Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240 - 18 July 2024

Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

Amel Ali Alhussan¹, Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,*, Marwa M. Eid^2,3, Abdelhameed Ibrahim⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1163-1177, 2024, DOI:10.32604/cmc.2024.049001 - 18 July 2024

Abstract To reduce the negative effects that conventional modes of transportation have on the environment, researchers are working to increase the use of electric vehicles. The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge. The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue. Nevertheless, the powering of these terminals presents challenges because of the high energy requirements, which may influence the quality of service. Modelling the maximum hourly capacity of each station based on… More >

Tengda Li, Gang Wang, Qiang Fu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2559-2586, 2024, DOI:10.32604/cmes.2024.052039 - 08 July 2024

Abstract Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation (DTA) and high-dimensional decision space with single agent, this paper combines the deep reinforcement learning (DRL) theory and an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG-D2) algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA. The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and considers the introduction of a double noise mechanism to increase the action exploration… More >

Rui Wang¹, Zhihui Liu^2,*, Hongdi Liu³, Baozhong Su⁴, Chuanyi Ma⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3035-3049, 2024, DOI:10.32604/cmes.2024.050806 - 08 July 2024

Abstract In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model’s receptive field is enhanced by introducing a multi-branch More >

Madiha Hameed^1,3, Aneela Zameer^1,*, Muhammad Asif Zahoor Raja²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2131-2164, 2024, DOI:10.32604/cmes.2024.050124 - 08 July 2024

Abstract The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially in skin cancer detection. These datasets contain tens of thousands of dermoscopic photographs, each accompanied by gold-standard lesion diagnosis metadata. Annual challenges associated with ISIC datasets have spurred significant advancements, with research papers reporting metrics surpassing those of human experts. Skin cancers are categorized into melanoma and non-melanoma types, with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated. This paper aims to address challenges in skin cancer detection… More >

Hongchi Liu¹, Xing Deng^1,*, Haijian Shao^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737 - 08 July 2024

Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More > Graphic Abstract

Ziqiao Zhou¹, Tianyang Zhou^1,*, Jinghao Xu², Junhu Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2613-2634, 2024, DOI:10.32604/cmes.2023.028553 - 08 July 2024

Abstract Intelligent penetration testing is of great significance for the improvement of the security of information systems, and the critical issue is the planning of penetration test paths. In view of the difficulty for attackers to obtain complete network information in realistic network scenarios, Reinforcement Learning (RL) is a promising solution to discover the optimal penetration path under incomplete information about the target network. Existing RL-based methods are challenged by the sizeable discrete action space, which leads to difficulties in the convergence. Moreover, most methods still rely on experts’ knowledge. To address these issues, this paper… More >

Yasser Jebbar^1,2,*, Fadhil Fluiful², Wisam Khudhayer³

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1279-1297, 2024, DOI:10.32604/fdmp.2024.050753 - 27 June 2024

Abstract This study includes an experimental and numerical analysis of the performances of a parabolic trough collector (PTC) with and without cylindrical turbulators. The PTC is designed with dimensions of 2.00 m in length and 1.00 m in width. The related reflector is made of lined sheets of aluminum, and the tubes are made of stainless steel used for the absorption of heat. They have an outer diameter of 0.051 m and a wall thickness of 0.002 m. Water, used as a heat transfer fluid (HTF), flows through the absorber tube at a mass flow rate… More >

Xiao Huang^1,2, Jin Yang^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1393-1406, 2024, DOI:10.32604/fdmp.2024.046360 - 27 June 2024

Abstract Cement-based materials are fundamental in the construction industry, and enhancing their properties is an ongoing challenge. The use of superabsorbent polymers (SAP) has gained significant attention as a possible way to improve the performance of cement-based materials due to their unique water-absorption and retention properties. This study investigates the multifaceted impact of kaolin intercalation-modified superabsorbent polymers (K-SAP) on the properties of cement mortar. The results show that K-SAP significantly affects the cement mortar’s rheological behavior, with distinct phases of water absorption and release, leading to changes in workability over time. Furthermore, K-SAP alters the hydration More >

Ping Xu^1,*, Zhiwei Zhang¹, Zhenguo Hou^2,3, Mankui Zheng¹, Jin Tong¹

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1299-1312, 2024, DOI:10.32604/fdmp.2023.046183 - 27 June 2024

Abstract In this paper, cement and dechlorinated Polyaluminum Chloride Residue (PACR) have been used to prepare a net slurry and mortar specimens. Two hydration activity indicators have been used to quantitatively analyze the dechlorinated PACR hydration activity. In particular, the effect of dechlorinated PACR content on the compressive strength of mortar has been assessed by means of compressive strength tests. Moreover, X-ray diffraction (XRD) and scanning electron microscopy (SEM) have been employed to observe the microstructure of the considered hydration products. The following results have been obtained. The 28th day activity index of the dechlorinated PACR… More >