Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms

2015 ◽  
Author(s):  
Jose Luis Seixas ◽  
Sylvio Barbon ◽  
Rafael Gomes Mantovani
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Medical Images ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Lower Member ◽  
Skin Ulcers

scholarly journals Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data

Metals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 557 ◽  
Author(s):  
Cristiano Fragassa ◽  
Matej Babic ◽  
Carlos Perez Bergmann ◽  
Giangiacomo Minak
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Mechanical Properties ◽  
Pattern Recognition ◽  
Cast Iron ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tensile Behaviour ◽  
Cast Alloys ◽  
Pattern Recognition Analysis

The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.

Finger Movement Pattern Recognition from Surface EMG Signals Using Machine Learning Algorithms

ICTMI 2017 ◽  
2019 ◽  
pp. 75-89 ◽  
Author(s):  
Shravan Krishnan ◽  
Ravi Akash ◽  
Dilip Kumar ◽  
Rishab Jain ◽  
Karthik Murali Madhavan Rathai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Algorithms ◽  
Movement Pattern ◽  
Surface Emg ◽  
Machine Learning Algorithms ◽  
Finger Movement

scholarly journals A Survey of kNN Algorithm

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Jingwen Sun ◽  
Weixing Du ◽  
Niancai Shi
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Related Literature ◽  
Machine Learning Classification ◽  
Advantages And Disadvantages ◽  
Implementation Steps

The kNN algorithm is a well-known pattern recognition method, which is one of the best text classifi cation algorithms. It is one of the simplest machine learning algorithms in machine learning classification algorithm. In this paper, we summarize the kNN algorithm and related literature, introduce the idea, principle, implementation steps and implementation code of kNN algorithm in detail, and analyze the advantages and disadvantages of the algorithm and its various improvement schemes. This paper also introduces the development of kNN algorithm, the important published papers. At the end of this paper, the application of kNN algorithm is introduced, and its implementation in text classifi cation is emphasized.

Preventing undesirable behavior of intelligent machines

Science ◽  
2019 ◽  
Vol 366 (6468) ◽  
pp. 999-1004 ◽  
Author(s):  
Philip S. Thomas ◽  
Bruno Castro da Silva ◽  
Andrew G. Barto ◽  
Stephen Giguere ◽  
Yuriy Brun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Data Analysis ◽  
Complex Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intelligent Machines ◽  
Dangerous Behavior ◽  
Flexible Framework ◽  
Undesirable Behavior

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior—that they do not, for example, cause harm to humans—is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke

2019 ◽  
Vol 26 (7) ◽  
pp. 6481-6491
Author(s):  
Jamile Silveira Tomiazzi ◽  
Danillo Roberto Pereira ◽  
Meire Aparecida Judai ◽  
Patrícia Alexandra Antunes ◽  
Ana Paula Alves Favareto
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Hearing Impairment ◽  
Cigarette Smoke ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

2021 ◽  
Author(s):  
Racheal S. Akinbo ◽  
Oladunni A. Daramola
Keyword(s):  
Machine Learning ◽  
Medical Imaging ◽  
Ensemble Learning ◽  
Medical Images ◽  
Learning Algorithms ◽  
Theoretical Background ◽  
Machine Learning Algorithms ◽  
Ensemble Machine Learning ◽  
Machine Learning Applications

The employment of machine learning algorithms in disease classification has evolved as a precision medicine for scientific innovation. The geometric growth in various machine learning systems has paved the way for more research in the medical imaging process. This research aims to promote the development of machine learning algorithms for the classification of medical images. Automated classification of medical images is a fascinating application of machine learning and they have the possibility of higher predictability and accuracy. The technological advancement in the processing of medical imaging will help to reduce the complexities of diseases and some existing constraints will be greatly minimized. This research exposes the main ensemble learning techniques as it covers the theoretical background of machine learning, applications, comparison of machine learning and deep learning, ensemble learning with reviews of state-of the art literature, framework, and analysis. The work extends to medical image types, applications, benefits, and operations. We proposed the application of the ensemble machine learning approach in the classification of medical images for better performance and accuracy. The integration of advanced technology in clinical imaging will help in the prompt classification, prediction, early detection, and a better interpretation of medical images, this will, in turn, improves the quality of life and expands the clinical bearing for machine learning applications.

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