Jiajun Sun, Shunshun Ji, Chao Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068668 - 09 December 2025

Abstract Asparagus stem blight is a devastating crop disease, and the early detection of its pathogenic spores is essential for effective disease control and prevention. However, spore detection is still hindered by complex backgrounds, small target sizes, and high annotation costs, which limit its practical application and widespread adoption. To address these issues, a semi-supervised spore detection framework is proposed for use under complex background conditions. Firstly, a difficulty perception scoring function is designed to quantify the detection difficulty of each image region. For regions with higher difficulty scores, a masking strategy is applied, while the… More >

Wanwei Huang^1,*, Huicong Yu¹, Jiawei Ren², Kun Wang³, Yanbu Guo¹, Lifeng Jin⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.068493 - 10 November 2025

Abstract Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity. These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy. This paper proposes an industrial Internet of Things intrusion detection feature selection algorithm based on an improved whale optimization algorithm (GSLDWOA). The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to, such as local optimality, long detection time, and reduced accuracy. First, the initial population’s diversity is increased using the Gaussian Mutation More >

S. N. Garibova^a,b,*, А. I. Isayev^a, S. A. Rzayeva^a, F. N. Mammadov^c

Chalcogenide Letters, Vol.22, No.1, pp. 1-9, 2025, DOI:10.15251/CL.2025.221.1

Abstract The structure particulars of amorphous Ge2Sb2Te5 thermally evaporated on glass substrates, as well as films annealed at temperatures of 500 and 700 K have been studied by the considering of experimentally established facts obtained from X-ray analysis and Raman spectroscopy measurements. The Debye-Scherrer and Williams-Hall methods were applied to the X-ray diffraction data for estimate the size of crystallites, interatomic distances, dislocation density and structure distortion degree. The features of heat treatment effect on numerical values of the above quantities at a given temperatures have been established. The analysis of the spectral distribution of Raman… More >

Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025

Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… More >

Khalil Ibrahim Lairedj¹, Zouaoui Chama¹, Amina Bagdaoui¹, Samia Larguech², Younes Menni^3,4,*, Nidhal Becheikh⁵, Lioua Kolsi^6,*, Badr M. Alshammari⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2419-2443, 2025, DOI:10.32604/cmes.2025.069396 - 31 August 2025

Abstract Brain tumor segmentation from Magnetic Resonance Imaging (MRI) supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans, making it a crucial yet challenging task. Supervised models such as 3D U-Net perform well in this domain, but their accuracy significantly improves with appropriate preprocessing. This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model (GGMM) to T1 contrast-enhanced MRI scans from the BraTS 2020 dataset. The Expectation-Maximization (EM) algorithm is employed to estimate parameters for four tissue classes, generating a More >

Amita Biswal, Dah-Jing Jwo^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 927-944, 2025, DOI:10.32604/cmes.2025.067299 - 31 July 2025

Abstract The extended Kalman filter (EKF) is extensively applied in integrated navigation systems that combine the global navigation satellite system (GNSS) and strap-down inertial navigation system (SINS). However, the performance of the EKF can be severely impacted by non-Gaussian noise and measurement noise uncertainties, making it difficult to achieve optimal GNSS/INS integration. Dealing with non-Gaussian noise remains a significant challenge in filter development today. Therefore, the maximum correntropy criterion (MCC) is utilized in EKFs to manage heavy-tailed measurement noise. However, its capability to handle non-Gaussian process noise and unknown disturbances remains largely unexplored. In this paper,… More >

Hyeong-Gyeong Kim¹, Sang-Min Choi², Hyeon Seo², Suwon Lee^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4381-4397, 2025, DOI:10.32604/cmc.2025.067024 - 30 July 2025

Abstract Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models. Existing defense mechanisms often suffer drawbacks, such as the need for model retraining, significant inference time overhead, and limited effectiveness against specific attack types. Achieving perfect defense against adversarial attacks remains elusive, emphasizing the importance of mitigation strategies. In this study, we propose a defense mechanism that applies random cropping and Gaussian filtering to input images to mitigate the impact of adversarial attacks. First, the image was randomly cropped to vary its dimensions and then placed… More >

Ziyang Wang¹, Lian Duan^1,2,*, Lei Kuang¹, Haibo Zhou¹, Ji’an Duan¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2587-2604, 2025, DOI:10.32604/cmc.2025.065648 - 03 July 2025

Abstract The packaging quality of coaxial laser diodes (CLDs) plays a pivotal role in determining their optical performance and long-term reliability. As the core packaging process, high-precision laser welding requires precise control of process parameters to suppress optical power loss. However, the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise. To address this challenge, a physics-informed (PI) and data-driven collaboration approach for welding parameter optimization is proposed. First, thermal-fluid-solid coupling finite element method (FEM) was employed to quantify the sensitivity of welding parameters to physical characteristics, including… More >

Pu Yang^1,*, Wanning Yan¹, Rong Li¹, Lei Chen², Lijie Guo²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 699-725, 2025, DOI:10.32604/cmc.2025.064947 - 09 June 2025

Abstract Lithium-ion batteries (LIBs) have been widely used in mobile energy storage systems because of their high energy density, long life, and strong environmental adaptability. Accurately estimating the state of health (SOH) for LIBs is promising and has been extensively studied for many years. However, the current prediction methods are susceptible to noise interference, and the estimation accuracy has room for improvement. Motivated by this, this paper proposes a novel battery SOH estimation method, the Beluga Whale Optimization (BWO) and Noise-Input Gaussian Process (NIGP) Stacked Model (BGNSM). This method integrates the BWO-optimized Gaussian Process Regression (GPR)… More >

Priyadharsini Selvaraj^1,*, Senthil Kumar Jagatheesaperumal², Karthiga Marimuthu¹, Oviya Saravanan¹, Bader Fahad Alkhamees³, Mohammad Mehedi Hassan^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1575-1594, 2025, DOI:10.32604/cmes.2025.064138 - 30 May 2025

Abstract With expeditious advancements in AI-driven facial manipulation techniques, particularly deepfake technology, there is growing concern over its potential misuse. Deepfakes pose a significant threat to society, particularly by infringing on individuals’ privacy. Amid significant endeavors to fabricate systems for identifying deepfake fabrications, existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations, thereby hindering their broad applicability to images and videos produced by unfamiliar technologies. In this manuscript, we endorse resilient training tactics to amplify generalization capabilities. In adversarial training, models are trained using More >