Thierry Mugenzi, Cahit Perkgoz^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.070381 - 10 November 2025

Abstract Missing data presents a crucial challenge in data analysis, especially in high-dimensional datasets, where missing data often leads to biased conclusions and degraded model performance. In this study, we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision. The proposed loss combines (i) a guided, masked mean squared error focusing on missing entries; (ii) a noise-aware regularization term to improve resilience against data corruption; and (iii) a variance penalty to encourage expressive yet stable reconstructions. We evaluate the proposed model across four missingness mechanisms, such as Missing… More >

Bo Zhu^1,#, Enzhi Dong^1,#, Zhonghua Cheng^1,*, Xianbiao Zhan², Kexin Jiang¹, Rongcai Wang ³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.069194 - 10 November 2025

Abstract With the increasing complexity of industrial automation, planetary gearboxes play a vital role in large-scale equipment transmission systems, directly impacting operational efficiency and safety. Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment, leading to excessive maintenance costs or potential failure risks. However, existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes. To address these challenges, this study proposes a novel condition-based maintenance framework for planetary gearboxes. A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals, which were then processed using a… More >

Huanan Yu¹, Xiang Zhang^1,*, Jian Wang²

Energy Engineering, Vol.122, No.12, pp. 5055-5071, 2025, DOI:10.32604/ee.2025.067152 - 27 November 2025

Abstract Recent advances in AC/DC hybrid power distribution systems have enhanced convenience in daily life. However, DC distribution introduces significant power quality challenges. To address the identification and classification of DC power quality disturbances, this paper proposes a novel methodology integrating Compressed Sensing (CS) with an enhanced Stacked Denoising Autoencoder (SDAE). The proposed approach first employs MATLAB/SIMULINK to model the DC distribution network and generate DC power quality disturbance signals. The measured original signals are then reconstructed using the compressive sensing-based generalized orthogonal matching pursuit (GOMP) algorithm to obtain sparse vectors as the final dataset. Subsequently, More >

Binu Sudhakaran Pillai¹, Raghavendra Kulkarni², Venkata Satya Suresh kumar Kondeti², Surendran Rajendran^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1141-1166, 2025, DOI:10.32604/cmes.2025.070348 - 30 October 2025

Abstract Future 6G communications will open up opportunities for innovative applications, including Cyber-Physical Systems, edge computing, supporting Industry 5.0, and digital agriculture. While automation is creating efficiencies, it can also create new cyber threats, such as vulnerabilities in trust and malicious node injection. Denial-of-Service (DoS) attacks can stop many forms of operations by overwhelming networks and systems with data noise. Current anomaly detection methods require extensive software changes and only detect static threats. Data collection is important for being accurate, but it is often a slow, tedious, and sometimes inefficient process. This paper proposes a new… More >

Nguyen Dong Phuong¹, Nanthakumar Srivilliputtur Subbiah¹, Yabin Jin², Xiaoying Zhuang^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 263-303, 2025, DOI:10.32604/cmes.2025.067100 - 30 October 2025

Abstract Traditional inverse neural network (INN) approaches for inverse design typically require auxiliary feedforward networks, leading to increased computational complexity and architectural dependencies. This study introduces a standalone INN methodology that eliminates the need for feedforward networks while maintaining high reconstruction accuracy. The approach integrates Principal Component Analysis (PCA) and Partial Least Squares (PLS) for optimized feature space learning, enabling the standalone INN to effectively capture bidirectional mappings between geometric parameters and mechanical properties. Validation using established numerical datasets demonstrates that the standalone INN architecture achieves reconstruction accuracy equal or better than traditional tandem approaches while More >

Ramadan Abdul-Rashid¹, Mohd Amiruddin Abd Rahman^1,*, Abdulaziz Yagoub Barnawi²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3213-3250, 2025, DOI:10.32604/cmes.2025.066589 - 30 September 2025

Abstract Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks (WSNs), especially in security-critical, time-sensitive applications. However, most existing protocols degrade substantially under malicious interference. We introduce iSTSP, an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust, precise synchronization even in hostile environments: (1) trust preprocessing that filters node participation using behavioral trust scoring; (2) anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time; (3) reliability-weighted consensus that prioritizes high-trust nodes during time aggregation; and (4) convergence-optimized synchronization… More >

Fatma S. Alrayes¹, Mohammed Zakariah^2,*, Mohammed K. Alzaylaee³, Syed Umar Amin⁴, Zafar Iqbal Khan⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3457-3484, 2025, DOI:10.32604/cmc.2025.068599 - 23 September 2025

Abstract Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information. The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks. The WUSTL-EHMS 2020 dataset trains and evaluates the model, constituting an imbalanced class distribution (87.46% normal traffic and 12.53% intrusion attacks). To address this imbalance, the study balances the effect of training Bias through Stratified K-fold cross-validation (K = 5), so that… More >

Seulki Han¹, Sangho Son², Won Sakong², Haemin Jung^3,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2893-2912, 2025, DOI:10.32604/cmc.2025.067211 - 23 September 2025

Abstract As cyber threats become increasingly sophisticated, Distributed Denial-of-Service (DDoS) attacks continue to pose a serious threat to network infrastructure, often disrupting critical services through overwhelming traffic. Although unsupervised anomaly detection using convolutional autoencoders (CAEs) has gained attention for its ability to model normal network behavior without requiring labeled data, conventional CAEs struggle to effectively distinguish between normal and attack traffic due to over-generalized reconstructions and naive anomaly scoring. To address these limitations, we propose CA-CAE, a novel anomaly detection framework designed to improve DDoS detection through asymmetric joint reconstruction learning and refined anomaly scoring. Our… More >

Shaoyong Hong¹, Bo Yang², Yan Chen², Hao Quan³, Shan Liu⁴, Minyi Tang^5,*, Jiawei Tian^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1091-1112, 2025, DOI:10.32604/cmes.2025.066165 - 31 July 2025

Abstract 3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and… More >

Prasanalakshmi Balaji^1,2, Sangita Babu³, Maode Ma⁴, Zhaoxi Fang², Syarifah Bahiyah Rahayu^5,6,*, Mariyam Aysha Bivi¹, Mahaveerakannan Renganathan⁷

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5831-5858, 2025, DOI:10.32604/cmc.2025.065260 - 30 July 2025

Abstract The rapid progression of the Internet of Things (IoT) technology enables its application across various sectors. However, IoT devices typically acquire inadequate computing power and user interfaces, making them susceptible to security threats. One significant risk to cloud networks is Distributed Denial-of-Service (DoS) attacks, where attackers aim to overcome a target system with excessive data and requests. Among these, low-rate DoS (LR-DoS) attacks present a particular challenge to detection. By sending bursts of attacks at irregular intervals, LR-DoS significantly degrades the targeted system’s Quality of Service (QoS). The low-rate nature of these attacks confuses their… More >