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Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

Computers, Materials & Continua 2022年第10期73卷 533-546页

作者： Mahmoud Ragab hesham A.abdushkour Alaa F.Nahhas Wajdi H.Aljedaibi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Nautical Science Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced *** recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare *** aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical *** recently developed deep learning(DL)models find a way for accurate lung nodule classification ***,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)*** proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise ***,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet *** addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule ***,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification *** experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various *** experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.

关键词： Lung cancer image classification computer aided diagnosis deep learning medical imaging parameter optimization

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

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Computers, Materials & Continua 2022年第9期72卷 4249-4265页

作者： Muhammad Irfan Ali Raza Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim hesham abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadWah Campus22060Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabad44000Pakistan School of Electrical Engineering and Computer Science National University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial ***,electricity demand and price forecasting play a significant role and can help in terms of reliability and *** to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG *** this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as *** minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant *** this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the ***,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test *** evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark *** proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

关键词： Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting

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Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

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Computers, Materials & Continua 2022年第7期72卷 2141-2158页

作者： Muhammad Irfan Nasir Ayub Faisal Althobiani Zain Ali Muhammad Idrees Saeed Ullah Saifur Rahman Abdullah Saeed Alwadie Saleh Mohammed Ghonaim hesham abdushkour Fahad Salem Alkahtani Samar Alqhtani Piotr Gas Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science Federal Urdu University of Science and TechnologyIslamabad44000Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical and Power Engineering AGH University of Science and Technology Mickiewicza 30 AvenueKrakow30-059Poland

One of the major concerns for the utilities in the Smart Grid(SG)is electricity *** the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously *** this purpose,we have taken historical data of energy thieves and normal *** avoid imbalance observation,biased estimates,we applied the interpolation ***,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further *** proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time *** minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark *** proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,***,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

关键词： Smart grids and meters electricity theft detection machine learning AdaBoost optimization techniques

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