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  • Open Access

    ARTICLE

    A Stacked BWO-NIGP Framework for Robust and Accurate SOH Estimation of Lithium-Ion Batteries under Noisy and Small-Sample Scenarios

    Pu Yang1,*, Wanning Yan1, Rong Li1, Lei Chen2, Lijie Guo2

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 699-725, 2025, DOI:10.32604/cmc.2025.064947 - 09 June 2025

    Abstract Lithium-ion batteries (LIBs) have been widely used in mobile energy storage systems because of their high energy density, long life, and strong environmental adaptability. Accurately estimating the state of health (SOH) for LIBs is promising and has been extensively studied for many years. However, the current prediction methods are susceptible to noise interference, and the estimation accuracy has room for improvement. Motivated by this, this paper proposes a novel battery SOH estimation method, the Beluga Whale Optimization (BWO) and Noise-Input Gaussian Process (NIGP) Stacked Model (BGNSM). This method integrates the BWO-optimized Gaussian Process Regression (GPR)… More >

  • Open Access

    ARTICLE

    A Mask-Guided Latent Low-Rank Representation Method for Infrared and Visible Image Fusion

    Kezhen Xie1,2, Syed Mohd Zahid Syed Zainal Ariffin1,*, Muhammad Izzad Ramli1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 997-1011, 2025, DOI:10.32604/cmc.2025.063469 - 09 June 2025

    Abstract Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images. However, existing methods often fail to distinguish salient objects from background regions, leading to detail suppression in salient regions due to global fusion strategies. This study presents a mask-guided latent low-rank representation fusion method to address this issue. First, the GrabCut algorithm is employed to extract a saliency mask, distinguishing salient regions from background regions. Then, latent low-rank representation (LatLRR) is applied to extract deep image features, enhancing More >

  • Open Access

    ARTICLE

    Salient Features Guided Augmentation for Enhanced Deep Learning Classification in Hematoxylin and Eosin Images

    Tengyue Li1,*, Shuangli Song1, Jiaming Zhou2, Simon Fong2,3, Geyue Li4, Qun Song3, Sabah Mohammed5, Weiwei Lin6, Juntao Gao7

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1711-1730, 2025, DOI:10.32604/cmc.2025.062489 - 09 June 2025

    Abstract Hematoxylin and Eosin (H&E) images, popularly used in the field of digital pathology, often pose challenges due to their limited color richness, hindering the differentiation of subtle cell features crucial for accurate classification. Enhancing the visibility of these elusive cell features helps train robust deep-learning models. However, the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community. To address this challenge, we introduce Salient Features Guided Augmentation (SFGA), an approach that strategically integrates machine learning and image processing. SFGA utilizes machine learning algorithms to identify… More >

  • Open Access

    ARTICLE

    Edge-Based Data Hiding and Extraction Algorithm to Increase Payload Capacity and Data Security

    Hanan Hardan1,*, Osama A. Khashan2,*, Mohammad Alshinwan1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1681-1710, 2025, DOI:10.32604/cmc.2025.061659 - 09 June 2025

    Abstract This study introduces an Edge-Based Data Hiding and Extraction Algorithm (EBDHEA) to address the problem of data embedding in images while preserving robust security and high image quality. The algorithm produces three classes of pixels from the pixels in the cover image: edges found by the Canny edge detection method, pixels arising from the expansion of neighboring edge pixels, and pixels that are neither edges nor components of the neighboring edge pixels. The number of Least Significant Bits (LSBs) that are used to hide data depends on these classifications. Furthermore, the lossless compression method, Huffman… More >

  • Open Access

    ARTICLE

    Enhancing Multi-Class Cyberbullying Classification with Hybrid Feature Extraction and Transformer-Based Models

    Suliman Mohamed Fati1,*, Mohammed A. Mahdi2, Mohamed A.G. Hazber2, Shahanawaj Ahamad3, Sawsan A. Saad4, Mohammed Gamal Ragab5, Mohammed Al-Shalabi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2109-2131, 2025, DOI:10.32604/cmes.2025.063092 - 30 May 2025

