Comparison of Various Machine Learning Techniques and Its Uses in Different Fields

Srivastava, Aditya; Saini, Sonia; Gupta, Deepa

doi:10.1109/iceca.2019.8822068

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…[1], [30], [31], [19], [34], [23], [43], [46], [48], [53], [57], [61], [75], [78], [81], [82], [83], [90], [97], [111], [115], [116] [58], [59], [60], [66], [67], [79], [102], [107], [108], [109], [117] Table 1 shows a summary of research articles analyzed in this research. The table portrays that machine learning techniques are being are applied to address problems in diverse fields.…”

mentioning

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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confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…As shown in Fig. 4, observations are highlighted in red, and random deviations' result (shown in green) from the basic relationship (shown in blue) between the independent variable (x) and the dependent variable (y) [21].…”

Section: A Linear Regressionmentioning

confidence: 99%

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Badawy

Ramadan

Hefny

2022

Preprint

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Aim This paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction, as well as identify inherent obstacles to applying these approaches in the healthcare prediction domain. Background Healthcare prediction has been a significant factor in saving human lives in recent years. In the domain of healthcare, there is a rapid development of intelligent systems for analyzing complicated relationships among data and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry. Thus comes the role of systems depending on machine learning as well as deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Methods The studies discussed in this paper have been presented in journals published by IEEE, Springer, and Elsevier. Machine learning, deep learning, healthcare, surgery, cardiology, radiology, hepatology, and nephrology are some of the terms used to search for these studies. The studies chosen for this survey are concerned with the use of machine learning as well as deep learning algorithms in healthcare prediction. Results A total of 40 working papers were selected and the methodology for each paper was clarified. Conclusion This paper presents a comprehensive survey as well as the current challenges in healthcare prediction. studies have shown that artificial intelligence plays a significant role in diseases diagnosing.

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“…[119] 6 Solutions Health 24 [1], [30], [31], [19], [34], [23], [43], [46], [48], [53], [57], [61], [75], [78], [81], [82], [83], [90], [97], [111], [115], [116] Solutions in Analytics and Artificial 13 [52], [3], [58], [59], [60], [66], [67], [79], [102], Intelligence [107], [108], [109], [117] Table 1 shows a summary of research articles analyzed in this research. The table portrays that machine learning techniques are being are applied to address problems in diverse fields.…”

Section: IIImentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Shukla¹

2022

IJSREM

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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 2021 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. Keywords: Research methods in machine learning, machine learning algorithms, machine learning techniques.

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Comparison of Various Machine Learning Techniques and Its Uses in Different Fields

Cited by 14 publications

References 7 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Research Methods in Machine Learning: A Content Analysis

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