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Titlebook: Machine Intelligence and Signal Analysis; M. Tanveer,Ram Bilas Pachori Conference proceedings 2019 Springer Nature Singapore Pte Ltd. 2019

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Conference proceedings 2019ce such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and el
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Excitation Modeling Method Based on Inverse Filtering for HMM-Based Speech Synthesis,modifying the natural residual segments in accordance with the target source features generated from HMMs. The proposed approach is incorporated into the HTS. Subjective evaluation results indicate that the proposed method enhances the quality of synthesis and is better than the traditional pulse and STRAIGHT-based excitation models.
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Detecting R-Peaks in Electrocardiogram Signal Using Hilbert Envelope, method has a very less detection error rate of 0.31% with a high sensitivity and positive predictivity of 99.83 and 99.86%, respectively. Furthermore, the results indicated that the performance of the proposed method is much better compared to other well-known methods in the presence of noise/artifacts, low-amplitude, and negative QRS complexes.
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Lung Nodule Identification and Classification from Distorted CT Images for Diagnosis and Detection ing nodules. Annotated images are used to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using relevant measures. Developed CAD system is found to identify nodules with high accuracy.
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