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Efficient Intelligent E-Learning Behavior-Based Analytics of Student’s Performance Using Deep Forest Model

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Computer Systems Science & Engineering 2024年第5期48卷 1133-1147页

作者： Raed Alotaibi Omar reyad Mohamed Esmail Karar Applied College Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Faculty of Computers and Artificial Intelligence Sohag UniversitySohag82524Egypt Faculty of electronic Engineering Menoufia UniversityMenouf32952Egypt

E-learning behavior data indicates several students’activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in *** article proposes a new analytics systemto support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning *** proposed e-learning analytics system includes a new deep forest *** consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural *** developed forest model can analyze each student’s activities during the use of an e-learning platform to give accurate expectations of the student’s performance before ending the semester and/or the final *** have been conducted on the Open University Learning Analytics Dataset(OULAD)of 32,593 *** proposed deep model showed a competitive accuracy score of 98.0%compared to artificial intelligence-based models,such as ConvolutionalNeuralNetwork(CNN)and Long Short-TermMemory(LSTM)in previous *** allows academic advisors to support expected failed students significantly and improve their academic level at the right ***,the proposed analytics system can enhance the quality of educational services for students in an innovative e-learning framework.

关键词： E-learning behavior data student evaluation artificial intelligence machine learning

Peak Load Evaluation Based on the Accumulated Annual Energy

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Journal of Energy and Power Engineering 2012年第11期6卷 1758-1763页

作者： Nabeel Tawalbeh reyad E1-khazali electrical Engineering Department Faculty of Engineering and Technology University of Jordan Amman 11942 Jordan ECE Department Khalifa University of Science Technology and Research Sharjah P.O. Box 573 UAE

This paper presents a nonlinear approach to estimate the consumed energy in electric power distribution feeders. The proposed method uses the statistical solution algorithm to analyze the active energy monthly consumption, which enables one to estimate the energy consumption during any period of the year. The energy readings and the normalized accumulated energy profile are used to estimate the hourly consumed active power, which can be used for future planning and sizing the equipment of the electrical system. The effectiveness of the proposed method is demonstrated by comparing the simulated results with that of real measured data.

关键词： Peak load accumulated energy diversity factor load pattern conversion factor load estimation.

Developed Fall Detection of elderly Patients in Internet of Healthcare Things

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Computers, Materials & Continua 2023年第8期76卷 1689-1700页

作者： Omar reyad Hazem Ibrahim Shehata Mohamed Esmail Karar College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Faculty of Computers and Artificial Intelligence Sohag UniversitySohag82524Egypt Faculty of Engineering Zagazig UniversityZagazig44519Egypt Faculty of electronic Engineering Menoufia UniversityMenouf32952Egypt

Falling is among the most harmful events older adults may *** the continuous growth of the aging population in many societies,developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes *** paper presents a new healthcare Internet of Health Things(IoHT)architecture built around an ensemble machine learning-based fall detection system(FDS)for older *** to deep neural networks,the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal *** number of cascaded random forest stages is automatically *** study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection *** SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a *** scenario in this dataset is classified and labeled as a fall or a *** comparison to the three machine learning models—K-nearest neighbors(KNN),decision tree(DT),and standard random forest(SRF),the proposed ensemble classifier outperformed the other models and achieved 98.4%*** developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.

关键词： elderly population fall detection wireless sensor networks Internet of health things ensemble machine learning

Serum homocysteine,lipid profile and BMI as atherosclerotic risk factors in children with numerical chromosomal aberrations

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World Journal of Pediatrics 2022年第6期18卷 443-448页

作者： Eman R.Youness Sherien Mel-Daly Hanaa reyad Abdallah Hala T.el-Bassyouni Hisham Megahed Azzah A.Khedr Marwa elhady Walaa Alsharany Abuelhamd Medical Biochemistry Department Medical Research DivisionNational Research CentreGizaEgypt Biological Anthropology Department Medical Research DivisionNational Research CentreGizaEgypt Clinical Genetics Department Human Genetics and Genome Research DivisionNational Research CentreEl-Tahrir StreetPO:12622GizaEgypt Human Cytogenetics Department Human Genetics and Genome Research DivisionNational Research CentreGizaEgypt Pediatric Department Faculty of Medicine for GirlsAl-Azhar UniversityCairoEgypt Department of Pediatrics Faculty of MedicineCairo UniversityCairoEgypt

Down syndrome,Turner syndrome,and Klinefelter syndrome are the most common chromosomal disorders *** syndrome is caused by an extra copy of the 21st chromosome and has an incidence of 1 in 800 to 1000 live *** syndrom... 详细信息

关键词： chromosomal Turner Serum

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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Computer Systems Science & Engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： elderly population fall detection wireless sensor networks internet of medical things deep forest

Adsorptive potential of Acacia nilotica based adsorbent for chromium(Ⅵ) from an aqueous phase

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Chinese Journal of Chemical Engineering 2018年第3期26卷 614-622页

