Abeer Alhuzali^1,*, Qamar Al-Qahtani¹, Asmaa Niyazi¹, Lama Alshehri¹, Fatemah Alharbi²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069491 - 10 November 2025

Abstract The surge in smishing attacks underscores the urgent need for robust, real-time detection systems powered by advanced deep learning models. This paper introduces PhishNet, a novel ensemble learning framework that integrates transformer-based models (RoBERTa) and large language models (LLMs) (GPT-OSS 120B, LLaMA3.3 70B, and Qwen3 32B) to enhance smishing detection performance significantly. To mitigate class imbalance, we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques. Our system employs a dual-layer voting mechanism: weighted majority voting among LLMs and a final ensemble vote to classify messages as ham, spam, or smishing. Experimental More >

Amjad Rehman^1,*, Tanzila Saba¹, Mona M. Jamjoom², Shaha Al-Otaibi³, Muhammad I. Khan¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068958 - 10 November 2025

Abstract Modern intrusion detection systems (MIDS) face persistent challenges in coping with the rapid evolution of cyber threats, high-volume network traffic, and imbalanced datasets. Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively. This study introduces an advanced, explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets, which reflects real-world network behavior through a blend of normal and diverse attack classes. The methodology begins with sophisticated data preprocessing, incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions, ensuring standardized and model-ready inputs.… More >

Puneetha B. H.^1,*, Manoj Kumar M. V.^2,*, Prashanth B. S.², Piyush Kumar Pareek³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-33, 2026, DOI:10.32604/cmc.2025.067706 - 10 November 2025

Abstract Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational, organizational, or regulatory factors. These changes, referred to as incremental concept drift, gradually alter the behavior or structure of processes, making their detection and localization a challenging task. Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift, particularly from a control-flow perspective. The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs, with a… More >

Huihui Yang¹, Saif Ur Rehman Khan^2,*, Omair Bilal², Chao Chen^1,*, Ming Zhao²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2401-2434, 2025, DOI:10.32604/cmes.2025.072148 - 26 November 2025

Abstract Detecting Alzheimer’s disease is essential for patient care, as an accurate diagnosis influences treatment options. Classifying dementia from non-dementia in brain MRIs is challenging due to features such as hippocampal atrophy, while manual diagnosis is susceptible to error. Optimal computer-aided diagnosis (CAD) systems are essential for improving accuracy and reducing misclassification risks. This study proposes an optimized ensemble method (CEOE-Net) that initiates with the selection of pre-trained models, including DenseNet121, ResNet50V2, and ResNet152V2 for unique feature extraction. Each selected model is enhanced with the inclusion of a channel attention (CA) block to improve the feature… More >

Ugur Madran, Saeed Mian Qaisar^*, Duygu Soyoglu

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1969-1992, 2025, DOI:10.32604/cmes.2025.072024 - 26 November 2025

Abstract Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids. It offers substantial benefits across social, environmental, and economic dimensions. To effectively realize these advantages, a fine-grained collection and analysis of smart meter data is essential. However, the high dimensionality and volume of such time-series present significant challenges, including increased computational load, data transmission overhead, latency, and complexity in real-time analysis. This study proposes a novel, computationally efficient framework for feature extraction and selection tailored to smart meter time-series data. The approach begins with an extensive offline analysis, where features are… More >

Yazeed Alkhrijah¹, Marwa Fahim², Syed Muhammad Usman³, Qasim Mehmood³, Shehzad Khalid^4,5,*, Mohamad A. Alawad¹, Haya Aldossary⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2339-2355, 2025, DOI:10.32604/cmes.2025.071512 - 26 November 2025

Abstract Atrial Fibrillation (AF) is a cardiac disorder characterized by irregular heart rhythms, typically diagnosed using Electrocardiogram (ECG) signals. In remote regions with limited healthcare personnel, automated AF detection is extremely important. Although recent studies have explored various machine learning and deep learning approaches, challenges such as signal noise and subtle variations between AF and other cardiac rhythms continue to hinder accurate classification. In this study, we propose a novel framework that integrates robust preprocessing, comprehensive feature extraction, and an ensemble classification strategy. In the first step, ECG signals are divided into equal-sized segments using a… More >

Mudasir Ali¹, Muhammad Faheem Mushtaq², Urooj Akram², Nagwan Abdel Samee^3,*, Mona M. Jamjoom⁴, Imran Ashraf^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1863-1901, 2025, DOI:10.32604/cmes.2025.070389 - 26 November 2025

Abstract Differentiating between regular and abnormal noises in machine-generated sounds is a crucial but difficult problem. For accurate audio signal classification, suitable and efficient techniques are needed, particularly machine learning approaches for automated classification. Due to the dynamic and diverse representative characteristics of audio data, the probability of achieving high classification accuracy is relatively low and requires further research efforts. This study proposes an ensemble model based on the LeNet and hierarchical attention mechanism (HAM) models with MFCC features to enhance the models’ capacity to handle bias. Additionally, CNNs, bidirectional LSTM (BiLSTM), CRNN, LSTM, capsule network More >

Adeel Akram¹, Tallha Akram², Ghada Atteia^3,*, Ayman Qahmash⁴, Sultan Alanazi⁵, Faisal Mohammad Alotaibi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2311-2337, 2025, DOI:10.32604/cmes.2025.069697 - 26 November 2025

Abstract Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score… More >

Alex Segura^*, Joaquim Meléndez

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 649-672, 2025, DOI:10.32604/cmes.2025.070954 - 30 October 2025

Abstract The increasing use of renewable energy sources, combined with the increase in electricity demand, has highlighted the importance of energy flexibility management in electrical grids. Energy flexibility is the capacity that generators and consumers have to change production and/or consumption to support grid operation, ensuring the stability and efficiency of the grid. Thus, Local Flexibility Markets (LFMs) are market-oriented mechanisms operated at different time horizons that support flexibility provision and trading at the distribution level, where the Distribution System Operators (DSOs) are the flexibility-demanding actors, and prosumers are the flexibility providers. This paper investigates the… More >

Nimra Bari¹, Tahir Saleem², Munam Shah³, Abdulmohsen Algarni⁴, Asma Patel^5,*, Insaf Ullah^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1167-1187, 2025, DOI:10.32604/cmes.2025.070311 - 30 October 2025

Abstract Today, phishing is an online attack designed to obtain sensitive information such as credit card and bank account numbers, passwords, and usernames. We can find several anti-phishing solutions, such as heuristic detection, virtual similarity detection, black and white lists, and machine learning (ML). However, phishing attempts remain a problem, and establishing an effective anti-phishing strategy is a work in progress. Furthermore, while most anti-phishing solutions achieve the highest levels of accuracy on a given dataset, their methods suffer from an increased number of false positives. These methods are ineffective against zero-hour attacks. Phishing sites with… More >