scholarly journals Re-classification of archival Ovarian Carcinoma diagnostics using immunohistologic digital quantification and algorithmic prognosis

2020 ◽  
Author(s):  
Camelia D. Vrabie ◽  
Mihnea I. Gangal ◽  
Marius D Gangal
Keyword(s):  
Ovarian Carcinoma ◽  
Open Source ◽  
Diagnostic Criteria ◽  
Low Cost ◽  
Research Question ◽  
Ground Truth ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Digital Analysis

Twenty years of research improved the classification of ovarian carcinoma, making the diagnostic relevant from a scientific and clinical perspective. Our research question was to find out if old studies are still pertinent under new diagnostic criteria and how we can use machine learning techniques for re-classification purposes. The same main investigator re-classified 60 cases of ovarian carcinoma after 15 years, using 2014 WHO diagnostic criteria. Selected pathology data only (macro, micro information and immunohistochemistry images coming from a seven-stain panel) were provided for digital analysis. Biomarker images were digitalized and quantified using open source software and a validated methodology. 1080 attributes were classified using a random forest (open source) algorithm, using a supervised learning technique (the training dataset used 180 attributes). Human results were considered ground truth for the digital analysis. The human analysis maintained the initial histopathologic diagnostic in 61.5% of cases. The digital prediction shows 80% accuracy and 73% precision when compared with human reclassified data. Based on results, we concluded that recycling of old studies is possible. Limitation of the study are the low number of cases analyzed, the total absence of clinical, treatment and prognostic data and a possible human criteria selection bias. Even if technical difficulties related to biomarker selection and histological analysis exist, digital investigation of existing, large archival registries is feasible, reliable and it can be done at a low cost.

Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload

2020 ◽  
pp. 1423-1439
Author(s):  
Zhiming Wu ◽  
Tao Lin ◽  
Ningjiu Tang
Keyword(s):  
Machine Learning ◽  
Interaction Design ◽  
Mental Stress ◽  
Mental Workload ◽  
Low Cost ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Psychological Evidence

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.

scholarly journals Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6477
Author(s):  
Túlio Fernandes de Almeida ◽  
Edgard Morya ◽  
Abner Cardoso Rodrigues ◽  
André Felipe Oliveira de Azevedo Dantas
Keyword(s):  
Open Source ◽  
Measurement System ◽  
Joint Angle ◽  
Low Cost ◽  
Ground Truth ◽  
Percentage Error ◽  
Angle Estimation ◽  
Adult Individual ◽  
Average Percentage ◽  
Real Scenario

The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.

Explore the Use of Handwriting Information and Machine Learning Techniques in Evaluating Mental Workload

2016 ◽  
Vol 12 (3) ◽  
pp. 18-32 ◽  
Author(s):  
Zhiming Wu ◽  
Tao Lin ◽  
Ningjiu Tang
Keyword(s):  
Machine Learning ◽  
Interaction Design ◽  
Mental Workload ◽  
Low Cost ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Open Research ◽  
Learning Techniques ◽  
Psychological Evidence

Mental workload is considered one of the most important factors in interaction design and how to detect a user's mental workload during tasks is still an open research question. Psychological evidence has already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental stress, but this phenomenon has not been explored adequately. The intention of this paper is to explore the possibility of evaluating mental workload with handwriting information by machine learning techniques. Machine learning techniques such as decision trees, support vector machine (SVM), and artificial neural network were used to predict mental workload levels in the authors' research. Results showed that it was possible to make prediction of mental workload levels automatically based on handwriting patterns with relatively high accuracy, especially on patterns of children. In addition, the proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost.

scholarly journals DEVELOPING A LAND COVER CLASSIFICATION OF SALT MARSHES USING UAS TIME-SERIES IMAGERY AND AN OPEN SOURCE WORKFLOW

