Mubeen Sabir¹, Zeshan Aslam Khan^2,*, Muhammad Waqar², Khizer Mehmood¹, Muhammad Junaid Ali Asif Raja³, Naveed Ishtiaq Chaudhary⁴, Khalid Mehmood Cheema⁵, Muhammad Asif Zahoor Raja⁴, Muhammad Farhan Khan⁶, Syed Sohail Ahmed⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 807-855, 2025, DOI:10.32604/cmes.2025.068131 - 30 October 2025

Abstract Fingerprint classification is a biometric method for crime prevention. For the successful completion of various tasks, such as official attendance, banking transactions, and membership requirements, fingerprint classification methods require improvement in terms of accuracy, speed, and the interpretability of non-linear demographic features. Researchers have introduced several CNN-based fingerprint classification models with improved accuracy, but these models often lack effective feature extraction mechanisms and complex multineural architectures. In addition, existing literature primarily focuses on gender classification rather than accurately, efficiently, and confidently classifying hands and fingers through the interpretability of prominent features. This research seeks to… More >

Fatima Abu Siryeh^*, Hussein Alrammahi, Abdullahi Abdu İbrahim

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5021-5046, 2025, DOI:10.32604/cmc.2025.065206 - 30 July 2025

Abstract Biometric template protection is essential for finger-based authentication systems, as template tampering and adversarial attacks threaten the security. This paper proposes a DCT-based fragile watermarking scheme incorporating AI-based tamper detection to improve the integrity and robustness of finger authentication. The system was tested against NIST SD4 and Anguli fingerprint datasets, wherein 10,000 watermarked fingerprints were employed for training. The designed approach recorded a tamper detection rate of 98.3%, performing 3–6% better than current DCT, SVD, and DWT-based watermarking approaches. The false positive rate (≤1.2%) and false negative rate (≤1.5%) were much lower compared to previous… More >

Yonghang Yan¹, Xin Xie¹, Hengyi Ren², Ying Cao^1,*, Hongwei Chang³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5035-5055, 2025, DOI:10.32604/cmc.2025.058276 - 06 March 2025

Abstract Fingerprint features, as unique and stable biometric identifiers, are crucial for identity verification. However, traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks, potentially leading to user data leakage. Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data, effectively addressing privacy and security concerns. However, variations in fingerprint data due to factors such as region, ethnicity, sensor quality, and environmental conditions result in significant heterogeneity across clients. This heterogeneity adversely impacts the generalization ability of the global model, limiting its performance across… More >

Dawei Xu^1,2,3, Min Wang¹, Yue Lv¹, Moxuan Fu², Yi Wu^4,5,*, Jian Zhao¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2027-2041, 2025, DOI:10.32604/cmc.2024.058178 - 17 February 2025

Abstract Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF… More >

Shira Cohen Rubin^1,*, Nadav Zacks¹, Ori Wand², Ophir Freund³, Evgeni Gershman³, Anna Breslavsky², Rotem Givoli-Vilensky¹, Anat Tzurel Ferber², Natalya Bilenko^1,4, Amir Bar-Shai³

European Cytokine Network, Vol.35, No.1, pp. 13-19, 2024, DOI:10.1684/ecn.2024.0494 - 05 January 2026

Abstract COVID-19 vaccination and acute infection result in cellular and humoral immune responses with various degrees of protection. While most studies have addressed the difference in humoral response between vaccination and acute infection, studies on the cellular response are scarce. We aimed to evaluate differences in immune response among vaccinated patients versus those who had recovered from COVID-19. Materials and Methods: This was a prospective study in a tertiary medical centre. The vaccinated group included health care workers, who had received a second dose of the BNT162b2 vaccine 30 days ago. The recovered group included adults… More >

Abdulraqeb Alhammadi^1,*, Zaid Ahmed Shamsan², Arijit De³

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1889-1905, 2024, DOI:10.32604/cmc.2024.051487 - 15 August 2024

Abstract Indoor localization systems are crucial in addressing the limitations of traditional global positioning system (GPS) in indoor environments due to signal attenuation issues. As complex indoor spaces become more sophisticated, indoor localization systems become essential for improving user experience, safety, and operational efficiency. Indoor localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database, but this can increase the computational burden in the online phase. Bayesian networks, which integrate prior knowledge or domain expertise, are an effective solution for accurately determining indoor user locations. These networks use probabilistic reasoning to model relationships among… More >

Yue Cao^1,2, Longsheng Bao¹, Xiaowei Zhang^1,*, Zhanfei Wang¹, Bingqian Li¹

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 485-503, 2024, DOI:10.32604/sdhm.2024.049698 - 05 June 2024

Abstract This study addresses the issue of inaccurate single damage fingerprint recognition during the process of bridge damage identification. To improve accuracy, the proposed approach involves fusing displacement mode difference and curvature mode difference data for single damage identification, and curvature mode difference and displacement mode wavelet coefficient difference data for two damage identification. The methodology begins by establishing a finite element model of the cable-stayed bridge and obtaining the original damage fingerprints, displacement modes, curvature modes, and wavelet coefficient differences of displacement modes through modal analysis. A fusion program based on the D-S evidence theory… More > Graphic Abstract

Di Wang, Yuefei Zhu, Jinlong Fei^*, Maohua Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2253-2276, 2024, DOI:10.32604/cmc.2024.049504 - 15 May 2024

Abstract Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target… More >

Yanmei Shi¹, Wei Yu^2,*, Yanxia Zhao^3,*, Yungang Jia⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 887-906, 2024, DOI:10.32604/cmes.2024.046140 - 16 April 2024

Abstract Web application fingerprint recognition is an effective security technology designed to identify and classify web applications, thereby enhancing the detection of potential threats and attacks. Traditional fingerprint recognition methods, which rely on preannotated feature matching, face inherent limitations due to the ever-evolving nature and diverse landscape of web applications. In response to these challenges, this work proposes an innovative web application fingerprint recognition method founded on clustering techniques. The method involves extensive data collection from the Tranco List, employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction. The core… More >

Qing Zhu^1,*, Linlin Gu^1,2, Huijie Lin^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 577-591, 2024, DOI:10.32604/cmes.2023.043307 - 16 April 2024

Abstract With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color More >