Mohammad Alshinwan^1,*, Walaa Alayed^2,*, Fatma A. Hashim³, Arar Al Tawil⁴

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086257 - 15 September 2026

Abstract The increasing trend of phishing sites is among the important threats against the Internet security, associated with monetary loss, data leakage, and identity swindle. In response to this urgent problem, this paper proposes a new phishing website detection framework based on the Modified Differential Evolution (mDE) algorithm in conjunction with state-of-the-art machine learning classifiers. The proposed mDE integrates with dynamic mutation and crossover strategies to improve the global search capability and the convergence speed, which is superior to traditional single optimization methods. We conduct experiments on two benchmark datasets: the UCI Phishing Websites dataset and… More >

Honglian Gao¹, Qingsong Zhang¹, Zeming Chen¹, Lianchen Li¹, Quanhui Liu¹, Yuxiang Tian¹, Xianfeng Xu^2,*

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.074052 - 30 August 2026

Abstract With the increasing penetration rate of distributed generators (DGs) in the distribution network, DGs with independent power supply capability provide strong support for distribution network fault recovery. Traditional fault reconfiguration methods often rely on static load priorities and fail to fully consider the time-varying dynamic characteristics of outage costs, resulting in shortcomings in the economy and adaptability of restoration strategies. To address this, this paper proposes a fault reconfiguration method that integrates day-ahead prediction and a dynamic load restoration set. Firstly, a Long Short-Term Memory network optimized by Variational Mode Decomposition and the Marine Predators… More >

Muhammad Idrees Khan^*, Hua-Dong Yao

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.087251 - 28 August 2026

Abstract Quantum computational fluid dynamics (QCFD) is an active but still immature research area, and quantum lattice Boltzmann methods (QLBM) provide a natural mesoscopic route because their collision–streaming structure can be decomposed into algorithmic blocks. This paper reviews QLBM and related hybrid quantum–classical fluid approaches from an engineering computational fluid dynamics (CFD) perspective, emphasizing physical scope, boundary realism, nonlinear collision treatment, measurement cost, hardware assumptions, and comparison with optimized classical baselines. The discussion is connected to computational aeroacoustics (CAA), where practical workflows already separate source generation, acoustic propagation, and design loops, creating possible insertion points for… More >

Rui Wu¹, Yehong Li², Hongjie Guo^3,*, Gangqiang Hu³, Changjun Zhou^3,*, Qile Zou⁴

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083715 - 13 August 2026

Abstract Federated learning (FL) enables collaborative training of deep neural architectures while preserving data privacy, yet its performance often deteriorates in non-IID scenarios, which stems from client-side distribution drift and divergent local updates induced by pervasive data heterogeneity. This challenge is particularly critical for maintaining the structural consistency and generalization of neural models across diverse, distributed sources with significant distribution shifts. In this paper, we investigate how to effectively mitigate label distribution shift and feature distribution skew to enhance the global representation stability of neural architectures. We propose Federated Learning with Consistency Optimization Algorithms (FedCO), a… More >

Piotr Musznicki¹, Marek Turzyński¹, Lyu Guanghua², Ghulam E Mustafa Abro^3,*, Viola Gierszewska¹, Arsalan Muhammad Soomar¹, Syed Hadi Hussain Shah²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082254 - 13 August 2026

Abstract This paper presents an accurate and efficient methodology for parameter extraction in complex impedance models using Differential Evolution (DE), an evolutionary optimization technique. The proposed approach targets equivalent RLC circuit topologies and aims to match measured impedance characteristics across a wide frequency spectrum. By formulating the extraction process as a global optimization problem, DE enables precise identification of component values, even for high-order models with multiple resonances. The method is implemented in Python using open-source libraries, facilitating reproducibility and integration into broader modeling workflows. Validation is performed on both analytically derived resonant circuits and physically More >

Islam T. Almalkawi^1,*, Samer Khasawneh¹, Hamza M. Alkhatib¹, Sabya Shtaiwi¹, Rami Halloush², Manel Guerrero Zapata³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.077025 - 13 August 2026

Abstract Current image steganography methods often struggle to balance security, payload capacity, and computational efficiency, with many spatial-domain techniques vulnerable to statistical steganalysis and complex methods incurring high overhead. To address persistent challenges in secure data communication, this paper introduces a novel hybrid chaotic-based multi-layered image security and steganography scheme to enhance resistance against detection while offering adaptable performance. The proposed scheme first integrates Fisher-Yates permutation driven by a Logistic Map PRNG, followed by stream cipher encryption using a Hénon Map-generated keystream to secure the secret image. Embedding is then performed via a unique three-pass chaotic More > Graphic Abstract

Kazutaka Toda¹, Yuntao Wang^1,*, Hyungrok Jo², Yang Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083407 - 27 July 2026

Abstract The rapid evolution of quantum computing poses a fundamental challenge to classical public-key cryptosystems, accelerating the adoption of lattice-based post-quantum cryptography in large-scale digital infrastructures, including Future Mobile Internet Technologies (FMIT) and their convergence applications (FMIT-CA). As lattice-based cryptography is expected to play an important role in such environments, accurate hardness estimation and parameter assessment of underlying lattice problems have become increasingly important. Since the security of these cryptographic schemes is closely related to the computational hardness of the Shortest Vector Problem (SVP), improving practical SVP-solving techniques contributes indirectly to the security evaluation of such… More >

Osama Al-Haj Hassan^1,*, Ammar Odeh¹, Abdullah Aref ², Ghassan Samara³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083861 - 23 July 2026

Abstract Mashups are among the key web technologies that provide end-users with customizable and personalized tools. Most mashup platforms are based on centralized architectures or do not employ fully decentralized architectures; therefore, in this paper, we propose a decentralized architecture for mashups that combines the strengths of structured and unstructured peer-to-peer networks. For the structured part, we rely on the Chord lookup protocol, and for the unstructured part, we build groups of nodes via two flavors of network flooding, namely, sequence number flooding and reverse path flooding. Brokers in the unstructured part would be responsible for More >

Adnan Hnaif^1,*, Hanadi Al-Shawabkah², Ayman Alqafaan², Mohammad Alia¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082998 - 23 July 2026

Abstract Many fast pattern-matching mechanisms are used in NIDS (Network Intrusion Detection Systems) to filter higher volumes of network traffic prior to invoking expensive rule verification stages. This filtering phase in signature-based engines, such as Snort, needs to preserve exact matching semantics while being able to process at high throughput on commodity hardware. Here, we introduce a hybrid CPU–GPU architecture-aware framework for exact multi-pattern matching based on the Weighted Exact Matching Algorithm (WEMA). WEMA performs the most relevant matching based on deterministic ordered indexing of category units, which eliminates chaotic control flow (which occurs with automata… More >

Ahmed Ismail Ebada^1,2, Yasmeen Abu-Seif^2,*, Hrushikesh Pardeshi^2,*, Nesma El-Sayed¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081804 - 23 July 2026

Abstract The integration of Deep Learning, Deep Reinforcement Learning, and massive Vision-Language-Action (VLA) foundation models has catalysed a profound paradigm shift in robotics, transitioning systems from rigid automation to dynamic, open-world autonomy. Despite transformative breakthroughs in fields such as healthcare, ranging from adaptive robotic rehabilitation to autonomous surgical manipulation and silver care, widespread real-world deployment remains severely bottlenecked. This limitation primarily stems from the “Reality Gap” inherent to sim-to-real transfer and a fundamental epistemological tension: the stochastic, “black-box” nature of unconstrained neural networks fundamentally conflicts with the deterministic, zero-violation safety guarantees demanded by physical robotics. To… More >