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| 1 | Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method显示文摘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 pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To 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 features.To 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 classification.Further,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 accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our 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. | 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 | 2022 | Computers, Materials & Continua2022，，9： | 2 |
| 2 | Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset显示文摘Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the brain.An EEG headset is a wearable device that records electrophysiological data from the brain.This paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV system.The electrode placement locations are modified under a 10–20 standard system.The fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and cost.First,the data is recorded from 20 subjects who used the EEG Headset,and signals were recorded.Secondly,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide changing.The raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,thesefindings have been compared to previously reported studies.We concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 percent.We also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use. | Riaz Muhammad Ahmed Ali M.Abid Anwar Toufique Ahmed Soomro Omar AlShorman Adel Alshahrani Mahmoud Masadeh Ghulam Md Ashraf Naif H.Ali Muhammad Irfan Athanasios Alexiou | 2023 | Intelligent Automation & Soft Computing2023，，3： | 0 |
| 3 | Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation显示文摘A brain tumor is a mass or growth of abnormal cells in the brain.In children and adults,brain tumor is considered one of the leading causes of death.There are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)tumors.Diagnosing brain tumors as early as possible is essential,as this can improve the chances of successful treatment and survival.Considering this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain tumors.These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas.The dataset consists of 120 patients.The pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region correctly.We have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient loss.From pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%accuracy.In contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region. | Muhammad Irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman | 2023 | Computers, Materials & Continua2023，，7： | 0 |
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