scholarly journals Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications

2020 ◽  
Vol 47 (5) ◽  
Author(s):  
Hyunseok Seo ◽  
Masoud Badiei Khuzani ◽  
Varun Vasudevan ◽  
Charles Huang ◽  
Hongyi Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Machine Learning Techniques ◽  
Technical Aspects ◽  
Biomedical Image ◽  
Learning Techniques ◽  
State Of Art

scholarly journals Survey of Machine Learning Techniques for Student Profile Modeling

2021 ◽  
Vol 16 (04) ◽  
pp. 136
Author(s):  
Touria Hamim ◽  
Faouzia Benabbou ◽  
Nawal Sael
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Machine Learning Techniques ◽  
Student Profile ◽  
Learning Techniques ◽  
Survey Results ◽  
Learning Platforms ◽  
Recruitment System ◽  
State Of Art ◽  
Profile Modeling

Developments in information technology have led to the emergence of several online platforms for educational purposes, such as e-learning platforms, e-recommendation systems, e-recruitment system, etc. These systems exploit advances in Machine Learning to provide services tailored to the needs and profile of students. In this paper, we propose a state of art on student profile modeling using machine learning techniques during last four years. We aim to analyze the most used and most efficient machine learning techniques in both online and face-to-face education context, for different objectives such as failure, dropout, orientation, academic performance, etc. and also analyze the dominant features used for each objective in order to achieve a global view of the student profile model. Decision Tree is the most used and the most efficient by most of research studies. And academic, personal identity and online behavior are the top characteristics used for the student profile. To strengthen the survey results, an experiment was carried out, based on the application of machine learning techniques extracted from the state of art analysis, on the same datasets. Decision tree gave the highest performance, which confirms the survey results.

scholarly journals How to Represent Paintings: A Painting Classification Using Artistic Comments

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1940
Author(s):  
Wentao Zhao ◽  
Dalin Zhou ◽  
Xinguo Qiu ◽  
Wei Jiang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Language Processing ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Topological Graph ◽  
Convolutional Network ◽  
Learning Techniques ◽  
Classification Tasks ◽  
State Of Art

The goal of large-scale automatic paintings analysis is to classify and retrieve images using machine learning techniques. The traditional methods use computer vision techniques on paintings to enable computers to represent the art content. In this work, we propose using a graph convolutional network and artistic comments rather than the painting color to classify type, school, timeframe and author of the paintings by implementing natural language processing (NLP) techniques. First, we build a single artistic comment graph based on co-occurrence relations and document word relations and then train an art graph convolutional network (ArtGCN) on the entire corpus. The nodes, which include the words and documents in the topological graph are initialized using a one-hot representation; then, the embeddings are learned jointly for both words and documents, supervised by the known-class training labels of the paintings. Through extensive experiments on different classification tasks using different input sources, we demonstrate that the proposed methods achieve state-of-art performance. In addition, ArtGCN can learn word and painting embeddings, and we find that they have a major role in describing the labels and retrieval paintings, respectively.

scholarly journals CobWeb 1.0: machine learning toolbox for tomographic imaging

2020 ◽  
Vol 13 (1) ◽  
pp. 315-334
Author(s):  
Swarup Chauhan ◽  
Kathleen Sell ◽  
Wolfram Rühaak ◽  
Thorsten Wille ◽  
Ingo Sass
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Image Segmentation ◽  
User Interface ◽  
Image Enhancement ◽  
Graphical User Interface ◽  
Machine Learning Techniques ◽  
Easy Access ◽  
Digital Rock ◽  
Learning Techniques

Abstract. Despite the availability of both commercial and open-source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pinpoint. More often, image segmentation is driven manually, where the performance remains limited to two phases. Discrepancies due to artefacts cause inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0, which is automated and explicitly tailored for accurate greyscale (multiphase) image segmentation using unsupervised and supervised machine learning techniques. In this study, we demonstrate image segmentation using unsupervised machine learning techniques. The simple and intuitive layout of the graphical user interface enables easy access to perform image enhancement and image segmentation, and further to obtain the accuracy of different segmented classes. The graphical user interface enables not only processing of a full 3-D digital rock dataset but also provides a quick and easy region-of-interest selection, where a representative elementary volume can be extracted and processed. The CobWeb software package covers image processing and machine learning libraries of MATLAB® used for image enhancement and image segmentation operations, which are compiled into series of Windows-executable binaries. Segmentation can be performed using unsupervised, supervised and ensemble classification tools. Additionally, based on the segmented phases, geometrical parameters such as pore size distribution, relative porosity trends and volume fraction can be calculated and visualized. The CobWeb software allows the export of data to various formats such as ParaView (.vtk), DSI Studio (.fib) for visualization and animation, and Microsoft® Excel and MATLAB® for numerical calculation and simulations. The capability of this new software is verified using high-resolution synchrotron tomography datasets, as well as lab-based (cone-beam) X-ray microtomography datasets. Regardless of the high spatial resolution (submicrometre), the synchrotron dataset contained edge enhancement artefacts which were eliminated using a novel dual filtering and dual segmentation procedure.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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