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Vascular depression for radiology: A review of the construct,methodology,and diagnosis

World Journal of Radiology 2020年第5期12卷 48-67页

作者： Sara N Rushia al amira safa shehab Jeffrey N Motter Dakota A Egglefield Sophie Schiff Joel R Sneed Ernst Garcon Department of Psychology The Graduate CenterCity University of New YorkNew YorkNY 10016United States Department of Psychology Queens CollegeCity University of New YorkQueensNY 11367United States Division of Geriatric Psychiatry New York State Psychiatric InstituteNew YorkNY 10032United States Department of Psychiatry Columbia University Medical CenterNew YorkNY 10032United States Department of Radiology Columbia University Medical CenterNew YorkNY 10032United States

Vascular depression(VD)as defined by magnetic resonance imaging(MRI)has been proposed as a unique subtype of late-life *** VD hypothesis posits that cerebrovascular disease,as characterized by the presence of MRIdefined white matter hyperintensities,contributes to and increases the risk for depression in older *** is also accompanied by cognitive impairment and poor antidepressant treatment *** VD diagnosis relies on MRI findings and yet this clinical entity is largely unfamiliar to neuroradiologists and is rarely,if ever,discussed in radiology *** primary purpose of this review is to introduce the MRI-defined VD construct to the neuroradiology *** reports are highlighted in order to illustrate the profile of VD in terms of radiological,clinical,and neuropsychological findings.A secondary purpose is to elucidate and elaborate on the measurement of cerebrovascular disease through visual rating scales and semi-and fully-automated volumetric *** methods are crucial for determining whether lesion burden or lesion severity is the dominant pathological contributor to ***,these rating methods have implications for the growing field of computer assisted *** VD has been found to have a profile that is distinct from other types of late-life depression,neuroradiologists,in conjunction with psychiatrists and psychologists,should consider VD in diagnosis and treatment planning.

关键词： Vascular depression Depression Magnetic resonance imaging Cerebrovascular disorders White matter hyperintensities Neuroradiology Case reports

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An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning algorithms

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Computers, Materials & Continua 2023年第10期77卷 1227-1249页

作者： Nourmeen Lotfy Abdulaziz shehab Mohammed Elhoseny Ahmed Abu-Elfetouh Department of Information Systems Faculty of Computers and Information ScienceMansoura UniversityMansoura35516Egypt Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates

Despite the extensive effort to improve intelligent educational tools for smart learning environments,automatic Arabic essay scoring remains a big research *** nature of the writing style of the Arabic language makes the problem even more *** study designs,implements,and evaluates an automatic Arabic essay scoring *** proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing ***,it comprises two main components:the grading engine and the adaptive fusion *** grading engine employs string-based and corpus-based similarity algorithms *** that,the adaptive fusion engine aims to prepare students’scores to be delivered to different feature selection algorithms,such as Recursive Feature Elimination and ***,some machine learning algorithms such as Decision Tree,Random Forest,Adaboost,Lasso,Bagging,and K-Nearest Neighbor are employed to improve the suggested system’s *** experimental results in the grading engine showed that Extracting DIStributionally similar words using the CO-occurrences similarity measure achieved the best correlation ***,in the adaptive fusion engine,the Random Forest algorithm outperforms all other machine learning algorithms using the(80%–20%)splitting method on the original *** achieves 91.30%,94.20%,0.023,0.106,and 0.153 in terms of Pearson’s Correlation Coefficient,Willmot’s Index of Agreement,Mean Square Error,Mean Absolute Error,and Root Mean Square Error metrics,respectively.

关键词： Arabic corpus-based similarity correlation machine learning string-based similarity text similarity

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