2018 ◽  
Vol XLII-4/W8 ◽  
pp. 155-162
Author(s):  
D. J. Myers ◽  
C. M. Schweik ◽  
R. Wicks ◽  
F. Bowlick ◽  
M. Carullo
Keyword(s):  
Land Cover ◽  
Open Source ◽  
Salt Marshes ◽  
Classification Accuracy ◽  
Low Cost ◽  
High Tide ◽  
Land Cover Classification ◽  
Image Stack ◽  
Time Point

<p><strong>Abstract.</strong> Salt marsh ecology classification is difficult using traditional coarse resolution remote sensing techniques. Salt marshes exhibit a spatial pattern of vegetation zonation that are visually identifiable using imagery that has an improved 0.04 meter per pixel resolution. This project applies high resolution unmanned aerial system (UAS) imagery to aid in multi-temporal classification of our study area (Horseneck Beach) in Westport, Massachusetts, USA. We flew a DJI Phantom Pro 3 at low- and high-tide to capture effects the changing tide has on vegetation in an effort to predict effects of the rising sea level on saline plant species. We implement an open source software workflow using OpenDroneMap and the Semi-Automatic Classification Plugin for QGIS to create the necessary orthomosaics and to conduct vegetation classification required of this project. We compare land cover classifications using one-time-point RGB imagery to a multi-time-point (low tide, high tide) RGB image stack to investigate whether the multi-time point stack improves land cover classification accuracy. We find it does. More generally, this paper provides a model for others wishing to use low-cost UAS equipment carrying a simple low-cost RGB camera, and free and open source for geospatial (FOSS4G) tools, to develop multi-band image stacks to improve land cover classification accuracy. Further, we provide some reflections and technical notes on our experience. The approach we present here could be extended to include other image layers that UAS can provide when equipped with other sensors, such as multi-spectral (e.g., NIR, thermal), or by adding another band with photogrammetry-produced digital elevation data.</p>

scholarly journals Classification of Fetal State through the application of Machine Learning techniques on Cardiotocography records: Towards Real World Application.

2021 ◽  
Author(s):  
Andrew M V Dadario ◽  
Christian Espinoza ◽  
Wellington Araujo Nogueira
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Cross Validation ◽  
Low Cost ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
Real World Application ◽  
Learning Techniques ◽  
Qualified Personnel

Objective Anticipating fetal risk is a major factor in reducing child and maternal mortality and suffering. In this context cardiotocography (CTG) is a low cost, well established procedure that has been around for decades, despite lacking consensus regarding its impact on outcomes. Machine learning emerged as an option for automatic classification of CTG records, as previous studies showed expert level results, but often came at the price of reduced generalization potential. With that in mind, the present study sought to improve statistical rigor of evaluation towards real world application. Materials and Methods In this study, a dataset of 2126 CTG recordings labeled as normal, suspect or pathological by the consensus of three expert obstetricians was used to create a baseline random forest model. This was followed by creating a lightgbm model tuned using gaussian process regression and post processed using cross validation ensembling. Performance was assessed using the area under the precision-recall curve (AUPRC) metric over 100 experiment executions, each using a testing set comprised of 30% of data stratified by the class label. Results The best model was a cross validation ensemble of lightgbm models that yielded 95.82% AUPRC. Conclusions The model is shown to produce consistent expert level performance at a less than negligible cost. At an estimated 0.78 USD per million predictions the model can generate value in settings with CTG qualified personnel and all the more in their absence.

scholarly journals Using Clustering Techniques  to Guide Refactoring of  Object-Oriented Classes

2021 ◽  
Author(s):  
◽  
Keith Cassell
Keyword(s):  
Software Development ◽  
Open Source ◽  
Low Cost ◽  
Research Question ◽  
Object Oriented ◽  
Fundamental Unit ◽  
Total Cost ◽  
Clustering Techniques ◽  
The Cost ◽  
Refactoring Tools

