Zhikai Chi¹, Jiaxu Ning^1,*, Delong Zhang¹, Changsheng Zhang^2,3

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085757 - 15 September 2026

Abstract Aiming at the problem that factory stamping parts have various types of defects, random locations, different sizes, and both known and unknown defects, it is difficult for traditional single inspection methods to achieve both accurate classification and generalized identification capabilities. To this end, the Hybrid Unsupervised Learning-Supervised Learning Fusion Defect Detection (HUSLFDD) model is proposed. The model adopts a dual-branch shared backbone network architecture, in which the supervised learning branch focuses on the accurate classification of known defects, and the unsupervised learning branch realizes feature capture and identification of unknown defects. The weighted fusion of… More >

Seyeon Son¹, Hyunki Lim^2,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085049 - 15 September 2026

Abstract Feature selection (FS) plays a crucial role in machine learning by reducing data dimensionality and improving learning efficiency. In many real-world scenarios, label information is unavailable, making unsupervised FS particularly important. While Genetic Algorithm (GA) offers a powerful global search mechanism for subset selection, it often suffers from premature convergence and struggles to refine solutions in complex search spaces. To address these limitations, we propose a hybrid GA that integrates an information-theoretic local search strategy for unsupervised FS. The proposed method integrates an information-theoretic local refinement procedure, consisting of DEL and ADD operations based on… More >

Sheraz Ali Hassan¹, Hamid Masood Khan¹, Muhammad Javed¹, Mohd Faizal Bin Yusof², Jamil Abedalrahim Jamil Alsayaydeh^3,*, Fida Muhammad Khan⁴, Inam Ullah^5,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084547 - 15 September 2026

Abstract The increasing frequency of natural and human-induced disasters has intensified the need for reliable methods to identify crisis-relevant information from Twitter/X streams. However, tweets are often short, noisy, informal, ambiguous, and context-dependent, making disaster-related tweet classification challenging. This study proposes a lightweight attention-enhanced hybrid deep learning framework for binary disaster-related tweet classification. The framework integrates CNN-based local feature extraction, recurrent contextual modeling, static pre-trained word embeddings, class weighting, training-only data augmentation, and learned neural attention. Two architectures, CNN–LSTM–Attention and CNN–BiGRU–Attention, are evaluated on the labeled Kaggle Disaster Tweets dataset using a leakage-aware protocol in which More >

Nidhika Chauhan^1,*, Navneet Kaur¹, Jawad Khan², Younhyun Jung², Haleem Farman³, Ahmed Sedik^3,4, Sohaib Bin Altaf Khattak³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084063 - 15 September 2026

Abstract The exponential growth of cloud data centers necessitates highly efficient resource allocation and task migration strategies. However, multi-dimensional memory fragmentation severely limits the efficacy of standard scheduling algorithms under heavy-tailed, real-world workloads. This paper proposes Fuzzy-SLW, a hybrid swarm-intelligence architecture that integrates a Mamdani fuzzy-inference pre-filter with a distributed Spark Lion-Whale Optimization (SLWO) core via Apache Spark. The fuzzy pre-filter mathematically prunes the search space using non-compressible hardware constraints, while the Spark execution model resolves the traditional serial bottleneck of swarm intelligence. Evaluated within a discrete-event environment utilizing the Google Cluster Trace (2019), Fuzzy-SLW demonstrates… More >

Muhammad Usman Ghani¹, Muhammad Javed¹, Zeeshan Ali Haider², Mohd Faizal Bin Yusof³, Jamil Abedalrahim Jamil Alsayaydeh^4,*, Inam Ullah^5,*, Fida Muhammad Khan²

