Rıdvan Yayla, Hakan Üçgün^*, Onur Ali Korkmaz

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4055-4087, 2025, DOI:10.32604/cmes.2025.072703 - 23 December 2025

Abstract Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a… More > Graphic Abstract

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 >

Mircea Ţălu^1,2,*

Journal of Cyber Security, Vol.7, pp. 565-587, 2025, DOI:10.32604/jcs.2025.073690 - 11 December 2025

Abstract The rapid proliferation of Internet of Things (IoT) devices has intensified the demand for cryptographic solutions that balance security, performance, and resource efficiency. However, existing studies often focus on isolated algorithmic families, lacking a comprehensive structural and experimental comparison across diverse lightweight cryptographic designs. This study addresses that gap by providing an integrated analysis of modern lightweight cryptographic algorithms spanning six structural classes—Substitution–Permutation Network (SPN), Feistel Network (FN), Generalized Feistel Network (GFN), Addition–Rotation–XOR (ARX), Nonlinear Feedback Shift Register (NLFSR), and Hybrid models—evaluated on resource-constrained IoT platforms. The key contributions include: (i) establishing a unified benchmarking… More >

Yu Tao¹, Tian Yin², Yang Jie^1,*

Journal on Artificial Intelligence, Vol.7, pp. 549-570, 2025, DOI:10.32604/jai.2025.072727 - 01 December 2025

Abstract To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units (IMU), this paper proposes a multi-sensor fusion attitude estimation method based on an improved Error-State Kalman Filter (ESKF). Several adaptive mechanisms are introduced within the standard ESKF framework: first, the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions; second, the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time, mitigating disturbances caused by external accelerations and magnetic fields. Additionally,… More >

Shaojian Zou^1,#, Lipeng Zhang^2,#, Xiang Chen^3,#, Zhuomin Wang², Xinhui Zhu², Dandong Luo⁴, Shengxun Mao^2,*, Zhen Zong^2,*

Oncology Research, Vol.33, No.12, pp. 3753-3788, 2025, DOI:10.32604/or.2025.067586 - 27 November 2025

Abstract Traditional cancer therapies have limitations like poor efficacy on advanced tumors, healthy tissue damage, side effects, and drug resistance, creating an urgent need for new strategies. Hydrogels have good biocompatibility and controlled release, while extracellular vesicles (EVs) enable targeting and bioactive transport. This review systematically summarizes hydrogels and EVs, focusing on the construction of hydrogel-EV delivery system, key influencing factors, drug delivery mechanisms, and tumor therapy apps, clarifying their synergies. The system overcomes single-carrier flaws, construction methods/key factors affect performance, preclinical studies have confirmed efficacy in multiple therapies, but large-scale production and in vivo stability challenges More >

Muflih Alhazmi¹, Waqar Ul Hassan², Saba Shaheen³, Mohammed M. A. Almazah⁴, Azmat Ullah Khan Niazi^3,*, Nafisa A. Albasheir⁵, Ameni Gargouri⁶, Naveed Iqbal⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1903-1940, 2025, DOI:10.32604/cmes.2025.072973 - 26 November 2025

Abstract This paper proposes a model-based control framework for vehicle platooning systems with second-order nonlinear dynamics operating over switching signed networks, time-varying delays, and deception attacks. The study includes two configurations: a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus (FNTBC) and Fixed-Time Bipartite Consensus (FXTBC), and a leader—follower structure ensuring structural balance and robustness against deceptive signals. In the leaderless model, a bipartite controller based on impulsive control theory, gauge transformation, and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays. The FNTBC achieves finite-time convergence depending on initial More >

Bin Zhang¹, En-Cheng Liou^2,*, Yi-Chih Tung³, Muhammad Usman^2,4, Chiung-An Chen^2,4, Chao-Shun Yang^2,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2551-2571, 2025, DOI:10.32604/cmes.2025.072471 - 26 November 2025

Abstract Indoor localization is a fundamental requirement for future 6G Intelligent Sensing and Communication (ISAC) systems, enabling precise navigation in environments where Global Positioning System (GPS) signals are unavailable. Existing methods, such as map-based navigation or site-specific fingerprinting, often require intensive data collection and lack generalization capability across different buildings, thereby limiting scalability. This study proposes a cross-site, map-free indoor localization framework that uses low-frequency sub-1 GHz radio signals and a Transformer-based neural network for robust positioning without prior environmental knowledge. The Transformer’s self-attention mechanisms allow it to capture spatial correlations among anchor nodes, facilitating accurate… More >

Meshari D. Alanazi¹, Gehan Elsayed^2,*, Turki M. Alanazi³, Anis Sahbani⁴, Amr Yousef^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2277-2309, 2025, DOI:10.32604/cmes.2025.070726 - 26 November 2025

Abstract Traffic congestion plays a significant role in intelligent transportation systems (ITS) due to rapid urbanization and increased vehicle concentration. The congestion is dependent on multiple factors, such as limited road occupancy and vehicle density. Therefore, the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment. Conventional prediction systems face difficulties in identifying highly congested areas, which leads to reduced prediction accuracy. The problem is addressed by integrating Graph Neural Networks (GNN) with the Lion Swarm Optimization (LSO) framework to tackle the congestion prediction problem. Initially, the traffic information is… More >

Mona Almofarreh¹, Amnah Alshahrani², Nouf Helal Alharbi³, Ahmed Omer Ahmed⁴, Hussain Alshahrani⁵, Abdulrahman Alzahrani^6,*, Mohammed Mujib Alshahrani⁷, Asma A. Alhashmi⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2631-2656, 2025, DOI:10.32604/cmes.2025.070545 - 26 November 2025

Abstract Zero-day attacks use unknown vulnerabilities that prevent being identified by cybersecurity detection tools. This study indicates that zero-day attacks have a significant impact on computer security. A conventional signature-based detection algorithm is not efficient at recognizing zero-day attacks, as the signatures of zero-day attacks are usually not previously accessible. A machine learning (ML)-based detection algorithm is proficient in capturing statistical features of attacks and, therefore, optimistic for zero-day attack detection. ML and deep learning (DL) are employed for designing intrusion detection systems. The improvement of absolute varieties of novel cyberattacks poses significant challenges for IDS… More >

Tamara Zhukabayeva^1,2, Zulfiqar Ahmad^1,3,*, Nurbolat Tasbolatuly⁴, Makpal Zhartybayeva¹, Yerik Mardenov^1,4, Nurdaulet Karabayev^1,*, Dilaram Baumuratova^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2573-2599, 2025, DOI:10.32604/cmes.2025.070426 - 26 November 2025

Abstract The Industrial Internet of Things (IIoT), combined with the Cyber-Physical Systems (CPS), is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems. There is a lack of explainability, challenges with imbalanced attack classes, and limited consideration of practical edge–cloud deployment strategies in prior works. In the proposed study, we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations (SHAP)-based Explainable AI (XAI) to attack detection and classification in IIoT-CPS settings. It includes not only unsupervised clustering (K-Means and DBSCAN) to extract… More >