,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet model.In addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule classification.Finally,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification performance.The experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various aspects.The experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.
基金The authors acknowledge the support from the Deanship of Scientific Research,Najran University.Kingdom of Saudi Arabia,for funding this work under the research groups funding program grant code number(NU/RG/SERC/11/3).
文摘The fiber nonlinearity and phase noise(PN)are the focused impairments in the optical communication system,induced by high-capacity transmission and high laser input power.The channels include high-capacity transmissions that cannot be achieved at the end side without aliasing because of fiber nonlinearity and PN impairments.Thus,addressing of these distortions is the basic objective for the 5G mobile network.In this paper,the fiber nonlinearity and PN are investigated using the assembled methodology of millimeter-wave and radio over fiber(mmWave-RoF).The analytical model is designed in terms of outage probability for the proposed mmWave-RoF system.The performance of mmWave-RoF against fiber nonlinearity and PN is studied for input power,output power and length using peak to average power ratio(PAPR)and bit error rate(BER)measuring parameters.The simulation outcomes present that the impacts of fiber nonlinearity and PNcan be balanced for a huge capacity mmWave-RoF model applying input power carefully.
