Intellectual Fuzzy System Air Pollution Control

Kravchenko, Yurii; Leshchenko, Olga; Dakhno, Nataliia; Дейнега, В. Н.; Shevchenko, Halyna; Trush, Oleksandr

doi:10.1109/atit50783.2020.9349334

Cited by 29 publications

(3 citation statements)

References 11 publications

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“…(Uddin et al, 2019). KNN works with nonlinear data, outperforming in low-dimensional datasets, and is feasible for multi-class classification (Kravchenko et al, 2020;Wang et al, 2020). In previous research, KNN attained accuracies exceeding 0.85 in customer churn and loan default predictions (Kaur & Kaur, 2020;Hassonah et al, 2019;Hong et al, 2022).…”

Section: Supervised Machine Learningmentioning

confidence: 97%

Enhancing Conversions and Lead Scoring in Online Professional Education

Yim,

Khaw,

Lim

et al. 2024

IJOMFA

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This study seeks to enhance lead conversion for online professional education providers by using supervised machine learning algorithms for lead conversion targeting and lead scoring, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Random Forst, Bagging, Boosting, and Stacking. A lead dataset was used to train and test the machine-learning models. The Recursive Feature Elimination (RFE) is used to establish a precise lead profile. The performance of the trained lead conversion models was evaluated and compared using the 10-Folds cross-validation method based on accuracy, precision, recall, and F1-score. The results show that Stacking is the best model with an accuracy of 0.9233, precision of 0.9391, and F1-score of 0.8939. Meanwhile, the Logistic Regression-based lead scoring model demonstrated promising potential for automating lead scoring. The results of the Logistic Regression-based lead scoring model achieved an accuracy of 0.9019, recall of 0.9019, precision of 0.9015, and F1-score of 0.9014. The optimal lead scoring threshold is 0.20, which stroked the optimal trade-off balance between accuracy, sensitivity, and specificity.

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Section: Supervised Machine Learningmentioning

confidence: 97%

Enhancing Conversions and Lead Scoring in Online Professional Education

Yim,

Khaw,

Lim

et al. 2024

IJOMFA

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show abstract

“…At the inference system level, fuzzy rules based on the fuzzy set theory and fuzzy rules in the form of IF-THEN statements are both used to draw conclusions [22] [28]. Zadeh divides into three basic operators, namely AND, OR, and NOT [29].…”

Section: System Inferencementioning

confidence: 99%

Tsukamoto Fuzzy Inference System on Internet of Things-Based for Room Temperature and Humidity Control

2023

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“…The development of computer vision algorithms through the use of neural networks attracts great attention of scientists. In works [7,8], the use of an artificial neural network of counter-propagation (CP-ANN), which has such advantages as the ability to learn and classify, is considered. CP-ANN neural networks still have some limitations in pattern recognition tasks when they encounter ambiguity during the learning process, leading to incorrect classification of the self-organizing Kohonen map (K-SOM).…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Recognition of aerial photography objects based on data sets with different aggregation of classes

Prystavka

Dukhnovska

Kovtun

et al. 2023

EEJET

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The object of this work is the recognition algorithms of aerial photography objects, namely, the analysis of recognition accuracy based on data sets with different aggregation classes. To solve this problem, an information system for object recognition based on aerial photography data has been developed. An architecture based on neural network architectures of the ConvNets group with structural modifications was chosen and used to create the information system. The use of a convolutional neural network of the ConvNets group in the architecture of the information system for the recognition of objects of aerial photography gives high accuracy rates when training the information system and validating its results. But the authors did not find any studies on the learning of the neural network of the ConvNets group. Therefore, it was decided to conduct an analysis in which case the ConvNets network will provide validation results with higher accuracy when the training takes place on datasets with or without class aggregation. The authors performed an analysis of the accuracy of recognition of aerial photography objects based on data sets with different aggregation classes. The dataset used for neural network training consisted of 3-channel labeled images of 64x64 pixels size. Based on the analysis, the optimal number of epochs for training is selected, which makes it possible to recognize aerial photography objects with greater accuracy and speed. It was concluded that greater accuracy in image classification is achieved for sampling without crossing data from different classes (without aggregation of classes). The result of the work is recommended for use in the automation of dataset filling and information filtering of visual images

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Intellectual Fuzzy System Air Pollution Control

Cited by 29 publications

References 11 publications

Enhancing Conversions and Lead Scoring in Online Professional Education

Enhancing Conversions and Lead Scoring in Online Professional Education

Tsukamoto Fuzzy Inference System on Internet of Things-Based for Room Temperature and Humidity Control

Recognition of aerial photography objects based on data sets with different aggregation of classes

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