Comparison of Open Source Data Mining Tools: Naive Bayes Algorithm Example

Özkan, Sibel Barin; Apaydin, Sultan Muhammed Fatih; Özkan, Yasin; Duzdar, Irem

doi:10.1109/ebbt.2019.8741664

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…The ML analysis was performed using the Knime Analytics Platform, a platform widely used in the biomedical engineering field [ 58 , 59 , 60 , 61 , 62 , 63 ].…”

Section: Methodsmentioning

confidence: 99%

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

Donisi,

Jacob,

Guerrini

et al. 2023

Bioengineering

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Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology—based on wearable sensors and artificial intelligence—to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

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“…The ML analysis was performed using the Knime Analytics Platform, a platform widely used in the biomedical engineering field [ 58 , 59 , 60 , 61 , 62 , 63 ].…”

Section: Methodsmentioning

confidence: 99%

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

Donisi,

Jacob,

Guerrini

et al. 2023

Bioengineering

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show abstract

“…Strong or naive and assumptions, regardless of the condition or event of each is a character of Naïve Bayes [2]. Several data mining operations by applying Naïve Bayes with image data and numerical data from several diseases to obtain classification results [3]. In addition, classiying the behavior of web users applies naïve Bayes in the hope of obtaining optimal word segmentation results [4].…”

Section: Introductionmentioning

confidence: 99%

The feature extraction for classifying words on social media with the Naïve Bayes algorithm

Lubis

Nasution

Sitompul

et al. 2022

IJ-AI

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To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.

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A Comprehensive Analysis of Proprietary and Open Source Data Mining Tools

Chowdhary¹,

Vikash²

2020

IJSRCSEIT

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The Powerful software tools and techniques required for the development of data mining applications. With the rapid development of technologies and business interest in using electronics and latest technologies plays important role in improvement of data mining field. Data mining access the meaningful and efficient information available in worldwide which is helps in decision making. This paper described the (a) various tools and techniques used by data mining applications. (b) compared features and limitations both in Proprietary and open sources data mining tools. (c) technical analysis of proprietary and open source data mining tools. On the basis of well-designed User interface, short time analysis, statistical and mathematical analysis user can select the best tool as per their requirements. Analysis of these tools makes easy to select appropriate tool.

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Comparison of Open Source Data Mining Tools: Naive Bayes Algorithm Example

Cited by 3 publications

References 1 publication

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

The feature extraction for classifying words on social media with the Naïve Bayes algorithm

A Comprehensive Analysis of Proprietary and Open Source Data Mining Tools

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