Jiajia Liu^1,2, Shuchen Pang³, Peng Xie³, Haitao Zhou³, Chenxi Du³, Haoran Hu³, Bo Tang³, Jianhua Liu³, Fei Jia¹, Huibing Zhang^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077824 - 09 April 2026

Abstract In the unmanned aerial vehicle (UAV) assisted edge computing system, the broadcast characteristics of the UAV signal, the high mobility of the UAV, and the limited airborne energy make the task offloading strategy face challenges such as increased risk of information disclosure, limited computing resources, and the trade-off between energy consumption and flight time. To address these issues, we propose a K-means in-depth reinforcement learning algorithm based on Soft Actor-Critic (SAC). The proposed method first leverages the K-means clustering algorithm to determine the optimal deployment of ground jammers based on the final distribution of mobile… More >

Qinglei Zhang, Zhenzhen Wang^*, Jianguo Duan, Jiyun Qin, Ying Zhou

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076562 - 09 April 2026

Abstract In recent years, increasing urban mobility and complex traffic dynamics have intensified the need for accurate traffic flow forecasting in intelligent transportation systems. However, existing models often struggle to jointly capture short-term fluctuations and long-term temporal dependencies under noisy and heterogeneous traffic conditions. To address this challenge, this paper proposes a hybrid traffic flow forecasting framework that integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the Mamba state-space model, and the Transformer architecture. The framework first applies DBSCAN to multidimensional traffic features to enhance traffic state representation and reduce noise. The prediction module alternates… More >

Kyoungmin Roh¹, Seungmin Lee², Seong-je Cho^2,*, Youngsup Hwang³, Dongjae Kim⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.074936 - 30 March 2026

Abstract Many machine learning–based Android malware detection often suffers from concept drift, where models trained on historical data fail to generalize to evolving threats. This paper proposes SCAN (Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection), a hybrid intelligent framework designed to mitigate concept drift without retraining. SCAN integrates Gaussian Mixture Models (GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster. A key challenge in clustering-based malware detection is cluster-wise class imbalance, where clusters contain disproportionate distributions of benign and malicious samples. SCAN addresses this issue through adaptive thresholding, which dynamically… More >

Lixing Tan^1,2, Liusiyu Chen¹, Yang Wang¹, Zhenyu Song^1,*, Zenan Lu^1,3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075959 - 12 March 2026

Abstract Anomaly detection is a vibrant research direction in controller area networks, which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles. However, existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features, resulting in fragmented feature representations that compromise model stability and accuracy. To improve the extraction of valuable features, this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering (EDESC-IDS). Following the… More >

Omar Almomani^1,*, Mahran Al-Zyoud¹, Ahmad Adel Abu-Shareha², Ammar Almomani^3,4,*, Said A. Salloum⁵, Khaled Mohammad Alomari⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075465 - 12 March 2026

Abstract In Wireless Sensor Networks (WSNs), survivability is a crucial issue that is greatly impacted by energy efficiency. Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints in WSNs. This paper presents an Adaptive Enhanced Grey Wolf Optimizer (AEGWO) for energy-efficient cluster head (CH) selection that mitigates the exploration–exploitation imbalance, preserves population diversity, and avoids premature convergence inherent in baseline GWO. The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation, a hybrid velocity-momentum update based on the dynamics of PSO, and… More >

Seunggyu Byeon¹, Jung-hun Lee², Jong-Deok Kim^3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074033 - 12 March 2026

Abstract This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity. Conventional pooling operations, such as max and average, apply rigid aggregation and often discard fine-grained boundary information. In contrast, our method computes soft memberships within each receptive field and aggregates cluster-wise responses through membership-weighted pooling, thereby preserving informative structure while reducing dimensionality. Being differentiable, the proposed layer operates as standard two-dimensional pooling. We evaluate our approach across various CNN backbones and open datasets, including CIFAR-10/100, STL-10, LFW, and ImageNette, and further probe small training set restrictions More >

Song Zhang, Yang Yu^*, Shuguang Li, Xue Li

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.072333 - 27 February 2026

Abstract Thermal storage electric heating (TSEH), as a prevalent variable load resource, offers significant potential for enhancing system flexibility when aggregated into a cluster. To address the uncertainties of renewable energy and load forecasting in active distribution networks (ADN), this paper proposes a multi-timescale coordinated optimal dispatch strategy that incorporates TSEH clusters. It utilizes the thermal storage characteristics and short-term regulation capabilities of TSEH, along with the rapid and gradual response characteristics of resources in active distribution grids, to develop a coordinated optimization dispatch mechanism for day-ahead, intraday, and real-time stages. It provides a coordinated optimized… More > Graphic Abstract

Lei Su^1,2,3, Wanli Feng^1,2,3, Cao Kan^1,2,3, Mingjiang Wei^1,2,3, Jihai Wang⁴, Pan Yu⁴, Lingxiao Yang^5,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071006 - 27 February 2026

Abstract To address the high costs and operational instability of distribution networks caused by the large-scale integration of distributed energy resources (DERs) (such as photovoltaic (PV) systems, wind turbines (WT), and energy storage (ES) devices), and the increased grid load fluctuations and safety risks due to uncoordinated electric vehicles (EVs) charging, this paper proposes a novel dual-scale hierarchical collaborative optimization strategy. This strategy decouples system-level economic dispatch from distributed EV agent control, effectively solving the resource coordination conflicts arising from the high computational complexity, poor scalability of existing centralized optimization, or the reliance on local information… More >

Md Shafiullah^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075269 - 26 February 2026

Abstract Various factors, including weak tie-lines into the electric power system (EPS) networks, can lead to low-frequency oscillations (LFOs), which are considered an instant, non-threatening situation, but slow-acting and poisonous. Considering the challenge mentioned, this article proposes a clustering-based machine learning (ML) framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer (PSS) parameters. To validate the proposed strategy, two distinct EPS networks are selected: the single-machine infinite-bus (SMIB) with a single-stage PSS and the unified power flow controller (UPFC) coordinated SMIB with a double-stage PSS. To… More >

Frank Traub^1,#, Muhammad A. Panezai^1,#, Michaela Moisch¹, Julia Melke¹, Leonard Schöbel¹, Tilmann Busse¹, Fei Xing², Jiachen Sun², Ulrike Ritz^1,*

Oncology Research, Vol.34, No.3, 2026, DOI:10.32604/or.2025.071919 - 24 February 2026

Abstract Objectives: Photodynamic therapy (PDT) is a minimally invasive method used in the treatment of various cancers and skin diseases, but it is not widely used in bone cancer, where the current therapy is often not effective and accompanied by side effects. Alternative and more effective therapies like PDT are needed. In this in-vitro study, the effect of the photosensitizer (PS) chlorin e6 (Ce6) on cancerous bone tumor cells using PDT was examined. Methods: A total of 27 tissue specimens from patients with primary bone cancers or bone metastases of different origins were genetically characterized and treated… More >