Md Shakhawat Hossain^1,2,#,*, Md Sahilur Rahman^2,#, Munim Ahmed², Anowar Hussen³, Zahid Ullah⁴, Mona Jamjoom⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3193-3215, 2025, DOI:10.32604/cmc.2025.065230 - 03 July 2025

Abstract One in every eight men in the US is diagnosed with prostate cancer, making it the most common cancer in men. Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate cancer patients. Traditionally, urological pathologists perform the grading by scoring the morphological pattern, known as the Gleason pattern, in histopathology images. However, this manual grading is highly subjective, suffers intra- and inter-pathologist variability and lacks reproducibility. An automated grading system could be more efficient, with no subjectivity and higher accuracy and reproducibility. Automated methods presented previously… More >

Maheen Anwar¹, Saima Farhan¹, Yasin Ul Haq², Waqar Azeem³, Muhammad Ilyas⁴, Razvan Cristian Voicu^5,*, Muhammad Hassan Tanveer⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3477-3502, 2025, DOI:10.32604/cmc.2025.065141 - 03 July 2025

Abstract Glaucoma, a chronic eye disease affecting millions worldwide, poses a substantial threat to eyesight and can result in permanent vision loss if left untreated. Manual identification of glaucoma is a complicated and time-consuming practice requiring specialized expertise and results may be subjective. To address these challenges, this research proposes a computer-aided diagnosis (CAD) approach using Artificial Intelligence (AI) techniques for binary and multiclass classification of glaucoma stages. An ensemble fusion mechanism that combines the outputs of three pre-trained convolutional neural network (ConvNet) models–ResNet-50, VGG-16, and InceptionV3 is utilized in this paper. This fusion technique enhances… More >

Khadija Bouzaachane^1,*, El Mahdi El Guarmah², Abdullah M. Alnajim³, Sheroz Khan⁴

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2391-2410, 2025, DOI:10.32604/cmc.2025.065031 - 03 July 2025

Abstract The rapid increase in the number of Internet of Things (IoT) devices, coupled with a rise in sophisticated cyberattacks, demands robust intrusion detection systems. This study presents a holistic, intelligent intrusion detection system. It uses a combined method that integrates machine learning (ML) and deep learning (DL) techniques to improve the protection of contemporary information technology (IT) systems. Unlike traditional signature-based or single-model methods, this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification. This combination provides a more nuanced and adaptable defense. The research utilizes the NF-UQ-NIDS-v2… More >

Liang Mu¹, Yurui Kang¹, Zixu Yan¹, Guangyu Zhu^2,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2495-2512, 2025, DOI:10.32604/cmc.2025.063208 - 03 July 2025

Abstract The accurate prediction and analysis of emergencies in Urban Rail Transit Systems (URTS) are essential for the development of effective early warning and prevention mechanisms. This study presents an integrated perception model designed to predict emergencies and analyze their causes based on historical unstructured emergency data. To address issues related to data structuredness and missing values, we employed label encoding and an Elastic Net Regularization-based Generative Adversarial Interpolation Network (ER-GAIN) for data structuring and imputation. Additionally, to mitigate the impact of imbalanced data on the predictive performance of emergencies, we introduced an Adaptive Boosting Ensemble… More >

Tianwen Zhao¹, Guoqing Chen^2,3, Cong Pang⁴, Piyapatr Busababodhin^3,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2883-2917, 2025, DOI:10.32604/cmes.2025.066442 - 30 June 2025

Abstract Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43%… More >

Rongwei Yu¹, Jie Yin^1,*, Jingyi Xiang¹, Qiyun Shao², Lina Wang¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 365-391, 2025, DOI:10.32604/cmc.2025.064438 - 09 June 2025

Abstract With the emergence of new attack techniques, traffic classifiers usually fail to maintain the expected performance in real-world network environments. In order to have sufficient generalizability to deal with unknown malicious samples, they require a large number of new samples for retraining. Considering the cost of data collection and labeling, data augmentation is an ideal solution. We propose an optimized noise-based traffic data augmentation system, ONTDAS. The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise. The noise is injected into the original samples for data augmentation. Then, an More >

Ruibo Wei^1,2, Yao Kong³, Mengzhao Li^1,2, Feng Liu^1,2,*, Fang Cheng^4,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1507-1528, 2025, DOI:10.32604/cmc.2025.063722 - 09 June 2025

Abstract To address uncertainties in satellite orbit error prediction, this study proposes a novel ensemble learning-based orbit prediction method specifically designed for the BeiDou navigation satellite system (BDS). Building on ephemeris data and perturbation corrections, two new models are proposed: attention-enhanced BPNN (AEBP) and Transformer-ResNet-BiLSTM (TR-BiLSTM). These models effectively capture both local and global dependencies in satellite orbit data. To further enhance prediction accuracy and stability, the outputs of these two models were integrated using the gradient boosting decision tree (GBDT) ensemble learning method, which was optimized through a grid search. The main contribution of this More >

Karim Gasmi¹, Olfa Hrizi^1,*, Najib Ben Aoun^2,3, Ibrahim Alrashdi¹, Ali Alqazzaz⁴, Omer Hamid⁵, Mohamed O. Altaieb¹, Alameen E. M. Abdalrahman¹, Lassaad Ben Ammar⁶, Manel Mrabet⁶, Omrane Necibi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2459-2489, 2025, DOI:10.32604/cmes.2025.065817 - 30 May 2025

Abstract The potential applications of multimodal physiological signals in healthcare, pain monitoring, and clinical decision support systems have garnered significant attention in biomedical research. Subjective self-reporting is the foundation of conventional pain assessment methods, which may be unreliable. Deep learning is a promising alternative to resolve this limitation through automated pain classification. This paper proposes an ensemble deep-learning framework for pain assessment. The framework makes use of features collected from electromyography (EMG), skin conductance level (SCL), and electrocardiography (ECG) signals. We integrate Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Bidirectional Gated Recurrent Units (BiGRU),… More >

Mohammed Al-Jabbar¹, Mohammed Alshahrani^1,*, Ebrahim Mohammed Senan^2,3, Ibrahim Abunadi⁴, Sultan Ahmed Almalki¹, Eman A Alshari^3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2429-2457, 2025, DOI:10.32604/cmes.2025.063938 - 30 May 2025

Abstract Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by tremors, rigidity, and decreased movement. PD poses risks to individuals’ lives and independence. Early detection of PD is essential because it allows timely intervention, which can slow disease progression and improve outcomes. Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD. In addition, the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis. Artificial intelligence (AI) techniques, especially deep and automated learning models, provide promising… More >

Jun Wang^1,2, Chaoren Ge^1,2, Yihong Li^1,2, Huimin Zhao^1,2, Qiang Fu^1,2,*, Kerang Cao^1,2, Hoekyung Jung^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5129-5153, 2025, DOI:10.32604/cmc.2025.062094 - 19 May 2025

Abstract Network Intrusion Detection System (NIDS) detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments. To improve the detection capability of minority-class attacks, this study proposes an intrusion detection method based on a two-layer structure. The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic, majority class attacks, and merged minority class attacks. The second layer further segments the minority class attacks through Stacking ensemble learning. The datasets are selected from the generic network dataset CIC-IDS2017, NSL-KDD, and the… More >