Nien-Tsu Hu^1,*, Hsiang-Tung Kao¹, Chin-Sheng Chen¹, Shih-Hao Chang²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074993 - 10 February 2026

Abstract Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering, particularly when both precision and efficiency must be ensured. Conventional control methods are often effective for stabilization but may not directly optimize long-term performance. To address this limitation, this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator. The proposed scheme adopts an actor–critic structure, where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation, and the actor network generates near-optimal control signals in real time. This dual… More >

Qasem Abu Al-Haija^1,*, Shahad Al Tamimi²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073540 - 10 February 2026

Abstract Adversarial Reinforcement Learning (ARL) models for intelligent devices and Network Intrusion Detection Systems (NIDS) improve system resilience against sophisticated cyber-attacks. As a core component of ARL, Adversarial Training (AT) enables NIDS agents to discover and prevent new attack paths by exposing them to competing examples, thereby increasing detection accuracy, reducing False Positives (FPs), and enhancing network security. To develop robust decision-making capabilities for real-world network disruptions and hostile activity, NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity. The accuracy and timeliness of… More >

Gang Hou^1,#, Aifeng Liu^1,#, Tao Zhao¹, Wenyuan Wei², Bo Li¹, Jiancheng Liu^3,*, Siwen Wei^4,5,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.073202 - 10 February 2026

Abstract Cooperative multi-UAV search requires jointly optimizing wide-area coverage, rapid target discovery, and endurance under sensing and motion constraints. Resolving this coupling enables scalable coordination with high data efficiency and mission reliability. We formulate this problem as a discounted Markov decision process on an occupancy grid with a cellwise Bayesian belief update, yielding a Markov state that couples agent poses with a probabilistic target field. On this belief–MDP we introduce a segment-conditioned latent-intent framework, in which a discrete intent head selects a latent skill every K steps and an intra-segment GRU policy generates per-step control conditioned on More >

Md. Ahsan Habib¹, Md. Anwar Hussen Wadud², M. F. Mridha^3,*, Md. Jakir Hossen^4,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.070235 - 10 February 2026

Abstract The problem of fake news detection (FND) is becoming increasingly important in the field of natural language processing (NLP) because of the rapid dissemination of misleading information on the web. Large language models (LLMs) such as GPT-4. Zero excels in natural language understanding tasks but can still struggle to distinguish between fact and fiction, particularly when applied in the wild. However, a key challenge of existing FND methods is that they only consider unimodal data (e.g., images), while more detailed multimodal data (e.g., user behaviour, temporal dynamics) is neglected, and the latter is crucial for… More >

Anis Zarrad¹, Thomas Armstrong², Jaber Jemai^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072768 - 12 January 2026

Abstract Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability. While prioritization selects the most relevant test cases for optimal coverage, ranking further refines their execution order to detect critical faults earlier. This study investigates machine learning techniques to enhance both prioritization and ranking, contributing to more effective and efficient testing processes. We first employ advanced feature engineering alongside ensemble models, including Gradient Boosted, Support Vector Machines, Random Forests, and Naive Bayes classifiers to optimize test case prioritization, achieving an accuracy score of 0.98847… More >

Yonghua Huo^1,2, Ying Liu^1,*, Anni Jiang³, Yang Yang³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072681 - 12 January 2026

Abstract With the advent of sixth-generation mobile communications (6G), space–air–ground integrated networks have become mainstream. This paper focuses on collaborative scheduling for mobile edge computing (MEC) under a three-tier heterogeneous architecture composed of mobile devices, unmanned aerial vehicles (UAVs), and macro base stations (BSs). This scenario typically faces fast channel fading, dynamic computational loads, and energy constraints, whereas classical queuing-theoretic or convex-optimization approaches struggle to yield robust solutions in highly dynamic settings. To address this issue, we formulate a multi-agent Markov decision process (MDP) for an air–ground-fused MEC system, unify link selection, bandwidth/power allocation, and task… More >

Sai Xu^1,*, Jun Liu^1,*, Shengyu Huang¹, Zhi Li²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071865 - 12 January 2026

Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >

Chao Li^1,3,#, Jianing Wang^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071242 - 12 January 2026

Abstract Underwater images frequently suffer from chromatic distortion, blurred details, and low contrast, posing significant challenges for enhancement. This paper introduces AquaTree, a novel underwater image enhancement (UIE) method that reformulates the task as a Markov Decision Process (MDP) through the integration of Monte Carlo Tree Search (MCTS) and deep reinforcement learning (DRL). The framework employs an action space of 25 enhancement operators, strategically grouped for basic attribute adjustment, color component balance, correction, and deblurring. Exploration within MCTS is guided by a dual-branch convolutional network, enabling intelligent sequential operator selection. Our core contributions include: (1) a More >

Rashid Ali^1,*, Alaa Omran Almagrabi^2,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070224 - 12 January 2026

Abstract Wi-Fi technology has evolved significantly since its introduction in 1997, advancing to Wi-Fi 6 as the latest standard, with Wi-Fi 7 currently under development. Despite these advancements, integrating machine learning into Wi-Fi networks remains challenging, especially in decentralized environments with multiple access points (mAPs). This paper is a short review that summarizes the potential applications of federated reinforcement learning (FRL) across eight key areas of Wi-Fi functionality, including channel access, link adaptation, beamforming, multi-user transmissions, channel bonding, multi-link operation, spatial reuse, and multi-basic servic set (multi-BSS) coordination. FRL is highlighted as a promising framework for More >

Changcheng Li^1,2, Weimeng Chang^1,2, Dahai Zhang^1,*, Jinghan He¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069389 - 27 December 2025

Abstract Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts. This paper proposes a novel partitioning method based on deep reinforcement learning. First, the partitioning decision process is formulated as a Markov decision process (MDP) model to maximize the modularity. Corresponding key partitioning constraints on parallel restoration are considered. Second, based on the partitioning objective and constraints, the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function. The soft bonus scaling mechanism… More >