Inherently Safer Design

Chakrabarty, Arnab; Mannan, Sam; Çağın, Tahir

doi:10.1016/b978-0-12-396975-0.00008-5

Cited by 6 publications

(2 citation statements)

References 67 publications

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“…According to [17], data preprocessing is crucial since it sets the data up in the most helpful way for the subsequent indepth analysis. Additionally, [18] highlighted that preprocessing data is crucial in getting the data ready for a model.…”

Section: Related Workmentioning

confidence: 99%

The Necessity of Exploratory Data Analysis How are preprocessing activities beneficial to Data Analysts and Professional Researchers in Academia

Muraina,

Adesanya,

Agoi

et al. 2023

IJSRCSE

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Data analysis is used in all academic disciplines. Still, research has shown that some studies can appear ambiguous when the analyzed data needs to be sufficiently illustrated to identify trends, patterns, and other assumptions. These assumptions typically enable researchers to present the statistical summary using pertinent and self-explanatory graphical representations. Analysts want to use a method that will assist them in condensing the dataset's critical characteristics for straightforward interpretation and presentation to the audience. In addition to presenting the impact of preprocessing activities in assuring an error-free dataset before actual analysis is done, this study uncovers the trick to conducting an adequate investigation on the dataset to have a clean dataset for accurate analysis interpretation. The most popular preprocessing procedures, including missing values, outliers, and variable transformation, are listed. The study used a descriptive survey design technique and focused on using a questionnaire instrument to gather data from respondents using a Google Forms App. The information was collected using criteria such as gender, amount of data analysis experiences, institution type, and roles within the academic community. Both face validity and construct validity methods were used to validate the instrument. Chrobach's Alpha yielded a dependability index of 0.88, indicating good reliability. Since the data was prepared and collected online using Google Forms, the data collecting and collation process only took four days. Software for appropriate visualization was used for the analysis. The results demonstrated that thoroughly exploring the data and removing any bias or outliers is the first step that any data analyst must take before starting a proper analysis process. The usage of some of the tools available for cleaning datasets was also outlined, and it was recommended that amateur analysts take the time to learn how to utilize them. Before beginning their final year projects, final-year undergraduate and postgraduate students should be exposed to all exploratory data analysis methods.

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Section: Related Workmentioning

confidence: 99%

The Necessity of Exploratory Data Analysis How are preprocessing activities beneficial to Data Analysts and Professional Researchers in Academia

Muraina,

Adesanya,

Agoi

et al. 2023

IJSRCSE

View full text Add to dashboard Cite

show abstract

“…The preprocessing stage is needed before training the machine learning model. During preprocessing, the data is cleaned and transformed to remove outliers and empty data [14,15].…”

Section: Data Preprocessingmentioning

confidence: 99%

Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Maulana

Prasetiyowati²,

Sibaroni³

2022

mib

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Precipitation is one of the most important climatic variables in many aspects of our daily lives. High rainfall intensity can cause floods, landslides, and other natural disasters. Therefore, rainfall prediction is important for predicting natural disasters, assisting farmers in production decisions, and crop harvesting. In this research, a system is built to create a rainfall prediction map using a machine learning approach and spatial interpolation algorithms in Java, Indonesia. In the field of weather prediction, the artificial neural network approach is a popular machine learning method. The artificial neural network (ANN) method is a method that has the advantage of studying connections in the previously unknown hidden layer between input data and output data through training procedures. By using the ANN method, historical weather and climate data can be applied to create a classification model and predict rainfall classes. The classification of data is determined based on the attributes of historical weather and climate data, namely temperature, humidity, air pressure, evaporation, sunlight, and the level of rainfall in the time range per day and month. From the results of the ANN modeling, it was found that the 5C month model with an accuracy value of 89% as the best monthly ANN model, and the 6C day model with an accuracy value of 81% as the best daily ANN model. After going through ANN modeling, there is a spatial interpolation algorithm that is often used to estimate rainfall, namely Ordinary Kriging. The Ordinary Kriging approach is used to reduce the estimated variance and estimate the rainfall value in the case study area. After going through Ordinary Kriging modeling, a rainfall prediction map for the next six months and seven days is made based on the coordinates as a result of the research. The results of this research are rainfall prediction maps for the next six months and the next seven days on Java Island.

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A Machine Learning-Based System to Recommend Appropriate Military Training Program for a Soldier

Rahman¹,

Dewan²,

Razzak³

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Inherently Safer Design

Cited by 6 publications

References 67 publications

The Necessity of Exploratory Data Analysis How are preprocessing activities beneficial to Data Analysts and Professional Researchers in Academia

The Necessity of Exploratory Data Analysis How are preprocessing activities beneficial to Data Analysts and Professional Researchers in Academia

Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

A Machine Learning-Based System to Recommend Appropriate Military Training Program for a Soldier

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