A Quick Review of Machine Learning Algorithms

Ray, Susmita

doi:10.1109/comitcon.2019.8862451

Cited by 649 publications

(333 citation statements)

References 3 publications

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“…Others like Decision trees perform better with small sets of data; however, the decision tree can be unstable. Support vector machine can handle complex functions and even scale up with dimensional data, however, it is not suitable with outliers [21].…”

Section: Figure 6: Number Of Algorithms Usedmentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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Section: Figure 6: Number Of Algorithms Usedmentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…When clusters are globular, efficiency suffers. [41]. Figure 3 illustrates the graphical representation of the K-means algorithm.…”

Section: B K-means Clustering Algorithmmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

165

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Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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“…The machines can make decisions in situations that they have not encountered before. Machine Learning (ML) is a large sub-area of AI that specializes in recognizing patterns in data to continuously learn from feedback [4]. One commonly used ML architecture is neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…-A CNN that translates images from the SSL hand alphabet; -An original approach based on transfer learning, which adapts the model to perform well using a limited training dataset; -An evaluation of the model on 8 study subjects, obtaining 85% of overall accuracy; -A publicly available implementation of our approach 4 and a web application where a static sign is entered as an input image by the user, and its corresponding text form is displayed on the screen. 5 The rest of this paper is structured as follows.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of Swedish Sign Language Using Convolutional Neural Networks and Transfer Learning

et al. 2021

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The automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consists of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.

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A Quick Review of Machine Learning Algorithms

Cited by 649 publications

References 3 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

The Role of Machine Learning Algorithms for Diagnosing Diseases

Interpretation of Swedish Sign Language Using Convolutional Neural Networks and Transfer Learning

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