Di Xiao^*, Wenting Jiang, Min Li

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079808 - 09 April 2026

Abstract The rapid advancement of the Internet of Things (IoT) has transformed edge devices from simple data collectors into intelligent units capable of local processing and collaborative learning. However, the vast amounts of sensitive data generated by these devices face severe constraints from “data silos” and risks of privacy breaches. Federated learning (FL), as a distributed collaborative paradigm that avoids sharing raw data, holds great promise in the IoT domain. Nevertheless, it remains vulnerable to gradient leakage threats. While traditional differential privacy (DP) techniques mitigate privacy risks, they often come at the cost of significantly reduced… More >

Giuseppe Marannano^*, Antonino Cirello, Tommaso Ingrassia

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077884 - 09 April 2026

Abstract In response to the growing need for adaptive optimization algorithms capable of handling complex, multimodal, and high-dimensional search spaces, this paper introduces the Structured Random Cycle-guided Algorithm (SRCA). SRCA is not presented as a fundamentally new optimization paradigm, but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection. Based on a cycle-structured architecture, directional and stochastic search behaviors are dynamically selected at the individual level. The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies, enabling a coherent and adaptive balance between exploration and exploitation throughout More >

Xu Chen, Li Yang^*, Guohao Qiu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077513 - 09 April 2026

Abstract Traffic prediction plays a crucial role in the efficient operation of satellite networks. However, due to resource consumption arising from redundant training of multiple individual prediction models, the dynamic and coupled spatial-temporal relationship of traffic, and maintenance of accurate traffic proportions, this problem is non-trivial to solve. Therefore, we consider this problem and makes the following contributions. First, a multi-granularity traffic prediction framework based on a shared feature extraction is designed to jointly predict total network traffic and service-specific traffic of satellite networks. This design ensures that both global and per service predictions benefit from… More >

Nahdi Saubari^1,2,*, Kunfeng Wang^1,*, Rachmat Muwardi^3,*, Andri Pranolo⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077087 - 09 April 2026

Abstract This study proposes an Adaptive Pooling method based on an alpha (α) parameter to enhance the effectiveness and stability of convolutional neural networks (CNNs) in image classification tasks. Conventional pooling techniques, such as max pooling and average pooling, often exhibit limited adaptability when applied to datasets with heterogeneous distributions and varying levels of complexity. To address this limitation, the proposed approach introduces an α parameter ranging from 0 to 1 that continuously regulates the contribution of maximum-based and average-based pooling operations in a unified and flexible framework. The proposed method is evaluated using two benchmark… More >

Yu Wang^1,2, Yabin Wang¹, Liang Wen¹, Bingyu Li¹, Mengze Qin¹, Fang Li¹, Zhonghua Cheng^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077026 - 09 April 2026

Abstract Remaining useful life (RUL) prediction for complex equipment is a critical technology for ensuring the safe and reliable operation of industrial systems. However, existing data-driven models commonly suffer from limitations such as weak cross-operational condition generalization, insufficient physical interpretability, and unstable training on non-stationary time-series data. To address these challenges, this paper proposes a temporal degradation prediction model that integrates context adaptation and physics-consistent constraints, named the Context-Adaptive Physics-informed Time-aware meta-Network (CAPTAIN). The model incorporates four core components: a Context-Aware Meta-Learning (CAML) module that enables lightweight parameter adaptation to diverse scenarios; Physics-Informed Neural Network (PINN)… More >

Jun Qi^1,*, Chao Yang², Xinliu Wang², Junyou Yang¹, Haixin Wang¹, Huaqin Chen^2,3, Zhenyan Li³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076511 - 09 April 2026

Abstract With the diversification of electricity trading forms driven by distributed energy technologies, the continuous growth of blockchain’s chained data structure poses dual challenges to traditional B+ tree indexes in terms of query efficiency and storage costs. This paper proposes a sliding window-based learned index construction method (SW-LI). The method consists of two key components. First, block timestamp–height samples are selected using a sliding window and used to train a linear regression model that captures the timestamp-to-height mapping. Second, an adaptive window adjustment mechanism is introduced: when the prediction error within a window exceeds a threshold,… More >

Abdulaziz A. Alsulami^1,*, Badraddin Alturki², Ahmad J. Tayeb², Rayan A. Alsemmeari², Raed Alsini¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076413 - 09 April 2026

Abstract Intrusion Detection Systems (IDS) play a critical role in protecting networked environments from cyberattacks. They have become increasingly important in smart environments such as the Internet of Things (IoT) systems. However, IDS for IoT networks face critical challenges due to hardware constraints, including limited computational resources and storage capacity, which lead to high feature dimensionality, prediction uncertainty, and increased processing cost. These factors make many conventional detection approaches unsuitable for real-time IoT deployment. To address these challenges, this paper proposes an adaptive intrusion detection framework that intelligently balances detection accuracy and computational efficiency. The proposed… More >

Jie Wang, Deming Lei^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076202 - 09 April 2026

Abstract Both flexible jobshop scheduling and parallel batch processing machine scheduling have been extensively considered; however, the flexible jobshop and parallel batch processing machine scheduling problem (FJPBPMSP) is prevalent in real-life manufacturing processes and is seldom investigated. In this study, FJPBPMSP is examined, where flexible processing and batch processing are performed sequentially. An adaptive imperialist competitive algorithm with cooperation (CAICA) is proposed to minimize makespan and total energy consumption simultaneously. In CAICA, a four-string representation is adopted, and initial empires with novel structures are formed by uniformly dividing the population. An adaptive assimilation and revolution are More >

Mohammed A. Ahmed¹, Jian Dong^2,*, Ronghua Shi², Ammar Nassr³, Hani Almaqtari³, Ala A. Alsanabani³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075616 - 09 April 2026

Abstract Facial Expression Recognition (FER) is an essential endeavor in computer vision, applicable in human-computer interaction, emotion assessment, and mental health surveillance. Although Convolutional Neural Networks (CNNs) have proven effective in Facial Emotion Recognition, they encounter difficulties in capturing long-range connections and global context. To address these constraints, we propose Spatial Quad-Similarity Network (SQSNet), an innovative hybrid framework that integrates the local feature extraction capabilities of CNNs with the global contextual modeling efficacy of Swin Transformers via a cohesive fusion technique. SQSNet introduces the Spatial Quad-Similarity (SQS) module, a feature refinement approach that amplifies discriminative characteristics… More >

He Tian^1,2, Ziliang Zhu^1,2, Jiangping Li^1,2, Ziyun Li^1,2, Baofeng Tang^1,2, Pengfei Ju^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075309 - 09 April 2026

Abstract As the number of electric vehicles continues to rise, pressure on charging infrastructure grows increasingly intense. Mobile charging technology, with its flexibility and deployability, has emerged as an effective solution. Within this technology, charging robots or vehicles must autonomously locate and dock with charging ports. Consequently, precise and stable charging port recognition constitutes both a prerequisite and the core bottleneck for achieving automated operations in mobile charging systems. However, in practical scenarios, charging ports often prove difficult to detect reliably due to factors such as physical obstructions, variations in lighting, and long shooting distances. To… More >