A Survey of kNN Algorithm

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.

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Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

Animals ◽

10.3390/ani11010241 ◽

2021 ◽

Vol 11 (1) ◽

pp. 241

Author(s):

Dongwon Seo ◽

Sunghyun Cho ◽

Prabuddha Manjula ◽

Nuri Choi ◽

Young-Kuk Kim ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Fixation Index ◽

Machine Learning Classification ◽

Genetic Components ◽

Marker Combination ◽

A Genome ◽

Minimum Number ◽

Native Chickens

A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

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EMG Signal Processing for Hand Motion Pattern Recognition Using Machine Learning Algorithms

10.33696/orthopaedics.1.005 ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Pattern Recognition ◽

Signal Processing ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Motion Pattern ◽

Hand Motion ◽

Emg Signal

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PSIX-15 Assessment of machine learning algorithms for prediction of Aleutian disease in American mink

Journal of Animal Science ◽

10.1093/jas/skab235.484 ◽

2021 ◽

Vol 99 (Supplement_3) ◽

pp. 264-265

Author(s):

Duy Ngoc Do ◽

Guoyu Hu ◽

Younes Miar

Keyword(s):

Machine Learning ◽

Random Forest ◽

Linear Models ◽

American Mink ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Enzyme Linked Immunosorbent Assay ◽

Linear Discriminant ◽

Machine Learning Classification

Abstract American mink (Neovison vison) is the major source of fur for the fur industries worldwide and Aleutian disease (AD) is causing severe financial losses to the mink industry. Different methods have been used to diagnose the AD in mink, but the combination of several methods can be the most appropriate approach for the selection of AD resilient mink. Iodine agglutination test (IAT) and counterimmunoelectrophoresis (CIEP) methods are commonly employed in test-and-remove strategy; meanwhile, enzyme-linked immunosorbent assay (ELISA) and packed-cell volume (PCV) methods are complementary. However, using multiple methods are expensive; and therefore, hindering the corrected use of AD tests in selection. This research presented the assessments of the AD classification based on machine learning algorithms. The Aleutian disease was tested on 1,830 individuals using these tests in an AD positive mink farm (Canadian Centre for Fur Animal Research, NS, Canada). The accuracy of classification for CIEP was evaluated based on the sex information, and IAT, ELISA and PCV test results implemented in seven machine learning classification algorithms (Random Forest, Artificial Neural Networks, C50Tree, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) using the Caret package in R. The accuracy of prediction varied among the methods. Overall, the Random Forest was the best-performing algorithm for the current dataset with an accuracy of 0.89 in the training data and 0.94 in the testing data. Our work demonstrated the utility and relative ease of using machine learning algorithms to assess the CIEP information, and consequently reducing the cost of AD tests. However, further works require the inclusion of production and reproduction information in the models and extension of phenotypic collection to increase the accuracy of current methods.

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

Metals ◽

10.3390/met9050557 ◽

2019 ◽

Vol 9 (5) ◽

pp. 557 ◽

Cited By ~ 8

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.

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Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases

Current Pharmaceutical Design ◽

10.2174/1381612824666181112114228 ◽

2019 ◽

Vol 24 (34) ◽

pp. 3998-4006

Author(s):

Shijie Fan ◽

Yu Chen ◽

Cheng Luo ◽

Fanwang Meng

Keyword(s):

Machine Learning ◽

Big Data ◽

Precision Medicine ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning Methods ◽

Advantages And Disadvantages ◽

Machine Learning Methods ◽

Accelerated Studies ◽

Applications Of Machine Learning

Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

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Npc Behavior Plugin Development for Game Engine Unity

Russian Digital Libraries Journal ◽

10.26907/1562-5419-2020-23-5-1044-1057 ◽

2020 ◽

Vol 23 (5) ◽

pp. 1044-1057

Author(s):

Leonid Nikolaevich Parenyuk ◽

Vlada Vladimirovna Kugurakova

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Real Time ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Game Engine ◽

Game Development ◽

Advantages And Disadvantages

There are various approaches for creating artificial intelligence in games, and each has both advantages and disadvantages. This study describes an authoring implementation of the NPC behavior task using machine learning algorithms that will be associated with the Unity environment in real time. This approach can be used in game development.

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