Yuzhen He¹, Zhiguo Qu^1,2,*, Le Sun¹

Journal of Quantum Computing, Vol.8, pp. 55-73, 2026, DOI:10.32604/jqc.2026.083993 - 26 June 2026

Abstract Quantum fuzzy neural networks (QFNNs) integrate fuzzy systems, neural networks, and quantum models, aiming to leverage their complementary strengths in handling uncertainty, parameter learning, and feature representation. However, a unified framework for effectively combining these three components remains lacking, and the existing literature reflects diverse and sometimes inconsistent modeling strategies. This paper provides a comprehensive review of the fundamental theories underlying QFNNs, including the core design principles and mathematical formulations, as well as the major categories of network architectures. Representative training strategies and typical application scenarios are also systematically examined. Furthermore, persistent issues in the More >

Jakkrawut Techo¹, Panupon Trairat¹, Karthikeyan Velmurugan^2,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.080474 - 18 June 2026

Abstract In this study, selective and non-selective absorber-coated trays were employed to dry carrots and pears. Two trays with a selective absorber coating (1 mm thickness) were used, each loaded with 600 g of sliced carrots and pears. Similarly, two additional trays with a non-selective absorber coating were utilised. Furthermore, the performance of both selective and non-selective absorber-coated trays was compared with conventional open sun drying. The selective absorber-coated tray demonstrated higher thermal energy absorption and enabled the drying of carrots within 2 days, resulting in a weight loss of 529 g. In contrast, owing to… More >

Ahmad Almufarreh¹, Rogaia Hassan Osman Hassan^2,3, Ashfaq Ahmad⁴, Muhammad Arshad^2,5,*, Choo Wou Onn⁶

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082264 - 15 June 2026

Abstract The explosive increase in connectivity has multiplied the volume and speed of network traffic, putting the world at greater risk from sophisticated and emerging cyber-attacks. Smart learning environments, which rely on cloud-based learning management systems, virtual classrooms, and interconnected educational devices, generate large volumes of dynamic network traffic that must be continuously monitored to protect sensitive academic data and ensure uninterrupted learning services. In this study, three supervised machine learning classifiers, namely Random Forest, Logistic Regression, and k-Nearest Neighbours (kNN), are designed and evaluated for anomaly detection using the UNSW-NB15 benchmark. Models are trained and… More >

Taghreed Alkhodaidi^1,*, Wadee Alhalabi¹, Miada Almasre²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081665 - 15 June 2026

Abstract The rapid growth of the IoMT has resulted in critical security threats to healthcare infrastructure, which require highly sophisticated IDSs that can detect a wide range of and unbalanced attack patterns. This study has addressed a critical challenge faced by network security data, which is class imbalance, by presenting a comprehensive evaluation of data balancing techniques on both a real-world standard data set, CICIoMT2024, and a synthetic data set, SynIoMT2026, which we generated to mimic the characteristics of the standard data set for developing a highly controlled data set. Three data balancing techniques, ADASYN, Sample… More >

Ammar Odeh^*, Osama Alhaj Hassan, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080940 - 15 June 2026

Abstract The rapid growth of sophisticated malware and the increasing diversity of computing environments have exposed critical limitations in traditional centralized malware detection systems, particularly in data privacy, scalability, and adaptability. This study proposes a privacy-preserving, collaborative malware-detection framework that leverages federated learning to improve detection accuracy while keeping sensitive data local to participating devices. The objective is to address emerging malware threats by combining behavioral and memory-based analysis within a decentralized learning paradigm. The proposed framework employs federated learning to train a global malware detection model without transferring raw data. Each client locally extracts discriminative… More >

Shafidah Shafian^1,*, Azlan Ismail^2,3

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080623 - 15 June 2026

Abstract Organic solar cells (OSCs) have progressed rapidly in recent years, driven by advances in donor polymers, non-fullerene acceptors, and increasingly complex binary and multicomponent blend architectures. Despite these achievements, device performance remains governed by strongly coupled molecular, morphological, and processing variables, making materials optimization inherently multidimensional and difficult to navigate using conventional trial-and-error approaches. The growing availability of experimental data and computational descriptors has therefore encouraged the integration of machine learning (ML) techniques into OSC research as a complementary strategy for accelerating materials discovery and device optimization. Among the available ML strategies, ensemble learning has… More >

Kamlesh Paswan¹, Rajnish Singh², Vivekanand Singh³, Brihaspati Singh⁴, Ankur Saxena⁵, Chandrmani Yadav^6,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080581 - 15 June 2026

Abstract Electrical Discharge Machining (EDM) is governed by highly coupled, nonlinear electro-thermal-mechanical phenomena involving plasma-mediated energy transfer, rapid heat conduction, phase transformation, and resolidification over micro to nanosecond time scales. From a computational materials science perspective, EDM serves as a prototypical problem of extreme, localised energy–matter interaction, where predictive modelling requires rigorous treatment of multiphysics coupling and scale bridging. This review presents a critical synthesis of theoretical and numerical frameworks for modelling advanced EDM configurations, including vibration-assisted and turning-based EDM, powder- and nano-additive-assisted EDM, and alternative dielectric environments. The review consolidates continuum-based formulations that describe the… More >

Yuyin Ma¹, Yufang Liu¹, Yijun Lu², Zhen Tian³, Fujiang Yuan⁴, Yanhong Peng^4,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080309 - 15 June 2026

Abstract Additive manufacturing (AM) has emerged as a transformative technology in modern manufacturing, offering unprecedented capabilities for producing complex geometries and customized components. However, the widespread adoption of AM is hindered by insufficient quality control, stemming from the multi-factor coupling characteristics of the manufacturing process. Machine learning (ML) presents a promising solution by enabling data-driven approaches to process optimization, quality prediction, and defect detection. This review examines the application landscape of ML techniques in AM through comprehensive analysis of recent literature. The study categorizes ML applications into four primary domains: real-time process monitoring and control, process… More >

Manjodh Kaur¹, Princy Randhawa^2,*, Jitendra Jaiswal², Deepak Dubal³, Ravindra N. Bulakhe^4,5, Deepanraj Balakrishnan⁶, Nithesh Naik^7,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079503 - 15 June 2026

Abstract This review emphasizes the growing role of artificial intelligence (AI) in transforming the materials discovery process into a data-driven and autonomous approach. It systematically traces the evolution of scientific paradigms in materials science and examines how machine learning, generative models, and AI agents are revolutionizing the design, screening, and optimization of materials. A key contribution is a detailed, step-by-step machine learning framework that guides researchers through data collection, preprocessing, feature engineering, model development, and validation, utilizing publicly available materials databases and computational tools. Additionally, the review discusses the latest advances in generative AI and autonomous More >

Zhen Yan¹, Jiani Huang¹, Yanlin Gu¹, Qingqing Xu¹, Yuyu Guo¹, Kun Lin², Juan Hou^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079204 - 15 June 2026

Abstract This study addresses the challenge of balancing “high deposition efficiency with large layer thickness” and “component mechanical integrity” in Laser Powder Bed Fusion (LPBF) additive manufacturing. Using 304L stainless steel as an example, a hybrid modeling strategy combining physical mechanism models and residual machine learning was proposed, achieving accurate prediction of densification at H = 60, 90, and 120 μm (test set R² = 0.833, MAE = 0.104). Within the Doehlert matrix experimental design framework, the coupled effects of laser power, scanning speed, and scanning spacing on densification behavior, microstructure evolution, and mechanical response at different… More >