Automatic assessment of students' software models using a simple heuristic and machine learning

2020 ◽  
Author(s):  
Younes Boubekeur ◽  
Gunter Mussbacher ◽  
Shane McIntosh
Keyword(s):  
Machine Learning ◽  
Automatic Assessment ◽  
Software Models ◽  
Simple Heuristic

scholarly journals Implementing Machine Learning in Interventional Cardiology: The Benefits are Worth the Trouble

2021 ◽  
Author(s):  
Walid Ben Ali ◽  
Ahmad Pesaranghader ◽  
Robert Avram ◽  
Reda Ibrahim ◽  
Thomas Modine ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
High Performance ◽  
Interventional Cardiology ◽  
Daily Practice ◽  
Automated Analysis ◽  
Coronary Intervention ◽  
Biomedical Data ◽  
Clinical Implementation ◽  
Automatic Assessment

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

scholarly journals On the benefit of logic-based machine learning to learn pairwise comparisons

2020 ◽  
Vol 9 (6) ◽  
pp. 2637-2649
Author(s):  
Nunung Nurul Qomariyah ◽  
Dimitar Kazakov ◽  
Ahmad Nurul Fajar
Keyword(s):  
Machine Learning ◽  
Description Logic ◽  
User Study ◽  
Inductive Learning ◽  
The Other ◽  
User Preference ◽  
Machine Learning Techniques ◽  
Web Searching ◽  
Learning To Learn ◽  
Software Models

In recent years, many daily processes such as internet web searching, e-mail filter-ing, social media services, e-commerce have benefited from machine learning tech-niques (ML). The implementation of ML techniques has been largely focused on blackbox methods where the general conclusions are not easily interpretable. Hence, theelaboration with other declarative software models to identify the correctness and com-pleteness of the models is not easy to perform. On the other hand, the emerge of somelogic-based machine learning techniques with their advantage of white box approachhave been proven to be well-suited for many software engineering tasks. In this paper,we propose the use of a logic-based approach to learn user preference in the form ofpairwise comparisons. APARELL as a novel approach of inductive learning is able tomodel the user’s preferences in description logic representation. This offers a rich, re-lational representation which is then can be used to produce a set of recommendations.A user study has been performed in our experiment to evaluate the implementation ofpairwise preference recommender system when compared to a standard list interface.The result of the experiment shows that the pairwise interface was significantly betterthan the other interface in many ways.

scholarly journals Machine Learning automatic assessment for glaucoma and myopia based on Corvis ST data

2022 ◽  
Vol 196 ◽  
pp. 454-460
Author(s):  
Danilo Leite ◽  
Maria Campelos ◽  
Ana Fernandes ◽  
Pedro Batista ◽  
João Beirão ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Assessment ◽  
Corvis St

scholarly journals ModelSet: a dataset for machine learning in model-driven engineering

2021 ◽  
Author(s):  
José Antonio Hernández López ◽  
Javier Luis Cánovas Izquierdo ◽  
Jesús Sánchez Cuadrado
Keyword(s):  
Machine Learning ◽  
Search Engine ◽  
Model Driven Engineering ◽  
Clustering Methods ◽  
Model Driven ◽  
Link Type ◽  
Software Models ◽  
Domain Expertise ◽  
Software Modelling ◽  
Real Scenario

AbstractThe application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io.

scholarly journals Implementing Machine Learning in Interventional Cardiology: The Benefits Are Worth the Trouble

2021 ◽  
Vol 8 ◽  
Author(s):  
Walid Ben Ali ◽  
Ahmad Pesaranghader ◽  
Robert Avram ◽  
Pavel Overtchouk ◽  
Nils Perrin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
High Performance ◽  
Interventional Cardiology ◽  
Daily Practice ◽  
Automated Analysis ◽  
Coronary Intervention ◽  
Biomedical Data ◽  
Clinical Implementation ◽  
Automatic Assessment

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.

scholarly journals Will the Machine Like Your Image?Automatic Assessment of Beauty in Images with Machine Learning Techniques †

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 34 ◽  
Author(s):  
Matteo Bodini
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Processing System ◽  
Machine Learning Techniques ◽  
Community Studies ◽  
Automatic Assessment ◽  
Future Directions ◽  
Computer Methods ◽  
Learning Techniques ◽  
Work Done

Although the concept of image quality has been a subject of study for the image processing community for more than forty years (where, with the term “quality”, we are referring to the accuracy with which an image processing system captures, processes, stores, compresses, transmits, and displays the signals that compose an image), notions related to aesthetics of photographs and images have only appeared for about ten years within the community. Studies devoted to aesthetics of images are multiplying today, taking advantage of the latest machine learning techniques and mostly due to the proliferation of huge communities and websites, specialized in digital photography sharing and archiving, such as Flickr, Imgur, DeviantArt, and Instagram. In this review, we examine the latest advances of computer methods that aim at computationally distinguishing high-quality from low-quality photos and images, relying on machine learning techniques. The paper is organized as follows: First, we introduce many approaches to aesthetics, studied in philosophy, neurobiology, experimental psychology, and sociology, to see what lighting they propose to researchers. Such points of view let us explain the weakness of the current consensus on the difficult aesthetics problem and the importance of the ongoing debates on it. Then, we analyze the work done in the community of pattern recognition and artificial intelligence on the task of automatic aesthetic assessment, and we both compare and critically examine the presented results. Finally, we describe many issues that have not been addressed, and starting from these, we outline some possible future directions.

scholarly journals Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 21 ◽  
Author(s):  
Oana Bălan ◽  
Gabriela Moise ◽  
Livia Petrescu ◽  
Alin Moldoveanu ◽  
Marius Leordeanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Affective Computing ◽  
Machine Learning Techniques ◽  
Emotion Classification ◽  
Automatic Assessment ◽  
Cognitive Changes ◽  
Video Signals ◽  
Learning Techniques ◽  
Education Marketing

Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. Our approach to emotion classification has future applicability in the field of affective computing, which includes all the methods used for the automatic assessment of emotions and their applications in healthcare, education, marketing, website personalization, recommender systems, video games, and social media.

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