Xiao Feng^1,2,3,*, Zheng Yuan¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1881-1895, 2024, DOI:10.32604/cmc.2023.047243 - 27 February 2024

Abstract First, we propose a cross-domain authentication architecture based on trust evaluation mechanism, including registration, certificate issuance, and cross-domain authentication processes. A direct trust evaluation mechanism based on the time decay factor is proposed, taking into account the influence of historical interaction records. We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data. We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record. Then we propose an autoencoder-based trust… More >

Xueting Cheng¹, Ziqi Zhang^2,*, Yueshuang Bao¹, Huiping Zheng¹

Energy Engineering, Vol.120, No.11, pp. 2517-2529, 2023, DOI:10.32604/ee.2023.042633 - 31 October 2023

Abstract At present, the proportion of new energy in the power grid is increasing, and the random fluctuations in power output increase the risk of cascading failures in the power grid. In this paper, we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering (DEC) algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids. First, considering the real-time operation status and system structure of new energy power grids, the scenario cascading failure risk More >

Lei Ling¹, Lijun Huang², Jie Wang², Li Zhang², Yue Wu², Yizhang Jiang¹, Kaijian Xia^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828 - 03 August 2023

Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a… More >

Xiong Xu¹, Chun Zhou^2,*, Chenggang Wang¹, Xiaoyan Zhang², Hua Meng²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 815-831, 2023, DOI:10.32604/iasc.2023.034656 - 29 April 2023

Abstract Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this… More >

Jiaqi Shao^1,#, Shuwen Chen^1,2,3,#,*, Jin Zhou^1,#, Huisheng Zhu¹, Ziyi Wang¹, Mackenzie Brown^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2173-2219, 2023, DOI:10.32604/cmes.2023.025499 - 09 March 2023

Abstract As a mainstream research direction in the field of image segmentation, medical image segmentation plays a key role in the quantification of lesions, three-dimensional reconstruction, region of interest extraction and so on. Compared with natural images, medical images have a variety of modes. Besides, the emphasis of information which is conveyed by images of different modes is quite different. Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors. Therefore, large quantities of automated medical image segmentation methods have been developed. However, until now, researchers have not developed… More >

P. M. Siva Raja^1,*, S. Brinthakumari², K. Ramanan³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2589-2599, 2023, DOI:10.32604/csse.2023.028542 - 09 February 2023

Abstract Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation. When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain, magnetic resonance imaging (MRI) is a great tool. It is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain picture. Radiologists have a difficult time sorting and classifying tumors from multiple images. Brain tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation (NTKFIBC-IS).… More >

Xianhui Gu^1,*, Xiaokan Wang¹, Shuang Liang²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 131-139, 2023, DOI:10.32604/cmc.2023.028756 - 22 September 2022

Abstract In order to solve the defect of large error in current employment quality evaluation, an employment quality evaluation model based on grey correlation degree method and fuzzy C-means (FCM) is proposed. Firstly, it analyzes the related research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, and normalizes the index data; Then, the weight value of employment quality evaluation index is determined by Grey relational analysis method, and some unimportant indexes are removed; Finally, the employment quality evaluation model is established by using fuzzy cluster analysis algorithm, and More >

P. Rahul^1,*, N. Kanthimathi¹, B. Kaarthick², M. Leeban Moses¹

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1583-1600, 2023, DOI:10.32604/csse.2023.027424 - 15 June 2022

Abstract Recently, the fundamental problem with Hybrid Mobile Ad-hoc Networks (H-MANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy More >

Ayman Altameem¹, Ramesh Chandra Poonia², Ankit Kumar³, Linesh Raja⁴, Abdul Khader Jilani Saudagar^5,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 553-566, 2023, DOI:10.32604/iasc.2023.027579 - 06 June 2022

Abstract Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods… More >

R. Ganesh Babu^1,*, D. Hemanand², V. Amudha³, S. Sugumaran⁴

Computer Systems Science and Engineering, Vol.44, No.1, pp. 441-456, 2023, DOI:10.32604/csse.2023.024049 - 01 June 2022

Abstract In recent decades, several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during transmission to a shorter distance while restricting the Primary Users (PUs) interference. The Cognitive Radio (CR) system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm (ASDIC) that shows better spectrum sensing among group of multiusers in terms of sensing error, power saving, and convergence time. In this research paper, the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.… More >