Baixuan Han¹, Yueping Peng^1,*, Zecong Ye², Hexiang Hao¹, Xuekai Zhang¹, Wei Tang¹, Wenchao Kang¹, Qilong Li¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.071071 - 09 December 2025

Abstract Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm, a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed. By integrating multi-head grouped self-attention mechanism and Partial-Conv, a two-way feature grouping fusion module (DFPF) was designed, which carried out effective channel segmentation and fusion strategies to reduce redundant calculations and memory access. C3K2 module was improved, and then unstructured pruning and feature distillation technology were used. The algorithm model is lightweight, and the feature extraction ability for airborne visual abnormal behavior… More >

Weiping Zeng¹, Xiangping Bryce Zhai^1,2,3,*, Cheng Sun¹, Liusha Jiang^1,2, Yicong Du³, Xuefeng Yan^1,3

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070777 - 09 December 2025

Abstract With the expanding applications of unmanned aerial vehicles (UAVs), precise flight evaluation has emerged as a critical enabler for efficient path planning, directly impacting operational performance and safety. Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints. However, these methods often overlook the correlation between pilot control quality and UAV flight dynamics, limiting their adaptability in complex scenarios. In this paper, we propose an intelligent flight evaluation model specifically designed to enhance multi-waypoint trajectory optimization algorithms. Our model leverages a decision tree to integrate attitude More >

Charlotte Olivia Namagembe, Mohamad Ibrahim, Md Arafatur Rahman^*, Prashant Pillai

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.070316 - 09 December 2025

Abstract The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification More >

Zeeshan Ali Haider¹, Inam Ullah^2,*, Ahmad Abu Shareha³, Rashid Nasimov⁴, Sufyan Ali Memon^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.071042 - 10 November 2025

Abstract The advent of sixth-generation (6G) networks introduces unprecedented challenges in achieving seamless connectivity, ultra-low latency, and efficient resource management in highly dynamic environments. Although fifth-generation (5G) networks transformed mobile broadband and machine-type communications at massive scales, their properties of scaling, interference management, and latency remain a limitation in dense high mobility settings. To overcome these limitations, artificial intelligence (AI) and unmanned aerial vehicles (UAVs) have emerged as potential solutions to develop versatile, dynamic, and energy-efficient communication systems. The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning (CoRL) to manage an autonomous network.… More >

Vi Hoai Nam¹, Chu Thi Minh Hue², Dang Van Anh^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070605 - 10 November 2025

Abstract Unmanned Aerial Vehicles (UAVs) have become integral components in smart city infrastructures, supporting applications such as emergency response, surveillance, and data collection. However, the high mobility and dynamic topology of Flying Ad Hoc Networks (FANETs) present significant challenges for maintaining reliable, low-latency communication. Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable. To overcome these limitations, this paper proposes an improved routing protocol based on reinforcement learning. This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware. The proposed method optimizes the selection of… More >

Zheyuan Jia, Fenglin Jin^*, Jun Xie, Yuan He

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069128 - 10 November 2025

Abstract This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks (SAGIN) through a novel Recursive Multi-Agent Proximal Policy Optimization (RMAPPO) algorithm. The exponential growth of mobile devices and data traffic has substantially increased network congestion, particularly in urban areas and regions with limited terrestrial infrastructure. Our approach jointly optimizes unmanned aerial vehicle (UAV) trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput, minimize energy consumption, and maintain equitable resource distribution. The proposed RMAPPO framework incorporates recurrent neural networks (RNNs) to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent More >

Zhiyong Deng¹, Yanchen Ye², Jiangling Guo^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069090 - 10 November 2025

Abstract With the rapid expansion of drone applications, accurate detection of objects in aerial imagery has become crucial for intelligent transportation, urban management, and emergency rescue missions. However, existing methods face numerous challenges in practical deployment, including scale variation handling, feature degradation, and complex backgrounds. To address these issues, we propose Edge-enhanced and Detail-Capturing You Only Look Once (EHDC-YOLO), a novel framework for object detection in Unmanned Aerial Vehicle (UAV) imagery. Based on the You Only Look Once version 11 nano (YOLOv11n) baseline, EHDC-YOLO systematically introduces several architectural enhancements: (1) a Multi-Scale Edge Enhancement (MSEE) module… More >

Shuangyuan Li^1,*, Zhengwei Wang², Jiaming Liang³, Yichen Wang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3993-4011, 2025, DOI:10.32604/cmes.2025.073896 - 23 December 2025

Abstract Object detection plays a crucial role in the field of computer vision, and small object detection has long been a challenging issue within this domain. In order to improve the performance of object detection on small targets, this paper proposes an enhanced structure for YOLOv5, termed ATC-YOLOv5. Firstly, a novel structure, AdaptiveTrans, is introduced into YOLOv5 to facilitate efficient communication between the encoder and the detector. Consequently, the network can better address the adaptability challenge posed by objects of different sizes in object detection. Additionally, the paper incorporates the CBAM (Convolutional Block Attention Module) attention More >

Hsiao-Chun Han¹, Der-Chen Huang^1,*, Chin-Ling Chen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 67-126, 2025, DOI:10.32604/cmes.2025.070944 - 30 October 2025

Abstract This study presents a systematic review of applications of artificial intelligence (abbreviated as AI) and blockchain in supply chain provenance traceability and legal forensics cover five sectors: integrated circuits (abbreviated as ICs), pharmaceuticals, electric vehicles (abbreviated as EVs), drones (abbreviated as UAVs), and robotics—in response to rising trade tensions and geopolitical conflicts, which have heightened concerns over product origin fraud and information security. While previous literature often focuses on single-industry contexts or isolated technologies, this review comprehensively surveys these sectors and categorizes 116 peer-reviewed studies by application domain, technical architecture, and functional objective. Special attention More >

Khadija Slimani^1,2,*, Samira Khoulji², Hamed Taherdoost^3,4, Mohamed Larbi Kerkeb⁵

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4667-4686, 2025, DOI:10.32604/cmc.2025.070173 - 23 October 2025

Abstract The integration of the dynamic adaptive routing (DAR) algorithm in unmanned aerial vehicle (UAV) networks offers a significant advancement in addressing the challenges posed by next-generation communication systems like 6G. DAR’s innovative framework incorporates real-time path adjustments, energy-aware routing, and predictive models, optimizing reliability, latency, and energy efficiency in UAV operations. This study demonstrated DAR’s superior performance in dynamic, large-scale environments, proving its adaptability and scalability for real-time applications. As 6G networks evolve, challenges such as bandwidth demands, global spectrum management, security vulnerabilities, and financial feasibility become prominent. DAR aligns with these demands by offering More >