作者： Raza Khalid Zaheer Aslam Aamir Abbas Waqar Ahmad Naveed Ramzan reyad Shawabkeh Department of Chemical Engineering University of Engineering and Technology Lahore 54890 Pakistan Department of Chemical Engineering King Fahd University of Petroleum and Minerals Dhahron 31261 Saudi Arabia

The objective of this research was to enhance adsorption capacity of Acacia nilotica （keekar） sawdust for the abatement of chromium bearing wastewater and to investigate the effect of process parameters on adsorption capacity. The sawdust was activated by acid wash and functionalized subsequently with formaldehyde. Functionalization of activated sawdust raised its chromium removal efficiency of almost 10% as compared to its adsorption removal efficiency of HCl treated sawdust in a batch adsorption study. Adsorption kinetic data provided better fitting with pseudo second order model. Maximum adsorption capacity calculated through the best fitting Langmuir model was 6.34 mg·g^-1 and 8.2 mg·g^-1 for HCl treated and formaldehyde functionalized sawdust adsorbents, respectively. The adsorption of Cr（VI） was endothermic when studied by varying temperature from 20℃ to 50℃ for both activated and functionalized adsorbents.

关键词： Acacia nilotica Chromium Adsorption Kinetics Isotherms Thermodynamics

SeqCor:correct the effect of guide RNA sequences in clustered regularly interspaced short palindromic repeats/Cas9 screening by machine learning algorithm

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Journal of Genetics and Genomics 2020年第11期47卷 672-680页

作者： Xiaojian Liu Yuanyuan Yang Yan Qiu Md reyad-ul-ferdous Qiurong Ding Yi Wang CAS Key Laboratory of Nutrition Metabolism and Food SafetyShanghai Institute of Nutrition and HealthShanghai Institutes for Biological SciencesUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Institute for Stem Cell and Regeneration Chinese Academy of SciencesBeijing 100101China Ministry of Education Key Laboratory of Contemporary Anthropology Department of Anthropology and Human GeneticsSchool of Life SciencesFudan UniversityShanghai 200438China Human Phenome Institute Fudan UniversityShanghai 200438China

Clustered regularly interspaced short palindromic repeats(CRISPR)/Cas9-based screening using various guide RNA(g RNA)libraries has been executed to identify functional components for a wide range of phenotypes with regard to numerous cell types and *** data from public CRISPR/Cas9-based screening experiments,we found that the sequences of g RNAs in the library influence CRISPR/Cas9-based *** building a standard strategy for correcting results of all g RNA libraries is impractical,we developed Seq Cor,an open-source programming bundle that enables researchers to address the result bias potentially triggered by the composition of g RNA sequences via the organization of g RNA in the library used in CRISPR/Cas9-based ***,Seq Cor completely computerizes the extraction of sequence features that may influence single-guide RNA knockout efficiency using a machine learning *** together,we have developed a software program bundle that ought to be beneficial to the CRISPR/Cas9-based screening platform.

关键词： SeqCor CRISPR/Cas9-based screening Machine learning

Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

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Computers, Materials & Continua 2021年第12期69卷 4095-4111页

作者： Mohamed Esmail Karar Omar reyad Abdel-Haleem Abdel-Aty Saud Owyed Mohd F.Hassan College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityEgypt Faculty of Science Sohag UniversitySohag82524Egypt Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Mathematics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Computer and Information Science CeRDaSUniversiti Teknologi PetronasMalaysia

Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural ***,Internet of Things(IoT)technology can be applied to monitor and detect harmful insect pests such as red palm weevils(RPWs)in the farms of date palm *** this paper,we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier,namely *** sound sensors,namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the *** trees are labeled based on the sensor node number to identify the infested ***,the acquired audio signals are sent to a cloud server for further on-line analysis by our fine-tuned deep transfer learning model,i.e.,*** proposed infestation classifier has been successfully validated on the public TreeVibes *** includes total short recordings of 1754 samples,such that the clean and infested signals are 1754 and 731 samples,*** to other deep learning models in the literature,our proposed InceptionResNet-V2 classifier achieved the best performance on the public database of TreeVibes audio *** resulted classification accuracy score was 97.18%.Using 10-fold cross validation,the fine-tuned InceptionResNet-V2 achieved the best average accuracy score and standard deviation of 94.53%and±1.69,*** the proposed intelligent IoT-aided detection system of RPWs in date palm farms is the main prospect of this research work.

关键词： Red palm weevils smart precision agriculture internet of things artificial intelligence

Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

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Computers, Materials & Continua 2021年第11期69卷 2295-2312页

作者： Mohamed Esmail Karar Omar reyad Mohammed Abd-elnaby Abdel-Haleem Abdel-Aty Marwa Ahmed Shouman College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Mathematics and Computer Science Faculty of ScienceSohag UniversitySohag82524Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Physics College of SciencesUniversity of BishaBisha61922Saudi Arabia Department of Physics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 ***,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 *** this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US *** to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware *** main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US *** image dataset(POCUS)was used to validate our proposed COVID-LWNet framework *** classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this *** results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,*** paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

关键词： Coronavirus medical image processing artificial intelligence ultrasound