Usage of KNN, Decision Tree and Random Forest Algorithms in Machine Learning and Performance Analysis with a Comparative Measure

Kumar, K. Uma Pavan; Gandhi, Ongole; Reddy, M.V. Mallikarjuna; Srinivasu, S. V. N.

doi:10.1007/978-981-15-9516-5_39

Cited by 8 publications

(2 citation statements)

References 6 publications

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“…Compared to other algorithms, it produces reliable and accurate results. They come in a variety of forms, including the K-nearest neighbor algorithm (KNN) [18], decision trees and random forests (DT/RF) [18], deep learning (DL) [19][20][21], and support vector regression (SVR) [22]. For accurate prediction, nearly one million values from the dataset are needed.…”

Section: About Here]mentioning

confidence: 99%

Futuristic deep learning algorithms for long-term solar power prediction

Kumar¹,

M²

2023

Preprint

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Solar power(SP) prediction using a futuristic deep-learning(DL) algorithm is an important piece of research. The SP data is accumulated in real-time with a 1 kW capacity that is available in our laboratory. The collected data is pre-processed via, initialization, normalization, and validation for accurate prediction. Data sets are normalized in order to fill in missing values using the K-nearest neighbor(KNN) algorithm and the interpolation method. Then, the data is validated using a newly proposed deep long short-term memory(DLSTM) with a recurrent neural network(RNN) algorithm for solar power prediction(SPP). To express the superiority of the DLSTM-RNN model, it is contrasted to other exciting models. All the DL algorithms are trained and tested using three different activation functions: Sigmoid, ReLU, and tanh with varying values of an epoch. Finally, the precision is evaluated in terms of different performance error indexes, such as the basic error index(BEI) and promoting percentage error index(PPEI).

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Section: About Here]mentioning

confidence: 99%

Futuristic deep learning algorithms for long-term solar power prediction

Kumar¹,

M²

2023

Preprint

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“…Various studies have used these techniques to analyze and predict different outcomes. For example, in a study by Uma Pavan Kumar et al the authors discuss the use of these algorithms and compare their performance [20]. Another study by Ali Shehadeh et al proposed using Modified Decision Tree regression, LightGBM, and XGBoost to predict the salvage value of construction equipment [21].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Mulyani,

Wijaya,

Trinidya

2024

COMPLETE

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This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.

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