Chao Li^1,3,#, Chen Wang^1,3,#, Caichang Ding^2,*, Yonghao Liao^1,3, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3129-3149, 2025, DOI:10.32604/cmc.2025.067440 - 23 September 2025

Abstract Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers. However, CNNs exhibit constrained performance in high-resolution image transmission, while Transformers incur high computational cost due to quadratic complexity. Recently, VMamba, a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities, has shown great potential in computer vision tasks. Inspired by this, we propose MNTSCC, an efficient VMamba-based nonlinear joint source-channel coding (JSCC) model for wireless image transmission. Specifically, MNTSCC comprises a VMamba-based nonlinear transform module, an MCAM entropy model, and a JSCC module. In the encoding stage, the… 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 >

Sidra Jubair¹, Jie Yang^1,2,*, Bilal Ali³, Walid Emam⁴, Yusra Tashkandy⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 511-534, 2025, DOI:10.32604/cmes.2025.066514 - 31 July 2025

Abstract Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning, particularly when class overlap significantly deteriorates classification performance. Traditional oversampling methods often generate synthetic samples without considering density variations, leading to redundant or misleading instances that exacerbate class overlap in high-density regions. To address these limitations, we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE, a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap. The originality of WGAN-VDE lies in its density-aware sample refinement, ensuring that synthetic samples are positioned in underrepresented More >

Anees Ara¹, Muhammad Mujahid¹, Amal Al-Rasheed^2,*, Shaha Al-Otaibi², Tanzila Saba¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2717-2731, 2025, DOI:10.32604/cmc.2025.065566 - 03 July 2025

Abstract DeepSeek Chinese artificial intelligence (AI) open-source model, has gained a lot of attention due to its economical training and efficient inference. DeepSeek, a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step, demonstrates remarkable reasoning capabilities of performing a wide range of tasks. DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries. Users possess divergent viewpoints regarding advanced models like DeepSeek, posting both their merits and shortcomings across several social media platforms. This research presents a new framework for predicting… More >

Nan Li^1,2,*, Jingxiong Dong², Liang Tao³, Liang Huang³

Energy Engineering, Vol.122, No.7, pp. 2957-2975, 2025, DOI:10.32604/ee.2025.062667 - 27 June 2025

Abstract With the continuous expansion and increasing complexity of power system scales, the binary classification for transient stability assessment in power systems can no longer meet the safety requirements of power system control and regulation. Therefore, this paper proposes a multi-class transient stability assessment model based on an improved Transformer. The model is designed with a dual-tower encoder structure: one encoder focuses on the time dependency of data, while the other focuses on the dynamic correlations between variables. Feature extraction is conducted from both time and variable perspectives to ensure the completeness of the feature extraction… More > Graphic Abstract

Lulu Zhang, Xuehui Du^*, Wenjuan Wang, Yu Cao, Xiangyu Wu, Shihao Wang

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1901-1919, 2025, DOI:10.32604/cmc.2025.064270 - 09 June 2025

Abstract To address the limitations of existing abnormal traffic detection methods, such as insufficient temporal and spatial feature extraction, high false positive rate (FPR), poor generalization, and class imbalance, this study proposed an intelligent detection method that combines a Stacked Convolutional Network (SCN), Bidirectional Long Short-Term Memory (BiLSTM) network, and Equalization Loss v2 (EQL v2). This method was divided into two components: a feature extraction model and a classification and detection model. First, SCN was constructed by combining a Convolutional Neural Network (CNN) with a Depthwise Separable Convolution (DSC) network to capture the abstract spatial features More >

David Milambo Kasumba^1,*, Guy Nkulu Wa Ngoie², Hyacinthe Tungadio Diambomba^1,3, Matthieu Kayembe Wa Kayembe⁴, Flory Kiseya Tshikala¹, Bonaventure Banza Wa Banza¹

Energy Engineering, Vol.122, No.6, pp. 2401-2416, 2025, DOI:10.32604/ee.2025.063144 - 29 May 2025

Abstract Electricity theft significantly impacts the reliability and sustainability of electricity services, particularly in developing regions. However, the socio-economic, infrastructural, and institutional drivers of theft remain inadequately explored. Here we examine electricity theft in Lubumbashi, Democratic Republic of Congo, focusing on its patterns, causes, and impacts on service quality. Theft rates exceeded 75% in peripheral municipalities like Katuba and Kampemba, driven by poverty, weak law enforcement, and poor infrastructure dominated by above-ground networks. In contrast, central areas like Kamalondo and Lubumbashi reported lower theft rates due to better urban planning and underground systems. We found that More >

ABDUR JAMIL¹, RIMSHA SIDDIQUE², FARYAL ALTAF³, DANIYAL WARRAICH⁴, FAIZAN AHMED⁵, ZAHEER QURESHI^6,*

Oncology Research, Vol.33, No.6, pp. 1289-1300, 2025, DOI:10.32604/or.2025.058790 - 29 May 2025

Abstract Background: Male breast cancer (MBC) is a rare but significant health concern, accounting for less than 1% of all breast cancer cases. Despite its low incidence, it presents unique clinical, genetic, and psychosocial challenges. Genetic predispositions, including BRCA2 mutations and hormonal imbalances, are key factors influencing the development of MBC. However, the rarity of the condition has led to limited research and fewer treatment guidelines specifically for male patients. Methods: A comprehensive literature review was conducted using PubMed, MEDLINE, and Embase databases to identify studies focusing on the epidemiology, risk factors, clinical presentation, diagnosis, treatment,… More >

I Made Putrama^1,2,*, Péter Martinek¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5699-5727, 2025, DOI:10.32604/cmc.2025.063465 - 19 May 2025

Abstract Imbalanced multiclass datasets pose challenges for machine learning algorithms. They often contain minority classes that are important for accurate predictions. However, when the data is sparsely distributed and overlaps with data points from other classes, it introduces noise. As a result, existing resampling methods may fail to preserve the original data patterns, further disrupting data quality and reducing model performance. This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling (NDESO), a hybrid method that integrates a data displacement strategy with a resampling technique to achieve data balance. It begins by computing the average distance of noisy… More >

Honglin Wang¹, Yaohua Xu^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4999-5018, 2025, DOI:10.32604/cmc.2025.061848 - 19 May 2025

Abstract Medical image classification is crucial in disease diagnosis, treatment planning, and clinical decision-making. We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation (BSDA) with a Vision Mamba-based model for medical image classification (MedMamba), enhanced by residual connection blocks, we named the model BSDA-Mamba. BSDA augments medical image data semantically, enhancing the model’s generalization ability and classification performance. MedMamba, a deep learning-based state space model, excels in capturing long-range dependencies in medical images. By incorporating residual connections, BSDA-Mamba further improves feature extraction capabilities. Through comprehensive experiments on eight medical image More >