Jie Li^1,3,*, Rongwen Wang², Yongtao Hu^1,3, Jinjun Li¹

Structural Durability & Health Monitoring, Vol.18, No.1, pp. 73-90, 2024, DOI:10.32604/sdhm.2023.044023

Abstract The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains. However, in real-world scenarios, accurate predictions are challenging due to various interferences. This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter (KF). The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments. By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals, it becomes possible… More > Graphic Abstract

Yifan Zhang¹, Qiancheng Yu^1,2,*, Lisi Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255

Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep forest algorithm to enhance the… More >

Cheng Han^1,*, Zhengguang Xu^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 515-536, 2024, DOI:10.32604/cmes.2023.043464

Abstract A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems, assuming that the system satisfies the generalized Lipschitz condition. As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics, the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables, which poses difficulties in predicting and estimating the system’s output. In this article, the temporal variation of the system is described by constructing pattern category variables, which are non-deterministic variables. Since pattern category variables have… More >

Weisi Chen^1,*, Walayat Hussain^2,*, Francesco Cauteruccio³, Xu Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388

Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and… More > Graphic Abstract

KEGANG JIA^1,#, YAWEI WANG^2,#, QI CAO^3,*, YOUYU WANG^1,*

Oncology Research, Vol.32, No.2, pp. 409-419, 2024, DOI:10.32604/or.2023.042863

Abstract Background: Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide. Therapeutic failure in lung cancer (LUAD) is heavily influenced by drug resistance. This challenge stems from the diverse cell populations within the tumor, each having unique genetic, epigenetic, and phenotypic profiles. Such variations lead to varied therapeutic responses, thereby contributing to tumor relapse and disease progression. Methods: The Genomics of Drug Sensitivity in Cancer (GDSC) database was used in this investigation to obtain the mRNA expression dataset, genomic mutation profile, and drug sensitivity information of NSCLS. Machine Learning (ML) methods, including Random Forest… More >

Xiang Wang¹, Liangsa Wang^2,*, Han Li¹, Yibin Guo¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2935-2969, 2023, DOI:10.32604/cmc.2023.044948

Abstract The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. To address these challenges, this paper introduces an improved whale optimization algorithm called OLCHWOA, incorporating a chaos mechanism and an opposition-based learning strategy. This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase, thereby enhancing the quality of the initial whale population. Additionally, including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations. The work and contributions of this paper are primarily reflected in two aspects. Firstly, an improved whale algorithm… More >

Shuqin Zhang, Xinyu Su^*, Yunfei Han, Tianhui Du, Peiyu Shi

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3993-4023, 2023, DOI:10.32604/cmc.2023.044526

Abstract Advanced Persistent Threat (APT) is now the most common network assault. However, the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks. They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats. To address the above problems, firstly, this paper constructs the multi-source threat element analysis ontology (MTEAO) by integrating multi-source network security knowledge bases. Subsequently, based on MTEAO, we propose a two-layer threat prediction model (TL-TPM) that combines the knowledge graph and the… More >

Asif Khan¹, Nada Boudjellal², Huaping Zhang^1,*, Arshad Ahmad³, Maqbool Khan³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3037-3055, 2023, DOI:10.32604/cmc.2023.044403

Abstract Predicting election outcomes is a crucial undertaking, and various methods are employed for this purpose, such as traditional opinion polling, and social media analysis. However, traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels, resulting in a limited depth of analysis and understanding. In light of this challenge, this study focuses on predicting elections at the state/regional level along with the country level, intending to offer a comprehensive analysis and deeper insights into the electoral process. To achieve this, the study introduces the Location-Based Election Prediction Model (LEPM), which utilizes social media data,… More >

S. Kannan^1,*, S. Anusuya²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3835-3851, 2023, DOI:10.32604/cmc.2023.042465

Abstract Gliomas, the most prevalent primary brain tumors, require accurate segmentation for diagnosis and risk assessment. In this paper, we develop a novel deep learning-based method, the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. The DHA-ISSP model combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms, enabling precise tumor segmentation and survival prediction. The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels, enhancing segmentation accuracy. By achieving remarkable results, our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge. With… More >

Shuangshuang Zhang^1,*, Kangliang Guo¹, Xinchen Gao¹, Haoran Yang¹, Jinfeng Zhang², Xing Han³

Energy Engineering, Vol.121, No.1, pp. 59-75, 2024, DOI:10.32604/ee.2023.041709

Abstract To improve the productivity of oil wells, perforation technology is usually used to improve the productivity of horizontal wells in oilfield exploitation. After the perforation operation, the perforation channel around the wellbore will form a near-well high-permeability reservoir area with the penetration depth as the radius, that is, the formation has different permeability characteristics with the perforation depth as the dividing line. Generally, the permeability is measured by the permeability tester, but this approach has a high workload and limited application. In this paper, according to the reservoir characteristics of perforated horizontal wells, the reservoir is divided into two areas:… More >