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Prediction of COVID-19 Transmission in the United States Using Google Search Trends

Computers, Materials & Continua 2022年第4期71卷 1751-1768页

作者： Meshrif Alruily Mohamed Ezz Ayman Mohamed mostafa Nacim Yanes mostafa abbas Yasser El-Manzalawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Faculty of Engineering Al-Azhar UniversityCairo11651Egypt Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt RIADI Laboratory La Manouba UniversityManouba2010Tunisia Department of Translational Data Science and Informatics GeisingerDanvillePA17822USA

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health *** to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the *** accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access *** objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related ***,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms *** the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,*** the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,*** 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,*** found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 *** also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

关键词： Forecasting COVID-19 transmission and mortality in the US stacked LSTM SARS-COV-2 and google COVID-19 search trends

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Solar thermal simulation and applications in greenhouse

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Information Processing in Agriculture 2018年第1期5卷 83-113页

作者： Morteza Taki abbas Rohani mostafa Rahmati-Joneidabad Department of Agricultural Machinery and Mechanization Ramin Agriculture and Natural Resources University of KhuzestanMollasaniIran Department of Biosystems Engineering Faculty of AgricultureFerdowsi University of MashhadIran Department of Horticultural Science Faculty of AgricultureRamin Agriculture and Natural Resources University of KhuzestanMollasaniIran

In this study,a comprehensive review focusing on key strategies of energy saving technologies based on simulation of heat and mass transfer and also artificial intelligent for climate controlling is *** the brief and concise assessment of existing greenhouse systems in terms of their role in total energy consumption;effective shape and structure,energy-efficient and new technologies are analyzed in detail for potential utilization in greenhouses for notable reductions in energy consumption and also go toward the *** technologies considered within the scope of this research are mainly renewable and sustainable based solutions such as photovoltaic(PV)modules,solar thermal(T)collectors,hybrid PV/T collectors and systems,phase change material(PCM)and underground based heat storage techniques,energy-efficient heat pumps,alternative facade materials for better thermal insulation and power *** findings from the research clearly reveal that up to 70%energy saving can be achieved through appropriate retrofit of conventional *** of solar greenhouses in Europe is more popular than *** some countries in Asia such as Iran,it is very restrict to invest on renewable projects because of cheap fossil *** it is recommended beside of investments by private investors,the Iranian government should also invest in the extension of solar energy in greenhouse by setting up a specialized agency or contracting *** should target the modeling and design the best shape of solar greenhouse for all agricultural areas to receive the maximum solar radiation and decrease the need of fossil fuels.

关键词： Agricultural greenhouse Sustainability Heat and mass transfer Modeling and simulation

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Transfer Learning-based Computer-aided Diagnosis System for Predicting Grades of Diabetic Retinopathy

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Computers, Materials & Continua 2022年第6期71卷 4573-4590页

作者： Qaisar abbas mostafa E.A.Ibrahim Abdul Rauf Baig College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityQalubiaBenha13518Egypt

Diabetic retinopathy(DR)diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate *** task is very difficult for ophthalmologists and ***,many computer-aided diagnosis(CAD)systems were developed to automate this screening process *** this paper,aCAD-DR system is proposed based on preprocessing and a pre-train transfer learningbased convolutional neural network(PCNN)to recognize the five stages of DR through retinal fundus *** develop this CAD-DR system,a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard pre-train PCNN model is improved to get high classification *** architecture of the PCNN model is based on three main ***,the training process of the proposed PCNN is accomplished by using the expected gradient length(EGL)to decrease the image labeling efforts during the training of the CNN ***,themost informative patches and images were automatically selected using a few pieces of training labeled ***,the PCNN method generated useful masks for prognostication and identified regions of ***,the DR-related lesions involved in the classification task such as micro-aneurysms,hemorrhages,and exudates were detected and then used for recognition of *** PCNN model is pre-trained using a high-end graphical processor unit(GPU)on the publicly available Kaggle *** obtained results demonstrate that the CAD-DR system outperforms compared to other state-of-the-art in terms of sensitivity(SE),specificity(SP),and accuracy(ACC).On the test set of 30,000 images,the CAD-DR system achieved an average SE of 93.20%,SP of 96.10%,and ACC of 98%.This result indicates that the proposed CAD-DR system is appropriate for the screening of the severity-level of DR.

