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Crystal structure guided machine learning for the discovery and design of intrinsically hard materials

Journal of Materiomics 2022年第3期8卷 678-684页

作者： Russlan Jaafreh tamer abuhmed Jung-Gu Kim Kotiba Hamad School of Advanced Materials Science&Engineering Sungkyunkwan UniversitySuwon16419South Korea College of Computing and Informatics Sungkyunkwan UniversitySuwon16419South Korea

In this work,a machine learning(ML)model was created to predict intrinsic hardness of various compounds using their crystal *** this purpose,an initial dataset,containing the hardness values of 270 compounds and counterpart applied loads,was employed in the learning *** on various features generated using crystal information,an ML model,with a high accuracy(R^(2)=0.942),was built using extreme gradient boosting(XGB)*** validations conducted by hardness measurements of various compounds,including MSi_(2)(M=Nb,Ce,V,and Ta),Al_(2)O_(3),and FeB_(4),showed that the XGB model was able to reproduce load-dependent hardness behaviors of these *** addition,this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements.

关键词： Machine learning XGB algorithm Intrinsic hardness Crystal chemistry OQMD ICSD MP

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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

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Computer Systems Science & Engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh tamer abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

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Computers, Materials & Continua 2021年第12期69卷 3531-3548页

作者： Nora Shoaip Amira Rezk Shaker EL-Sappagh tamer abuhmed Sherif Barakat Mohammed Elmogy Department of Information Systems Faculty of Computers and InformationMansoura UniversityEgypt Centro Singular de Investigaci’on en Tecnolox’ias Intelixentes(CiTIUS)Universidade de Santiago de Compostela Santiago de Compostela15782Spain Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Department of Computer Science and Engineering College of ComputingSungkyunkwan UniversityRepublic of Korea Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia *** is a global health problem.99%of clinical trials have failed to limit the progression of this *** risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical *** for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological *** this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive *** an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very ***,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning *** models are random forest(RF),JRip,and J48,which have been used along with the voting *** dataset was used for this *** proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference ***,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.

关键词： Mild cognitive impairment Alzheimer’s disease knowledge based semantic web rule language reasoning system ADNI dataset machine learning techniques

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Timing and Classication of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

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Computers, Materials & Continua 2021年第4期 393-409页

作者： Mai Ramadan Ibraheem Jilan Adel Alaa Eldin Balbaa Shaker El-Sappagh tamer abuhmed Mohammed Elmogy Faculty of Computers and Information Kafrelsheikh UniversityEgypt Faculty of Physical Therapy Kafrelsheikh UniversityEgypt Faculty of Physical Therapy Nahda UniversityNUBEgypt Universidade de Santiago de Compostela Santiago de CompostelaSpain College of Computing Sungkyunkwan UniversitySeoul06351Korea Faculty of Computers and Information Mansoura UniversityMansoura35516Egy

:Surface electromyogram(sEMG)processing and classication can assist neurophysiological standardization and evaluation and provide habitational *** timing of muscle activation is critical in determining various medical conditions when looking at sEMG *** muscle activation timing allows identication of muscle locations and feature validation for precise *** work aims to develop a predictive model to investigate and interpret Patellofemoral(PF)osteoarthritis based on features extracted from the sEMG signal using pattern *** this end,sEMG signals were acquired from ve core muscles over about 200 reads from healthy adult patients while they were going ***,offset,and time duration for the Transversus Abdominus(TrA),Vastus Medialis Obliquus(VMO),Gluteus Medius(GM),Vastus Lateralis(VL),and Multidus Muscles(ML)were acquired to construct a classication *** proposed classication model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature *** activation feature space of muscle timing is used to train several large margin classiers to modulate muscle activations and account for such activation *** fast large margin classier achieved higher performance and faster convergence than support vector machines(SVMs)and other state-of-the-art *** proposed sEMG classication framework achieved an average accuracy of 98.8%after 7 s training time,improving other classication techniques in previous literature.

关键词： literature.Keywords:Muscle activation onset time LS-SVM surface electromyogram patellofemoral osteoarthritis the timing of core muscles

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