Semantic Similarity-based Automatic Grading System for Computer Programs Evaluation

2021 ◽  
Vol 8 (6) ◽  
Keyword(s):  
Semantic Similarity ◽  
Computer Programs ◽  
Grading System ◽  
Automatic Grading

scholarly journals Automatic Grading of Stroke Symptoms for Rapid Assessment Using Optimized Machine Learning and 4-Limb Kinematics: Clinical Validation Study (Preprint)

2020 ◽  
Author(s):  
Eunjeong Park ◽  
Kijeong Lee ◽  
Taehwa Han ◽  
Hyo Suk Nam
Keyword(s):  
Machine Learning ◽  
Medical Staff ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Grading System ◽  
Operating Characteristics ◽  
Stroke Patients ◽  
Motor Weakness ◽  
Automatic Grading

BACKGROUND Subtle abnormal motor signs are indications of serious neurological diseases. Although neurological deficits require fast initiation of treatment in a restricted time, it is difficult for nonspecialists to detect and objectively assess the symptoms. In the clinical environment, diagnoses and decisions are based on clinical grading methods, including the National Institutes of Health Stroke Scale (NIHSS) score or the Medical Research Council (MRC) score, which have been used to measure motor weakness. Objective grading in various environments is necessitated for consistent agreement among patients, caregivers, paramedics, and medical staff to facilitate rapid diagnoses and dispatches to appropriate medical centers. OBJECTIVE In this study, we aimed to develop an autonomous grading system for stroke patients. We investigated the feasibility of our new system to assess motor weakness and grade NIHSS and MRC scores of 4 limbs, similar to the clinical examinations performed by medical staff. METHODS We implemented an automatic grading system composed of a measuring unit with wearable sensors and a grading unit with optimized machine learning. Inertial sensors were attached to measure subtle weaknesses caused by paralysis of upper and lower limbs. We collected 60 instances of data with kinematic features of motor disorders from neurological examination and demographic information of stroke patients with NIHSS 0 or 1 and MRC 7, 8, or 9 grades in a stroke unit. Training data with 240 instances were generated using a synthetic minority oversampling technique to complement the imbalanced number of data between classes and low number of training data. We trained 2 representative machine learning algorithms, an ensemble and a support vector machine (SVM), to implement auto-NIHSS and auto-MRC grading. The optimized algorithms performed a 5-fold cross-validation and were searched by Bayes optimization in 30 trials. The trained model was tested with the 60 original hold-out instances for performance evaluation in accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve (AUC). RESULTS The proposed system can grade NIHSS scores with an accuracy of 83.3% and an AUC of 0.912 using an optimized ensemble algorithm, and it can grade with an accuracy of 80.0% and an AUC of 0.860 using an optimized SVM algorithm. The auto-MRC grading achieved an accuracy of 76.7% and a mean AUC of 0.870 in SVM classification and an accuracy of 78.3% and a mean AUC of 0.877 in ensemble classification. CONCLUSIONS The automatic grading system quantifies proximal weakness in real time and assesses symptoms through automatic grading. The pilot outcomes demonstrated the feasibility of remote monitoring of motor weakness caused by stroke. The system can facilitate consistent grading with instant assessment and expedite dispatches to appropriate hospitals and treatment initiation by sharing auto-MRC and auto-NIHSS scores between prehospital and hospital responses as an objective observation.

scholarly journals Semi-automatic grading system in histologic and immunohistochemistry analysis to evaluate in vitro chondrogenesis

2012 ◽  
Vol 17 (2) ◽  
pp. 167 ◽  
Author(s):  
María Lucía Gutiérrez ◽  
Johana Guevara ◽  
Luis Alejandro Barrera
Keyword(s):  
Alcian Blue ◽  
Mesenchymal Stromal Cells ◽  
Stromal Cells ◽  
Functional System ◽  
Grading System ◽  
Differentiation Potential ◽  
Blinded Observer ◽  
Automatic Grading ◽  
Imagej Software

During embryological limb formation mesenchymal cells condense and differentiate into chondrocytes, in a process known as chondrogenesis. These chondrocytes synthesize glycosaminoglycans (GAGs), thus playing an important role in this process. A simplified system in vitro chondrogenesis, using adult mesenchymal stromal cells (MSCs) has been demonstrated. This differentiation potential is usually assessed by histological staining. <strong>Objective</strong>. Establishment of a semi-automatic grading system for histochemistry stains and immunohistochemistry assays. <strong>Materials and methods</strong>. For chondrogenesis cells were cultured for three weeks in aggregates with inducing media. Total GAGs were measured using dimethylmethylene blue (DMB) method. For histological analyses aggregates were stained with Alcian blue for total GAGs detection and immunohistochemistry (IHC) for aggrecan was performed. Semi-automatic grading for all slides was obtained after ImageJ analysis. <strong>Results</strong>. MSCs cultured as aggregates in chondrogenic differentiation media had similar protein concentrations for all time points, suggesting cellularity remained homogenous during culture. Total GAGs was higher for aggregates cultured in chondrogenic compared to complete media. The same trend was observed for Alcian blue stain grades by blinded observer and analysis using ImageJ software. Aggrecan’s IHC analysis had a decreasing tendency with time for aggregates in chondrogenic media for blinded observer and ImageJ evaluation. <strong>Conclusion</strong>. We developed a functional system for semi-automatic slide grading. We corroborated these results by biochemical analysis with comparable results. To our knowledge, for in vitro chondrogenesis, this is the first report to evaluate stains using<br />this methodology. This procedure might be useful for other applications in the field of Biology and Medical Sciences.<br /><strong>Key words</strong>: mesenchymal stromal cells, in vitro chondrogenesis, glycosaminoglycans, ImageJ

scholarly journals Considerations when using an Automatic Grading System within Computer Science Modules

2021 ◽  
Author(s):  
Amy Thompson ◽  
Aidan Mooney ◽  
Mark Noone ◽  
Emlyn Hegarty-Kelly
Keyword(s):  
Computer Science ◽  
Grading System ◽  
Valuable Insight ◽  
Specific System ◽  
Grading Systems ◽  
Automatic Grading ◽  
Insight Into

This paper aims to investigate the effectiveness of automatic grading systems, with a focus on their uses within Computer Science. Automatic grading systems have seen a rise in popularity in recent years with publications concerning automatic grading systems usually linked to a specific system. This paper will discuss the factors that need to be considered when using automatic grading, regardless of which system is being used, and will make recommendations for each factor. This discussion is based on the authors' experience of using an automatic grading system in a CS1 environment. From the research conducted, many elements should be considered when using these systems. These include how the code will be tested, the need for plagiarism checks and how marks are awarded. The findings of this study suggest there is a lack of defined standards when using these systems. This analysis of the considerations provides valuable insight into how these systems should be used and what the standards should be built on.

Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software

2020 ◽  
Vol 45 (12) ◽  
pp. 1550-1555
Author(s):  
Xiang-Ning Wang ◽  
Ling Dai ◽  
Shu-Ting Li ◽  
Hong-Yu Kong ◽  
Bin Sheng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Grading System ◽  
Automatic Grading
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