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 >

Traian-Radu Ploscă^1,*, Alexandru-Mihai Pescaru², Bianca-Valeria Rus¹, Daniel-Ioan Curiac^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069707 - 10 November 2025

Abstract Objective expertise evaluation of individuals, as a prerequisite stage for team formation, has been a long-term desideratum in large software development companies. With the rapid advancements in machine learning methods, based on reliable existing data stored in project management tools’ datasets, automating this evaluation process becomes a natural step forward. In this context, our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems. For this, we mathematically formalize two categories of expertise: technology-specific expertise, which denotes the skills required for a particular technology, and general expertise, which encapsulates overall knowledge More >

Yeasul Kim¹, Chaeeun Won¹, Hwankuk Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.069608 - 10 November 2025

Abstract With the increasing emphasis on personal information protection, encryption through security protocols has emerged as a critical requirement in data transmission and reception processes. Nevertheless, IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices, spanning a range of devices from non-encrypted ones to fully encrypted ones. Given the limited visibility into payloads in this context, this study investigates AI-based attack detection methods that leverage encrypted traffic metadata, eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices. Using the UNSW-NB15 and CICIoT-2023 dataset, encrypted and… More >

Jiqiang Xia¹, Qi Zhan¹, Le Tian^1,2,3,*, Yuxiang Hu^1,2,3, Jianhua Peng⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069533 - 10 November 2025

Abstract The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency, high-throughput communication, necessitating frequent and flexible updates to network routing configurations. However, maintaining consistent forwarding states during these updates is challenging, particularly when rerouting multiple flows simultaneously. Existing approaches pay little attention to multi-flow update, where improper update sequences across data plane nodes may construct deadlock dependencies. Moreover, these methods typically involve excessive control-data plane interactions, incurring significant resource overhead and performance degradation. This paper presents P4LoF, an efficient loop-free update approach that enables the controller to reroute multiple flows through More >

Kim Sao Nguyen, Ngoc Dung Bui^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069355 - 10 November 2025

Abstract Reversible data hiding (RDH) enables secret data embedding while preserving complete cover image recovery, making it crucial for applications requiring image integrity. The pixel value ordering (PVO) technique used in multi-stego images provides good image quality but often results in low embedding capability. To address these challenges, this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image. The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order. Four secret bits are embedded into each block’s maximum pixel value, while three additional More >

Hamza Murad Khan¹, Shakila Basheer², Mohammad Tabrez Quasim³, Raja`a Al-Naimi⁴, Vijaykumar Varadarajan⁵, Anwar Khan^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069327 - 10 November 2025

Abstract With the increasing growth of online news, fake electronic news detection has become one of the most important paradigms of modern research. Traditional electronic news detection techniques are generally based on contextual understanding, sequential dependencies, and/or data imbalance. This makes distinction between genuine and fabricated news a challenging task. To address this problem, we propose a novel hybrid architecture, T5-SA-LSTM, which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attention-enhanced (SA) Long Short-Term Memory (LSTM). The LSTM is trained using the Adam optimizer, which provides faster and more stable convergence compared… More >

Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

Jizhao Liu, Yuanyuan Shao^*, Shuqin Zhang, Fangfang Shan, Jun Li

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.068998 - 10 November 2025

Abstract Lateral movement represents the most covert and critical phase of Advanced Persistent Threats (APTs), and its detection still faces two primary challenges: sample scarcity and “cold start” of new entities. To address these challenges, we propose an Uncertainty-Driven Graph Embedding-Enhanced Lateral Movement Detection framework (UGEA-LMD). First, the framework employs event-level incremental encoding on a continuous-time graph to capture fine-grained behavioral evolution, enabling newly appearing nodes to retain temporal contextual awareness even in the absence of historical interactions and thereby fundamentally mitigating the cold-start problem. Second, in the embedding space, we model the dependency structure among… More >

Amjad Rehman^1,*, Tanzila Saba¹, Mona M. Jamjoom², Shaha Al-Otaibi³, Muhammad I. Khan¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068958 - 10 November 2025

Abstract Modern intrusion detection systems (MIDS) face persistent challenges in coping with the rapid evolution of cyber threats, high-volume network traffic, and imbalanced datasets. Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively. This study introduces an advanced, explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets, which reflects real-world network behavior through a blend of normal and diverse attack classes. The methodology begins with sophisticated data preprocessing, incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions, ensuring standardized and model-ready inputs.… More >

Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… More >