Water Quality Prediction Using KNN Imputer and Multilayer Perceptron

Juna, Afaq; Umer, Muhammad; Sadiq, Saima; Karamti, Hanen; Eshmawi, Ala’ Abdulmajid; Mohamed, Abdullah; Ashraf, Imran

doi:10.3390/w14172592

Cited by 82 publications

(26 citation statements)

References 32 publications

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“…The imputation technique you choose is important because it may have a big influence on how well your model performs. A popular approach for imputing missing data is the KNN imputer, which may be used instead of more conventional imputation techniques like [ 59 ]. By utilizing the Euclidean distance matrix to locate the closest neighbors, it substitutes missing values.…”

Section: Methodsmentioning

confidence: 99%

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Aljrees

2024

PLoS ONE

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Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

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Section: Methodsmentioning

confidence: 99%

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Aljrees

2024

PLoS ONE

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“…Given the gradient of the loss function, the weights can be updated as follows. where and are the new and current weights, is the learning rate, and is the gradient of the loss function calculated by an optimization algorithm, such as Adam, Stochastic Gradient Descent (SGD), RMSprop, Adagrad, Momentum, Nestrov and Accelerated Gradient 34 , 35 .…”

Section: Methodsmentioning

confidence: 99%

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Yousefzadeh,

Kazemi,

Al-Maamari

2024

Sci Rep

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One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.

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“…k-NN is the first choice for medical data mining. k-NN is a straightforward instance-based classifier [ 34 , 35 ]. A supervised learning model called k-NN compares new data to existing cases to determine how similar they are, then groups the new data with those cases that have the highest similarity.…”

Section: Materials and Methodsmentioning

confidence: 99%

Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

Alturki

Umer

Ishaq

et al. 2023

Cancers

Self Cite

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Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descent using features extracted by deep convolutional layers for the efficient classification of tumorous victims from the normal. From the first and second-order brain tumor features, deep convolutional features are extracted for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classification. The proposed voting classifier along with convoluted features produces results that show the highest accuracy of 99.9%. Compared to cutting-edge methods, the proposed approach has demonstrated improved accuracy.

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Water Quality Prediction Using KNN Imputer and Multilayer Perceptron

Cited by 82 publications

References 32 publications

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

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