Machine Learning and Hebrew NLP for Automated Assessment of Open-Ended Questions in Biology

2022 ◽  
Author(s):  
Moriah Ariely ◽  
Tanya Nazaretsky ◽  
Giora Alexandron
Keyword(s):  
Machine Learning ◽  
Automated Assessment

scholarly journals Evidence-based guidelines for developing automated conservation assessment methods

2021 ◽  
Author(s):  
Barnaby E. Walker ◽  
Tarciso C.C. Leão ◽  
Steven P. Bachman ◽  
Eve Lucas ◽  
Eimear Nic Lughadha
Keyword(s):  
Machine Learning ◽  
Data Cleaning ◽  
Training Sample ◽  
Assessment Methods ◽  
Evidence Based ◽  
Automated Assessment ◽  
Threat Status ◽  
Taxonomic Groups ◽  
Evidence Based Guidelines ◽  
Occurrence Records

Assessing species’ risk of extinction is a vital first step in setting conservation priorities. However, assessment endeavours like the IUCN Red List of Threatened Species still have significant gaps in their coverage of some taxonomic groups. Automated assessment (AA) methods are gaining popularity to rapidly fill these gaps, taking advantage of improvements in computing and digitally available information. However, implicit choices made when developing and reporting automated assessment methods could prevent their successful adoption or, even worse, their predictions could lead to poor allocation of conservation resources.We systematically explored how the choice of data cleaning, taxonomic group, training sample, and automation method affected predicted threat status. We used occurrence records from GBIF to generate assessments for three distinct taxonomic groups using four different automated assessment methods. We measured each method’s performance and coverage after applying increasingly stringent cleaning to the input occurrence data. We used these results to build evidence-based guidelines for developing and reporting automated assessments.Automatically cleaned data from GBIF resulted in comparable performance to occurrence records cleaned manually by an expert. However, all types of data cleaning removed species and limited the coverage of automated assessments. This limitation was more severe for some groups of species than others. Overall, machine learning-based methods performed well on all taxonomic groups, even with minimal data cleaning.We recommend using a machine learning-based method on minimally cleaned data to get the best compromise between performance and species coverage. However, our results demonstrate that the optimal data cleaning, training sample, and automation method depends on the focal group of species. Therefore, we recommend evaluating new AA methods across multiple groups and providing additional context with extinction risk predictions for users to make informed decisions.

A system for automated motor function assessment in stroke patients using motion sensing technology (Preprint)

2019 ◽  
Author(s):  
Bo Sheng ◽  
Xiangbin Wang ◽  
Meijin Hou ◽  
Jia Huang ◽  
Shuping Xiong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Motor Function ◽  
Upper Limb ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Motion Sensing ◽  
Stroke Patients ◽  
Automated Assessment ◽  
Sensing Technology ◽  
Function Assessment

BACKGROUND Upper-limb motor function assessment for stroke patients is essential for planning interventions to maximise function and independence. OBJECTIVE The general aim of this study is to realize the automated assessment of upper-limb motor function after stroke by using motion sensing technology and machine learning algorithms. METHODS The proposed system contained two subsystems. A motion tracking subsystem was developed for measuring the kinematic data of participants through Kinect V2. A motor function assessment subsystem was developed to realize the automated assessment based on a feed-forward neural network (FFNN)-based assessment model. The assessment model was developed by thirty-two kinematic metrics calculated from the gathered data. In addition, five metrics with ten-fold cross-validation were used for performance evaluation (accuracy, F1-score, specificity, sensitivity, and AUC), and the developed FFNN-based model was compared with ten well-known machine learning algorithm-based models. RESULTS 26 participants between 20 to 79 years of age, living in the Fuzhou city (Fujian) were recruited from October 2018 to January 2019. A total of 2400 patterns were calculated as the dataset after data processing. The experimental results showed that the proposed system presented satisfactory performance: overall accuracy ranged from 0.87 to 0.96, and F1-score ranged from 0.83 to 0.93. The FFNN-based assessment model presented promising comprehensive performance: it ranked top two for all tasks in the metrics of overall accuracy and F1-score. CONCLUSIONS The proposed system presents a promising method for automated assessment of upper-limb motor function in stroke patients. The system could be implemented in clinical applications as a cheap and portable assessment tool and provide decision support for physiotherapists as well.

Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images

2016 ◽  
Vol 29 (5) ◽  
pp. 723-731 ◽  
Author(s):  
Yu Xin Yang ◽  
Mei Sian Chong ◽  
Laura Tay ◽  
Suzanne Yew ◽  
Audrey Yeo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Automated Assessment

scholarly journals A machine learning approach for automated assessment of retinal vasculature in the oxygen induced retinopathy model

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Javier Mazzaferri ◽  
Bruno Larrivée ◽  
Bertan Cakir ◽  
Przemyslaw Sapieha ◽  
Santiago Costantino
Keyword(s):  
Machine Learning ◽  
Retinal Vasculature ◽  
Learning Approach ◽  
Automated Assessment ◽  
Oxygen Induced Retinopathy ◽  
Machine Learning Approach

scholarly journals The Application of Machine Learning and Neural Networks to Automated Text and Visual Assignment Verification Used as Assistance to Educators

2020 ◽  
pp. 39-45
Author(s):  
Н. О. Бесшапошников ◽  
М. С. Дьяченко ◽  
А. Г. Леонов ◽  
М. А. Матюшин ◽  
А. E. Орловский
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Teaching Methods ◽  
Online Teaching ◽  
Assessment Tools ◽  
Automated Assessment ◽  
Distant Learning ◽  
E Learning ◽  
Extensive Introduction ◽  
Learning Platforms

Процесс цифровизации образования, активно проводимый в нашей стране и по всему миру, позволил более широко применить в учебном процессе современные приемы преподавания, перенося часть педагогической нагрузки с очного формата на дистанционный. Проектируемые и используемые цифровые образовательные платформы уже сейчас включают в себя не только оцифрованный лекционный видеоматериал и электронные формы учебников, но и элементы автоматизации проверки выполненных учащимися заданий. Расширение области применения автоматической проверки решенных учащимися задач и выполненных упражнений является объективной необходимостью, в противном случае при дистанционных формах образовательного процесса резко возрастает нагрузка на педагога, который должен выделять значительное время на проверку увеличившегося самостоятельной работы школьников и студентов. Кроме того, при дистанционном преподавании снижается эффект личного присутствия педагога, когда учитель и ученики разделены экранами компьютеров. Существенной помощью может стать использование интеллектуальных помощников преподавателя и автоматизированных систем проверки, построенных методами машинного обучения и технологии нейронных сетей. В настоящей статье рассмотрены подходы к решению поставленных задач по автоматической проверке графических заданий и выявлению заимствований в текстовом виде. Показаны возможные варианты реализации этих функций с использованием технологий искусственного интеллекта. The digitalization of education in Russia and worldwide enables a more extensive introduction of advanced teaching methods through a partial switch from offline to online teaching. The existing and coming e-learning platforms feature not only digital lecture videos and e-textbooks, but some automated assessment/grading tools. There is a need to expand the coverage of such tools to avoid the extreme burden of online teaching as the educator has to allocate significant time for assessing the increased amount of high school/university student assignments. Also, distant learning diminishes the effect of the educator personal presence since the teacher and the student are separated by their computer screens. Smart educator assistants and automated assessment tools based on machine learning and neural networks can significantly alleviate the problem. This study offers some strategies for automated assessment of graphic assignments and checks for plagiarism. Possible AI-based implementations of such features are presented.

A Machine-Learning Approach to the Automated Assessment of Joint Synovitis Activity

2016 ◽  
pp. 440-450
Author(s):  
Konrad Wojciechowski ◽  
Bogdan Smolka ◽  
Rafal Cupek ◽  
Adam Ziebinski ◽  
Karolina Nurzynska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Automated Assessment ◽  
Machine Learning Approach
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