关键词： Diabetic Retinopathy retinal fundus images computer-aided diagnosis system deep learning transfer learning convolutional neural network

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Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

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Computers, Materials & Continua 2022年第4期71卷 1999-2017页

作者： Qaisar abbas mostafa EAIbrahim Shakir Khan Abdul Rauf Baig College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityQalubiaBenha13518Egypt

Traffic accidents are caused by driver fatigue or distraction in many *** prevent accidents,several low-cost hypovigilance(hypo-V)systems were developed in the past based on a multimodal-hybrid(physiological and behavioral)feature *** in this paper,real-time driver inattention and fatigue(Hypo-Driver)detection system is proposed through multi-view cameras and biosignal sensors to extract hybrid *** considered features are derived from non-intrusive sensors that are related to the changes in driving behavior and visual facial *** get enhanced visual facial features in uncontrolled environment,three cameras are deployed on multiview points(0◦,45◦,and 90◦)of the *** develop a Hypo-Driver system,the physiological signals(electroencephalography(EEG),electrocardiography(ECG),electro-myography(sEMG),and electrooculography(EOG))and behavioral information(PERCLOS70-80-90%,mouth aspect ratio(MAR),eye aspect ratio(EAR),blinking frequency(BF),head-titled ratio(HT-R))are collected and pre-processed,then followed by feature selection and fusion *** driver behaviors are classified into five stages such as normal,fatigue,visual inattention,cognitive inattention,and *** improved hypo-Driver system utilized trained behavioral features by a convolutional neural network(CNNs),recurrent neural network and long short-term memory(RNN-LSTM)model is used to extract physiological *** fusion of these features,the Hypo-Driver system is classified hypo-V into five stages based on trained layers and dropout-layer in the deep-residual neural network(DRNN)*** test the performance of a hypo-Driver system,data from 20 drivers are *** results of Hypo-Driver compared to state-of-theart methods are *** to the state-of-the-art Hypo-V system,on average,the Hypo-Driver system achieved a detection accuracy(AC)of 96.5%.The obtained results indicate that the Hypo-Driver system based on multimodal and multivie

关键词： Internet of things(IoT) intelligent transportation sensors multiview points transfer learning convolutional neural network recurrent neural network residual neural network multimodal features

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Prediction of representative deformation modulus of longwall panel roof rock strata using Mamdani fuzzy system

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International Journal of Mining Science and Technology 2015年第1期25卷 23-30页

作者： Mohammad Rezaei mostafa Asadizadeh abbas Majdi Mohammad Farouq Hossaini School of Mining Engineering College of Engineering University of Tehran

Deformation modulus is the important parameter in stability analysis of tunnels, dams and mining struc- tures. In this paper, two predictive models including Mamdani fuzzy system （MFS） and multivariable regression analysis （MVRA） were developed to predict deformation modulus based on data obtained from dilatometer tests carried out in Bakhtiary dam site and additional data collected from longwall coal mines. Models inputs were considered to be rock quality designation, overburden height, weathering, unconfined compressive strength, bedding inclination to core axis, joint roughness coefficient and fill thickness. To control the models performance, calculating indices such as root mean square error （RMSE）, variance account for （VAF） and determination coefficient （R^2） were used. The MFS results show the significant prediction accuracy along with high performance compared to MVRA results. Finally, the sensitivity analysis of MFS results shows that the most and the least effective parameters on deformation modulus are weatherin~ and overburden height, respectively.

关键词： Deformation modulusDilatometer testMamdani fuzzy systemMultivariable regression analysis

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An ensemble deep learning model for cyber threat hunting in industrial internet of things

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： abbas Yazdinejad mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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Experimental Study on Improving Performance and Productivity of Pyramid Solar StillUsing Rotation Technique

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Frontiers in Heat and Mass Transfer 2024年第3期22卷 955-976页

作者： Ali Abdullah abbas Baiee Sasan Asiaei Sayed mostafa Hosseinalipour School of Mechanical Engineering Iran University of Science and TechnologyTehranIran

Globally,potable water scarcity is pervasive *** solar distillation device is a straightforward apparatus that has been purposefully engineered to convert non-potable water into potable *** experimental study is distinctive due to the implementation of a rotational mechanism within the pyramidal solar still(PSS),which serves to enhance the evaporation and condensation *** objective of this research study is to examine the impact of integrating rotational motion into pyramidal solar stills on various processes:water distillation,evaporation,condensation,heat transfer,and energy waste reduction,shadow effects,and low water temperature in saline ***,the study aims to enhance the production of distilled *** economic evaluation was undertaken in order to ascertain the extent of cost *** measuring freshwater productivity and thermal performance were conducted over a three-month period at the University of Science and Technology in *** entire pyramid structure was rotated using a direct current motor driven by a photovoltaic *** research methodology entailed the operation of a PSS with varying rotational speeds(0.125,0.25,1,and 1.5 rpm)and without rotation,from 9 am to 4 *** findings suggested that the productivity of the distillation apparatus in terms of distilled water increased as the rotation speed rose,with the most pronounced increase occurring at 1 rpm in comparison to the other *** presence of turbulence in the water enhanced the heat transfer occurring between the absorber plate and *** 2:00 *** an experimental day,this effect was observed when the absorber plate temperature reached 79.1°C at 1.5 *** contrast,its temperature decreased to 78°C when not in a state of rotation,as the intensity of solar radiation was higher in the non-rotation *** 1 rpm,the solar pyramid distiller achieved a 30.2%increase in output compared to its non-rotating *** 1 rp

