RADIAL-BASED SIGNAL-PROCESSING COMBINED WITH METHODS OF MACHINE LEARNING

2013 ◽  
Vol 27 (06) ◽  
pp. 1350018
Author(s):  
F. WEICHERT ◽  
M. GASPAR ◽  
M. WAGNER
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
A Priori ◽  
Three Dimensional ◽  
Classification Problem ◽  
Support Vector ◽  
Multiscale Approach ◽  
Two Dimensional ◽  
Dimensional Classification ◽  
Novel Approach

The present paper describes a novel approach to performing feature extraction and classification in possibly layered circular structures, as seen in two-dimensional cutting planes of three-dimensional tube-shaped objects. The algorithm can therefore be used to analyze histological specimens of blood vessels as well as intravascular ultrasound (IVUS) datasets. The approach uses a radial signal-based extraction of textural features in combination with methods of machine learning to integrate a priori domain knowledge. The algorithm in principle solves a two-dimensional classification problem that is reduced to parallel viable time series analysis. A multiscale approach hereby determines a feature vector for each analysis using either a Wavelet-transform (WT) or a S-transform (ST). The classification is done by methods of machine learning — here support vector machines. A modified marching squares algorithm extracts the polygonal segments for the two-dimensional classification. The accuracy is above 80% even in datasets with a considerable quantity of artifacts, while the mean accuracy is above 90%. The benefit of the approach therefore mainly lies in its robustness, efficient calculation, and the integration of domain knowledge.

scholarly journals Two-Dimensional Image-Based Screening Tool for Infants with Positional Cranial Deformities: A Machine Learning Approach

Diagnostics ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 495 ◽  
Author(s):  
Cecilia A. Callejas Pastor ◽  
Il-Young Jung ◽  
Shinhye Seo ◽  
Soon Bin Kwon ◽  
Yunseo Ku ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parental Monitoring ◽  
Screening Method ◽  
Three Dimensional ◽  
Correlation Coefficients ◽  
Asymmetry Index ◽  
Support Vector ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Machine Learning Approach

Positional cranial deformities are relatively common conditions, characterized by asymmetry and changes in skull shape. Although three-dimensional (3D) scanning is the gold standard for diagnosing such deformities, it requires expensive laser scanners and skilled maneuvering. We therefore developed an inexpensive, fast, and convenient screening method to classify cranial deformities in infants, based on single two-dimensional vertex cranial images. In total, 174 measurements from 80 subjects were recorded. Our screening software performs image processing and machine learning-based estimation related to the deformity indices of the cranial ratio (CR) and cranial vault asymmetry index (CVAI) to determine the severity levels of brachycephaly and plagiocephaly. For performance evaluations, the estimated CR and CVAI values were compared to the reference data obtained using a 3D cranial scanner. The CR and CVAI correlation coefficients obtained via support vector regression were 0.85 and 0.89, respectively. When the trained model was evaluated using the unseen test data for the three CR and three CVAI classes, an 86.7% classification accuracy of the proposed method was obtained for both brachycephaly and plagiocephaly. The results showed that our method for screening cranial deformities in infants could aid clinical evaluations and parental monitoring of the progression of deformities at home.

scholarly journals Machine Learning for Statistical Modeling

2021 ◽  
Vol 26 (3) ◽  
pp. 1-17
Author(s):  
Urmimala Roy ◽  
Tanmoy Pramanik ◽  
Subhendu Roy ◽  
Avhishek Chatterjee ◽  
Leonard F. Register ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Distribution ◽  
Switching Time ◽  
A Priori ◽  
Random Access ◽  
Spin Transfer Torque ◽  
Support Vector ◽  
Micromagnetic Simulations ◽  
Physical Systems ◽  
Computational Resources

We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 10 6 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.

scholarly journals Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110135
Author(s):  
Florian Jaton
Keyword(s):  
Machine Learning ◽  
William James ◽  
A Priori ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ground Truthing ◽  
Set Up ◽  
The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

scholarly journals A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

2014 ◽  
Vol 5 (3) ◽  
pp. 82-96 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Sanja Pfeifer ◽  
Nataša Šarlija
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Classification Problem ◽  
High Dimensional ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dimensional Classification ◽  
Artificial Neural

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

AUTONOMOUS BEHAVIORS OF GRAPHICAL AVATARS BASED ON MACHINE LEARNING

2012 ◽  
Vol 26 (02) ◽  
pp. 1251002
Author(s):  
YUESHENG HE ◽  
YUAN YAN TANG
Keyword(s):  
Machine Learning ◽  
Hierarchical Structure ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Research Area ◽  
Machine Learning Techniques ◽  
3D Animation ◽  
Graphical Environment ◽  
Novel Approach ◽  
The Difference

Graphical avatars have gained popularity in many application domains such as three-dimensional (3D) animation movies and animated simulations for product design. However, the methods to edit avatars' behaviors in the 3D graphical environment remained to be a challenging research topic. Since the hand-crafted methods are time-consuming and inefficient, the automatic actions of the avatars are required. To achieve the autonomous behaviors of the avatars, artificial intelligence should be used in this research area. In this paper, we present a novel approach to construct a system of automatic avatars in the 3D graphical environments based on the machine learning techniques. Specific framework is created for controlling the behaviors of avatars, such as classifying the difference among the environments and using hierarchical structure to describe these actions. Because of the requirement of simulating the interactions between avatars and environments after the classification of the environment, Reinforcement Learning is used to compute the policy to control the avatar intelligently in the 3D environment for the solution of the problem of different situations. Thus, our approach has solved problems such as where the levels of the missions will be defined and how the learning algorithm will be used to control the avatars. In this paper, our method to achieve these goals will be presented. The main contributions of this paper are presenting a hierarchical structure to control avatars automatically, developing a method for avatars to recognize environment and presenting an approach for making the policy of avatars' actions intelligently.

scholarly journals Automated classification of blood cell neutrophils.

