Classification of Arrhythmia Beats Using Optimized K-Nearest Neighbor Classifier

Mohebbanaaz,; Kumari, L. V. Rajani; Sai, Y. Padma

doi:10.1007/978-981-33-6081-5_31

Cited by 26 publications

(7 citation statements)

References 11 publications

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“…Feature extraction is a vital stage in data mining given that it allows for the discovery of previously unknown information from enormous databases and the correction of inaccurate data included in the datasets. 226,227 Fig. 14 shows the proposed methodology for the use of feature extraction on numerous descriptor families for high-performance EESSs.…”

Section: Feature Extractionmentioning

confidence: 99%

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

et al. 2023

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Section: Feature Extractionmentioning

confidence: 99%

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

et al. 2023

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“…Optimized KNN, GWO-neural network optimization and Neural network methods were employed for medical signal classification. [16][17] [18].…”

Section: 2mentioning

confidence: 99%

An Automatic Rice Grain Classification for Agricultural Products Marketing

Rayudu,

Pampana,

Myneni

et al. 2023

Atlantis Highlights in Computer Sciences

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Use of Technology in agriculture and marketing of agricultural products is essential in sustainable and quality production. Some of the usage areas of these technologies are quality control and classification of grains. To optimize the rice production and processing industry, ensuring best product quality and meeting consumer demands effectively, different varieties of rice grain need to be classified accurately and consistently. Manual classification of rice is laborious, time consuming, inconsistent, and inefficient. Our main objective is developing an Artificial Intelligence (AI) based automated model that can analyze and classify rice grains with high accuracy, allowing for higher throughput and increased productivity. In such, we proposed a Machine Learning (ML) based approach to classify five classes of rice varieties. Investigated the results of five classifiers namely, Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF). The RF classifier has given 99.40% accuracy in classifying the five varieties of rice grains.

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“…The k-neighbors classifier (KNC) [39] is the simplest and non-parametric algorithm machine learning model for classification problems. The KNC model calculates the similarity between the data points and places the new input points into a similar category that is similar to each other.…”

Section: Employed Machine Learning Techniquesmentioning

confidence: 99%

A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics

et al. 2022

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Polycystic ovary syndrome (PCOS) is a critical disorder in women during their reproduction phase. The PCOS disorder is commonly caused by excess male hormone and androgen levels. The follicles are the collections of fluid developed by ovaries and may fail to release eggs regularly. The PCOS results in miscarriage, infertility issues, and complications during pregnancy. According to a recent report, PCOS is diagnosed in 31.3% of women from Asia. Studies show that 69% to 70% of women did not avail of a detecting cure for PCOS. A research study is needed to save women from critical complications by identifying PCOS early. The main aim of our research is to predict PCOS using advanced machine learning techniques. The dataset based on clinical and physical parameters of women is utilized for building study models. A novel feature selection approach is proposed based on the optimized chi-squared (CS-PCOS) mechanism. The ten hyper-parametrized machine learning models are applied in comparison. Using the novel CS-PCOS approach, the gaussian naive bayes (GNB) outperformed machine learning models and state-of-the-art studies. The GNB achieved 100% accuracy, precision, recall, and f1-scores with minimal time computations of 0.002 seconds. The k-fold cross-validation of GNB achieved a 100% accuracy score. The proposed GNB model achieved accurate results for critical PCOS prediction. Our study reveals that the dataset features prolactin (PRL), blood pressure systolic, blood pressure diastolic, thyroid stimulating hormone (TSH), relative risk (RR-breaths), and pregnancy are the prominent factors having high involvement in PCOS prediction. Our research study helps the medical community overcome the miscarriage rate and provide a cure to women through the early detection of PCOS.

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Classification of Arrhythmia Beats Using Optimized K-Nearest Neighbor Classifier

Cited by 26 publications

References 11 publications

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

An Automatic Rice Grain Classification for Agricultural Products Marketing

A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics

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