Xi Li^1,2, Runpu Nie^1,*, Zhaoyong Fan², Lianying Zou², Zhenhua Xiao², Kaile Dong¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2959-2984, 2025, DOI:10.32604/cmc.2025.066740 - 23 September 2025

Abstract With the rapid development of robotics, grasp prediction has become fundamental to achieving intelligent physical interactions. To enhance grasp detection accuracy in unstructured environments, we propose a novel Cross-Multiscale Adaptive Collaborative and Fusion Grasp Detection Network (CMACF-Net). Addressing the limitations of conventional methods in capturing multi-scale spatial features, CMACF-Net introduces the Quantized Multi-scale Global Attention Module (QMGAM), which enables precise multi-scale spatial calibration and adaptive spatial-channel interaction, ultimately yielding a more robust and discriminative feature representation. To reduce the degradation of local features and the loss of high-frequency information, the Cross-scale Context Integration Module (CCI) More >

Charles Munyao^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 329-346, 2025, DOI:10.32604/jai.2025.069226 - 22 September 2025

Abstract The natural language processing (NLP) domain has witnessed significant advancements with the emergence of transformer-based models, which have reshaped the text understanding and generation landscape. While their capabilities are well recognized, there remains a limited systematic synthesis of how these models perform across tasks, scale efficiently, adapt to domains, and address ethical challenges. Therefore, the aim of this paper was to analyze the performance of transformer-based models across various NLP tasks, their scalability, domain adaptation, and the ethical implications of such models. This meta-analysis paper synthesizes findings from 25 peer-reviewed studies on NLP transformer-based models,… More >

Geoffry Mutiso^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 275-288, 2025, DOI:10.32604/jai.2025.069768 - 05 September 2025

Abstract Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained on More >

Huichao Cao^1,*, Honghe Du¹, Dongnian Jiang¹, Wei Li¹, Lei Du¹, Jianfeng Yang²

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1305-1325, 2025, DOI:10.32604/sdhm.2025.066002 - 05 September 2025

Abstract In the production processes of modern industry, accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring “safe, stable, long-term, full load and optimal” operation of the production process. The benzene-to-ethylene ratio control system is a complex system based on an MPC-PID double-layer architecture. Taking into consideration the interaction between levels, coupling between loops and conditions of incomplete operation data, this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology. Firstly, according to the results of the pre-assessment of the system layers… More >

Caiping Huang^*, Hui Li, Zihang Yu

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1287-1304, 2025, DOI:10.32604/sdhm.2025.065930 - 05 September 2025

Abstract Semantic segmentation of concrete bridge defect images frequently encounters challenges due to insufficient precision and the limited computational capabilities of mobile devices, thereby considerably affecting the reliability of bridge defect monitoring and health assessment. To tackle these issues, a concrete defects dataset (including spalling, crack, and exposed steel rebar) was curated and multiple semantic segmentation models were developed. In these models, a deep convolutional network or a lightweight convolutional network were employed as the backbone feature extraction networks, with different loss functions configured and various attention mechanism modules introduced for conducting multi-angle comparative research. The… 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 >

Zhenxiang He^*, Rulin Wu, Xinyuan Wang

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1631-1651, 2025, DOI:10.32604/cmc.2025.067252 - 29 August 2025

Abstract With the continuous advancement of steganographic techniques, the task of image steganalysis has become increasingly challenging, posing significant obstacles to the fields of information security and digital forensics. Although existing deep learning methods have achieved certain progress in steganography detection, they still encounter several difficulties in real-world applications. Specifically, current methods often struggle to accurately focus on steganography sensitive regions, leading to limited detection accuracy. Moreover, feature information is frequently lost during transmission, which further reduces the model’s generalization ability. These issues not only compromise the reliability of steganography detection but also hinder its applicability… More >

Soufiane Khedairia¹, Akram Bennour^2,*, Mouaaz Nahas³, Aida Chefrour¹, Rashiq Rafiq Marie⁴, Mohammed Al-Sarem⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1267-1285, 2025, DOI:10.32604/cmc.2025.066601 - 29 August 2025

Abstract These days, social media has grown to be an integral part of people’s lives. However, it involves the possibility of exposure to “fake news,” which may contain information that is intentionally or inaccurately false to promote particular political or economic interests. The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model (CMCG) to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection. The proposed approach includes… More >

Lang Ding, Qinmu Wu^*, Jiaheng Li, Tao Hong, Linqing Bian

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1999-2020, 2025, DOI:10.32604/cmc.2025.066423 - 29 August 2025

Abstract Trajectory prediction is a critical task in autonomous driving systems. It enables vehicles to anticipate the future movements of surrounding traffic participants, which facilitates safe and human-like decision-making in the planning and control layers. However, most existing approaches rely on end-to-end deep learning architectures that overlook the influence of driving style on trajectory prediction. These methods often lack explicit modeling of semantic driving behavior and effective interaction mechanisms, leading to potentially unrealistic predictions. To address these limitations, we propose the Driving Style Guided Trajectory Prediction framework (DSG-TP), which incorporates a probabilistic representation of driving style… More >

Yuanqing Ding^1,2, Hanming Zhai¹, Qiming Ma¹, Liang Zhang¹, Lei Shao², Fanliang Bu^1,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 905-923, 2025, DOI:10.32604/cmc.2025.066307 - 29 August 2025

Abstract As the use of deepfake facial videos proliferate, the associated threats to social security and integrity cannot be overstated. Effective methods for detecting forged facial videos are thus urgently needed. While many deep learning-based facial forgery detection approaches show promise, they often fail to delve deeply into the complex relationships between image features and forgery indicators, limiting their effectiveness to specific forgery techniques. To address this challenge, we propose a dual-branch collaborative deepfake detection network. The network processes video frame images as input, where a specialized noise extraction module initially extracts the noise feature maps.… More >