Mohammad Hamidi^1,*, Florentin Smarandache², Mohadeseh Taghinezhad¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1907-1923, 2024, DOI:10.32604/cmes.2023.030284

Abstract This paper explores the defects in fuzzy (hyper) graphs (as complex (hyper) networks) and extends the fuzzy (hyper) graphs to fuzzy (quasi) superhypergraphs as a new concept. We have modeled the fuzzy superhypergraphs as complex superhypernetworks in order to make a relation between labeled objects in the form of details and generalities. Indeed, the structure of fuzzy (quasi) superhypergraphs collects groups of labeled objects and analyzes them in the form of the part to part of objects, the part of objects to the whole group of objects, and the whole to the whole group of objects at the same time.… More >

Difeng Zhu¹, Zhimou Zhu², Xuan Gong¹, Demao Ye¹, Chao Li^3,*, Jingjing Chen^4,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3083-3100, 2023, DOI:10.32604/iasc.2023.040517

Abstract Traffic prediction is a necessary function in intelligent transportation systems to alleviate traffic congestion. Graph learning methods mainly focus on the spatiotemporal dimension, but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments. There exist two issues: 1) deep integration of the spatiotemporal information and 2) global spatial dependencies for structural properties. To address these issues, we propose a nonlinear spatiotemporal optimization method, which introduces hypergraph convolution networks (HGCN). The method utilizes the higher-order spatial features of the road network captured by HGCN, and dynamically integrates them with the historical data to… More >

Zhengtao Xi¹, Chaofan Song², Jiahui Zheng³, Haifeng Shi³, Zhuqing Jiao^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2243-2266, 2023, DOI:10.32604/cmes.2023.023544

Abstract The structure and function of brain networks have been altered in patients with end-stage renal disease (ESRD). Manifold regularization (MR) only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions. To solve this issue, we developed a method to construct a dynamic brain functional network (DBFN) based on dynamic hypergraph MR (DHMR) and applied it to the classification of ESRD associated with mild cognitive impairment (ESRDaMCI). The construction of DBFN with Pearson’s correlation (PC) was transformed into an optimization model. Node convolution and hyperedge convolution superposition were adopted to… More > Graphic Abstract

N. Venkata Subramanian¹, V. S. Shankar Sriram^2,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3279-3294, 2023, DOI:10.32604/iasc.2023.023700

Abstract

Live Virtual Machine (VM) migration is one of the foremost techniques for progressing Cloud Data Centers’ (CDC) proficiency as it leads to better resource usage. The workload of CDC is often dynamic in nature, it is better to envisage the upcoming workload for early detection of overload status, underload status and to trigger the migration at an appropriate point wherein enough number of resources are available. Though various statistical and machine learning approaches are widely applied for resource usage prediction, they often failed to handle the increase of non-linear CDC data. To overcome this issue, a novel Hypergrah based Convolutional… More >

Jing Peng^1,2,3, Jingfu Yang², Chaoyang Xia², Xiaojie Li², Yanfen Guo², Ying Fu², Xinlai Chen⁴, Zhe Cui^1,3,*

Computer Systems Science and Engineering, Vol.42, No.1, pp. 319-333, 2022, DOI:10.32604/csse.2022.022314

Abstract Cardiac anatomy segmentation is essential for cardiomyopathy clinical diagnosis and treatment planning. Thus, accurate delineation of target volumes at risk in cardiac anatomy is important. However, manual delineation is a time-consuming and labor-intensive process for cardiologists and has been shown to lead to significant inter-and intra-practitioner variability. Thus, computer-aided or fully automatic segmentation methods are required. They can significantly economize on manpower and improve treatment efficiency. Recently, deep convolutional neural network (CNN) based methods have achieved remarkable successes in various kinds of vision tasks, such as classification, segmentation and object detection. Semantic segmentation can be considered as a pixel-wise task,… More >

Mengyuan Liao¹, Jing Duan^2,3,*, Rong Zhang^2,3, Xu Zhou^2,3, Xi Wu¹, Xin Wang⁴, Jinrong Hu¹

Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 787-801, 2021, DOI:10.32604/iasc.2021.018190

Abstract In the field of weather modification, it is important to accurately identify the ice crystal particles in ice clouds. When ice crystal habits are correctly identified, cloud structure can be further understood and cloud seeding and other methods of weather modification can be used to change the microstructure of the cloud. Consequently, weather phenomena can be changed at an appropriate time to support human production and quality of life. However, ice crystal morphology is varied. Traditional ice crystal particle classification methods are based on expert experience, which is subjective and unreliable for the identification of the categories by threshold setting.… More >