Premanand S., Sathiya Narayanan^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 25-45, 2023, DOI:10.32604/cmc.2023.042590

Abstract Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the… More >

Zhimei Li¹, Kuan Tian², Keping Wang², Zhengyi Li², Haoli Qin^1,*, Hu Li^2,*

Journal of Renewable Materials, Vol.11, No.11, pp. 3847-3865, 2023, DOI:10.32604/jrm.2023.030122

Abstract Sustainable acquisition of bioactive compounds from biomass-based platform molecules is a green alternative for existing CO₂-emitting fossil-fuel technologies. Herein, a core–shell magnetic biocarbon catalyst functionalized with sulfonic acid (Fe₃O₄@SiO₂@chitosan-SO₃H, MBC-SO₃H) was prepared to be efficient for the synthesis of various N-substituted pyrroles (up to 99% yield) from bio-based hexanedione and amines under mild conditions. The abundance of Brønsted acid sites in the MBC-SO₃H ensured smooth condensation of 2,5-hexanedione with a variety of amines to produce N-substituted pyrroles. The reaction was illustrated to follow the conventional PallKnorr coupling pathway, which includes three cascade reaction steps: amination, loop closure… More > Graphic Abstract

Weizheng Wang^1,3, Xiangqi Wang^2,*, Xianmin Pan¹, Xingxing Gong³, Jian Liang³, Pradip Kumar Sharma⁴, Osama Alfarraj⁵, Wael Said⁶

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3859-3876, 2023, DOI:10.32604/cmc.2023.041346

Abstract Image-denoising techniques are widely used to defend against Adversarial Examples (AEs). However, denoising alone cannot completely eliminate adversarial perturbations. The remaining perturbations tend to amplify as they propagate through deeper layers of the network, leading to misclassifications. Moreover, image denoising compromises the classification accuracy of original examples. To address these challenges in AE defense through image denoising, this paper proposes a novel AE detection technique. The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network (CNN) network structures. The used detector model integrates the classification results of different models as the input to… More >

Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3097-3112, 2023, DOI:10.32604/cmc.2023.040914

Abstract Predicting traffic flow is a crucial component of an intelligent transportation system. Precisely monitoring and predicting traffic flow remains a challenging endeavor. However, existing methods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes, resulting in the loss of essential information and lower forecast performance. On the other hand, the availability of spatiotemporal data is limited. This research offers alternative spatiotemporal data with three specific features as input, vehicle type (5 types), holidays (3 types), and weather (10 conditions). In this study, the proposed model… More >

Hong Zhu^*, Lisha Gao, Yitian Sha, Nan Xiang, Yue Wu, Shuo Han

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3363-3377, 2023, DOI:10.32604/cmc.2023.040731

Abstract Load forecasting is a crucial aspect of intelligent Virtual Power Plant (VPP) management and a means of balancing the relationship between distributed power grids and traditional power grids. However, due to the continuous emergence of power consumption peaks, the power supply quality of the power grid cannot be guaranteed. Therefore, an intelligent calculation method is required to effectively predict the load, enabling better power grid dispatching and ensuring the stable operation of the power grid. This paper proposes a decentralized heterogeneous federated distillation learning algorithm (DHFDL) to promote trusted federated learning (FL) between different federates… More >

Yan Li¹, Xiaoguang Zhang^1,*, Tianyu Gong¹, Qi Dong¹, Hailong Zhu¹, Tianqiang Zhang¹, Yanji Jiang^2,3

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3691-3705, 2023, DOI:10.32604/cmc.2023.040492

Abstract Automatic text summarization (ATS) plays a significant role in Natural Language Processing (NLP). Abstractive summarization produces summaries by identifying and compressing the most important information in a document. However, there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics. In particular, Chinese complaint reports, generated by urban complainers and collected by government employees, describe existing resident problems in daily life. Meanwhile, the reflected problems are required to respond speedily. Therefore, automatic summarization tasks for these reports have been More >

Gaoyuan Yu¹, Yunfeng Lou², Hang Dong³, Junjie Li¹, Xianlong Jin^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2795-2816, 2023, DOI:10.32604/cmc.2023.037375

Abstract The strict and high-standard requirements for the safety and stability of major engineering systems make it a tough challenge for large-scale finite element modal analysis. At the same time, realizing the systematic analysis of the entire large structure of these engineering systems is extremely meaningful in practice. This article proposes a multilevel hierarchical parallel algorithm for large-scale finite element modal analysis to reduce the parallel computational efficiency loss when using heterogeneous multicore distributed storage computers in solving large-scale finite element modal analysis. Based on two-level partitioning and four-transformation strategies, the proposed algorithm not only improves… More >

DONGYE YANG^1,#,*, DONGDONG GUO^2,3,#, YUNMEI PENG², DONGMENG LIU¹, YANQIU FU¹, FEN SUN², LISHI ZHOU¹, JIAQI GUO¹, LAIQING HUANG^2,3,*

BIOCELL, Vol.47, No.9, pp. 2037-2049, 2023, DOI:10.32604/biocell.2023.028863

Abstract Introduction: Sulfatase 2 (SULF2), an endogenous extracellular sulfatase, can remove 6-O-sulfate groups of glucosamine residues from heparan sulfate (HS) chains to modulate the Wnt/β-catenin signaling pathway, which plays an important role in both liver carcinogenesis and embryogenesis. Side population (SP) cells are widely identified as stem-like cancer cells and are closely related to carcinoma metastasis, recurrence, and poor patient prognosis. However, the roles of SULF2 in SP cells of hepatomas are unclear, and the underlying mechanism is undefined. Objectives: This study aimed to compare the heterogeneity between SP cells and non-side population (NSP) cells derived from… More > Graphic Abstract

Junpeng Lyu¹, Hai Mei^1,2, Liping Zu¹, Lisheng Liu^1,2,*, Liangliang Chu^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 391-410, 2024, DOI:10.32604/cmes.2023.030614

Abstract This article proposes a modeling method for C/C-ZrC composite materials. According to the superposition of Gaussian random field, the original gray model is obtained, and the threshold segmentation method is used to generate the C-ZrC inclusion model. Finally, the fiber structure is added to construct the microstructure of the three-phase plain weave composite. The reconstructed inclusions can meet the randomness of the shape and have a uniform distribution. Using an algorithm based on asymptotic homogenization and finite element method, the equivalent thermal conductivity prediction of the microstructure finite element model was carried out, and the… More >

Yulei Wang¹, Li Feng^1,*, Shumin Yao^1,2, Hong Liang¹, Haoxu Shi¹, Yuqiang Chen³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 639-661, 2024, DOI:10.32604/cmes.2023.029565

Abstract Interference management is one of the most important issues in the device-to-device (D2D)-enabled heterogeneous cellular networks (HetCNets) due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum. To alleviate the interference, an efficient interference management way is to set exclusion zones around the cellular receivers. In this paper, we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets. The main difficulties contain three aspects: 1) how to model the location randomness of base stations, cellular and D2D More >