Darlington Chigozie Okeke^*

Journal of Cyber Security, Vol.8, pp. 319-356, 2026, DOI:10.32604/jcs.2026.080477 - 08 June 2026

Abstract Background: The evolution of modern networked systems in complexity, volume, and diversity has markedly increased the cyber-attack area. Conventional signature-based intrusion detection systems (IDS) will no longer be adequate for identifying advanced threats. A data-driven, adaptive approach that can identify malicious network activity is provided by machine learning (ML) techniques. This review aims to study, compare, and analyze ML-based approaches in IDS and improve the security defense mechanism. Methods: This systematic review followed the PRISMA 2020 guidelines. ML-based IDS peer-reviewed papers were identified from five scientific databases. Abstracts, full texts, and titles were filtered using… More >

Quynh-Anh Thi Bui^1,*, Son Hoang Trinh¹, Maryam Sayadi², Reza Khanali³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081794 - 27 May 2026

Abstract Geopolymer concrete has attracted increasing attention as a lower-carbon alternative to ordinary Portland cement concrete because it can utilize aluminosilicate-rich industrial by-products while still achieving satisfactory mechanical performance. However, the 28-day compressive strength of steel fiber-reinforced geopolymer concrete (SFGPC) is governed by multiple interacting mixture variables, which makes reliable prediction difficult, especially for medium-sized experimental datasets. This study developed an interpretable deep-learning framework to predict the 28-day compressive strength (CS28) of SFGPC using an original experimental dataset of 189 mixtures produced under a consistent laboratory protocol in Vietnam. The dataset covered nine mixture variables, including… More >

Irfan Saif Ud Din¹, Imran Siddique^2,3,4,5, Zohaib Zahid¹, Muhammad Nadeem⁶, Ibrahim Alraddadi^2,*, Taha Radwan^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.081726 - 27 May 2026

Abstract This study examines the variable thermal conductivity and electroosmotic performance of Sutterby hybrid nanofluid (SBHNF) thin film flow over a stretched inclined sheet using an artificial neural network (ANN)-based on NARX (Multilayer Nonlinear Autoregressive Networks with Exogenous Inputs) multiple-layer backpropagation simulation with the Levenberg-Marquardt algorithm (LMA). AA7075 and AA7072 nanoparticles suspended in sodium alginate (SA) base fluid make up the hybrid nanofluid (HNF), which was selected due to its improved heat transfer properties and superior thermal conductivity. The model’s practical applicability is enhanced by melting heat, nonlinear thermal radiation, boundary slip, and Newtonian heating effects,… More >

Manal Mohammed AL-Tamimi^1,2,*, Siti Norul Huda Sheikh Abdullah^1,*, Mohammad Khatim Hasan¹, Mohammed Azmi Al-Betar^3,4, Maw Shin Sim⁵, Abdulrahman Mohammed AL-Tamimi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079891 - 27 May 2026

Abstract Pan-cancer survival prediction remains a major challenge in personalized oncology due to profound tumor heterogeneity and the complexity of high-dimensional molecular data. Diverse risk profiles across cancer types and noisy, sparse features hinder deep learning models from capturing robust prognostic patterns. Prior pan-cancer studies predominantly focus on multimodal integration or unimodal gene expression analysis, leaving other informative modalities such as Copy Number Variation (CNV) and miRNA expression underexplored. We introduce a new formulation of mixture-of-experts (MoE) survival modeling that recasts expert assignment as a clinically interpretable risk-space decomposition problem. The proposed framework, CoxGuided-SE, constructs an… More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

Ghadi Almaktoom, Suliman Aladhadh, Salim El Khediri^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078774 - 27 May 2026

Abstract The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy. The primary focus of the research is on detecting phishing attacks, with no interest in how the data is processed. Attackers use fraudulent methods to obtain valuable, confidential information, resulting in billions of dollars in financial losses for enterprises. In our review, we examined the methods used in phishing-detection studies. We concluded that the two main sections, centralized and decentralized methods, were the centralized ones, which aggregate data in a central server and thus violate data protection regulations, such as GDPR.… More >

Kalim Sattar¹, Malik Muhammad Saad Missen², Syeda Zoupash Zahra^1,3, Najia Saher⁴, Rab Nawaz Bashir^3,5,6,*, Oumaima Saidani⁷, Shahid Kamal⁵, Muhammad I. Khan⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078569 - 27 May 2026

Abstract Tropical Cyclone (TC) genesis forecasting is an important aspect of early warning systems, as it allows the adoption of early warnings and mitigation plans. However, existing methods often rely on binary classification or fail to capture the complex spatio-temporal dependencies that govern TC formation. To address this limitation, this study introduces STAG-Net, a novel Spatio-Temporal Attention-Gated Network designed to directly predict the geographical coordinates of TC genesis. The model uses multivariate variables of meteorological factors such as u-wind, v-wind, relative humidity, temperature, and large-scale dynamic features using a Convolutional Neural Network (CNN), Gated Recurrent Units… More >

Amel Ksibi^1,*, Manel Ayadi¹, Hela Elmannai², Monia Hamdi², Ala Saleh Alluhaidan¹, Imen Ksibi³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078347 - 27 May 2026

Abstract Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention. Traditional clinical assessments rely on subjective observations, often missing minor motor abnormalities until they become severe, typically after 12 months of age. This article presents a novel deep learning model, TransCP-Net (Transformer-based Cerebral Palsy Network), designed for early detection of infant cerebral palsy through spatiotemporal pose representation learning. The architecture employs hierarchical spatial and temporal attention to analyze complex motion patterns in video sequences, integrating multi-modal data for improved accuracy. TransCP-Net incorporates specialized preprocessing, including More >

Oluwatosin Ahmed Amodu¹, Zurina Mohd Hanapi^1,*, Chedia Jarray², Huda Althumali³, Faten A. Saif ⁴, Raja Azlina Raja Mahmood¹, Mohammed Sani Adam⁵, Nor Fadzilah Abdullah⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.077054 - 27 May 2026

Abstract Non-terrestrial networks (NTNs)—including UAVs, HAPs, and satellite systems—are rapidly becoming key enablers of wide-area, resilient connectivity for large-scale IoT applications. As these platforms integrate with terrestrial networks to form space–air–ground architectures, optimization challenges related to trajectory, resource management, energy efficiency, and security become increasingly complex. Machine learning (ML) has emerged as a central tool for addressing these challenges by enabling adaptive, data-driven decision-making under uncertainty. This survey presents an optimization-centric review of ML-based NTN-assisted IoT systems focusing on aspect-specific datasets. Using a structured methodology involving dataset curation, keyword filtering, metadata analysis, and citation-based paper selection,… More >

Abdulrahman Dira Khalaf^1,2,*, Hazlina Hamdan^1,*, Alfian Abdul Halin¹, Noridayu Manshor¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.075537 - 27 May 2026

Abstract Current automated lesion segmentation methods have limited success, particularly for segmenting small, irregular, or heterogeneous lesions. Moreover, such models require significant computational power, which restricts their scalability and clinical application. To overcome these limitations, a lightweight LANET, which is a layer-attention network based on an encoder–decoder deep-learning architecture, has the explicit goal of increasing the segmentation performance and computational efficiency. The LANET is coupled with three new modules: (i) an attention module that includes a depthwise separable convolution operator to reduce the number of parameters, (ii) a custom attention mechanism, and (iii) an atrous spatial… More > Graphic Abstract