Nesreen M. Alharbi^*, Ahmed Hamza Osman, Arwa A. Mashat, Hasan J. Alyamani

Computer Systems Science and Engineering, Vol.48, No.1, pp. 175-198, 2024, DOI:10.32604/csse.2023.041044

Abstract Recent years have witnessed significant advancements in the field of character recognition, thanks to the revolutionary introduction of machine learning techniques. Among various types of character recognition, offline Handwritten Character Recognition (HCR) is comparatively more challenging as it lacks temporal information, such as stroke count and direction, ink pressure, and unexpected handwriting variability. These issues contribute to a poor level of precision, which calls for the adoption of anomaly detection techniques to enhance Optical Character Recognition (OCR) schemes. Previous studies have not researched unsupervised anomaly detection using MLP for handwriting recognition. Therefore, this study proposes a novel approach for enhanced… More >

Walid Aydi^1,3,*, Fuad S. Alduais^2,4

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4033-4050, 2022, DOI:10.32604/cmc.2022.023119

Abstract The Weibull distribution is regarded as among the finest in the family of failure distributions. One of the most commonly used parameters of the Weibull distribution (WD) is the ordinary least squares (OLS) technique, which is useful in reliability and lifetime modeling. In this study, we propose an approach based on the ordinary least squares and the multilayer perceptron (MLP) neural network called the OLSMLP that is based on the resilience of the OLS method. The MLP solves the problem of heteroscedasticity that distorts the estimation of the parameters of the WD due to the presence of outliers, and eases… More >