Khalid Haseeb¹, Imran Qureshi^2,*, Naveed Abbas¹, Muhammad Ali³, Muhammad Arif Shah⁴, Qaisar Abbas²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4349-4362, 2025, DOI:10.32604/cmes.2025.072326 - 23 December 2025

Abstract The rapid evolution of smart cities has led to the deployment of Cyber-Physical IoT Systems (CPS-IoT) for real-time monitoring, intelligent decision-making, and efficient resource management, particularly in intelligent transportation and vehicular networks. Edge intelligence plays a crucial role in these systems by enabling low-latency processing and localized optimization for dynamic, data-intensive, and vehicular environments. However, challenges such as high computational overhead, uneven load distribution, and inefficient utilization of communication resources significantly hinder scalability and responsiveness. Our research presents a robust framework that integrates artificial intelligence and edge-level traffic prediction for CPS-IoT systems. Distributed computing for More >

Fatemeh Mobasheri¹, Masoud Hosseinpoor^1,*, Ammar Yahia^1,2, Farhad Pourkamali-Anaraki³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3309-3347, 2025, DOI:10.32604/cmes.2025.072271 - 23 December 2025

Abstract Self-consolidating concrete (SCC) is an important innovation in concrete technology due to its superior properties. However, predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes. This study combines machine learning (ML) models with conformal prediction (CP) to address these issues, offering prediction intervals that quantify uncertainty and reliability. A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods, including Random Forest (RF), Gradient Boosting Regressor (GBR), Extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM), along with CP techniques, More >

Jeongsu Park¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3913-3948, 2025, DOI:10.32604/cmes.2025.072261 - 23 December 2025

Abstract Time series anomaly detection is critical in domains such as manufacturing, finance, and cybersecurity. Recent generative AI models, particularly Transformer- and Autoencoder-based architectures, show strong accuracy but their robustness under noisy conditions is less understood. This study evaluates three representative models—AnomalyTransformer, TranAD, and USAD—on the Server Machine Dataset (SMD) and cross-domain benchmarks including the Soil Moisture Active Passive (SMAP) dataset, the Mars Science Laboratory (MSL) dataset, and the Secure Water Treatment (SWaT) testbed. Seven noise settings (five canonical, two mixed) at multiple intensities are tested under fixed clean-data training, with variations in window, stride, and More > Graphic Abstract

Muhammad Saad Farooqui¹, Aizaz Ahmad Khattak², Bakri Hossain Awaji³, Nazik Alturki⁴, Noha Alnazzawi⁵, Muhammad Hanif^6,*, Muhammad Shahbaz Khan²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4395-4439, 2025, DOI:10.32604/cmes.2025.072023 - 23 December 2025

Abstract Quality of Service (QoS) assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service (DDoS), spoofing, and botnet intrusions. This paper presents AutoSHARC, a feedback-driven, explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier. AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones, followed by post-hoc SHAP-guided retraining to refine feature importance, forming a feedback loop where only the most impactful attributes are More >

Mohamed Kouki¹, Nahid Osman², Mona Gafar³, Ragab A. El-Sehiemy^4,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3783-3811, 2025, DOI:10.32604/cmes.2025.071964 - 23 December 2025

Abstract The growing integration of nondispatchable renewable energy sources (PV, wind) and the need to cut CO2 emissions make energy management crucial. Microgrids provide a framework for RES integration but face challenges from intermittency, fluctuating loads, cost optimization, and uncertainty in real-time balancing. Accurate short-term forecasting of solar generation and demand is vital for reliable and sustainable operation. While stochastic and machine learning methods are used, they struggle with limited data, complex temporal patterns, and scalability. Key challenges include capturing seasonal to weekly variations and modeling sudden fluctuations in generation and consumption. To address… More >

Shuoting Xiao^1,*, Nikita Igorevich Fomin¹, Kirill Anatolyevich Khvostunkov², Chong Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2821-2847, 2025, DOI:10.32604/cmes.2025.071961 - 23 December 2025

Abstract Precast concrete structures have gained popularity due to their advantages. However, the seismic performance of their connection joints remains an area of ongoing research and improvement. Grouted Sleeve Connection (GSC) offers a solution for connecting reinforcements in precast components, but their vulnerability to internal defects, such as construction errors and material variability, can significantly impact performance. This article presents a finite element analysis (FEA) to evaluate the impact of internal grouting defects in GSC on the structural performance of precast reinforced concrete columns. Four finite element models representing GSC with varying degrees of defects were… More > Graphic Abstract

Michał Guminiak¹, Marcin Kamiński^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3133-3163, 2025, DOI:10.32604/cmes.2025.071887 - 23 December 2025

Abstract The analysis of the dynamics of surface girders is of great importance in the design of engineering structures such as steel welded bridge plane girders or concrete plate-column structures. This work is an extension of the classical deterministic problem of free vibrations of thin (Kirchhoff) plates. The main aim of this work is the study of stochastic eigenvibrations of thin (Kirchhoff) elastic plates resting on internal continuous and column supports by the Boundary Element Method (BEM). This work is a continuation of previous research related to the random approach in plate analysis using the BEM.… More >

Lijuan Zhang¹, Yu Zhou², Jiawei Tian^3,*, Fupei Guo⁴, Xiang Zhang⁴, Bo Yang^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3949-3968, 2025, DOI:10.32604/cmes.2025.071869 - 23 December 2025

Abstract In endoscopic surgery, the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces from endoscopic image sequences. To address these challenges, we propose a novel Hybrid Triangular Matching (HTM) modeling framework for soft tissue feature tracking. Specifically, HTM constructs a geometric model of the detected blobs on the soft tissue surface by applying the Watershed algorithm for blob detection and integrating the Delaunay triangulation with a newly designed triangle search segmentation algorithm. By leveraging barycentric coordinate theory, More >

Tanvir Fatima Naik Bukht^1,2, Yanfeng Wu¹, Nouf Abdullah Almujally³, Shuoa S. AItarbi⁴, Hameedur Rahman², Ahmad Jalal^2,5,*, Hui Liu^1,6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4013-4036, 2025, DOI:10.32604/cmes.2025.071850 - 23 December 2025

Abstract Human activity recognition (HAR) is crucial in fields like robotics, surveillance, and healthcare, enabling systems to understand and respond to human actions. Current models often struggle with complex datasets, making accurate recognition challenging. This study proposes a quantum-integrated Convolutional Neural Network (QI-CNN) to enhance HAR performance. The traditional models demonstrate weak performance in transferring learned knowledge between diverse complex data collections, including D3D-HOI and Sysu 3D HOI. HAR requires better extraction models and techniques that must address current challenges to achieve improved accuracy and scalability. The model aims to enhance HAR task performance by combining… More >

Deema Alzamil^1,2,*, Bader Alkhamees², Mohammad Mehedi Hassan^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2971-3027, 2025, DOI:10.32604/cmes.2025.070867 - 23 December 2025

Abstract Breast cancer diagnosis relies heavily on many kinds of information from diverse sources—like mammogram images, ultrasound scans, patient records, and genetic tests—but most AI tools look at only one of these at a time, which limits their ability to produce accurate and comprehensive decisions. In recent years, multimodal learning has emerged, enabling the integration of heterogeneous data to improve performance and diagnostic accuracy. However, doctors cannot always see how or why these AI tools make their choices, which is a significant bottleneck in their reliability, along with adoption in clinical settings. Hence, people are adding… More >