Mostafa Nassar¹, Anas M. Ali^1,2, Walid El-Shafai^1,3, Adel Saleeb¹, Fathi E. Abd El-Samie¹, Naglaa F. Soliman⁴, Hussah Nasser AlEisa^5,*, Hossam Eldin H. Ahmed¹

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2235-2247, 2023, DOI:10.32604/csse.2023.032156 - 03 November 2022

Abstract Traditional security systems are exposed to many various attacks, which represents a major challenge for the spread of the Internet in the future. Innovative techniques have been suggested for detecting attacks using machine learning and deep learning. The significant advantage of deep learning is that it is highly efficient, but it needs a large training time with a lot of data. Therefore, in this paper, we present a new feature reduction strategy based on Distributed Cumulative Histograms (DCH) to distinguish between dataset features to locate the most effective features. Cumulative histograms assess the dataset instance More >

Fatma M. El-Ghamry^1,2, Walid El-Shafai², Mahmouad I. Abdalla¹, Ghada M. El-Banby³, Abeer D. Algarni^4,*, Moawad I. Dessouky², Adel S. Elfishawy², Fathi E. Abd El-Samie^2,4, Naglaa F. Soliman^1,4

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4457-4487, 2022, DOI:10.32604/cmc.2022.022328 - 14 January 2022

Abstract Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for More >