Publications by authors named "Emad H Abualsauod"

Article Synopsis
  • This study presents a new method for detecting heart problems and QRS complexes using machine learning, particularly support vector machine (SVM) classifiers.
  • The method demonstrated impressive performance, achieving a low detection error rate of 0.45% for cardiac irregularities and accurately classifying four types of ECG beats.
  • The SVM classifiers showed high accuracy rates of 96.67% and 98.39%, indicating their strong potential for analyzing cardiac abnormalities and categorizing ECG signals based on specific characteristics.
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The potential of quantifying the variations in IR active bands was explored while using the chemometric analysis of FTIR spectra for selecting orthopedic biomaterial of industrial scale i.e., ultra-high molecular weight PE (UHMWPE).

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A waste marine plant was used to produce a wet-laid nonwoven web for multifunction applications. To study the effect of some parameters related to the web characteristics (sheet weight, binder ratio, and pulp ratio) on the mechanical and physical properties of the web, we used a Box-Behnken design plan with three levels. The diagram of the superposed contours graphic method was used to find the optimum parameters of the process for the application of the Posidonia nonwoven fiber on an insulation field.

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The selection of suitable composite material for high-strength industrial applications, from the list of available alternatives, is a tedious task as it requires an optimized structural performance-based solution. This study aimed to optimize the concentration of fillers, i.e.

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