Peng Xiao¹, Haiyu Xu¹, Peng Xu¹, Zhiwei Guo^1,*, Amr Tolba^2,*, Osama Alfarraj²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2999-3022, 2025, DOI:10.32604/cmc.2025.066937 - 23 September 2025

Abstract Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare. In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology, as well as the existence of information redundancy in cross-modal data fusion, this paper proposes a multimodal fusion framework based on cross-modal collaboration and weighted attention mechanism. In terms of feature extraction, the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture, overcoming the limitations of traditional single-modality models in… More >

Heng Wang¹, Jian-Hua Qin^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1037-1052, 2025, DOI:10.32604/cmc.2025.067650 - 29 August 2025

Abstract Vehicle detection is a crucial aspect of intelligent transportation systems (ITS) and autonomous driving technologies. The complexity and diversity of real-world road environments, coupled with traffic congestion, pose significant challenges to the accuracy and real-time performance of vehicle detection models. To address these challenges, this paper introduces a fast and accurate vehicle detection algorithm named BES-Net. Firstly, the BoTNet module is integrated into the backbone network to bolster the model’s long-distance dependency, address the complexities and diversity of road environments, and accelerate the detection speed of the BES-Net network. Secondly, to accommodate the varying sizes… More >

Muhammad Naeem^*, Abdul Majid

Journal on Artificial Intelligence, Vol.7, pp. 115-141, 2025, DOI:10.32604/jai.2025.062446 - 30 May 2025

Abstract Brain tumor identification is a challenging task in neuro-oncology. The brain’s complex anatomy makes it a crucial part of the central nervous system. Accurate tumor classification is crucial for clinical diagnosis and treatment planning. This research presents a significant advancement in the multi-classification of brain tumors. This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d, a Convolutional Neural Network (CNN) with a multi-head self-attention (MHSA) mechanism. This combination harnesses the strengths of the feature extraction, feature representation by CNN, and long-range dependencies by MHSA. Magnetic Resonance Imaging (MRI) datasets were employed to check… More >

Xinlu Zong^*, Fan Yu, Zhen Chen, Xue Xia

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3517-3537, 2025, DOI:10.32604/cmc.2024.057494 - 17 February 2025

Abstract Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a More >

Yahia Said^1,*, Yahya Alassaf², Taoufik Saidani³, Refka Ghodhbani³, Olfa Ben Rhaiem⁴, Ali Ahmad Alalawi¹

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.058903 - 19 December 2024

Abstract The integration of Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITS) holds transformative potential for real-time traffic monitoring, a critical component of emerging smart city infrastructure. UAVs offer unique advantages over stationary traffic cameras, including greater flexibility in monitoring large and dynamic urban areas. However, detecting small, densely packed vehicles in UAV imagery remains a significant challenge due to occlusion, variations in lighting, and the complexity of urban landscapes. Conventional models often struggle with these issues, leading to inaccurate detections and reduced performance in practical applications. To address these challenges, this paper introduces CFEMNet,… More >

Zhongyang Wang, Hu Zhu, Feng Liu^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 605-623, 2024, DOI:10.32604/cmc.2024.050959 - 18 July 2024

Abstract Visual object tracking plays a crucial role in computer vision. In recent years, researchers have proposed various methods to achieve high-performance object tracking. Among these, methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information. However, current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information. In this paper, we introduce self-calibration multi-head self-attention Transformer (SMSTracker) as a solution to these challenges. It employs a hybrid tensor decomposition self-organizing multi-head self-attention transformer mechanism, which not only… More >

Jun Wang, Changfu Si, Zhen Wang, Qiang Fu^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4297-4318, 2024, DOI:10.32604/cmc.2024.050223 - 20 June 2024

Abstract Nowadays, with the rapid development of industrial Internet technology, on the one hand, advanced industrial control systems (ICS) have improved industrial production efficiency. However, there are more and more cyber-attacks targeting industrial control systems. To ensure the security of industrial networks, intrusion detection systems have been widely used in industrial control systems, and deep neural networks have always been an effective method for identifying cyber attacks. Current intrusion detection methods still suffer from low accuracy and a high false alarm rate. Therefore, it is important to build a more efficient intrusion detection model. This paper… More >

Bin Luo^1,2,3, Liangguo Chen^1,2,3, Shuhua Ruan^1,2,3,*, Yonggang Luo^2,3,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1731-1754, 2024, DOI:10.32604/cmc.2023.045739 - 27 February 2024

Abstract Considering the stealthiness and persistence of Advanced Persistent Threats (APTs), system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host. Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks, and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection, which requires lots of manual efforts to locate attack entities. This paper proposes an APT-exploited process detection approach called ThreatSniffer, which constructs the benign provenance graph from attack-free audit… More >