Study the Influence of Normalization/Transformation process on the Accuracy of Supervised Classification

Raju, Vanitha Narayan; Lakshmi, K. Prasanna; Jain, Vinod Mahesh; Kalidindi, Archana; Padma, V.

doi:10.1109/icssit48917.2020.9214160

Cited by 180 publications

(77 citation statements)

References 3 publications

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“…It was discovered that scale in terms of geometric properties was different for each microbe. Hence, normalization of the dataset was done using minmax algorithm [ 70 ]. This was followed by the comparison of two strategies.…”

Section: Resultsmentioning

confidence: 99%

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Dhindsa

Bhatia

Agrawal

et al. 2021

Entropy

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The accurate classification of microbes is critical in today’s context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).

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Section: Resultsmentioning

confidence: 99%

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Dhindsa

Bhatia

Agrawal

et al. 2021

Entropy

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“…It is used in this study so that the data scale is not too different; this is intended so that the average of the observed data becomes 0 and the standard deviation becomes 1. The standard used in this study is the standard scaler in python [24]. Eq.…”

Section: Standardizationmentioning

confidence: 99%

A New Approach for Detection of Viral Respiratory Infections Using E-nose Through Sweat from Armpit with Fully Connected Deep Network

2022

IJIES

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Viral respiratory infections are the most common diseases suffered by all age groups worldwide. The gold standard for diagnosing viral respiratory infection is through the molecular method, but this diagnosis is expensive, requires sophisticated equipment, can only be performed by well-trained medical staff, and is painful. Volatile Organic Compounds (VOCs) are compounds released from the human body that can be a marker of disease and based on numerous studies it also contains VOCs. An electronic nose (E-nose) is a device that can be used to identify disease. This study proposes a new approach for the detection of viral respiratory infections through sweat from the armpit using an E-nose consisting of 5 semiconductor gases and a single-board computer. Several statistical parameters are used to obtain features and the detection algorithm used is Fully Connected Deep Network (FCDN). Several sizes of hidden layers were tested to obtain the best FCDN model. This study also proposes the selection of the best FCDN model which is a trade-off between complexity and accuracy, so that the model stored in E-nose is a model that not only has good accuracy but is also not too complex. The experimental results show that using 29 statistical parameters and 2 hidden layers generate the highest accuracy of 0.940 for the detection of 2 classes, namely positive and negative, with sensitivity and specificity of 0.967 and 0.915, respectively, where the best FCDN model has a total of 90,561 parameters.

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“…This article uses Min-Max-Scaler technique (Raju et al, 2020) to normalize the road transportation and COVID-19 confirmed cases data. Min-Max maps the minimum and maximum values of a dataset into 0 and 1 respectively.…”

Section: Data Pre-processingmentioning

confidence: 99%

An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic

Ardakani

Xia

Cheshmehzangi

et al. 2022

Genus

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The world still suffers from the COVID-19 pandemic, which was identified in late 2019. The number of COVID-19 confirmed cases are increasing every day, and many governments are taking various measures and policies, such as city lockdown. It seriously treats people’s lives and health conditions, and it is highly required to immediately take appropriate actions to minimise the virus spread and manage the COVID-19 outbreak. This paper aims to study the impact of the lockdown schedule on pandemic prevention and control in Ningbo, China. For this, machine learning techniques such as the K-nearest neighbours and Random Forest are used to predict the number of COVID-19 confirmed cases according to five scenarios, including no lockdown and 2 weeks, 1, 3, and 6 months postponed lockdown. According to the results, the random forest machine learning technique outperforms the K-nearest neighbours model in terms of mean squared error and R-square. The results support that taking an early lockdown measure minimises the number of COVID-19 confirmed cases in a city and addresses that late actions lead to a sharp COVID-19 outbreak.

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Study the Influence of Normalization/Transformation process on the Accuracy of Supervised Classification

Cited by 180 publications

References 3 publications

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

A New Approach for Detection of Viral Respiratory Infections Using E-nose Through Sweat from Armpit with Fully Connected Deep Network

An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic

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