Uncertainty in Machine Learning Applications: A Practice-Driven Classification of Uncertainty

2018 ◽  
pp. 431-438 ◽  
Author(s):  
Michael Kläs ◽  
Anna Maria Vollmer
Keyword(s):  
Machine Learning ◽  
Machine Learning Applications

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

scholarly journals Newly Proposed Technique for Autism Spectrum Disorder based Machine Learning

2021 ◽  
Vol 13 (2) ◽  
pp. 1-15
Author(s):  
Sherif Kamel ◽  
Rehab Al-harbi
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Screening Methods ◽  
Disease Prediction ◽  
Healthcare Organizations ◽  
Machine Learning Applications ◽  
Research Stage

The rapid growth in the number of autism disorder among toddlers needs for the development of easily implemented and effective screening methods. In this current era, the causes of Autism Spectrum Disorder (ASD) do not know yet, however, the diagnosis and detection of ASD is based on behaviours and symptoms. This paper aims to improve ASD disease prediction accuracy among toddlers by using the Logistic Regression model of Machine Learning, through the collected health care dataset and by using an algorithm for rapid classification of the behaviours to check whether the children are having autism diseases or not according to information in the dataset. Therefore, Machine Learning decreasing the time needed to detect the disorder, then providing the necessary health services early for infected toddlers to enhance their lifestyle. In healthcare, most machine learning applications are in the research stage, and to take the advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges.

scholarly journals Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets

2020 ◽  
Vol 10 (23) ◽  
pp. 8481
Author(s):  
Cesar Federico Caiafa ◽  
Jordi Solé-Casals ◽  
Pere Marti-Puig ◽  
Sun Zhe ◽  
Toshihisa Tanaka
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Unsupervised Classification ◽  
Decomposition Methods ◽  
Signal Decomposition ◽  
Learning Performance ◽  
Decomposition Approach ◽  
Data Completion ◽  
Machine Learning Applications

In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review

2021 ◽  
pp. 1-11
Author(s):  
Stephanie M. Helman ◽  
Elizabeth A. Herrup ◽  
Adam B. Christopher ◽  
Salah S. Al-Zaiti
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Clinical Applications ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Support Vector ◽  
Learning Techniques ◽  
Machine Learning Applications

Abstract Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.

scholarly journals Classification of Phonocardiography Signals Using Imbalanced Machine Learning Techniques

2020 ◽  
pp. 103-106
Author(s):  
Mustafa Berkant Selek ◽  
Sude Pehlivan ◽  
Yalcin Isler
Keyword(s):  
Machine Learning ◽  
Frequency Domain ◽  
Cardiovascular Health ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Random Forest Algorithm ◽  
Learning Techniques ◽  
Machine Learning Applications

Cardiovascular diseases, which involve heart and blood vessel dysfunctions, cause a higher number of deaths than any other disease in the world. Throughout history, many approaches have been developed to analyze cardiovascular health by diagnosing such conditions. One of the methodologies is recording and analyzing heart sounds to distinguish normal and abnormal functioning of the heart, which is called Phonocardiography. With the emergence of machine learning applications in healthcare, this process can be automated via the extraction of various features from phonocardiography signals and performing classification with several machine learning algorithms. Many studies have been conducted to extract time and frequency domain features from the phonocardiography signals by segmenting them first into individual heart cycles, and then by classifying them using different machine learning and deep learning approaches. In this study, various time and frequency domain features have been extracted using the complete signal rather than just segments of it. Random Forest algorithm was found to be the most successful algorithm in terms of accuracy as well as recall and precision.

scholarly journals Review of Machine Learning Applications and Datasets in Classification of Acute Leukemia

2021 ◽  
Vol 11 (6) ◽  
pp. 1-9
Author(s):  
Jaishree Ranganathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Leukemia ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Types ◽  
Research Community ◽  
Machine Learning Applications ◽  
Different Sources

Cancer is an extremely heterogenous disease. Leukemia is a cancer of the white blood cells and some other cell types. Diagnosing leukemia is laborious in a multitude of areas including heamatology. Machine Learning (ML) is the branch of Artificial Intelligence. There is an emerging trend in ML models for data classification. This review aimed to describe the literature of ML in the classification of datasets for acute leukemia. In addition to describing the existing literature, this work aims to identify different sources of publicly available data that could be utilised for research and development of intelligent machine learning applications for classification. To best of the knowledge there is no such work that contributes such information to the research community.

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.

scholarly journals Review of Machine Learning Applications and Datasets in Classification of Acute Leukemia

2021 ◽  
Vol 11 (6) ◽  
pp. 1-9
Author(s):  
Jaishree Ranganathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Leukemia ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Types ◽  
Research Community ◽  
Machine Learning Applications ◽  
Different Sources

Cancer is an extremely heterogenous disease. Leukemia is a cancer of the white blood cells and some other cell types. Diagnosing leukemia is laborious in a multitude of areas including heamatology. Machine Learning (ML) is the branch of Artificial Intelligence. There is an emerging trend in ML models for data classification. This review aimed to describe the literature of ML in the classification of datasets for acute leukemia. In addition to describing the existing literature, this work aims to identify different sources of publicly available data that could be utilised for research and development of intelligent machine learning applications for classification. To best of the knowledge there is no such work that contributes such information to the research community.

Classification of spatially resolved molecular fingerprints for machine learning applications and development of a codebase for their implementation

2018 ◽  
Vol 3 (3) ◽  
pp. 431-441 ◽  
Author(s):  
Mardochee Reveil ◽  
Paulette Clancy
Keyword(s):  
Machine Learning ◽  
Molecular Fingerprints ◽  
Spatially Resolved ◽  
Direct Mapping ◽  
Structures And Properties ◽  
Machine Learning Applications

Direct mapping between material structures and properties for various classes of materials is often the ultimate goal of materials researchers.

Sign in / Sign up

Export Citation Format

Share Document