Dong-Fang Wu^1,2, Jiaojiao Deng^1,2, Zhiwei Ye^1,2,*, Rong Gao^1,2, Fan Ma^1,2, Dingfeng Song^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085308 - 13 August 2026

Abstract Anomaly detection is used to identify data points deviating from normal patterns and plays a significant role in fields such as fault diagnosis, biomedicine, and cybersecurity. However, anomaly detection tasks in practical applications often involve high-dimensional data which presents challenges such as severe feature redundancy, hidden anomaly patterns, and difficulty in capturing uncertainty. These issues make it difficult to effectively identify anomalies, thereby limiting the model’s discriminative power and stability. To address these challenges, we propose a high-dimensional anomaly detection model, MSFO-EIF, integrating multi-strategy feature optimization with rough set modeling. First, in the feature space… More >

Sultan Shutyan Albalawi¹, Mohd Yamani Idna Idris^1,2,*, Ainuddin Wahid Bin Abdul Wahab¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083860 - 13 August 2026

Abstract As Distributed Denial-of-Service (DDoS) attacks grow in size and complexity, standard intrusion detection systems are hitting a wall. Most struggle to generalize well, require too much computational power, or lack model diversity. To tackle this, we developed TS-SCHO-TSE, a unified hybrid framework for DDoS detection. Unlike traditional methods that treat feature selection and ensemble building as isolated, step-by-step tasks, our approach combines everything. We map feature masks, classifier states, voting weights, and hyperparameters into a single mixed discrete-continuous search space to optimize them all at once. We tested our system against classical functions (F1–F23), the More >

Israt Jahan Munny¹, Anup Majumder², Bibhas Roy Chowdhury Piyas^3,*, Fahmid Al Farid^4,5,*, Md. Rafsan Jani², Fatama Jannat Tisha³, Israt Jahan³, Abu Saleh Musa Miah⁶, Hezerul Abdul Karim^4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083167 - 13 August 2026

Abstract Orange is one of the most economically significant citrus crops worldwide, which is essential for the global food distribution network and supports rural livelihoods. However, its high susceptibility to destructive diseases results in substantial yield losses and long-term economic damage. Despite recent advances in smart agriculture, early and precise disease diagnosis remains challenging due to visual resemblance among disease symptoms, high computational cost, and limited model interpretability. To overcome these difficulties, we introduce a novel lightweight and Region of Interest (ROI)-guided explainable machine learning framework to identify orange disease that integrates a strategic feature selection… More >

Yiyan Zhang^1,*, Yi Xin², Qin Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082841 - 27 July 2026

Abstract Medical data has specificity compared to other fields of data, and the description of medical data characteristics is still in a qualitative stage. This study included 293 sub-datasets of 138 independent datasets. First, data preprocessing was performed using methods such as incomplete data removal, inconsistent data normalization, and data integration. Then, the characteristics of 293 research datasets were quantified using 26 indicators in three categories: simple indicators, statistical indicators, and informational indicators. Furthermore, statistical analysis was performed on the above-mentioned quantitative characteristics, and stepwise regression and decision tree methods were used for modeling learning. The… More >

Wenchang Yu¹, Xiaoqin Ma^1,2, Zheqing Zhang¹, Kezhong Lu^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083713 - 23 July 2026

Abstract Feature selection grounded in neighborhood rough sets has attracted sustained research attention owing to its principled treatment of classification uncertainty. However, existing forward greedy algorithms typically evaluate uncertainty over the entire object universe at each iteration, resulting in prohibitive computational complexity on large-scale datasets. To address this inefficiency, we introduce a new uncertainty index built upon Boundary Object Sets (BOS). BOS are defined as objects whose neighborhood granules intersect with multiple decision classes, thereby capturing intrinsic classification ambiguity. The proposed measure quantifies the proportion of these boundary objects relative to the total universe size. Grounded More >

Mohammed Hassan Alnemari^1,2,*, Abdelrahman Osman Elfaki³, Anas Bushnag¹, Mohamed Hussien Mohamed Nerma¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082976 - 23 July 2026

Abstract Sparse finite impulse response (FIR) filters reduce computational cost on resource-constrained devices, but selecting the sparsification threshold λ is typically left to grid search or hand tuning. We propose a two-stage method: a 67,331-parameter surrogate network predicts (Ap,As,S) (passband ripple in dB, stopband attenuation in dB, sparsity in %) from a filter specification and a candidate λ, and split conformal prediction (CP) calibrates ± intervals around each prediction. We then select λ by minimizing a worst-case penalty computed on the conservative ends of the intervals (the upper bound on Ap and… More >

Botambu Collins, Jin-Taek Seong^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082245 - 30 June 2026

Abstract Classical image denoising methods remain relevant in practical scenarios where training data or noise models are unavailable, yet their performance is highly sensitive to parameter selection. Non-Local Means (NLM) is a representative example whose effectiveness depends critically on smoothing strength, patch size, and search window configuration. This paper formulates NLM parameter selection as a black-box optimization problem under unknown noise conditions and employs adaptive metaheuristic optimization strategies for this task. We propose an adaptive optimization framework that integrates rank-based perturbation, opposition-based learning, Lévy-flight exploration, and noise-aware parameter constraints to improve robustness and convergence. The proposed More >

Gaoteng Yuan^1,*, Ping Qiu², Qika Lin³, Jianchu Lin¹, Xiang Li¹, Dongping Gao⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081152 - 30 June 2026

Abstract Epilepsy is a chronic neurological disorder characterized by recurrent seizures, posing significant challenges to patients’ quality of life. Accurate classification of seizure states is crucial for effective intervention. This paper presents a deep learning-based approach for epileptic seizure classification by integrating multi-feature analysis of electroencephalogram (EEG) signals. The proposed method begins with signal preprocessing, including denoising, segmentation, and label construction. Subsequently, a comprehensive set of temporal, spectral, and wavelet-based features—such as signal mean, power, heart rate, and wavelet coefficients—is extracted. Feature selection is then performed using the Maximal Information Coefficient (MIC) to identify the most… More > Graphic Abstract

Hao Jiang¹, Lucheng Sun², Liguo Shuai^1,*, Zhihan Li^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.6, 2026, DOI:10.32604/fdmp.2026.082152 - 30 June 2026

Abstract Underwater bio-inspired robots have emerged as a promising alternative to conventional propeller-driven autonomous underwater vehicles and remotely operated vehicles because of their potential for high propulsive efficiency, superior maneuverability, reduced acoustic signatures, and enhanced environmental adaptability. Unlike rigid propellers operating under approximately steady inflow conditions, bio-inspired propulsion relies on strongly unsteady hydrodynamic mechanisms, including vortex generation and shedding, added-mass effects, boundary-layer evolution, and flexible fluid–structure interaction (FSI). These processes fundamentally govern thrust production, energy conversion, and maneuvering performance, yet a systematic synthesis connecting hydrodynamic mechanisms with engineering implementation remains limited. This review addresses that gap… More >

Xiaoyong Zhao¹, Jiamin Wu^2,*, Lei Wang²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.077179 - 15 June 2026

Abstract With the continuous improvement of the performance of large language models, how to further enhance their ability in complex tasks has become a key issue. The task of abnormal text detection poses a challenge to the model in identifying non-standard semantics due to its semantic complexity and high-risk features. However, existing fine-tuning methods rely heavily on static data selection strategies, making it difficult to adapt to the dynamic evolution of model capabilities, resulting in low training efficiency. This article proposes ADS (Adaptive Dataset Selection), an adaptive framework for selecting data in anomaly text detection. ADS… More >