Empirical comparison of machine learning algorithms for bug prediction in open source software

2017 ◽  
Author(s):  
Ruchika Malhotra ◽  
Laavanye Bahl ◽  
Sushant Sehgal ◽  
Pragati Priya
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Empirical Comparison

scholarly journals Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs

2021 ◽  
Vol 94 (1126) ◽  
pp. 20210221
Author(s):  
Bino Abel Varghese ◽  
Heeseop Shin ◽  
Bhushan Desai ◽  
Ali Gholamrezanezhad ◽  
Xiaomeng Lei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Open Source ◽  
Open Source Software ◽  
Learning Algorithms ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Venn Diagram ◽  
First Order ◽  
Texture Metrics

Objectives For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. Methods In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. Results Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. Conclusions: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. Advances in knowledge We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.

scholarly journals An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sho Nakagome ◽  
Trieu Phat Luu ◽  
Yongtian He ◽  
Akshay Sujatha Ravindran ◽  
Jose L. Contreras-Vidal
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Empirical Comparison

PREDICTING SOFTWARE CHANGE IN AN OPEN SOURCE SOFTWARE USING MACHINE LEARNING ALGORITHMS

2013 ◽  
Vol 20 (06) ◽  
pp. 1350025
Author(s):  
RUCHIKA MALHOTRA ◽  
ANKITA JAIN BANSAL
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Roc Analysis ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Preventive Actions ◽  
Change Proneness ◽  
The Relationship

Due to various reasons such as ever increasing demands of the customer or change in the environment or detection of a bug, changes are incorporated in a software. This results in multiple versions or evolving nature of a software. Identification of parts of a software that are more prone to changes than others is one of the important activities. Identifying change prone classes will help developers to take focused and timely preventive actions on the classes of the software with similar characteristics in the future releases. In this paper, we have studied the relationship between various object oriented (OO) metrics and change proneness. We collected a set of OO metrics and change data of each class that appeared in two versions of an open source dataset, 'Java TreeView', i.e., version 1.1.6 and version 1.0.3. Besides this, we have also predicted various models that can be used to identify change prone classes, using machine learning and statistical techniques and then compared their performance. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the models predicted using both machine learning and statistical methods demonstrate good performance in terms of predicting change prone classes. Based on the results, it is reasonable to claim that quality models have a significant relevance with OO metrics and hence can be used by researchers for early prediction of change prone classes.

scholarly journals Applying Machine Learning Techniques to Predict the Maintainability of Open Source Software

2019 ◽  
Vol 8 (5S3) ◽  
pp. 192-195
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Software Applications ◽  
Learning Techniques ◽  
Software Maintainability ◽  
Quality Aspect

Software maintainability is a vital quality aspect as per ISO standards. This has been a concern since decades and even today, it is of top priority. At present, majority of the software applications, particularly open source software are being developed using Object-Oriented methodologies. Researchers in the earlier past have used statistical techniques on metric data extracted from software to evaluate maintainability. Recently, machine learning models and algorithms are also being used in a majority of research works to predict maintainability. In this research, we performed an empirical case study on an open source software jfreechart by applying machine learning algorithms. The objective was to study the relationships between certain metrics and maintainability.

scholarly journals I Know Where You Are Going: Predicting Flight Destinations of Corporate and State Aircraft

2021 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Marc Jourdan ◽  
Karolis Martinkus ◽  
David Roschewitz ◽  
Martin Strohmeier
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Large Scale ◽  
Historical Data ◽  
Learning Algorithms ◽  
Automated Analysis ◽  
Machine Learning Algorithms ◽  
Commercial Aviation ◽  
Open Source Intelligence

As data of aircraft movements have become freely accessible on a large scale through means of crowdsourcing, their open source intelligence (OSINT) value has been illustrated in many different domains. Potentially sensitive movements of all stakeholders outside commercial aviation are potentially affected, from corporate jets to military and government aircraft. Until now, this OSINT value was shown only on historical data, where automated analysis on flight destinations has been effective to find information on potential mergers & acquisition deals or diplomatic relationships between governments. In practice, obtaining such information as early as possible is crucial. Hence, in this work, we predict the destinations of state and corporate aircraft on live data, while the targets are still in the air. We use machine learning algorithms to predict the area of landing up to 2 h in advance. We evaluate our approach on more than 500,000 flights during 2018 obtained from the OpenSky Network.

scholarly journals Nutrix - Artificial Intelligence for Nutritionists

2021 ◽  
Author(s):  
Jorge Crespo Alvarez ◽  
Bryan Ferreira Hernández ◽  
Sandra Sumalla Cano
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Open Source ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Decision Making Process

This work, developed under the NUTRIX Project, has the objective to develop artificial intelligence algorithms based on the open source platform Knime that allows to characterize and predict the adherence of individuals to diet before starting the treatment. The machine learning algorithms developed under this project have significantly increased the confidence (a priory probability) that a patient leaves the treatment (diet) before starting: from 17,6% up to 96,5% which can be used as valuable guidance during the decision-making process of professionals in the area of ​dietetics and nutrition.

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