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.083690 - 15 September 2026

Abstract The Internet of Things (IoT) networks in smart cities experience high-dimensional, time-dependent traffic types, and the detection of attacks is difficult in a timely fashion, particularly in the case of imbalanced classes of attacks. Two hybrid deep learning-based intrusion detection frameworks, TimeSpaceNet and ContextFusionNet, are proposed for IoT intrusion detection: TimeSpaceNet, a CNN-LSTM model enhanced with spatial-temporal normalization, and ContextFusionNet, a CNN-BiLSTM model strengthened with contextual fusion attention. For both models, class imbalance is addressed with SMOTE, and training convergence is assisted by the ADOPT optimizer. All the models are tested on the IoT Bot… More >

Reon Yoshida¹, Keiko Ono^2,*, Kentaro Ohki³, Takuya Futagami²

Journal of Quantum Computing, Vol.8, pp. 123-144, 2026, DOI:10.32604/jqc.2026.086882 - 07 September 2026

Abstract Quantum machine learning (QML) has attracted significant attention for its potential to accelerate computation and improve efficiency, particularly through quantum feature maps that may enable the separation of data not linearly separable in classical spaces. Although this capability remains largely theoretical, it represents a promising direction for addressing complex learning tasks. However, current quantum devices suffer from low error tolerance and a limited number of qubits, which has spurred interest in hybrid quantum–classical approaches. One such application is EEG-based emotion recognition, which involves complex, nonlinear signals and substantial inter-subject variability. While prior studies have applied… More >

Pennelli Saila Kumari¹, Shaik Mohammed Ibrahim¹, Bhavanam Naga Lakshmi², Giulio Lorenzini^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.8, 2026, DOI:10.32604/fdmp.2026.083129 - 04 September 2026

Abstract This study examines transient heat and mass transfer characteristics in a Casson-based hybrid nanofluid (Au–Cu/water) flowing over a time-dependent stretching elastic surface in the presence of porous media and viscous dissipation. The mathematical model further incorporates the effects of magnetic fields, thermal radiation, chemical reactions, and velocity slip conditions to capture realistic transport phenomena encountered in advanced thermal systems. Through suitable similarity transformations, the governing partial differential equations are reduced to a system of nonlinear ordinary differential equations, which are solved numerically using the MATLAB bvp4c solver. To identify optimal operating conditions, Response Surface Methodology… More >

Wensheng Li¹, Zeyang Zhang¹, Xinjie Chai², Facheng Qiu^1,*

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.080472 - 31 August 2026

Abstract Hydrodynamic jet technologies have emerged as a promising approach for advanced wastewater treatment, offering engineering advantages through operational efficiency and system simplicity. As a critical review, this work examines three major hydrodynamic approaches—atomized jets, cavitation jets, and pulsed-jet systems—and analyzes their degradation mechanisms and practical applications in pollutant abatement. By systematically evaluating current research trends, this review elucidates the synergistic interplay between hydrodynamic effects, including turbulent shear, microbubble implosion, and reactive radical generation, and contaminant-decomposition pathways. Quantitative evidence from the reviewed literature further reveals significant performance enhancements achieved by these technologies. Optimized cavitating jets (e.g.,… More > Graphic Abstract

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 >

Masood Karamoozian¹, Zarrin Mahdavipour², Mohammed Ameen³, Faisal Binzagr⁴, Abdolraheem Khader^2,*, Ahmed Hamza Osman³, Ali Ahmed⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085421 - 28 August 2026

Abstract Achieving net-zero carbon emissions in the building sector requires computational tools that are simultaneously efficient, physically rigorous, and scalable to complex multi-domain design spaces. Traditional physics-based simulators such as EnergyPlus and finite-element analysis (FEM) packages deliver high fidelity but are computationally prohibitive for large-scale parametric exploration and real-time multi-objective optimization. This study introduces a hybrid Physics-Informed Graph Neural Network (PI-GNN) surrogate that directly leverages semantic Building Information Modeling (BIM) data to enable rapid, accurate, and physically consistent multi-physics prediction and optimization of prefabricated modular buildings. Building components and their physical and functional relationships are encoded… More >