Dawei Qiu¹, Peng Wu^1,*, Xiaoming Zhang^2,*, Renjie Xu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1753-1769, 2025, DOI:10.32604/cmc.2025.062599 - 16 April 2025

Abstract Recently, many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences. However, these methods tend to focus more on low-frequency information while neglecting high-frequency information, which makes them ineffective in balancing users’ long- and short-term preferences. At the same time, many methods overlook the potential of frequency domain methods, ignoring their efficiency in processing frequency information. To overcome this limitation, we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation, namely HyTiFRec. Specifically, we design two hybrid filter modules: the learnable… More >

Kamil Yasen^1,#, Heyan Jin^2,#, Sijie Yang², Li Zhan², Xuyang Zhang², Ke Qin^1,3, Ye Li^2,3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3277-3301, 2025, DOI:10.32604/cmc.2025.061037 - 16 April 2025

Abstract Research on mass gathering events is critical for ensuring public security and maintaining social order. However, most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting, and there is a relative lack of research on mass gathering behaviors. We believe real-time detection and monitoring of mass gathering behaviors are essential for migrating potential security risks and emergencies. Therefore, it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur, enabling prompt and effective responses. To address this problem, we propose… More >

Panru Liang¹, Guojiang Xin^1,*, Xiaolei Yi², Hao Liang³, Changsong Ding¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2481-2504, 2025, DOI:10.32604/cmc.2025.060961 - 16 April 2025

Abstract Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases, facilitating treatment evaluations, and designing surgical plans. Due to the pancreas’s tiny size, significant variability in shape and location, and low contrast with surrounding tissues, achieving high segmentation accuracy remains challenging. To improve segmentation precision, we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas from CT images. Firstly, an EfficientNetV2 encoder is employed to extract complex and multi-level features, enhancing the model’s ability to capture the pancreas’s intricate morphology. Then, a residual multi-branch dilated attention… More >

Xinyi Song¹, Haixiang Zhang^1,*, Shaohua Li²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2735-2750, 2025, DOI:10.32604/cmc.2025.059006 - 16 April 2025

Abstract In recent years, Transformer has achieved remarkable results in the field of computer vision, with its built-in attention layers effectively modeling global dependencies in images by transforming image features into token forms. However, Transformers often face high computational costs when processing large-scale image data, which limits their feasibility in real-time applications. To address this issue, we propose Token Masked Pose Transformers (TMPose), constructing an efficient Transformer network for pose estimation. This network applies semantic-level masking to tokens and employs three different masking strategies to optimize model performance, aiming to reduce computational complexity. Experimental results show More >

Elaine Yi-Ling Wu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1185-1214, 2025, DOI:10.32604/cmes.2025.064636 - 11 April 2025

Abstract Hybrid renewable energy systems (HRES) offer cost-effectiveness, low-emission power solutions, and reduced dependence on fossil fuels. However, the renewable energy allocation problem remains challenging due to complex system interactions and multiple operational constraints. This study develops a novel Multi-Neighborhood Enhanced Harris Hawks Optimization (MNEHHO) algorithm to address the allocation of HRES components. The proposed approach integrates key technical parameters, including charge-discharge efficiency, storage device configurations, and renewable energy fraction. We formulate a comprehensive mathematical model that simultaneously minimizes levelized energy costs and pollutant emissions while maintaining system reliability. The MNEHHO algorithm employs multiple neighborhood structures… More >

Yunhan Ling¹, Huajun Peng², Yiqing Shi^1,*, Chao Xu¹, Jingzhen Yan¹, Jingjing Wang¹, Hui Ma^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 767-808, 2025, DOI:10.32604/cmes.2025.061763 - 11 April 2025

Abstract The traditional first-order reliability method (FORM) often encounters challenges with non-convergence of results or excessive calculation when analyzing complex engineering problems. To improve the global convergence speed of structural reliability analysis, an improved coati optimization algorithm (COA) is proposed in this paper. In this study, the social learning strategy is used to improve the coati optimization algorithm (SL-COA), which improves the convergence speed and robustness of the new heuristic optimization algorithm. Then, the SL-COA is compared with the latest heuristic optimization algorithms such as the original COA, whale optimization algorithm (WOA), and osprey optimization algorithm… More >

Kaku Haribabu^1,*, Prasath R¹, Praveen Joe IR²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 413-448, 2025, DOI:10.32604/cmes.2025.059406 - 11 April 2025

Abstract Thyroid nodules, a common disorder in the endocrine system, require accurate segmentation in ultrasound images for effective diagnosis and treatment. However, achieving precise segmentation remains a challenge due to various factors, including scattering noise, low contrast, and limited resolution in ultrasound images. Although existing segmentation models have made progress, they still suffer from several limitations, such as high error rates, low generalizability, overfitting, limited feature learning capability, etc. To address these challenges, this paper proposes a Multi-level Relation Transformer-based U-Net (MLRT-UNet) to improve thyroid nodule segmentation. The MLRT-UNet leverages a novel Relation Transformer, which processes… More >

Hussain H. Al-Kayiem^1,*, Raed A. Jessam², Sinan S. Hamdi³, Ali M. Tukkee^4,5

Energy Engineering, Vol.122, No.4, pp. 1311-1335, 2025, DOI:10.32604/ee.2025.061709 - 31 March 2025

Abstract Size reduction of the gas turbines (GT) by reducing the inlet S-shaped diffuser length increases the power-to-weight ratio. It improves the techno-economic features of the GT by lesser fuel consumption. However, this Length reduction of a bare S-shaped diffuser to an aggressive S-shaped diffuser would risk flow separation and performance reduction of the diffuser and the air intake of the GT. The objective of this research is to propose and assess fitted energy promoters (EPs) to enhance the S-shaped diffuser performance by controlling and modifying the flow in the high bending zone of the diffuser.… More >

Yuanhang Zhang¹, Xu An Wang^1,2,*, Weiwei Jiang³, Mingyu Zhou¹, Xiaoxuan Xu¹, Hao Liu¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1533-1553, 2025, DOI:10.32604/cmc.2025.062910 - 26 March 2025

Abstract Cloud storage, a core component of cloud computing, plays a vital role in the storage and management of data. Electronic Health Records (EHRs), which document users’ health information, are typically stored on cloud servers. However, users’ sensitive data would then become unregulated. In the event of data loss, cloud storage providers might conceal the fact that data has been compromised to protect their reputation and mitigate losses. Ensuring the integrity of data stored in the cloud remains a pressing issue that urgently needs to be addressed. In this paper, we propose a data auditing scheme… More >

Aadil Mushtaq Pandith¹, Manni Kumar², Naveen Kumar³, Nitin Goyal^4,*, Sachin Ahuja², Yonis Gulzar⁵, Rashi Rastogi⁶, Rupesh Gupta⁷

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 259-279, 2025, DOI:10.32604/cmc.2025.062608 - 26 March 2025

Abstract Underwater wireless sensor networks (UWSNs) rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink. However, many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments. Additionally, conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink, commonly known as the energy hole issue. Moreover, cluster-based aggregation methods face significant challenges such as cluster head (CH) failures and collisions within clusters that degrade overall network performance. To address these limitations,… More >