Yue Wan, Nan Jiang^*, Ziyu Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3017-3034, 2024, DOI:10.32604/cmes.2024.051101

Abstract The Social Internet of Things (SIoT) integrates the Internet of Things (IoT) and social networks, taking into account the social attributes of objects and diversifying the relationship between humans and objects, which overcomes the limitations of the IoT’s focus on associations between objects. Artificial Intelligence (AI) technology is rapidly evolving. It is critical to build trustworthy and transparent systems, especially with system security issues coming to the surface. This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT, aiming to build an SIoT hypergraph generation… More >

Shuai Zhang, Liguo Weng^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2635-2654, 2023, DOI:10.32604/cmes.2023.025405

Abstract Transmission line (TL) Parameter Identification (PI) method plays an essential role in the transmission system. The existing PI methods usually have two limitations: (1) These methods only model for single TL, and can not consider the topology connection of multiple branches for simultaneous identification. (2) Transient bad data is ignored by methods, and the random selection of terminal section data may cause the distortion of PI and have serious consequences. Therefore, a multi-task PI model considering multiple TLs’ spatial constraints and massive electrical section data is proposed in this paper. The Graph Attention Network module More > Graphic Abstract

Jie Chen¹, Jiabao Xu¹, Xuefeng Xi^1,*, Zhiming Cui¹, Victor S. Sheng²

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 747-761, 2023, DOI:10.32604/cmes.2022.020771

Abstract Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose a Randomly Wired Graph Neural Network (RWGNN) by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary ambiguity and polysemy) of Chinese NER. Besides, we develop a pipeline to explain the RWGNN by using Saliency Map and Adversarial Attacks. Experimental results demonstrate that our approach can identify More >

Mohamed Ali Rakrouki^1,*, Nawaf Alharbe¹, Mashael Khayyat², Abeer Aljohani¹

Computer Systems Science and Engineering, Vol.45, No.1, pp. 395-408, 2023, DOI:10.32604/csse.2023.029032

Abstract Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new More >

P. Perumal^*

Computer Systems Science and Engineering, Vol.43, No.1, pp. 203-218, 2022, DOI:10.32604/csse.2022.020459

Abstract With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased. Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task. In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR). Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering… More >

Liangliang Shi^1,2, Peili Lu³, Junchi Yan^4,5,*

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 873-885, 2020, DOI:10.32604/iasc.2020.010121

Abstract Causality learning has been an important tool for decision making, especially for financial analytics. Given the time series data, most existing works construct the causality network with the traditional regression models and estimate the causality by pairs. To fulfil a holistic one-shot inference procedure over the whole network, we propose a new causal inference method for the multidimensional time series data, specifically related to some case studies for the industrial finance analytics. Specifically, the time series are first converted to the event sequences with timestamps by fluctuation the detection, and then a multidimensional point process More >

S.K. Pal¹, D.A. Linkens²

CMC-Computers, Materials & Continua, Vol.2, No.2, pp. 113-118, 2005, DOI:10.3970/cmc.2005.002.113

Abstract In hot rolling, the final thickness of the strip is achieved through plastic deformation of the original stock by a series of counter-rotating rollers. In this work, static recrystallization kinetics in between two stages of steel rolling has been modelled, and simulation studies have also been performed to find out the effect of entry temperature on the recrystallization kinetics. A viable bond graph model has been developed to study the kinetics of the process. Low-carbon steel has been considered for this purpose. More >

A. Merabtine¹, S. Mokraoui¹, R. Benelmir¹, N. Laraqi²

FDMP-Fluid Dynamics & Materials Processing, Vol.8, No.2, pp. 215-240, 2012, DOI:10.3970/fdmp.2012.008.215

Abstract Modeling of the thermal behavior of buildings needs effective strategies of analysis and tools. This is particularly true when conduction of heat through walls and/or slabs has to be properly taken into account. This article is concerned with a new modeling strategy for solving the transient heat conduction equation in a finite medium (with extensive background application to the different elements of a building structure). The developed approach is based on the Bond Graph technique, a graphical modeling language which is particularly suitable to the treatment of problems involving energy transfer. With this model, two More >

Wenpeng Lu^1,*, Fanqing Meng², Shoujin Wang³, Guoqiang Zhang⁴, Xu Zhang¹, Antai Ouyang⁵, Xiaodong Zhang⁶

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 197-212, 2019, DOI:10.32604/cmc.2019.06068

Abstract Word sense disambiguation (WSD) is a fundamental but significant task in natural language processing, which directly affects the performance of upper applications. However, WSD is very challenging due to the problem of knowledge bottleneck, i.e., it is hard to acquire abundant disambiguation knowledge, especially in Chinese. To solve this problem, this paper proposes a graph-based Chinese WSD method with multi-knowledge integration. Particularly, a graph model combining various Chinese and English knowledge resources by word sense mapping is designed. Firstly, the content words in a Chinese ambiguous sentence are extracted and mapped to English words with More >