Novel Approach for Reinforcement the Extraction of ECG Signal for Twin Fetuses Based on Modified BSS

Al-Dujaili, Mohammed Jawad; Mezeel, Mushtaq Talib

doi:10.1007/s11277-021-08337-y

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…PCA has some drawbacks despite its ease of use and widespread application, including its sensitivity to outliers and the fact that it can only identify linear correlations in the data. It is still a powerful and widespread tool for data analysis [43,44]. The following are the steps in this method: The first step involves transforming the input image which is in the form of a two-dimensional matrix, into a one-dimensional vector and create the feature matrix before combining these vectors.…”

Section: 4mentioning

confidence: 99%

Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers

Al-Dujaili

Alrikabi

abed

et al. 2023

Int. J. Interact. Mob. Technol.

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In the realm of robotics and interactive systems, gender recognition is a crucial problem. Considering the several uses it has in security, web search, human-computer interactions, etc., gender recognition from facial photos has garnered a lot of attention. The need to use and enhance gender recognition techniques is felt more strongly today due to a significant development in the design of facial recognition systems. Relatively speaking to other approaches, the progress gained in this area thus far is not exceptional. Thus, a novel method has been adopted in this study to improve accuracy in comparison to earlier research. To create the best rate of accuracy and efficiency in the suggested method of this research, we choose a minimal set of characteristics. Testing on the FERET and UTK-Face datasets reveals that our suggested algorithm has a lower degree of inaccuracy. In this article, the input image of the person's face is pre-processed to extract the right features from the face once the person's face has been recognized. Gender separation is achieved using Multi-class Support Vector Machine (SVM) Classifiers after features from normalized images have been extracted using Histogram Oriented Gradient (HOG), Gabor Filters, and Speeded Up Robust Features (SURF), as well as their combination to select the most appropriate feature from them as input for gender classification. As a feature reduction feature, the Principal Component Analysis (PCA) algorithm is also employed. Using the proposed approach, 98.75% gender recognition precision has been accomplished on the FERET database and a runtime performance of 0.4 Sec. on the UTK-Face database, 97.43% gender recognition accuracy has been accomplished and a runtime performance of 0.5 Sec.

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Section: 4mentioning

confidence: 99%

Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers

Al-Dujaili

Alrikabi

abed

et al. 2023

Int. J. Interact. Mob. Technol.

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“…Blind source separation (BSS) [1][2] technology refers to the process of separating each source signal only using the observed signal when the transmission channel and the source signals are unknown. BSS algorithm has been widely used in biomedicine [3][4][5] , signal processing [6][7][8][9] , and mobile communication [10][11] . In recent years, single-channel BSS (SCBSS), as an extreme case of BSS, has gradually become a major research topic in the eld of signal processing.…”

Section: Introductionmentioning

confidence: 99%

Improved Single-Channel Blind Source Separation Algorithm based on Variational Mode Decomposition

Zhou

Fan³

et al. 2022

Preprint

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In this paper, we propose an improved single-channel blind source separation (SCBSS) algorithm based on variational modal decomposition (VMD). VMD algorithm is a classical algorithm in SCBSS. The algorithm effectively avoids the end effect and mode mixing, and makes the decomposition more stable. However, the algorithm can not adaptively determine the number of decomposition layers. To solve this problem, we propose an improved adaptive VMD algorithm, which determines the decomposition layer of different signals by setting different spectral correlation coefficient thresholds. Combined with the improved source number estimation algorithm, signal preprocessing algorithm and JADE algorithm, SCBSS of the received mixed signal can be realized. Simulation results show that the algorithm can accurately separate source signals without bit error under different SNR and degrees of spectrum aliasing. The algorithm is effective and feasible.

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A Novel FrWT Based Arrhythmia Detection in ECG Signal Using YWARA and PCA

Gupta¹,

Mittal

2021

Wireless Pers Commun

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Novel Approach for Reinforcement the Extraction of ECG Signal for Twin Fetuses Based on Modified BSS

Cited by 8 publications

References 21 publications

Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers

Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers

Improved Single-Channel Blind Source Separation Algorithm based on Variational Mode Decomposition

A Novel FrWT Based Arrhythmia Detection in ECG Signal Using YWARA and PCA

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