Jun Xiong^*, Peng Yang, Bohan Chen, Zeming Chen

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069438 - 27 December 2025

Abstract The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system. However, various defects could be produced in the secondary equipment during long-term operation. The complex relationship between the defect phenomenon and multi-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods, which limits the real-time and accuracy of defect identification. Therefore, a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed. The defect data of secondary equipment is… More >

Chuang-Chieh Lin¹, Yung-Shen Huang², Shih-Yeh Chen^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.072890 - 09 December 2025

Abstract With the rapid development of generative artificial intelligence (GenAI), the task of story visualization, which transforms natural language narratives into coherent and consistent image sequences, has attracted growing research attention. However, existing methods still face limitations in balancing multi-frame character consistency and generation efficiency, which restricts their feasibility for large-scale practical applications. To address this issue, this study proposes a modular cloud-based distributed system built on Stable Diffusion. By separating the character generation and story generation processes, and integrating multi-feature control techniques, a caching mechanism, and an asynchronous task queue architecture, the system enhances generation… More >

Tahira Khalil¹, Sadeeq Jan^2,*, Rania M. Ghoniem³, Muhammad Imran Khan Khalil¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.072655 - 09 December 2025

Abstract The contemporary era is characterized by rapid technological advancements, particularly in the fields of communication and multimedia. Digital media has significantly influenced the daily lives of individuals of all ages. One of the emerging domains in digital media is the creation of cartoons and animated videos. The accessibility of the internet has led to a surge in the consumption of cartoons among young children, presenting challenges in monitoring and controlling the content they view. The prevalence of cartoon videos containing potentially violent scenes has raised concerns regarding their impact, especially on young and impressionable minds.… More >

Dike Chen^1,2,3, Zhiyong Qin², Ji Zhang², Hongyuan Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.072494 - 09 December 2025

Abstract To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle (UAV) aerial imagery, which often lead to missed and false detections, we propose Multi-scale Feature Fusion YOLO (MFF-YOLO), an enhanced algorithm based on YOLOv8s. Our approach introduces a Multi-scale Feature Fusion Strategy (MFFS), comprising the Multiple Features C2f (MFC) module and the Scale Sequence Feature Fusion (SSFF) module, to improve feature integration across different network levels. This enables more effective capture of fine-grained details and sequential multi-scale features. Furthermore, we incorporate Inner-CIoU, an improved loss function that uses auxiliary More >

Zilin Zhang¹, Yan Liu^1,*, Jia Liu², Senbao Hou³, Yuping Zhang¹, Chenyuan Wang¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-14, 2026, DOI:10.32604/cmc.2025.072286 - 09 December 2025

Abstract With the growing demand for more comprehensive and nuanced sentiment understanding, Multimodal Sentiment Analysis (MSA) has gained significant traction in recent years and continues to attract widespread attention in the academic community. Despite notable advances, existing approaches still face critical challenges in both information modeling and modality fusion. On one hand, many current methods rely heavily on encoders to extract global features from each modality, which limits their ability to capture latent fine-grained emotional cues within modalities. On the other hand, prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to… More >

Zhonghao Wang^1,2, Xin Liu^1,2,*, Changhua Yue³, Haiwen Yuan⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.071813 - 09 December 2025

Abstract To address critical challenges in nighttime ship detection—high small-target missed detection (over 20%), insufficient lightweighting, and limited generalization due to scarce, low-quality datasets—this study proposes a systematic solution. First, a high-quality Night-Ships dataset is constructed via CycleGAN-based day-night transfer, combined with a dual-threshold cleaning strategy (Laplacian variance sharpness filtering and brightness-color deviation screening). Second, a Cross-stage Lightweight Fusion-You Only Look Once version 8 (CLF-YOLOv8) is proposed with key improvements: the Neck network is reconstructed by replacing Cross Stage Partial (CSP) structure with the Cross Stage Partial Multi-Scale Convolutional Block (CSP-MSCB) and integrating Bidirectional Feature Pyramid More >

Rashid Jahangir^1,*, Nazik Alturki², Muhammad Asif Nauman³, Faiqa Hanif¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071574 - 09 December 2025

Abstract Human activity recognition (HAR) is a method to predict human activities from sensor signals using machine learning (ML) techniques. HAR systems have several applications in various domains, including medicine, surveillance, behavioral monitoring, and posture analysis. Extraction of suitable information from sensor data is an important part of the HAR process to recognize activities accurately. Several research studies on HAR have utilized Mel frequency cepstral coefficients (MFCCs) because of their effectiveness in capturing the periodic pattern of sensor signals. However, existing MFCC-based approaches often fail to capture sufficient temporal variability, which limits their ability to distinguish… More >

Ans D. Alghamdi^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-35, 2026, DOI:10.32604/cmc.2025.071210 - 09 December 2025

Abstract Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities. Current approaches struggle with cross-modal alignment, temporal consistency, and robust handling of noisy or incomplete inputs across multiple modalities. We propose MultiAgent-Chain of Thought (CoT), a novel multi-agent chain-of-thought reasoning framework where specialized agents for text, vision, and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms. Our architecture incorporates self-reflection modules, conflict resolution protocols, and dynamic rationale alignment to enhance consistency, factual accuracy, and user engagement. More >

Xu Tao¹, Qiang Xiao², Zhaoqi Jin², Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.070790 - 09 December 2025

Abstract Image fusion technology aims to generate a more informative single image by integrating complementary information from multi-modal images. Despite the significant progress of deep learning-based fusion methods, existing algorithms are often limited to single or dual-dimensional feature interactions, thus struggling to fully exploit the profound complementarity between multi-modal images. To address this, this paper proposes a parallel multi-dimensional complementary fusion network, termed PMCFusion, for the task of infrared and visible image fusion. The core of this method is its unique parallel three-branch fusion module, PTFM, which pioneers the parallel synergistic perception and efficient integration of… More >

Ran Liu, Yawen Chen, Dong Yang^*, Jingjing Yang^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070734 - 09 December 2025

Abstract In the field of smart agriculture, accurate and efficient object detection technology is crucial for automated crop management. A particularly challenging task in this domain is small object detection, such as the identification of immature fruits or early stage disease spots. These objects pose significant difficulties due to their small pixel coverage, limited feature information, substantial scale variations, and high susceptibility to complex background interference. These challenges frequently result in inadequate accuracy and robustness in current detection models. This study addresses two critical needs in the cashew cultivation industry—fruit maturity and anthracnose detection—by proposing an… More >