Emad Sami Jaha^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3645-3678, 2025, DOI:10.32604/cmes.2025.068681 - 30 September 2025

Abstract The human ear has been substantiated as a viable nonintrusive biometric modality for identification or verification. Among many feasible techniques for ear biometric recognition, convolutional neural network (CNN) models have recently offered high-performance and reliable systems. However, their performance can still be further improved using the capabilities of soft biometrics, a research question yet to be investigated. This research aims to augment the traditional CNN-based ear recognition performance by adding increased discriminatory ear soft biometric traits. It proposes a novel framework of augmented ear identification/verification using a group of discriminative categorical soft biometrics and deriving… More > Graphic Abstract

Yazeed Alkhrijah^1,2, Shehzad Khalid³, Syed Muhammad Usman^4,*, Amina Jameel³, Danish Hamid⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1113-1141, 2025, DOI:10.32604/cmes.2025.068726 - 31 July 2025

Abstract Arabic Sign Language (ArSL) recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing (DHH) community. Researchers have proposed multiple methods for automated recognition of ArSL; however, these methods face multiple challenges that include high gesture variability, occlusions, limited signer diversity, and the scarcity of large annotated datasets. Existing methods, often relying solely on either skeletal data or video-based features, struggle with generalization and robustness, especially in dynamic and real-world conditions. This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint… More >

Shi Qiu¹, Hongbing Lu^1,*, Jun Shu², Ting Liang³, Tao Zhou⁴

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2495-2510, 2024, DOI:10.32604/cmc.2024.052476 - 15 August 2024

Abstract Colorectal cancer, a malignant lesion of the intestines, significantly affects human health and life, emphasizing the necessity of early detection and treatment. Accurate segmentation of colorectal cancer regions directly impacts subsequent staging, treatment methods, and prognostic outcomes. While colonoscopy is an effective method for detecting colorectal cancer, its data collection approach can cause patient discomfort. To address this, current research utilizes Computed Tomography (CT) imaging; however, conventional CT images only capture transient states, lacking sufficient representational capability to precisely locate colorectal cancer. This study utilizes enhanced CT images, constructing a deep feature network from the… More >

Ala’a R. Al-Shamasneh¹, Rabha W. Ibrahim^2,3,4,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 795-810, 2024, DOI:10.32604/cmc.2023.042755 - 30 January 2024

Abstract The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic. One of the most popular methods for manipulating digital images is image splicing, which involves copying a specific area from one image and pasting it into another. Attempts were made to mitigate the effects of image splicing, which continues to be a significant research challenge. This study proposes a new splicing detection model, combining Sonine functions-derived convex-based features and deep features. Two stages make up the proposed method. The first step entails feature extraction, then… More >

Mehdi Hassan^1,2, Safdar Ali³, Jin Young Kim^2,*, Muhammad Sanaullah⁴, Hani Alquhayz⁵, Khushbakht Safdar⁶

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1643-1663, 2023, DOI:10.32604/cmc.2023.038578 - 30 August 2023

Abstract Breast cancer resistance protein (BCRP) is an important resistance protein that significantly impacts anticancer drug discovery, treatment, and rehabilitation. Early identification of BCRP substrates is quite a challenging task. This study aims to predict early substrate structure, which can help to optimize anticancer drug development and clinical diagnosis. For this study, a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning (TL) for automatic deep feature (DF) extraction followed by classification with linear discriminant analysis algorithm (TLRNDF-LDA). This study utilized structural fingerprints, which are exploited by DF contrary to conventional More >

Safdar Khan¹, Muhammad Attique Khan², Jamal Hussain Shah^1,*, Faheem Shehzad², Taerang Kim³, Jae-Hyuk Cha³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2337-2360, 2023, DOI:10.32604/csse.2023.040410 - 28 July 2023

Abstract Human Suspicious Activity Recognition (HSAR) is a critical and active research area in computer vision that relies on artificial intelligence reasoning. Significant advances have been made in this field recently due to important applications such as video surveillance. In video surveillance, humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although numerous techniques have been introduced in the literature for routine human actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for… More >

Sobia Nawaz¹, Sidra Rasheed², Wania Sami³, Lal Hussain^4,5,*, Amjad Aldweesh^6,*, Elsayed Tag eldin⁷, Umair Ahmad Salaria^8,9, Mohammad Shahbaz Khan¹⁰

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5213-5228, 2023, DOI:10.32604/cmc.2023.037543 - 29 April 2023

Abstract This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and… More >

Muhammad Attique Khan¹, Awais Khan¹, Majed Alhaisoni², Abdullah Alqahtani³, Ammar Armghan⁴, Sara A. Althubiti⁵, Fayadh Alenezi⁴, Senghour Mey⁶, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5087-5103, 2023, DOI:10.32604/cmc.2023.033856 - 29 April 2023

Abstract Human gait recognition (HGR) is the process of identifying a subject (human) based on their walking pattern. Each subject is a unique walking pattern and cannot be simulated by other subjects. But, gait recognition is not easy and makes the system difficult if any object is carried by a subject, such as a bag or coat. This article proposes an automated architecture based on deep features optimization for HGR. To our knowledge, it is the first architecture in which features are fused using multiset canonical correlation analysis (MCCA). In the proposed method, original video frames… More >

Kumar Abhishek^1,*, Sheikh Badar ud din Tahir²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1391-1409, 2023, DOI:10.32604/cmc.2023.035894 - 06 February 2023

Abstract Human verification and activity analysis (HVAA) are primarily employed to observe, track, and monitor human motion patterns using red-green-blue (RGB) images and videos. Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times. Numerous models rely on various parameters, such as the detection rate, position, and direction of human body components in RGB images. This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features. To use human interaction image sequences as input data, we first perform a few denoising steps.… More >

Tehseen Ahsan^1,*, Sohail Khalid¹, Shaheryar Najam¹, Muhammad Attique Khan², Ye Jin Kim³, Byoungchol Chang⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1089-1105, 2023, DOI:10.32604/cmc.2023.034563 - 06 February 2023

Abstract Human action recognition (HAR) attempts to understand a subject’s behavior and assign a label to each action performed. It is more appealing because it has a wide range of applications in computer vision, such as video surveillance and smart cities. Many attempts have been made in the literature to develop an effective and robust framework for HAR. Still, the process remains difficult and may result in reduced accuracy due to several challenges, such as similarity among actions, extraction of essential features, and reduction of irrelevant features. In this work, we proposed an end-to-end framework using… More >