Mohamed Elkawkagy¹, Ibrahim A. Elgendy^2,*, Ammar Muthanna^3,4, Reem Ibrahim Alkanhel⁵, Heba Elbeh¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 393-415, 2025, DOI:10.32604/cmc.2025.063766 - 09 June 2025

Abstract Hierarchical Task Network (HTN) planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures. However, achieving optimal solutions in HTN planning remains a challenge, especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently. This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization (ACO) algorithm into the refinement process. The Ant System algorithm, inspired by the foraging behavior of ants, is well-suited for addressing optimization problems by efficiently exploring solution spaces. By incorporating ACO… More >

Tehreem Fatima¹, Kewen Xia^1,*, Wenbiao Yang², Qurat Ul Ain¹, Poornima Lankani Perera¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 811-826, 2025, DOI:10.32604/cmc.2025.063686 - 09 June 2025

Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >

Qiang Guo^1,2,*, Rubo Zhang¹, Bingbing Zhang³

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1307-1327, 2025, DOI:10.32604/cmc.2025.063288 - 09 June 2025

Abstract Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications, and the major challenge is false positives that occur during pedestrian detection. The emergence of various Convolutional Neural Network-based detection strategies substantially enhances pedestrian detection accuracy but still does not solve this problem well. This paper deeply analyzes the detection framework of the two-stage CNN detection methods and finds out false positives in detection results are due to its training strategy misclassifying some false proposals, thus weakening the classification capability of the following… More >

Reem Alkanhel^1,#, Abuzar B. M. Adam^2,#, Samia Allaoua Chelloug¹, Dina S. M. Hassan^1,*, Mohammed Saleh Ali Muthanna³, Ammar Muthanna⁴

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 607-624, 2025, DOI:10.32604/cmc.2025.063206 - 09 June 2025

Abstract The 3GPP standard defines the requirements for next-generation wireless networks, with particular attention to Ultra-Reliable Low-Latency Communications (URLLC), critical for applications such as Unmanned Aerial Vehicles (UAVs). In this context, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technique to improve spectrum efficiency and user fairness by allowing multiple users to share the same frequency resources. However, optimizing key parameters–such as beamforming, rate allocation, and UAV trajectory–presents significant challenges due to the nonconvex nature of the problem, especially under stringent URLLC constraints. This paper proposes an advanced deep learning-driven approach to address the resulting… More >

Hussam Qushtom, Ahmad Hasasneh^*, Sari Masri

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1379-1395, 2025, DOI:10.32604/cmc.2025.061995 - 09 June 2025

Abstract This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works,… More >

Haichao Zhou^*, Wei Zhang, Tinghui Huang, Haoran Li

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1113-1131, 2025, DOI:10.32604/fdmp.2025.064991 - 30 May 2025

Abstract The configuration of a spoiler plays a crucial role in the aerodynamics of a vehicle. In particular, investigating the impact of spoiler design on aerodynamic performance is essential for effectively reducing drag and optimizing efficiency. This study focuses on the 35° Ahmed body as the test model and examines six different spoiler types mounted on its slant surface. Using the Lattice Boltzmann Method (LBM) in XFlow and the Large Eddy Simulation (LES) technique, the aerodynamic effects of these spoilers were analyzed. The numerical approach was validated against published experimental data. Results indicate that aerodynamic drag More >

Denis Polezhaev^*, Alexey Vjatkin, Victor Kozlov

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1051-1061, 2025, DOI:10.32604/fdmp.2025.062000 - 30 May 2025

Abstract The dynamics of fluid and non-buoyant particles in a librating horizontal annulus is studied experimentally. In the absence of librations, the granular material forms a cylindrical layer near the outer boundary of the annulus and undergoes rigid-body rotation with the fluid and the annulus. It is demonstrated that the librational liquefaction of the granular material results in pattern formation. This self-organization process stems from the excitation of inertial modes induced by the oscillatory motion of liquefied granular material under the influence of the gravitational force. The inertial wave induces vortical fluid flow which entrains particles More > Graphic Abstract

Tao Wang^1,2, Haiyang Yu^1,*, Jie Gao², Fei Wang², Xinlong Zhang^3,*, Hao Yang², Guirong Di², Pengrun Wang²

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.5, pp. 1191-1200, 2025, DOI:10.32604/fdmp.2025.058865 - 30 May 2025

Abstract Oilfields worldwide are increasingly grappling with challenges such as early water breakthrough and high water production, yet direct, targeted solutions remain elusive. In recent years, chemical flooding techniques designed for tertiary oil recovery have garnered significant attention, with microgel flooding emerging as a particularly prominent area of research. Despite its promise, the complex mechanisms underlying microgel flooding have been rarely investigated numerically. This study aims to address these gaps by characterizing the distribution of microgel concentration and viscosity within different pore structures. To enhance the accuracy of these characterizations, the viscosity of microgels is adjusted More >

Umit Cigdem Turhal¹, Yasemin Onal^1,*, Kutalmis Turhal²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2307-2332, 2025, DOI:10.32604/cmes.2025.064269 - 30 May 2025

Abstract The reliability and efficiency of photovoltaic (PV) systems are essential for sustainable energy production, requiring accurate fault detection to minimize energy losses. This study proposes a hybrid model integrating Neighborhood Components Analysis (NCA) with a Convolutional Neural Network (CNN) to improve fault detection and diagnosis. Unlike Principal Component Analysis (PCA), which may compromise class relationships during feature extraction, NCA preserves these relationships, enhancing classification performance. The hybrid model combines NCA with CNN, a fundamental deep learning architecture, to enhance fault detection and diagnosis capabilities. The performance of the proposed NCA-CNN model was evaluated against other More > Graphic Abstract

Tallha Akram^1,*, Fahdah Almarshad¹, Anas Alsuhaibani¹, Syed Rameez Naqvi^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2333-2359, 2025, DOI:10.32604/cmes.2025.064079 - 30 May 2025

Abstract Melanoma is the deadliest form of skin cancer, with an increasing incidence over recent years. Over the past decade, researchers have recognized the potential of computer vision algorithms to aid in the early diagnosis of melanoma. As a result, a number of works have been dedicated to developing efficient machine learning models for its accurate classification; still, there remains a large window for improvement necessitating further research efforts. Limitations of the existing methods include lower accuracy and high computational complexity, which may be addressed by identifying and selecting the most discriminative features to improve classification… More >