    Abstract Cyberbullying on social media poses significant psychological risks, yet most detection systems oversimplify the task by focusing on binary classification, ignoring nuanced categories like passive-aggressive remarks or indirect slurs. To address this gap, we propose a hybrid framework combining Term Frequency-Inverse Document Frequency (TF-IDF), word-to-vector (Word2Vec), and Bidirectional Encoder Representations from Transformers (BERT) based models for multi-class cyberbullying detection. Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships, fused with BERT’s contextual embeddings to capture syntactic and semantic complexities. We evaluate the framework on a publicly available dataset of 47,000 annotated social… More >

  • Open Access

    ARTICLE

    Multi-Stage Hierarchical Feature Extraction for Efficient 3D Medical Image Segmentation

    Jion Kim, Jayeon Kim, Byeong-Seok Shin*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5429-5443, 2025, DOI:10.32604/cmc.2025.063815 - 19 May 2025

    Abstract Research has been conducted to reduce resource consumption in 3D medical image segmentation for diverse resource-constrained environments. However, decreasing the number of parameters to enhance computational efficiency can also lead to performance degradation. Moreover, these methods face challenges in balancing global and local features, increasing the risk of errors in multi-scale segmentation. This issue is particularly pronounced when segmenting small and complex structures within the human body. To address this problem, we propose a multi-stage hierarchical architecture composed of a detector and a segmentor. The detector extracts regions of interest (ROIs) in a 3D image, while More >

  • Open Access

    ARTICLE

    TSMS-InceptionNeXt: A Framework for Image-Based Combustion State Recognition in Counterflow Burners via Feature Extraction Optimization

    Huiling Yu1, Xibei Jia2, Yongfeng Niu1, Yizhuo Zhang1,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4329-4352, 2025, DOI:10.32604/cmc.2025.061882 - 19 May 2025

    Abstract The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose… More >

  • Open Access

    ARTICLE

    A Two-Stage Feature Extraction Approach for Green Energy Consumers in Retail Electricity Markets Using Clustering and TF–IDF Algorithms

    Wei Yang1, Weicong Tan1, Zhijian Zeng1, Ren Li1, Jie Qin1, Yuting Xie1, Yongjun Zhang2, Runting Cheng2, Dongliang Xiao2,*

    Energy Engineering, Vol.122, No.5, pp. 1697-1713, 2025, DOI:10.32604/ee.2025.060571 - 25 April 2025

    Abstract The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies, which poses greater challenges for the market service for green energy consumers. This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and term frequency-inverse document frequency (TF–IDF) algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market. First, the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and… More >

  • Open Access

    ARTICLE

    TIPS: Tailored Information Extraction in Public Security Using Domain-Enhanced Large Language Model

    Yue Liu1, Qinglang Guo2, Chunyao Yang1, Yong Liao1,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2555-2572, 2025, DOI:10.32604/cmc.2025.060318 - 16 April 2025

    Abstract Processing police incident data in public security involves complex natural language processing (NLP) tasks, including information extraction. This data contains extensive entity information—such as people, locations, and events—while also involving reasoning tasks like personnel classification, relationship judgment, and implicit inference. Moreover, utilizing models for extracting information from police incident data poses a significant challenge—data scarcity, which limits the effectiveness of traditional rule-based and machine-learning methods. To address these, we propose TIPS. In collaboration with public security experts, we used de-identified police incident data to create templates that enable large language models (LLMs) to populate data More >

  • Open Access

    REVIEW

    Reviving Contaminated Soils: Microbe-Aided Phytoremediation for Sustainable Metal Pollution Cleanup

    Chengyi Zou1, Sara Zafar2,*, Umbreen Bibi2, Manzar Abbas3, Zuhair Hasnain4,*

    Phyton-International Journal of Experimental Botany, Vol.94, No.3, pp. 603-621, 2025, DOI:10.32604/phyton.2025.062560 - 31 March 2025

    Abstract Soil metal pollution is a global issue due to its toxic nature affecting ecosystems and human health. This has become a concern since metals are non-biodegradable and toxic. Most of the reclamation methods currently used for soils rely on the use of physical and chemical means, which tend to be very expensive and result in secondary environmental damage. However, microbe-aided phytoremediation is gaining attention as it is an eco-friendly, affordable, and technically advanced method to restore the ecosystem. It is essential to understand the complex interaction between plants and microbes. The primary function of plant… More > Graphic Abstract

    Reviving Contaminated Soils: Microbe-Aided Phytoremediation for Sustainable Metal Pollution Cleanup

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