A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification

2018 ◽  
Author(s):  
R. Saravanan ◽  
Pothula Sujatha
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Data Classification ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
State Of Art ◽  
Art Techniques

scholarly journals Searching for improvements in predicting human eye colour from DNA

2021 ◽  
Author(s):  
Magdalena Kukla-Bartoszek ◽  
Paweł Teisseyre ◽  
Ewelina Pośpiech ◽  
Joanna Karłowska-Pik ◽  
Piotr Zieliński ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sequencing Analysis ◽  
Learning Approaches ◽  
Human Eye ◽  
Software Analysis ◽  
Whole Exome ◽  
Eye Colour

AbstractIncreasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

Machine Learning in Gifted Education: A Demonstration Using Neural Networks

2019 ◽  
Vol 63 (4) ◽  
pp. 243-252 ◽  
Author(s):  
Jaret Hodges ◽  
Soumya Mohan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Learning ◽  
Language Processing ◽  
Gifted Education ◽  
Learning Algorithms ◽  
Simulated Data ◽  
Machine Learning Algorithms ◽  
Automated Driving ◽  
The Neural Network

Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

scholarly journals Analyzing the Performance Factors of Machine Learning Algorithms for COVID'19 Data

2020 ◽  
Vol 9 (9) ◽  
pp. 149-155
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Learning Algorithms ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Algorithm Selection ◽  
Data Set ◽  
Performance Factors ◽  
Selection Decision

Machine learning is a branch of Artificial intelligence which provides algorithms that can learn from data and improve from experience, without human intervention. Now a day's many of the machine learning algorithms playing a vital role in data analytics. Such algorithms are possible to apply with the recent pandemic COVID situation across the globe. Machine learning algorithms are classified into 3 different groups based on the type of learning process, such as supervised learning, unsupervised learning, and reinforcement learning. By considering the medical observations on the COVID across the globe it has been discussed and concluded to analyze under the supervised learning process. The data set is acquired from the reliable source, it is processed and fed into the classification algorithms. Since learning behaviors are carried out by knowing the input data and expected output data. The data is labeled and has been classified based on labels. In the proposed work, three different algorithms are used to experiment with the COVID'19 dataset and compared for their efficiency and algorithm selection decision is made.

scholarly journals Machine Learning Approaches on Diabetic Retinopathy Prediction

2020 ◽  
pp. 341-345
Author(s):  
Gowri Prasad ◽  
Vrinda Raveendran ◽  
Vidya B M ◽  
Tejavati Hedge
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Comparative Study ◽  
Blood Sugar ◽  
Efficient Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Early Age ◽  
Learning Approaches

Diabetic retinopathy is a eye disorder which is developed due to high blood sugar that affects the neurons in retina. A dangerous fact about this disease is that it can lead to blindness. The possible cure is through detection of disease at early age. This can be done using different machine learning algorithms. This paper does a comparative study on different machine learning algorithms that can be used for early detection of diabetic retinopathy. This study is done to find out the most efficient algorithm suitable for the process and to increase the efficiency of the particular algorithm.

Machine Learning and Data Mining in Bioinformatics

2012 ◽  
pp. 695-703
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

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