1977 ◽  
Vol 25 (7) ◽  
pp. 633-640 ◽  
Author(s):  
J K Mui ◽  
K S Fu ◽  
J W Bacus
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Classification Accuracy ◽  
Three Dimensional ◽  
Classification Problem ◽  
Differential Count ◽  
Two Dimensional ◽  
Automated Classification ◽  
Exact Shape

The classification of white blood cell neutrophils into band neutrophils (bands) and segmented neutrophils (segs) is a subproblem of the white blood cell differential count. This classification problem is not well defined for at least two reasons: (a) there are no unique quantitative definitions for bands and segs and (b) existing definitions use the shape of the nucleus as the only discriminating criterion. When cells are classified on a slide, decisions are made from the two-dimensional views of these three-dimensional cells. A problem arises because the exact shape of the nucleus becomes indeterminate when the nucleus overlaps so that the filament is hidden. To assess the importance of this problem, this paper quantitates the classification errors due to overlapped nuclei (ON). The results indicate that, using only neutrophils without ON, the classification accuracy is 89%. For neutrophils with ON, the classification accuracy is 65%. This suggests a classification strategy of first classifying neutrophils into three categories: (a) bands without ON, (b) segs without ON and (c) neutrophils with ON. Category III can then be further classified into segs and bands by other stretegies.

Towards Multi-Classification of Human Motions Using Micro IMU and SVM Training Process

2009 ◽  
Vol 60-61 ◽  
pp. 189-193 ◽  
Author(s):  
Guang Yi Shi ◽  
Yue Xian Zou ◽  
Wen J. Li ◽  
Yu Feng Jin ◽  
Pei Guan
Keyword(s):  
Optimal Parameter ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Human Motion ◽  
Measurement Unit ◽  
Support Vector ◽  
Training Process ◽  
Motion Feature ◽  
Novel Approach ◽  
Multi Classification

This paper introduces a novel approach for human motion recognition via motion feature vectors collected by A Micro Inertial Measurement Unit (µIMU). First, µIMU that is 56x23x15mm3 in size was built. The unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU), which can transmit human motion information through a serial port to a computer. Second, a human motion database was setup by recording the motion data from the µIMU. The motions include fall, walk, stand, run and step upstairs. Third, Support Vector Machine (SVM) training process was used for human motion multi-classification. FFT was used for feature generation and optimal parameter searching process was done for the best SVM kernel function. Experimental results showed that for the given 5 different motions, the total correct recognition rate is 92%, of which the fall motion can be classified from others with 100% recognition rate.

A Multi-Classification Method Based on Gaussian Processes

2012 ◽  
Vol 198-199 ◽  
pp. 1333-1337 ◽  
Author(s):  
San Xi Wei ◽  
Zong Hai Sun
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Good Accuracy ◽  
Classification Problem ◽  
Decision Time ◽  
Support Vector ◽  
Regression Problem ◽  
Multi Classification

Gaussian processes (GPs) is a very promising technology that has been applied both in the regression problem and the classification problem. In recent years, models based on Gaussian process priors have attracted much attention in the machine learning. Binary (or two-class, C=2) classification using Gaussian process is a very well-developed method. In this paper, a Multi-classification (C>2) method is illustrated, which is based on Binary GPs classification. A good accuracy can be obtained through this method. Meanwhile, a comparison about decision time and accuracy between this method and Support Vector Machine (SVM) is made during the experiments.

A tree-based learning approach for document structure analysis and its application to web search

2014 ◽  
Vol 21 (4) ◽  
pp. 569-605 ◽  
Author(s):  
F. CANAN PEMBE ◽  
TUNGA GÜNGÖR
Keyword(s):  
Machine Learning ◽  
Web Search ◽  
Learning Algorithm ◽  
Classification Problem ◽  
Support Vector ◽  
Learning Approach ◽  
Rule Based ◽  
Document Structure ◽  
Rule Based Approach ◽  
Extraction Model

AbstractIn this paper, we study the problem of structural analysis of Web documents aiming at extracting the sectional hierarchy of a document. In general, a document can be represented as a hierarchy of sections and subsections with corresponding headings and subheadings. We developed two machine learning models: heading extraction model and hierarchy extraction model. Heading extraction was formulated as a classification problem whereas a tree-based learning approach was employed in hierarchy extraction. For this purpose, we developed an incremental learning algorithm based on support vector machines and perceptrons. The models were evaluated in detail with respect to the performance of the heading and hierarchy extraction tasks. For comparison, a baseline rule-based approach was used that relies on heuristics and HTML document object model tree processing. The machine learning approach, which is a fully automatic approach, outperformed the rule-based approach. We also analyzed the effect of document structuring on automatic summarization in the context of Web search. The results of the task-based evaluation on TREC queries showed that structured summaries are superior to unstructured summaries both in terms of accuracy and user ratings, and enable the users to determine the relevancy of search results more accurately than search engine snippets.

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