Spoken Term Detection Techniques

Mary, Leena; Deekshitha, G

doi:10.1007/978-3-319-97761-4_5

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…It combines the strengths of CNNs and LSTMs to capture spatial and temporal dependencies in sequential data. An extended recurrent neural network (RNN) is an LSTM, which can better consider time dependency [24]. Because LSTM cannot incorporate spatial correlation, ConvLSTM can address this issue.…”

Section: Bi-directional Convolutional Long-short-term Memory (Bconvlstm)mentioning

confidence: 99%

A Hybrid Deep Learning Technique for Feature Selection and Classification of Chronic Kidney Disease

2023

IJIES

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A significant global public health concern is the widespread presence of chronic kidney disease (CKD). High mortality rates are associated with the disease, particularly in developing nations. Since there are no visible earlystage signs, CKD frequently goes undiagnosed. In the meantime, preventing the disease from progressing requires early detection and prompt clinical care. To help clinicians discover CKD early, deep learning (DL) techniques can give an effective and affordable diagnosis. This research proposes a unique hybrid DL approach to classify CKD. In a pre-processing step, eliminate the missing values and reduce noise from data, data transformation, and outlier detection. After that, using the improved capsule network (Improved CapsNet) method to extract the features. Then, select essential features using the improved spotted hyena optimizer (ISHO) algorithm to better classification with less time. Finally, employ hybrid deep learning techniques of BConvLSTM and DNetCNN to classify the CKD. A recently introduced CKD prediction algorithm and well-known classifiers were used as benchmarks for the proposed approaches. The proposed model, which was trained with the smaller feature set, outperformed other classifiers with a classification accuracy rate of 99.89%. The experimental findings also demonstrate the positive effect of feature selection on the performance of the different techniques. The proposed technique has developed a reliable predictive system for recognizing CKD and may be extended to more unbalanced medical datasets to identify diseases reliably.

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Section: Bi-directional Convolutional Long-short-term Memory (Bconvlstm)mentioning

confidence: 99%

A Hybrid Deep Learning Technique for Feature Selection and Classification of Chronic Kidney Disease

2023

IJIES

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show abstract

Spoken Term Detection Techniques

Cited by 1 publication

References 15 publications

A Hybrid Deep Learning Technique for Feature Selection and Classification of Chronic Kidney Disease

A Hybrid Deep Learning Technique for Feature Selection and Classification of Chronic Kidney Disease

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