Farhad Mortezapour Shiri^1,*, Ehsan Ahmadi², Mohammadreza Rezaee¹, Thinagaran Perumal¹

Journal on Artificial Intelligence, Vol.6, pp. 85-103, 2024, DOI:10.32604/jai.2024.048911

Abstract Automatic detection of student engagement levels from videos, which is a spatio-temporal classification problem is crucial for enhancing the quality of online education. This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos. The evaluation of these models utilizes the DAiSEE dataset, a public repository capturing student affective states in e-learning scenarios. The initial model integrates EfficientNetV2-L with Gated Recurrent Unit (GRU) and attains an accuracy of 61.45%. Subsequently, the second model combines EfficientNetV2-L with bidirectional GRU (Bi-GRU), yielding an accuracy of 61.56%. The… More >

Di Qiu^1,3,4, Rongpei Shi^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1017-1028, 2024, DOI:10.32604/cmes.2024.048797

Abstract For media with inclusions (e.g., precipitates, voids, reinforcements, and others), the difference in lattice parameter and the elastic modulus between the matrix and inclusions cause stress concentration at the interfaces. These stress fields depend on the inclusions’ size, shape, and distribution and will respond instantly to the evolving microstructure. This study develops a phase-field model concerning modulus heterogeneity. The effect of modulus heterogeneity on the growth process and equilibrium state of the α plate in Ti-6Al-4V during precipitation is evaluated. The α precipitate exhibits strong anisotropy in shape upon cooling due to the interplay of the elastic strain and interfacial… More >

Haojie Lian¹, Jiaqi Wang¹, Leilei Chen^2,*, Shengze Li³, Ruochen Cao⁴, Qingyuan Hu⁵, Peiyun Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549

Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations… More >

Dawei Ruan¹, Mingwei Hu^1,2,3,4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 821-844, 2024, DOI:10.32604/cmes.2024.048392

Abstract The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale, multifunctional, and complex orientations. The vibration caused by the high-frequency units during regular operation is supported by the foundation of the units, and the magnitude of vibration and the operating frequency fluctuate in different engineering contexts, leading to variations in the dynamic response of the foundation. The high-frequency units yield significantly diverse outcomes under different startup conditions and times, resulting in failure to meet operational requirements, influencing the normal function of the tunnel, and causing harm to the foundation structure, personnel, and property in severe cases. This article… More >

Weiwu Jiang¹, Xiaowei Gao^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 41-76, 2024, DOI:10.32604/cmes.2024.048313

Abstract The collocation method is a widely used numerical method for science and engineering problems governed by partial differential equations. This paper provides a comprehensive review of collocation methods and their applications, focused on elasticity, heat conduction, electromagnetic field analysis, and fluid dynamics. The merits of the collocation method can be attributed to the need for element mesh, simple implementation, high computational efficiency, and ease in handling irregular domain problems since the collocation method is a type of node-based numerical method. Beginning with the fundamental principles of the collocation method, the discretization process in the continuous domain is elucidated, and how… More >

Haosong Gou¹, Gaoyi Zhang¹, Renê Ripardo Calixto², Senthil Kumar Jagatheesaperumal³, Victor Hugo C. de Albuquerque^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1077-1102, 2024, DOI:10.32604/cmes.2024.047806

Abstract Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally,… More >

Lyuchao Liao^1,2, Hankun Xiao^2,*, Pengqi Xing², Zhenhua Gan^1,2, Youpeng He², Jiajun Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452

Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational… More >

Huaiguang Wu, Yibo Peng, Yaqiong He^*, Jinlin Fan

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 77-108, 2024, DOI:10.32604/cmes.2024.046758

Abstract In recent years, the number of smart contracts deployed on blockchain has exploded. However, the issue of vulnerability has caused incalculable losses. Due to the irreversible and immutability of smart contracts, vulnerability detection has become particularly important. With the popular use of neural network model, there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts. This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts. Subsequently, it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection. These… More > Graphic Abstract

Jamal N. Al Abbasi¹, Ahmed Z. Afify^2,*, Badr Alnssyan^3,*, Mustafa S. Shama^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 513-536, 2024, DOI:10.32604/cmes.2024.046533

Abstract This study proposes a new flexible family of distributions called the Lambert-G family. The Lambert family is very flexible and exhibits desirable properties. Its three-parameter special sub-models provide all significant monotonic and non-monotonic failure rates. A special sub-model of the Lambert family called the Lambert-Lomax (LL) distribution is investigated. General expressions for the LL statistical properties are established. Characterizations of the LL distribution are addressed mathematically based on its hazard function. The estimation of the LL parameters is discussed using six estimation methods. The performance of this estimation method is explored through simulation experiments. The usefulness and flexibility of the… More >

JIYU WANG^1,2, HUIXIA LI^1,2, DEYUAN ZHOU^1,2, LIHONG BAI^1,2, KEJING TANG^1,2,3,*

BIOCELL, Vol.48, No.4, pp. 623-637, 2024, DOI:10.32604/biocell.2024.030420

Abstract Background: For patients with lung cancer, timely identification of new lung lesions as infectious or non-infectious, and accurate identification of pathogens is very important in improving OS of patients. As a new auxiliary examination, metagenomic next-generation sequencing (mNGS) is believed to be more accurate in diagnosing infectious diseases in patients without underlying diseases, compared with conventional microbial tests (CMTs). We designed this study to find out whether mNGS has better performance in distinguishing infectious and non-infectious diseases in lung cancer patients using bronchoalveolar lavage fluid (BALF). Materials and Methods: This study was a real-world retrospective review based on electronic medical… More >