Yahia Said^1,2,*, Yahya Alassaf³, Refka Ghodhbani⁴, Taoufik Saidani⁴, Olfa Ben Rhaiem⁵

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3005-3018, 2025, DOI:10.32604/cmc.2025.060928 - 17 February 2025

Abstract Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic… More >

Ibrar Afzal¹, Noor ul Amin^1,*, Zulfiqar Ahmad^1,*, Abdulmohsen Algarni²

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1377-1399, 2025, DOI:10.32604/cmc.2024.057755 - 03 January 2025

Abstract The deployment of the Internet of Things (IoT) with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses, smart cities, and smart transportation systems. Fog computing tackles a range of challenges, including processing, storage, bandwidth, latency, and reliability, by locally distributing secure information through end nodes. Consisting of endpoints, fog nodes, and back-end cloud infrastructure, it provides advanced capabilities beyond traditional cloud computing. In smart environments, particularly within smart city transportation systems, the abundance of devices and nodes poses significant challenges related… More >

Shorouq Alshawabkeh, Li Wu^*, Daojun Dong, Yao Cheng, Liping Li

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 561-577, 2025, DOI:10.32604/cmc.2024.057213 - 03 January 2025

Abstract Detecting pavement cracks is critical for road safety and infrastructure management. Traditional methods, relying on manual inspection and basic image processing, are time-consuming and prone to errors. Recent deep-learning (DL) methods automate crack detection, but many still struggle with variable crack patterns and environmental conditions. This study aims to address these limitations by introducing the MaskerTransformer, a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network (Mask R-CNN) with the global contextual awareness of Vision Transformer (ViT). The research focuses on leveraging the strengths of both architectures… More >

Khadija El Fellah^1,*, Ikram El Azami^2,*, Adil El Makrani², Habiba Bouijij³, Oussama El Azzouzy⁴

Computer Systems Science and Engineering, Vol.49, pp. 99-122, 2025, DOI:10.32604/csse.2024.057754 - 03 January 2025

Abstract The rapid growth of the automotive industry has raised significant concerns about the security of connected vehicles and their integrated supply chains, which are increasingly vulnerable to advanced cyber threats. Traditional authentication methods have proven insufficient, exposing systems to risks such as Sybil, Denial of Service (DoS), and Eclipse attacks. This study critically examines the limitations of current security protocols, focusing on authentication and data exchange vulnerabilities, and explores blockchain technology as a potential solution. Blockchain’s decentralized and cryptographically secure framework can significantly enhance Vehicle-to-Vehicle (V2V) communication, ensure data integrity, and enable transparent, immutable transactions More >

Tatyana P. Lyubimova¹, Sergey A. Plotnikov², Albert N. Sharifulin², Vladimir Ya. Modorskii², Sergey S. Neshev², Stanislav L. Kalyulin^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.12, pp. 2775-2788, 2024, DOI:10.32604/fdmp.2024.054498 - 23 December 2024

Abstract Transporting and storing hydrogen is a complex technological task. A typical problem relates to the need to minimize the strength of fluid motion and heat transfer near the walls of the container. In this work this problem is tackled numerically assuming an infinite cavity of pipe square cross-section, located in a constant external temperature gradient. In particular, a method based on the application of vibrations to suppress the gravitational convection mechanism is explored. A parametric investigation is conducted and the limits of applicability of the method for small Grashof numbers (10e4) are determined. It is More >

Yahia Said^1,*, Yahya Alassaf², Taoufik Saidani³, Refka Ghodhbani³, Olfa Ben Rhaiem⁴, Ali Ahmad Alalawi¹

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.058903 - 19 December 2024

Abstract The integration of Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITS) holds transformative potential for real-time traffic monitoring, a critical component of emerging smart city infrastructure. UAVs offer unique advantages over stationary traffic cameras, including greater flexibility in monitoring large and dynamic urban areas. However, detecting small, densely packed vehicles in UAV imagery remains a significant challenge due to occlusion, variations in lighting, and the complexity of urban landscapes. Conventional models often struggle with these issues, leading to inaccurate detections and reduced performance in practical applications. To address these challenges, this paper introduces CFEMNet,… More >

Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2161-2194, 2024, DOI:10.32604/cmc.2024.058888 - 18 November 2024

Abstract Forecasting travel demand requires a grasp of individual decision-making behavior. However, transport mode choice (TMC) is determined by personal and contextual factors that vary from person to person. Numerous characteristics have a substantial impact on travel behavior (TB), which makes it important to take into account while studying transport options. Traditional statistical techniques frequently presume linear correlations, but real-world data rarely follows these presumptions, which may make it harder to grasp the complex interactions. Thorough systematic review was conducted to examine how machine learning (ML) approaches might successfully capture nonlinear correlations that conventional methods may… More >

Pengfei Duan^1,*, Luling Li¹, Jianhui Liu¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.4, pp. 1-1, 2024, DOI:10.32604/icces.2024.012431

Abstract The supply of hydrogen-blended natural gas to civil and industrial users can assist downstream firm to achieve carbon emission reduction, and ensure energy security as an alternative gas source. This application mode has been widely concerned by urban gas enterprises. This paper focuses on the leakage problem of hydrogen-blended pipelines in utility tunnel due to corrosion and other reasons. Using dimensional analysis method, a model experiment is designed to verify that the three-dimensional compressible fluid model coupled with transport equations can effectively simulate the concentration change of hydrogen-blended natural gas after leakage in the utility… More >

Yijun Gao^1,2, Yong Wang^1,*, Qing Na¹, Jiawei Zhang¹, Aixiang Wu¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2443-2458, 2024, DOI:10.32604/fdmp.2024.051385 - 28 October 2024

Abstract Filling methods in the mining industry can maximize the recovery of mineral resources and protect the underground and surface environments. In recent years, such methods have been widely used in metal mines where pipeline transportation typically plays a decisive role in the safety and stability of the entire filling system. Because the filling slurry contains a large percentage of solid coarse particles, the involved pipeline is typically eroded and often damaged during such a process. A possible solution is the so-called nesting repair technology. In the present study, nesting a 127 mm outer diameter pipeline… More >

Fan Yang^1,2,*, Honggang Mi^1,2, Jian Wu^1,2, Qi Yang^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.11, pp. 2637-2656, 2024, DOI:10.32604/fdmp.2024.048574 - 28 October 2024

Abstract The gas production of deep coalbed methane wells in Linxing-Shenfu block decreases rapidly, the water output is high, the supporting effect is poor, the effective supporting fracture size is limited, and the migration mechanism of proppant in deep coal reservoir is not clear at present. To investigate the migration behavior of proppants in complex fractures during the volume reconstruction of deep coal and rock reservoirs, an optimization test on the conductivity of low-density proppants and simulations of proppant migration in complex fractures of deep coal reservoirs were conducted. The study systematically analyzed the impact of… More >