关键词： Pyramidal solar still rotation solar radiation gain evaporation condensation productivity thermal efficiency

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Determining endemic values of cutaneous leishmaniasis in Iranian Fars province by retrospectively detected clusters and receiver operating characteristic curve analysis

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Asian Pacific Journal of Tropical Biomedicine 2019年第9期9卷 359-364页

作者： Marjan Zare abbas Rezaianzadeh Hamidreza Tabatabaee Hossain Faramarzi Mohsen Aliakbarpour mostafa Ebrahimi Department of Epidemiology School of HealthShiraz University of Medical SciencesShirazIran Colorectal Research Center Shiraz University of Medical ScienceShirazIran Research Center for Health Sciences Shiraz University of Medical SciencesShirazIran Department of Community Medicine Medical SchoolShiraz University of Medical SciencesShirazIran Department of Communicable Diseases Shiraz University of Medical ScienceShirazIran

Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and the endemic values of cutaneous leishmaniasis were determined by retrospective clusters derived from spatiotemporal permutation modeling on a time-series design. The accuracy of the values was assessed using receiver operating characteristic(ROC) curve. SPSS version 22, Arc GIS, and ITSM 2002 software tools were used for analysis. Results: Nine statistically significant retrospective clusters(P<0.05) resulted in finding seven significant and accurate endemic values(P<0.1). These valid endemic scores were generalized to the other 18 cities based on 6 different climates in the province. Conclusions: Retrospectively detected clusters with the help of ROC curve analysis could help determine cutaneous leishmaniasis endemic values which are essential for future prediction and prevention policies in the area.

关键词： Cutaneous leishmaniasis Retrospective clusters Receiver operating characteristic curve Endemic values Fars province

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Spatiotemporal clustering of cutaneous leishmaniasis in Fars province,Iran

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Asian Pacific Journal of Tropical Biomedicine 2017年第10期7卷 862-869页

作者： Marjan Zare abbas Rezaianzadeh Hamidreza Tabatabaee Mohsen Aliakbarpoor Hossein Faramarzi mostafa Ebrahimi Department of Epidemiology School of HealthShiraz University of Medical SciencesShirazIran Department of Social Medicine School of HealthShiraz University of Medical SciencesShirazIran

Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis(CL) in Fars province, ***: Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. Time-series data were recorded from 29 201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using Sa TScan ***: This study resulted in statistically significant spatiotemporal clusters of CL(P < 0.05). The most likely cluster contained 350 cases from 1 July 2010 to 30 November2010. Besides, 5 secondary clusters were detected in different periods of time. Finally,statistically significant sub-clusters were found within the three large clusters(P < 0.05).Conclusions: Transmission of CL followed spatiotemporal pattern in Fars province,Iran. This can have an important effect on future studies on prediction and prevention of CL.

关键词： Cutaneous leishmaniasis Scan statistics Spatiotemporal clustering Clusters within a cluster

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Applied machine learning in greenhouse simulation;new application and analysis

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Information Processing in Agriculture 2018年第2期5卷 253-268页

作者： Morteza Taki Saman Abdanan Mehdizadeh abbas Rohani Majid Rahnama mostafa Rahmati-Joneidabad Department of Agricultural Machinery and Mechanization Ramin Agriculture and Natural Resources University of KhuzestanMollasaniIran Department of Biosystems Engineering Faculty of AgricultureFerdowsi University of MashhadMashhadIran Department of Horticultural Science Faculty of AgricultureRamin Agriculture and Natural Resources University of KhuzestanMollasaniIran

Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot *** greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve *** the main goal of this study is to select the best method between Artificial Neural Network(ANN)and Support Vector Machine(SVM)to estimate three different variables include inside air,soil and plant temperatures(Ta,Ts,Tp)and also energy exchange in a polyethylene greenhouse in Shahreza city,Isfahan province,*** environmental factors which influencing all the inside temperatures such as outside air temperature,wind speed and outside solar radiation were collected as data *** this research,13 different training algorithms were used for ANN models(MLPRBF).Based on K-fold cross validation and Randomized Complete Block(RCB)methodology,the best model was *** results showed that the type of training algorithm and kernel function are very important factors in ANN(RBF and MLP)and SVM models performance,*** RBF,MLP and SVM models showed that the performance of RBF to predict Ta,Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 *** range of RMSE and MAPE factors for RBF model to predict Ta,Tp and Ts were between 0.07 and 0.12C and 0.28-0.50%,*** and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data *** performance of best model(RBF)to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy.

关键词： Black box method Energy lost Environmental situation Energy exchange

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