Renyi Entropy Predictive Data Mining And Weighted Xavier Deep Neural Classifier For Heart Disease Prediction DOI Open Access

M. Revathy Meenal,

S. Vennila

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 16, 2025

During the past few years, Frequent Pattern Mining (FPM) has received interest of several researchers that necessitate extracting items from transactions, and sequences datasets, clarifying heart disease diagnosis materializes commonly, recognizing specific arrangements. In this era with healthcare involving significant evolutions, unforeseeable movement enormous amount data concerning classification lead way to new issues in FPM, such as space time complexity. However, most research work concentrates on identifying patterns relating transpires frequently, where within every transaction were known a priori. To address present scenario, selecting predominant or frequent is essential using relevant FPM models. The primary objective enhance mining results reduce misclassification rate Cardiovascular Disease (CVD) dataset samples. This proposes novel method called Renyi Entropy Homogenized Weighted Xavier-based Deep Neural Classifier (REHWX-DNC) for prediction. tackle first challenge, Entropy-based (RE-FPM) algorithm proposed, which filters low-quality features function. handle second issue, HWX-DNC model designed assist minimizing by employing Swish activation A CVD synthesis can be analyzed obtain accuracy study, REGEX-DNC improved compared state-of-the-art methods. Some indicators, including prediction accuracy, time, level, F1-total, are considered calculate predictor, checking REHWX-DNC proposed efficient trustworthy predicting disease.

Язык: Английский

Audio Fingerprinting to Achieve Greater Accuracy and Maximum Speed with Multi-Model CNN-RNN-LSTM in Speaker Identification DOI Open Access

Rajani Kumari Inapagolla,

K. Ramesh Babu

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 20, 2025

The process of matching speech data with database records is known as speaker identification. major objective this paper to find the accuracy and speed in comparison training set from RAVDESS test signal using neural network methods Convolutional Neural Network (CNN), Recurrent (RNN) along Long Short-Term Memory (LSTM) combination audio fingerprinting technique. Speech most fundamental form human communication language primary means exchange among humans. An essential component social interaction pitch tone changes are grouped together while accounting for a wide range issues. fingerprint voice was produced after background noise eliminated. Dataset multilayer perception, Audio CNN, RNN LSTM contrast results measures. machine will ultimately display gender determination relation words per second terms no epochs has been observed .and show that every classifier dataset performs faster higher accuracy.

Язык: Английский

Процитировано

3
Renyi Entropy Predictive Data Mining And Weighted Xavier Deep Neural Classifier For Heart Disease Prediction DOI Open Access

M. Revathy Meenal,

S. Vennila

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 16, 2025

During the past few years, Frequent Pattern Mining (FPM) has received interest of several researchers that necessitate extracting items from transactions, and sequences datasets, clarifying heart disease diagnosis materializes commonly, recognizing specific arrangements. In this era with healthcare involving significant evolutions, unforeseeable movement enormous amount data concerning classification lead way to new issues in FPM, such as space time complexity. However, most research work concentrates on identifying patterns relating transpires frequently, where within every transaction were known a priori. To address present scenario, selecting predominant or frequent is essential using relevant FPM models. The primary objective enhance mining results reduce misclassification rate Cardiovascular Disease (CVD) dataset samples. This proposes novel method called Renyi Entropy Homogenized Weighted Xavier-based Deep Neural Classifier (REHWX-DNC) for prediction. tackle first challenge, Entropy-based (RE-FPM) algorithm proposed, which filters low-quality features function. handle second issue, HWX-DNC model designed assist minimizing by employing Swish activation A CVD synthesis can be analyzed obtain accuracy study, REGEX-DNC improved compared state-of-the-art methods. Some indicators, including prediction accuracy, time, level, F1-total, are considered calculate predictor, checking REHWX-DNC proposed efficient trustworthy predicting disease.

Язык: Английский

Процитировано

2