Hiba Sameer Saeed^#, Amenah Dahim Abbood^#,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4375-4405, 2025, DOI:10.32604/cmc.2025.067783 - 23 October 2025

Abstract In recent years, the evolution of the community structure in social networks has gained significant attention. Due to the rapid and continuous evolution of real-world networks over time. This makes the process of identifying communities and tracking their topology changes challenging. To tackle these challenges, it is necessary to find efficient methodologies for analyzing the behavior patterns of dynamic communities. Several previous reviews have introduced algorithms and models for community detection. However, these methods have not been very accurate in identifying communities. Moreover, none of the reviewed papers made an apparent effort to link algorithms… More >

Osama M. Alshehri¹, Ahmad Shaf^2,*, Muhammad Irfan^3,*, Mohammed M. Jalal⁴, Malik A. Altayar⁴, Mohammed H. Abu-Alghayth⁵, Humood Al Shmrany⁶, Tariq Ali⁷, Toufique A. Soomro⁸, Ali G. Alkhathami⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1165-1196, 2025, DOI:10.32604/cmes.2025.067150 - 31 July 2025

Abstract Background: Accurate classification of normal blood cells is a critical foundation for automated hematological analysis, including the detection of pathological conditions like leukemia. While convolutional neural networks (CNNs) excel in local feature extraction, their ability to capture global contextual relationships in complex cellular morphologies is limited. This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification, laying the groundwork for future leukemia diagnostics. Methods: The proposed architecture integrates pre-trained CNNs (ResNet50, EfficientNetB3, InceptionV3, CustomCNN) with Vision Transformer (ViT) layers to combine local and global feature modeling. Four hybrid models were evaluated on… More >

Asaad Algarni¹, Iqra Aijaz Abro², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Hui Liu^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5879-5896, 2025, DOI:10.32604/cmc.2025.064601 - 30 July 2025

Abstract Inertial Sensor-based Daily Activity Recognition (IS-DAR) requires adaptable, data-efficient methods for effective multi-sensor use. This study presents an advanced detection system using body-worn sensors to accurately recognize activities. A structured pipeline enhances IS-DAR by applying signal preprocessing, feature extraction and optimization, followed by classification. Before segmentation, a Chebyshev filter removes noise, and Blackman windowing improves signal representation. Discriminative features—Gaussian Mixture Model (GMM) with Mel-Frequency Cepstral Coefficients (MFCC), spectral entropy, quaternion-based features, and Gammatone Cepstral Coefficients (GCC)—are fused to expand the feature space. Unlike existing approaches, the proposed IS-DAR system uniquely integrates diverse handcrafted features using… More >

Jungpil Shin^1,*, Md. Al Mehedi Hasan², Md. Maniruzzaman³, Satoshi Nishimura¹, Sultan Alfarhood⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3615-3638, 2025, DOI:10.32604/cmes.2025.064588 - 30 June 2025

Abstract Activity recognition is a challenging topic in the field of computer vision that has various applications, including surveillance systems, industrial automation, and human-computer interaction. Today, the demand for automation has greatly increased across industries worldwide. Real-time detection requires edge devices with limited computational time. This study proposes a novel hybrid deep learning system for human activity recognition (HAR), aiming to enhance the recognition accuracy and reduce the computational time. The proposed system combines a pre-trained image classification model with a sequence analysis model. First, the dataset was divided into a training set (70%), validation set… More > Graphic Abstract

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 >

Ghada Atteia^1,*, Mohammed Dabboor², Konstantinos Karantzalos³, Maali Alabdulhafith¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1747-1767, 2025, DOI:10.32604/cmc.2025.063363 - 09 June 2025

Abstract This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of… More >

Hoang Thi Minh Chau^1,2,3, Tran Thi Ngan^4,*, Nguyen Long Giang⁵, Tran Manh Tuan⁶, Tran Kim Chau⁷

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4915-4937, 2025, DOI:10.32604/cmc.2025.062784 - 19 May 2025

Abstract Global climate change, along with the rapid increase of the population, has put significant pressure on water security. A water reservoir is an effective solution for adjusting and ensuring water supply. In particular, the reservoir water level is an essential physical indicator for the reservoirs. Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies. In recent years, deep learning models have been widely applied to solve forecasting problems. In this study, we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,… More >

Suhaila Abuowaida^1,*, Hamza Abu Owida², Deema Mohammed Alsekait^3,*, Nawaf Alshdaifat⁴, Diaa Salama AbdElminaam^5,6, 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 >

Chayut Bunterngchit¹, Laith H. Baniata², Mohammad H. Baniata³, Ashraf ALDabbas⁴, Mohannad A. Khair⁵, Thanaphon Chearanai⁶, Sangwoo Kang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 517-536, 2025, DOI:10.32604/cmc.2025.060368 - 26 March 2025

Abstract Stroke is a leading cause of death and disability worldwide, significantly impairing motor and cognitive functions. Effective rehabilitation is often hindered by the heterogeneity of stroke lesions, variability in recovery patterns, and the complexity of electroencephalography (EEG) signals, which are often contaminated by artifacts. Accurate classification of motor imagery (MI) tasks, involving the mental simulation of movements, is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability. To address these challenges, this study introduces a graph-attentive convolutional long short-term memory (LSTM) network (GACL-Net), a novel hybrid deep learning model… More >

Shoaib Hassan^1,*, Qianmu Li^1,*, Muhammad Zubair², Rakan A. Alsowail³, Muhammad Umair²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4041-4067, 2025, DOI:10.32604/cmc.2025.059832 - 06 March 2025

Abstract As the trend to use the latest machine learning models to automate requirements engineering processes continues, security requirements classification is tuning into the most researched field in the software engineering community. Previous literature studies have proposed numerous models for the classification of security requirements. However, adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning algorithms. Moreover, most of the researchers focus only on the classification of requirements with security keywords. They did not consider other nonfunctional requirements (NFR) directly or… More >