Auto Modelling for Machine Learning: A Comparison Implementation between RapidMiner and Python

Baharun, Norhayati; Razi, Nor Faezah Mohamad; Masrom, Suraya; Yusri, Nor Ain Mohamad; Rahman, Abdullah Sani Abd

doi:10.46338/ijetae0522_03

Cited by 28 publications

(2 citation statements)

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“…Furthermore, ML‐based models are able to handle heavy data sets effectively within a limited time. Moreover, the introduction of automated ML tools such as TPOT, Orange, and RapidMiner, amongst others, has made ML applications easier and accessible to individuals who are not experts in programming languages (Baharun et al., 2022; Demšar & Zupan, 2013; Randal et al., 2016). However, ML‐based models are as good as the quality and quantity of the data used in developing them.…”

Section: Machine Learning‐based Modelsmentioning

confidence: 99%

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Taiwo

Seghier

Zayed

2023

Water Resources Research

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Water distribution networks (WDNs) are undoubtedly a critical part of water utility assets, as they are responsible for water transmission (Atef et al., 2016;Berardi et al., 2014). It has been reported that, on average, 80% of water utility expenditures are spent on the management of WDNs (Poulakis et al., 2003). However, available evidence shows that the failure of WDNs is increasing at a fast rate, which negatively impacts individuals' social, economic, and health status (Steffelbauer et al., 2022;Yazdani & Jeffrey, 2012).In the USA and Canada, around 700 water pipes fail daily, thereby contributing to the loss of over 2 trillion gallons of clean water annually (Fan et al., 2022). In 2017, more than 2.2 billion m 3 of water was loss in China (Liao et al., 2021). The enormous financial commitment associated with the failure of water distribution networks cannot be overemphasized. According to the American Water Work Association (AWWA), the USA needs to invest around $1 trillion in replacing and repairing the deteriorating components of their WDNs (Fan et al., 2022). In Australia, the estimated cost of repairing and maintaining WDNs is about AUD 1.4 billion (Weeraddana et al., 2020). In South Korea, 52.5% of the water pipes will require rehabilitation by 2024, as indicated in a study by Seo et al. (2015). In the case of Colombia, a developing country, approximately 50% of water is lost due to water pipe failures (Giraldo-González & Rodríguez, 2020). From the aforementioned, the failure of WDNs, which mostly consist of water pipes, is a global issue that requires utmost attention. WDNs are founded underground; therefore, these infrastructures need to be designed to resist traffic, soil, internal, and overburden pressures, and other environment and operation related loads (Berardi et al., 2008). In essence, the reliability of WDNs must not be compromised during their service life; hence, catastrophic failure

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Section: Machine Learning‐based Modelsmentioning

confidence: 99%

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Taiwo

Seghier

Zayed

2023

Water Resources Research

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“…In evaluating the performance of the clustering algorithm in profiling; the percentage of accuracy, precision, and recall rate of the classification was measured using RapidMiner software. RapidMiner is a data mining platform that enables focuses on machine learning and data mining [30]- [32]. In this study, RapidMiner is used to classify and predict data using the Naïve Bayes, Decision Tree, and Random Forest.…”

Section: Introductionmentioning

confidence: 99%

Comparing Data Mining Classification for Online Fraud Victim Profile in Indonesia

2023

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Classification is one of the most often employed data mining techniques. It focuses on developing a classification model or function, also known as a classifier, and predicting the class of objects whose class label is unknown. Categorizing applications include pattern recognition, medical diagnosis, identifying weaknesses in organizational systems, and classifying changes in the financial markets. The objectives of this study are to develop a profile of a victim of online fraud and to contrast the approaches frequently used in data mining for classification based on Accuracy, Classification Error, Precision, and Recall. The survey was conducted using Google Forms, which is an online platform. Naive Bayes, Decision Tree, and Random Forest algorithms are popular models for classification in data mining. Based on the sociodemographics of Indonesia's online crime victims, these models are used to classify and predict. The result shows that Naïve Bayes and Decision Tree are slightly superior to the Random Forest Model. Naive Bayes and Decision Tree have an accuracy value of 77.3%, while Random Forest values 76.8%.

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Systematic Structures and Silicon Stories Shaping the Saga of Software Solutions

Bhuvana,

Vekariya,

Singh

et al. 2024

Lecture Notes in Networks and Systems

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Auto Modelling for Machine Learning: A Comparison Implementation between RapidMiner and Python

Cited by 28 publications

References 0 publications

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Toward Sustainable Water Infrastructure: The State‐Of‐The‐Art for Modeling the Failure Probability of Water Pipes

Comparing Data Mining Classification for Online Fraud Victim Profile in Indonesia

Systematic Structures and Silicon Stories Shaping the Saga of Software Solutions

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