Kadriye Simsek Alan^*, Busra Senel Kahyaoglu

Journal on Artificial Intelligence, Vol.8, pp. 203-230, 2026, DOI:10.32604/jai.2026.078359 - 15 April 2026

Abstract Cardiovascular diseases remain one of the leading causes of mortality worldwide, making early and reliable diagnosis a critical challenge for modern healthcare systems. In this study, a systematic comparative performance analysis of widely used machine learning algorithms is conducted for the early detection of heart disease using tabular clinical data. Rather than proposing a novel model architecture, the primary objective is to provide a fair, reproducible, and clinically meaningful evaluation of commonly adopted classifiers under consistent experimental conditions. The Kaggle Heart Failure dataset is employed, and multiple machine learning models—including tuned Random Forest, tuned XGBoost,… More >

Yan-Hao Huang^*, Chung-Ming Kao

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079381 - 09 April 2026

Abstract Photovoltaic (PV) equivalent-circuit models are widely used for performance evaluation and diagnostics, but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape current–voltage (I–V) behavior. For nonlinear and strongly coupled PV models, conventional global sensitivity analysis can be computationally demanding and offer limited insight into effect direction and operating-point dependence. This study presents an method-oriented framework that integrates nature-inspired optimization with surrogate-based explainable global sensitivity analysis under a specified operating condition. The Starfish Optimization Algorithm (SFOA) is first used for parameter identification by searching for the optimal parameter set that… More >

Giuseppe Marannano^*, Antonino Cirello, Tommaso Ingrassia

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077884 - 09 April 2026

Abstract In response to the growing need for adaptive optimization algorithms capable of handling complex, multimodal, and high-dimensional search spaces, this paper introduces the Structured Random Cycle-guided Algorithm (SRCA). SRCA is not presented as a fundamentally new optimization paradigm, but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection. Based on a cycle-structured architecture, directional and stochastic search behaviors are dynamically selected at the individual level. The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies, enabling a coherent and adaptive balance between exploration and exploitation throughout More >

Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.073949 - 31 March 2026

Abstract The study of long-term pavement performance is a fundamental topic in the field of highway engineering. Through comprehensive and in-depth research on the pavement system, the previous scattered, one-sided, superficial, and perceptual knowledge and experience are summarized and sublimated into a systematic and complete engineering theory, thereby providing powerful guidance and assistance for the practice of pavement design, construction, maintenance, operation, and management. In this research, the mentoring system deployment technology for automatic monitoring is carried out for long-term pavement performance. By burying a variety of sensors in different parts of the road surface, base,… More >

Mojgan Mohammadi¹, Naser Khaji^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078945 - 30 March 2026

Abstract The failure of liquid storage tanks, one of the most critical infrastructure systems widely used, during severe earthquakes can have direct or indirect impacts on public safety. The significance of their safe performance even after destructive earthquakes and their potential for operational use underscores the necessity of appropriate seismic design. Hence, seismic isolation, specifically base isolation, has gained attention as a seismic control method to reduce damage to these infrastructures by increasing their vibration period. One prevalent type of seismic isolator used for tanks and other structures is the friction pendulum system (FPS) isolator. However,… More >

Peiying Zhang^1,2, Yihong Yu^1,2, Jia Luo^3,4,*, Nguyen Gia Ba⁵, Lizhuang Tan^6,7, Lei Shi⁸

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076690 - 12 March 2026

Abstract Next-Generation Networks (NGNs) demand high resilience, dynamic adaptability, and efficient resource utilization to enable ubiquitous connectivity. In this context, the Space-Air-Ground Integrated Network (SAGIN) architecture is uniquely positioned to meet these requirements. However, conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics, such as its heterogeneous structure, dynamic topology, and constrained resources, leading to suboptimal performance under disruptions such as node failures or cyberattacks. To meet these demands for SAGIN, this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation. Methodologically, we define three core routing performance metrics,… More >

Tran Minh Bao¹, Kumar Shashvat², Nguyen Gia Nhu^3,*, Dac-Nhuong Le⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075161 - 12 March 2026

Abstract The sudden growth of harmful web pages, including spam and phishing URLs, poses a greater threat to global cybersecurity than ever before. These URLs are commonly utilised to trick people into divulging confidential details or to stealthily deploy malware. To address this issue, we aimed to assess the efficiency of popular machine learning and neural network models in identifying such harmful links. To serve our research needs, we employed two different datasets: the PhiUSIIL dataset, which is specifically designed to address phishing URL detection, and another dataset developed to uncover spam links by examining the… More >

Sumbul Azeem¹, Shazia Javed^1,*, Farheen Ibraheem², Uzma Bashir¹, Nazar Waheed³, Khursheed Aurangzeb⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.071977 - 12 March 2026

Abstract Data serves as the foundation for training and testing machine learning and artificial intelligence models. The most fundamental part of data is its attributes or features. The feature set size changes from one dataset to another. Only the relevant features contribute meaningfully to classification accuracy. The presence of irrelevant features reduces the system’s effectiveness. Classification performance often deteriorates on high-dimensional datasets due to the large search space. Thus, one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the… More >

Xuan Ruan¹, Han Yan², Donglin Hu¹, Min Zhang², Ying Li¹, Di Hai¹, Bo Yang^3,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071166 - 27 February 2026

Abstract An optimized volt-ampere reactive (VAR) control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators (SVGs). The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions, and quantifies SVG high-speed compensation capability, enabling seamless transition from localized VAR management to a globally coordinated strategy. An enhanced adaptive gain-sharing knowledge optimizer (AGSK-SD) integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions, renewable source reactive-power set-points, and SVG output.… More >

Borja Bordel Sánchez^*, Ramón Alcarria, Tomás Robles

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.072603 - 26 February 2026

Abstract In this paper, we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks. This system enables end nodes to select the optimum time and scheme to transmit private data safely. In 6G dynamic heterogeneous infrastructures, unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy. Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service (QoS). As the transport network is built of ad hoc nodes, there is no guarantee about their trustworthiness or behavior, and transversal functionalities are delegated to the extreme nodes. However, More >