Yingui Qiu¹, Shuai Huang¹, Danial Jahed Armaghani², Biswajeet Pradhan³, Annan Zhou⁴, Jian Zhou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2873-2897, 2024, DOI:10.32604/cmes.2023.029938

Abstract As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field… More >

Mudassir Khalil¹, Muhammad Imran Sharif^2,*, Ahmed Naeem³, Muhammad Umar Chaudhry¹, Hafiz Tayyab Rauf^4,*, Adham E. Ragab⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2031-2047, 2023, DOI:10.32604/cmc.2023.043687

Abstract Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in their nascent stages can significantly enhance the chances of patient survival. While there are various types of brain tumors, each with unique characteristics and treatment protocols, tumors are often minuscule during their initial stages, making manual diagnosis challenging, time-consuming, and potentially ambiguous. Current techniques predominantly used in hospitals involve manual detection via MRI scans, which can be costly, error-prone, and time-intensive. An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases. This research… More >

Qing Xia¹, Yonghua Li^2,*, Dongxu Zhang², Yufeng Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1769-1794, 2024, DOI:10.32604/cmes.2023.030724

Abstract For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network… More >

Shazia Shamas¹, Surya Narayan Panda^1,*, Ishu Sharma^1,*, Kalpna Guleria¹, Aman Singh^2,3,4, Ahmad Ali AlZubi⁵, Mallak Ahmad AlZubi⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712

Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired… More >

Jiang Li, Jiutao Zhao, Qinhui Liu^*, Laizheng Zhu, Jinyi Guo, Weijiu Zhang

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 223-244, 2023, DOI:10.32604/cmc.2023.042429

Abstract Cutting parameters have a significant impact on the machining effect. In order to reduce the machining time and improve the machining quality, this paper proposes an optimization algorithm based on Bp neural network-Improved Multi-Objective Particle Swarm (Bp-DWMOPSO). Firstly, this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm. Secondly, the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established. Finally, the Bp-DWMOPSO algorithm is designed based on the established models. In order to verify the effectiveness of the algorithm, this paper obtains the required data through More >

Fang Yu¹, Wenbin Bi², Ning Cao^3,*, Jun Luo⁴, Diantang An⁵, Liqiang Ding⁴, Russell Higgs⁶

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3921-3931, 2023, DOI:10.32604/cmc.2023.028330

Abstract The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption. Aiming at the problem of dynamic permission management in common access control methods, a new access control method based on smart contract under blockchain is proposed, which improves the intelligence level under blockchain technology. Firstly, the Internet attribute access control model based on smart contract is established. For the dynamic access of heterogeneous devices, the management contract, permission judgment contract and access… More >

Loc Q. Huynh¹, Ly V. Tran¹, Phuc N. K. Phan¹, Zhiqiu Yu², Son V. T. Dao^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2281-2300, 2023, DOI:10.32604/cmc.2023.034872

Abstract Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles. In this paper, we propose a novel path planning algorithm–Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer, Particle swarm optimization (PSO), for fine-tuning and enhancement. In Phase 1, the start and goal trees are initialized at the starting and goal positions, respectively, and the intermediary tree is initialized at a random unexplored region… More >

Shuaijun Wang¹, Wenqiong Tu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09926

Abstract Cellulose nanofibrils (CNFs) and the continuously assembled microfibers have shown transversely isotropic behavior in many studies. Due to fact that the size of CNFs and the assembled microfibers is at the nano and micro scale, respectively, the characterization of their mechanical properties is extremely challenge. That greatly hinders the accurate multi-scale modeling and design of CNFs-based materials. In our study, we have characterized the elastic constants of both CNFs microfibers and CNFs through a Multi-scale Optimization Inversion technology. Through the tensile test of CNFs microfibers reinforced resin with different volume fractions and the micromechanics model More >

Kaili Shao¹, Hui Fu¹, Ying Song², Bo Wang^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454

Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem… More >

Xujun Li^*, Guodong Fang, Liming Rao, Tengze Zhang

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2671-2688, 2023, DOI:10.32604/csse.2023.038154

Abstract To improve the tracking accuracy of persons in the surveillance video, we proposed an algorithm for multi-target tracking persons based on deep learning. In this paper, we used You Only Look Once v5 (YOLOv5) to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to do cascade matching and Intersection Over Union (IOU) matching of person targets between different frames. To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification (ReID) network in the process of cascade… More >