Naïve Bayes Approach for Expert System Design of Children Skin Identification Based on Android

Hartatik, Hartatik; Purnomo, A; Hartono, Rudi; Munawaroh, H

doi:10.1088/1757-899x/333/1/012105

Cited by 8 publications

(7 citation statements)

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“…Gaussian Naïve Bayes is supervised learning technique which relies on Bayes theorem that is employed for classification challenge and specifically for text classification because it is more suited to high dimensional training datasets [22]. It is considered one of the top 10 classifier techniques in data mining [23].…”

Section: Gaussian Naive Bayes (Gnb)mentioning

confidence: 99%

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

et al. 2022

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Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. Our survey provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction,tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies,including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.

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Section: Gaussian Naive Bayes (Gnb)mentioning

confidence: 99%

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

et al. 2022

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“…The Bayes method has attributes that are interconnected with each other. The approach used by the Bayes theorem is to calculate the probability of an event in certain conditions (Sukahar, 2014), (Hartatik, 2017), which is based on priors and posteriores.…”

Section: Calculation Of Assistant Selection With Bayesmentioning

confidence: 99%

Efficiency of the Course Practicum Assistant Selection Process Using Bayesian Algorithms

Hartatik

2022

MULTI RES LIT

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The selection of the Practicum Assistant Course is the main problem in the process of selecting the role of the practicum assistant in big data. In the process of big data, the process of selecting assistant laboratorium subjects pay attention to the processes that occur before in the prospective assistant lab practicum (previous distribution). In this study we use the Bayesian classification method for the selection of practicum assistant courses. The Bayesian algorithm is applied in the process of selecting a practicum asiststen which is received with prior knowledge and posterior . In this consideration, the initial (prior) data is used as a consideration to determine whether a student in a certain semester can become a practicum assistant in the appropriate course of interest. The benefits obtained are efficiency in choosing a practicum assistant with consideration of previous events, P (θ | x). Based on the results obtained, the results of this study show the efficiency of the selection based on the previous few who became the background of the candidates for the practicum assistant course

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“…The bene t of employing NBC classi er is the ability to predict the parameters required for the classi cation process using a small amount of training datasetsAlthough KNN is a highly accurate classi er, increasing accuracy needs additional data for training, and k value selection will also have an impact on the classi cation outcomes. The amount of data used in training will considerably affect the length of time required for computation, Naive Bayes will be employed in this work as a classi er with limited data, and its correctness in classifying skin conditions based on feature extraction it will be assessed [9].…”

Section: Introductionmentioning

confidence: 99%

Skin Disease Detection using Image Processing and Machine Learning

Prasad,

priya,

Jahnavi

et al. 2023

Preprint

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In these modern times, due to air pollution human skin is affected in many ways and these have become the most prevalent diseases worldwide. It may cause diseases like Actinic keratosis, Melanoma, Psoriasis, Eczema Skin disease is the fourth leading cause of non-toxic disease globally, affecting 7.9–60% of individuals in all age groups. The advancement of medical technology has led easier ways in identification of these diseases but the cost of such diagnosis is very expensive and limited. So, Image processing techniques guide to build computerized screening systems at initial stage. In this research work, we have come with a method to identify and classify the diseases. The implementation of the project consists of a dual stage approach to detect skin diseases based on their classification and severity. The second stage involves the training and testing of images using Naive- Bayes classifier to identify the skin disease.

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Naïve Bayes Approach for Expert System Design of Children Skin Identification Based on Android

Cited by 8 publications

References 0 publications

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

Efficiency of the Course Practicum Assistant Selection Process Using Bayesian Algorithms

Skin Disease Detection using Image Processing and Machine Learning

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