Prediction of conotoxin superfamilies by the Naive Bayes classifier

Huo, Haiyan; Yang, Lei

doi:10.1109/cisp-bmei.2017.8302275

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Of the 116 data is divided into 58 Data landslides and 58 area non-landslide conclude the performance and accuracy of naï ve Bayes slightly higher with a value of 87.50% of the logistic regression with an amount of 82.61%. For prediction cases, Hou and Yang also used Naï ve Bayes to predict Conotoxin Superfamilies [16]. This study concluded that from 305 data conotoxins, the accuracy value was 84.92%.…”

Section: Related Studiesmentioning

confidence: 98%

Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

Yudianto¹,

Agustin²,

James³

et al. 2021

IJMECS

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Indonesia has been known as an agrarian country because of its fertile soil and is very suitable for agricultural land, including rice. Yogyakarta is one of the most significant granary regions in Indonesia, especially in the Sleman region. However, one of the main challenges in rice planting in recent years is the erratic rainfall patterns caused by climate anomalies due to the El Nino and La Nina phenomena. As a result of this phenomenon, farmers have difficulty determining planting time and harvest time and planting other plants. Therefore, we make rainfall predictions to recommend planting varieties with Moving Average and Naive Bayes Methods in Sleman District. The results showed that moving averages well use in predicting rainfall. From these results, we can estimate that in 2020 rice production will below. That can saw from the calculation of the probability of naive Bayes on rice plants being low at 0.999 and 0.923. So that the recommended intercrops planted in 2020 are corn and peanuts. We also find that rainfall prediction with Moving Average using data from several previous years in the same month is more accurate than using data from four past months or periods.

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Section: Related Studiesmentioning

confidence: 98%

Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

Yudianto¹,

Agustin²,

James³

et al. 2021

IJMECS

View full text Add to dashboard Cite

show abstract

“…There are many classifiers for recognizing protein function, such as Rough Set [ 18 ], Naive Bayes [ 19 ] and SVM [ 20 ], of which SVM is very effective. As a supervised learning model, SVM has been widely used in many domains due to its simplicity and efficiency, such as protein subcellular prediction [ 21 ], HIV-1 and HIV-2 proteins prediction [ 22 ], gene selection [ 23 ], protein subcellular localization [ 24 ], pre-microRNA prediction [ 25 ] and membrane protein function prediction.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier

et al. 2018

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Research on cytokine recognition is of great significance in the medical field due to the fact cytokines benefit the diagnosis and treatment of diseases, but the current methods for cytokine recognition have many shortcomings, such as low sensitivity and low F-score. Therefore, this paper proposes a new method on the basis of feature combination. The features are extracted from compositions of amino acids, physicochemical properties, secondary structures, and evolutionary information. The classifier used in this paper is SVM. Experiments show that our method is better than other methods in terms of accuracy, sensitivity, specificity, F-score and Matthew’s correlation coefficient.

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Prediction of conotoxin superfamilies by the Naive Bayes classifier

Cited by 2 publications

References 24 publications

Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

Rainfall Forecasting to Recommend Crops Varieties Using Moving Average and Naive Bayes Methods

A New Method for Recognizing Cytokines Based on Feature Combination and a Support Vector Machine Classifier

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