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  • Open Access

    ARTICLE

    UltraSegNet: A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

    Suhaila Abuowaida1,*, Hamza Abu Owida2, Deema Mohammed Alsekait3,*, Nawaf Alshdaifat4, Diaa Salama AbdElminaam5,6, Mohammad Alshinwan4

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3303-3333, 2025, DOI:10.32604/cmc.2025.063470 - 16 April 2025

    Abstract Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise, dependency on the operator, and the variation of image quality. This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations: This work adds three things: (1) a changed ResNet-50 backbone with sequential 3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries; (2) a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory; and (3) an adaptive feature fusion strategy that changes local and… More >

  • Open Access

    ARTICLE

    HyTiFRec: Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation

    Dawei Qiu1, Peng Wu1,*, Xiaoming Zhang2,*, 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 >

  • Open Access

    ARTICLE

    A Category-Agnostic Hybrid Contrastive Learning Method for Few-Shot Point Cloud Object Detection

    Xuejing Li*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1667-1681, 2025, DOI:10.32604/cmc.2025.062161 - 16 April 2025

    Abstract Few-shot point cloud 3D object detection (FS3D) aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes. Due to imbalanced training data, existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes, which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects. To address these issues, this thesis proposes a… More >

  • Open Access

    ARTICLE

    Real-Time Proportional-Integral-Derivative (PID) Tuning Based on Back Propagation (BP) Neural Network for Intelligent Vehicle Motion Control

    Liang Zhou1, Qiyao Hu1,2,3,*, Xianlin Peng4,5, Qianlong Liu6

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2375-2401, 2025, DOI:10.32604/cmc.2025.061894 - 16 April 2025

    Abstract Over 1.3 million people die annually in traffic accidents, and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems. In modern industrial and technological applications and collaborative edge intelligence, control systems are crucial for ensuring efficiency and safety. However, deficiencies in these systems can lead to significant operational risks. This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control, particularly the limitations of traditional Proportional-Integral-Derivative (PID) controllers in managing nonlinear and time-varying dynamics, such as varying road conditions… More >

  • Open Access

    ARTICLE

    Leveraging Transformers for Detection of Arabic Cyberbullying on Social Media: Hybrid Arabic Transformers

    Amjad A. Alsuwaylimi1,*, Zaid S. Alenezi2

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3165-3185, 2025, DOI:10.32604/cmc.2025.061674 - 16 April 2025

    Abstract Cyberbullying is a remarkable issue in the Arabic-speaking world, affecting children, organizations, and businesses. Various efforts have been made to combat this problem through proposed models using machine learning (ML) and deep learning (DL) approaches utilizing natural language processing (NLP) methods and by proposing relevant datasets. However, most of these endeavors focused predominantly on the English language, leaving a substantial gap in addressing Arabic cyberbullying. Given the complexities of the Arabic language, transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained… More >

  • Open Access

    ARTICLE

    A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation

    Hamza Murad Khan1, Anwar Khan1,*, Santos Gracia Villar2,3,4, Luis Alonso Dzul Lopez2,5,6, Abdulaziz Almaleh7, Abdullah M. Al-Qahtani8

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3369-3388, 2025, DOI:10.32604/cmc.2025.060474 - 16 April 2025

    Abstract Traffic forecasting with high precision aids Intelligent Transport Systems (ITS) in formulating and optimizing traffic management strategies. The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity. To address this problem, this paper uses the Tree-structured Parzen Estimator (TPE) to tune the hyperparameters of the Long Short-term Memory (LSTM) deep learning framework. The Tree-structured Parzen Estimator (TPE) uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples. This ensures fast convergence in… More >

  • Open Access

    ARTICLE

    Multi-Neighborhood Enhanced Harris Hawks Optimization for Efficient Allocation of Hybrid Renewable Energy System with Cost and Emission Reduction

    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 >

  • Open Access

    ARTICLE

    Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

    Xiangyang Cao1,2, Yaojie Zheng1,2, Hanbin Xiao1,2,*, Min Xiao2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 289-334, 2025, DOI:10.32604/cmes.2025.063954 - 11 April 2025

    Abstract This study investigates the Maximum Power Point Tracking (MPPT) control method of offshore wind-photovoltaic hybrid power generation system with offshore crane-assisted. A new algorithm of Global Fast Integral Sliding Mode Control (GFISMC) is proposed based on the tip speed ratio method and sliding mode control. The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter. An offshore wind power generation system model is presented to verify the algorithm effect. An offshore More >

  • Open Access

    ARTICLE

    Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

    Noveela Iftikhar1, Mujeeb Ur Rehman1, Mumtaz Ali Shah2, Mohammed J. F. Alenazi3, Jehad Ali4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 639-671, 2025, DOI:10.32604/cmes.2025.062788 - 11 April 2025

    Abstract Intrusion attempts against Internet of Things (IoT) devices have significantly increased in the last few years. These devices are now easy targets for hackers because of their built-in security flaws. Combining a Self-Organizing Map (SOM) hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting (XGBoost) for multi-class classification can improve network traffic intrusion detection. The proposed model is evaluated on the NSL-KDD dataset. The hybrid approach outperforms the baseline line models, Multilayer perceptron model, and SOM-KNN (k-nearest neighbors) model in precision, recall, and F1-score, highlighting the proposed More >

  • Open Access

    ARTICLE

    MOCBOA: Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

    Nour Elhouda Chalabi1, Abdelouahab Attia2, Abdulaziz S. Almazyad3, Ali Wagdy Mohamed4,5, Frank Werner6, Pradeep Jangir7, Mohammad Shokouhifar8,9,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 967-1008, 2025, DOI:10.32604/cmes.2025.062332 - 11 April 2025

    Abstract Multi-objective optimization is critical for problem-solving in engineering, economics, and AI. This study introduces the Multi-Objective Chef-Based Optimization Algorithm (MOCBOA), an upgraded version of the Chef-Based Optimization Algorithm (CBOA) that addresses distinct objectives. Our approach is unique in systematically examining four dominance relations—Pareto, Epsilon, Cone-epsilon, and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front. Our comparison investigation, which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering, mechanical design, and power systems, reveals that the dominance approach More >

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