<p>Much of the cost of software development is maintenance. Well structured software tends to be cheaper to maintain than poorly structured software, because it is easier to analyze and modify. The research described in this thesis concentrates on determining how to improve the structure of object-oriented classes, the fundamental unit of organization for object-oriented programs. Some refactoring tools can mechanically restructure object-oriented classes, given the appropriate inputs regarding what attributes and methods belong in the revised classes. We address the research question of determining what belongs in those classes, i.e., determining which methods and attributes most belong together and how those methods and attributes can be organized into classes. Clustering techniques can be useful for grouping entities that belong together; however, doing so requires matching an appropriate algorithm to the domain task and choosing appropriate inputs. This thesis identifies clustering techniques suitable for determining the redistribution of existing attributes and methods among object-oriented classes, and discusses the strengths and weaknesses of these techniques. It then describes experiments using these techniques as the basis for refactoring open source Java classes and the changes in the class quality metrics that resulted. Based on these results and on others reported in the literature, it recommends particular clustering techniques for particular refactoring problems. These clustering techniques have been incorporated into an open source refactoring tool that provides low-cost assistance to programmers maintaining object-oriented classes. Such maintenance can reduce the total cost of software development.</p>

scholarly journals Using Clustering Techniques  to Guide Refactoring of  Object-Oriented Classes

2021 ◽  
Author(s):  
◽  
Keith Cassell
Keyword(s):  
Software Development ◽  
Open Source ◽  
Low Cost ◽  
Research Question ◽  
Object Oriented ◽  
Fundamental Unit ◽  
Total Cost ◽  
Clustering Techniques ◽  
The Cost ◽  
Refactoring Tools

<p>Much of the cost of software development is maintenance. Well structured software tends to be cheaper to maintain than poorly structured software, because it is easier to analyze and modify. The research described in this thesis concentrates on determining how to improve the structure of object-oriented classes, the fundamental unit of organization for object-oriented programs. Some refactoring tools can mechanically restructure object-oriented classes, given the appropriate inputs regarding what attributes and methods belong in the revised classes. We address the research question of determining what belongs in those classes, i.e., determining which methods and attributes most belong together and how those methods and attributes can be organized into classes. Clustering techniques can be useful for grouping entities that belong together; however, doing so requires matching an appropriate algorithm to the domain task and choosing appropriate inputs. This thesis identifies clustering techniques suitable for determining the redistribution of existing attributes and methods among object-oriented classes, and discusses the strengths and weaknesses of these techniques. It then describes experiments using these techniques as the basis for refactoring open source Java classes and the changes in the class quality metrics that resulted. Based on these results and on others reported in the literature, it recommends particular clustering techniques for particular refactoring problems. These clustering techniques have been incorporated into an open source refactoring tool that provides low-cost assistance to programmers maintaining object-oriented classes. Such maintenance can reduce the total cost of software development.</p>

scholarly journals EARBOX: An open tool for high-throughput measurements of the spatial organization of maize ears and inference of novel traits

2021 ◽  
Author(s):  
Vincent Oury ◽  
Timothe Leroux ◽  
Olivier Turc ◽  
Romain Chapuis ◽  
Carine Palaffre ◽  
...  
Keyword(s):  
Open Source ◽  
High Throughput ◽  
Spatial Organization ◽  
Low Cost ◽  
Plant Genetic Resources ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Spatial Features ◽  
Novel Traits ◽  
Maize Ear

Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results: We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organization, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions: The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

Rapid mapping accuracy assessment using low-cost UAV and open-source Structure from Motion

2020 ◽  
Vol 52 ◽  
pp. 55-61
Author(s):  
Ettore Potente ◽  
Cosimo Cagnazzo ◽  
Alessandro Deodati ◽  
Giuseppe Mastronuzzi
Keyword(s):  
Open Source ◽  
Structure From Motion ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Mapping Accuracy ◽  
Rapid Mapping ◽  
Source Structure

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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