Yantao Shen^1,*, Yang Lu², Zhili Wang¹, Xin Xv³, Feng Qi¹, Ningzhe Xing⁴, Ziyu Zhao⁵

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 259-268, 2021, DOI:10.32604/iasc.2021.013704

Abstract With the continuous development of the Internet, the domain name system (DNS) as infrastructure is playing an increasingly important role. However, traditional DNS architecture is centralized, and there are some security problems such as the right concentration and power abuse. This paper combines blockchain technology with DNS technology and proposes a domain name service model based on the permissioned blockchain. At first, this paper designs a top-level domain chain (TLDChain) model to conduct consensus on block transactions and achieve decentralization of domain name service. Then, this paper introduces a data model to upload data. At the same time, to improve… More >

Suresh Krishnamoorthy^1,*, Kumaratharan Narayanaswamy²

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 89-102, 2021, DOI:10.32604/iasc.2021.013643

Abstract Increase in demand for multimedia and quality services requires 5G networks to resolve issues such as slicing, allocation, forwarding, and control using techniques such as software-defined networking (SDN) and network function virtualization. In this study, the optimum number of SDN multi-controllers are implemented based on a multi-criterion advanced genetic algorithm that takes into consideration three key parameters: Switch controller latency, hopcount, and link utilization. Preprocessing is the first step, in which delay, delay paths, hopcount, and hoppaths are computed as an information matrix (Infomat). Randomization is the second step, and consists of initially placing controllers randomly, followed by an analytical… More >

Xiaojuan Yuan^1,2,*

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 249-257, 2021, DOI:10.32604/iasc.2021.013637

Abstract This paper briefly introduced the structure of heterogeneous cellular network and two algorithms which are used for optimizing the network resource allocation scheme: dynamic game algorithm based on spectrum allocation and the game allocation algorithm based on power allocation and alliance. After that, the two algorithms were simulated in MATLAB software and compared with another power iterative allocation algorithm based on non-cooperative game. The results showed that the system energy efficiency of the three algorithms decreased with the increase of the number of small base stations in the network; with the increase of the number of users in the network,… More >

Yuqi Wang¹, Sujie Shao^1,*, Shaoyong Guo¹, Ruijun Chai¹, Feng Qi¹, Michel Kadoch²

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 287-298, 2021, DOI:10.32604/iasc.2021.013543

Abstract Electric power Internet of Things (IoT) is a network system that can meet multiple requirements of the power grid, such as infrastructure, environment recognition, interconnection, perception and control. Long Range Radio Wide Area Network (LoRaWAN) with the advantages of ultra-long transmission and ultra-low power consumption, becomes the most widely used protocol in the electric power IoT. However, its extremely simple star topology also leads to several problems. When most of terminals depend on one or several gateways for communication, the gateways with heavier communication tasks have poorer communication quality. The load of each gateway is unbalanced, which is hardly conducive… More >

Ping-Yu Hsu¹, Ming-Shien Cheng^2,*, Chih-Hao Wen³, Yen-Huei Ko¹

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 269-285, 2021, DOI:10.32604/iasc.2021.013404

Abstract Cross-domain technology application is the application of technology from one field to another to create a wide range of application opportunities. To successfully identify emerging technological application cross sections of patent documents is vital to the competitive advantage of companies, and even nations. An automatic process is needed to save precious resources of human experts and exploit huge numbers of patent documents. Chinese patent documents are the source data of our experiment. In this study, an identification algorithm was developed on the basis of a cross-collection mixture model to identify cross section and emerging technology from patents written in Chinese.… More >

Qing Ai^1,2,*, Yude Kang¹, Anna Wang²

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 205-220, 2021, DOI:10.32604/iasc.2021.013357

Abstract Multi-label learning is a meaningful supervised learning task in which each sample may belong to multiple labels simultaneously. Due to this characteristic, multi-label learning is more complicated and more difficult than multi-class classification learning. The multi-label twin support vector machine (MLTSVM) [], which is an effective multi-label learning algorithm based on the twin support vector machine (TSVM), has been widely studied because of its good classification performance. To obtain good generalization performance, the MLTSVM often needs a large number of labelled samples. In practical engineering problems, it is very time consuming and difficult to obtain all labels of all samples… More >

Xiaohong Ji¹, Junjun Du¹, Guoqing Jia^1,*, Weidong Fang^2,3

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 221-231, 2021, DOI:10.32604/iasc.2021.013145

Abstract Sparse code multiple access (SCMA) is a new non-orthogonal multiple access scheme suitable for 5G communication, which can effectively improve spectrum efficiency and support massive connections. Multiple users in the SCMA system realize the sharing of the same time-frequency resources by mapping data into codewords of a special code book (Code Book, CB). A typical SCMA system increases the spectrum utilization to 150%. In order to further improve the system spectrum utilization and increase the number of user connections, this paper proposes an uplink SCMA codebook reuse transmission and reception scheme (CB-Reuse-SCMA), which reuse a codebook to multiple users. The… More >

Peng Mei¹, Gangyi Ding¹, Qiankun Jin¹, Fuquan Zhang^2,*, Yeh-Cheng Chen³

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 159-171, 2021, DOI:10.32604/iasc.2021.012813

Abstract Community structure is a key component in complex network systems. This paper aims to improve the effectiveness of community detection and community discovery in complex network systems by providing directions for the reconstruction and optimization of community structures to expand the application of intelligent optimization algorithms in community structures. First, deep learning algorithms and ant colony algorithms are used to elaborate the community detection and community discovery in complex networks. Next, we introduce the technology of transfer learning and propose an algorithm of deep self-encoder modeling based on transfer learning (DSEM-TL). The DSEM-TL algorithm’s indicators include normalized mutual information and… More >

Zejian Zhang¹, Dawei Wang^2,*, Xiao-Zhi Gao³

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 173-189, 2021, DOI:10.32604/iasc.2021.012805

Abstract The interval type-2 fuzzy sets can describe nonlinear plants with uncertain parameters. It exists in nonlinearity. The parameter uncertainties extensively exist in the nonlinear practical Networked Control Systems (NCSs), and it is paramount to investigate the stabilization of the NCSs on account of the section type-2 fuzzy systems. Notice that most of the existing research work is only on account of the convention Parallel Distribution Compensation (PDC). For overcoming the weak point of the PDC and acquire certain guard stability conditions, the state tickling regulator under imperfect premise matching can be constructed to steady the NCSs using the section type-2… More >

Chunhua Zhou^*, Jianjing Shen, Xiaofeng Guo, Zhenyu Zhou

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 127-141, 2021, DOI:10.32604/iasc.2021.012532

Abstract In the process of interaction between users and battlefield situation information, combat tasks are the key factors that affect users’ information selection. In order to solve the problems of battlefield situation information recommendation (BSIR) for combat tasks, we propose a task-oriented battlefield situation information hybrid recommendation model (TBSI-HRM) based on tensor factorization and deep learning. In the model, in order to achieve high-precision personalized recommendations, we use Tensor Factorization (TF) to extract correlation relations and features from historical interaction data, and use Deep Neural Network (DNN) to learn hidden feature vectors of users, battlefield situation information and combat tasks from… More >