scholarly journals Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

2021 ◽  
Vol 15 ◽  
Author(s):  
Alexandre Routier ◽  
Ninon Burgos ◽  
Mauricio Díaz ◽  
Michael Bacci ◽  
Simona Bottani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Scalar Fields ◽  
Machine Learning Algorithms ◽  
Software Platform ◽  
Imaging Data ◽  
Clinical Neuroscience ◽  
Neuroimaging Data ◽  
Share Data

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

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 SIMON: open-source knowledge discovery platform

2020 ◽  
Author(s):  
Adriana Tomic ◽  
Ivan Tomic ◽  
Levi Waldron ◽  
Ludwig Geistlinger ◽  
Max Kuhn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knowledge Discovery ◽  
Open Source ◽  
Graphical User Interface ◽  
Open Source Software ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Biomedical Data ◽  
Programming Skills

AbstractData analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

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 Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware

2021 ◽  
Author(s):  
Jonathan Shapey ◽  
Thomas Dowrick ◽  
Rémi Delaunay ◽  
Eleanor C. Mackle ◽  
Stephen Thompson ◽  
...  
Keyword(s):  
Skull Base ◽  
Open Source ◽  
Open Source Software ◽  
Skull Base Surgery ◽  
Patient Specific ◽  
Software Platform ◽  
Image Guided ◽  
Phantom Model ◽  
Technical Requirements ◽  
Navigated Ultrasound

Abstract Purpose Image-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery. Methods We established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system’s ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model. Results Experimental validation of the system’s navigated ultrasound component demonstrated accuracy of $$<4.5\,\hbox {mm}$$ < 4.5 mm and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of $$<15\,\hbox {min}$$ < 15 min and performed with a clinically acceptable level of accuracy. Conclusion We present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements.

scholarly journals OptFlux: an open-source software platform for in silico metabolic engineering

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
Isabel Rocha ◽  
Paulo Maia ◽  
Pedro Evangelista ◽  
Paulo Vilaça ◽  
Simão Soares ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Open Source ◽  
Open Source Software ◽  
In Silico ◽  
Software Platform

scholarly journals KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes

2017 ◽  
Vol 05 (06) ◽  
pp. E477-E483 ◽  
Author(s):  
Anastasios Koulaouzidis ◽  
Dimitris Iakovidis ◽  
Diana Yung ◽  
Emanuele Rondonotti ◽  
Uri Kopylov ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Capsule Endoscopy ◽  
Diagnostic Yield ◽  
Jaccard Index ◽  
Machine Learning Algorithms ◽  
Medical Decision ◽  
Annotation Tool ◽  
Medical Decision Support ◽  
Medical Decision Support Systems

Abstract Background and aims Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE. Methods Open-source software was used for the KID database. Clinicians contribute anonymized, annotated CE images and videos. Graphic annotations are supported by an open-access annotation tool (Ratsnake). We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA. The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers. Results The MLA performed best in measuring lymphangiectasias with a JI of 81 ± 6 %. The other lesion types were: angioectasias (JI 64 ± 11 %), aphthae (JI 64 ± 8 %), chylous cysts (JI 70 ± 14 %), polypoid lesions (JI 75 ± 21 %), and ulcers (JI 56 ± 9 %). Conclusion MLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL). Automated lesion measurement is therefore feasible. KID is currently the only open-source CE database developed specifically to aid development of MDSS. Our experiment demonstrates this potential.

scholarly journals Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches

2021 ◽  
Vol 35 (1) ◽  
pp. 11-21
Author(s):  
Himani Tyagi ◽  
Rajendra Kumar
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Detection System ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Testing Time ◽  
Learning Approaches ◽  
Reduction Techniques ◽  
Share Data

IoT is characterized by communication between things (devices) that constantly share data, analyze, and make decisions while connected to the internet. This interconnected architecture is attracting cyber criminals to expose the IoT system to failure. Therefore, it becomes imperative to develop a system that can accurately and automatically detect anomalies and attacks occurring in IoT networks. Therefore, in this paper, an Intrsuion Detection System (IDS) based on extracted novel feature set synthesizing BoT-IoT dataset is developed that can swiftly, accurately and automatically differentiate benign and malicious traffic. Instead of using available feature reduction techniques like PCA that can change the core meaning of variables, a unique feature set consisting of only seven lightweight features is developed that is also IoT specific and attack traffic independent. Also, the results shown in the study demonstrates the effectiveness of fabricated seven features in detecting four wide variety of attacks namely DDoS, DoS, Reconnaissance, and Information Theft. Furthermore, this study also proves the applicability and efficiency of supervised machine learning algorithms (KNN, LR, SVM, MLP, DT, RF) in IoT security. The performance of the proposed system is validated using performance Metrics like accuracy, precision, recall, F-Score and ROC. Though the accuracy of Decision Tree (99.9%) and Randon Forest (99.9%) Classifiers are same but other metrics like training and testing time shows Random Forest comparatively better.

Prospects of Open Source Software for Maximizing the User Expectations in Heterogeneous Network

2021 ◽  
pp. 257-271
Author(s):  
Pushpa Singh ◽  
Rajeev Agrawal
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Research Work ◽  
Software Tool ◽  
Knn Classifier ◽  
User Expectations

This article focuses on the prospects of open source software and tools for maximizing the user expectations in heterogeneous networks. The open source software Python is used as a software tool in this research work for implementing machine learning technique for the categorization of the types of user in a heterogeneous network (HN). The KNN classifier available in Python defines the type of user category in real time to predict the available users in a particular category for maximizing profit for a business organization.

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