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C. Sivamani¹, M. Rajeswari², E. Golden Julie³, Y. Harold Robinson⁴, Vimal Shanmuganathan⁵, Seifedine Kadry⁶, Yunyoung Nam^7,*
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 1-13, 2021, DOI:10.32604/iasc.2021.013705
（This article belongs to this Special Issue: Recent Trends in Artificial Intelligence for Automated Complex Industrial Systems)
Abstract Road accident is a potential risk to the lives of both drivers and passers-by. Many road accidents occur due to the improper condition of the vehicle tyres after long term usage. Thus, tyres need to be inspected and analyzed while manufacturing to avoid serious road problems. However, tyre wear is a multifaceted happening. It normally needs the non-linearly on many limitations, like tyre formation and plan, vehicle category, conditions of the road. Yet, tyre wear has numerous profitable and environmental inferences particularly due to maintenance costs and traffic safety implications. Thus, the risk to calculate tyre wear is therefore of… More >

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AbdulHafeez Muhammad¹, Ansar Siddique^2,*, Quadri Noorulhasan Naveed³, Usman Saleem¹, Mohd Abul Hasan⁴, Basit Shahzad⁵
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 15-34, 2021, DOI:10.32604/iasc.2021.014427
（This article belongs to this Special Issue: Soft Computing Methods for Innovative Software Practices)
Abstract The major emphasis of Software Engineering (SE) discipline is to produce successful software systems. The success of software projects is estimated through quadruple measures including budget, cost, scope, and quality. To meet this aim of SE, several software development processes are presented in the literature. Such processes are categorized into two different methodologies which are known as traditional and agile software development methodologies. The issue with traditional software development methodologies is that they had not shown any remarkable progress towards the fundamental goal of SE. Consequently, software development organizations have started to adopt agile methodologies in the pursuit of successful… More >

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Naima Bousnina¹, Sanaa Ghouzali^2,*, Mounia Mikram^1,3, Maryam Lafkih¹, Ohoud Nafea⁴, Muna Al-Razgan², Wadood Abdul⁴
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 35-51, 2021, DOI:10.32604/iasc.2021.014694
（This article belongs to this Special Issue: Soft Computing in Intrusion Detection)
Abstract Biometric template disclosure starts gaining an important concern in deploying practical biometric authentication systems, where an assailant compromises the database for illegitimate access. To protect biometric templates from disclosure attacks, biometric authentication systems should meet these four requirements: security, diversity, revocability, and performance. Different methods have been suggested in the literature such as feature transformation techniques and biometric cryptosystems. However, no single method could satisfy the four requirements, giving rise to the deployment of hybrid mechanisms. In this context, the current paper proposes a hybrid system for multimodal biometric template protection to provide robustness against template database attacks. Herein, a… More >

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Jong-Yih Kuo^*, Hui-Chi Lin, Chien-Hung Liu
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 53-68, 2021, DOI:10.32604/iasc.2021.014437
Abstract Predicting salary trends of students after employment is vital for helping students to develop their career plans. Particularly, salary is not only considered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of graduates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determining the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a multi-output deep neural network to… More >

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Adeeb M. Alhomoud^*
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 69-88, 2021, DOI:10.32604/iasc.2021.014773
Abstract This research article provides an up-to-date review of spatial-domain steganography. Maintaining the communication as secure as possible when transmitting secret data through any available communication channels is the target of steganography. Currently, image steganography is the most developed field, with several techniques are provided for different image formats. Therefore, the general image steganography including the fundamental concepts, the terminology, and the applications are highlighted in this paper. Further, the paper depicts the essential characteristics between information hiding and cryptography systems. In addition, recent well-known techniques in the spatial-domain steganography, such as LSB and pixel value differencing, are discussed in detail… More >

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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 >

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Shahzad Latif¹, Suhail Akraam², Arif Jamal Malik³, Aaqif Afzaal Abbasi³, Muhammad Habib³, Sangsoon Lim^4,*
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 103-114, 2021, DOI:10.32604/iasc.2021.014388
Abstract

In recent years, wireless channel optimization technologies witnessed tremendous improvements. In this regard, research for developing wireless spectrum for accommodating a wider range of wireless devices increased. This also helped in resolving spectrum scarcity issues. Cognitive Radio (CR) is a type of wireless communication in which a transceiver can intelligently detect which communication channels are being used. To avoid interference, it instantly moves traffic into vacant channels by avoiding the occupied ones. Cognitive Radio (CR) technology showed the potential to deal with the spectrum shortage problem. The spectrum assignment is often considered as a key research challenge in Cognitive Radio… More >

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Qi Chen, Wei Cui^*, Jianqiu Sun, Xingguang Li, Xuyu Tian
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 115-126, 2021, DOI:10.32604/iasc.2021.011617
Abstract The existing range-Doppler algorithms for SAR imaging are affected by a fast-time Doppler effect so they cannot be directly applied to FMCW SAR. Moreover, range migration is more evident in squint mode. To reveal the influence of the continuous motion of FMCW SAR in the squint mode on the echo signal and optimize the imaging process, an improved range-Doppler algorithm is based on squint FMCW SAR imaging is proposed in this paper. Firstly, the imaging geometry model and echo signal model of FMCW SAR are analyzed and deduced. The problem of Doppler center offset under squint mode is eliminated by… More >

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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 >

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1061

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Tao Ni¹, Lingtao Huang^1,*, Huanfei Zheng², Hongyan Zhang¹
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 143-158, 2021, DOI:10.32604/iasc.2021.012268
Abstract To overcome the problem that a teleoperation system loses robustness when the target moves outside the robot’s visual field or it is far away from the desired position, and to improve the operability and controllability of a master-slave teleoperation system, we present an image servo based haptic feedback (ISBHF) control method for teleoperating. The ISBHF control method involves extracting target feature points and constructing image servo based virtual force. First, the image characteristics of the environment and targets are identified and extracted by a 3D reconstruction method. A composite image Jacobian matrix is used to construct virtual guidance force based… More >

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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 >

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1404

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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 >

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Yan Zhou^1,*, Yue Li¹, Yunxing Zhang²
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 191-203, 2021, DOI:10.32604/iasc.2021.014142
Abstract Service pricing is a bottleneck in the development of innovation services, as it is the issue of most concern to the suppliers and demanders. In this paper, a negotiated pricing model that is based on the multiobjective genetic algorithm is developed for innovation services. Regarding the process of service pricing as a multiobjective problem, the objective functions which include the service price, service efficiency, and service quality for the suppliers and the demanders are constructed. Because the solution of a multiobjective problem is typically a series of alternatives, an additional negotiation process is necessary in determining the final decision. A… More >

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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 >

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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 >

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1082

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Zonglin Fu¹, Pei Hu¹, Wei Li², Jeng-Shyang Pan^1,*, Shuchuan Chu¹
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 233-247, 2021, DOI:10.32604/iasc.2021.014192
（This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)
Abstract The Equilibrium Optimizer (EO) algorithm is a novel meta-heuristic algorithm based on the strength of physics. To achieve better global search capability, a Parallel Equilibrium Optimizer algorithm, named PEO, is proposed in this paper. PEO is inspired by the idea of parallelism and adopts two different communication strategies between groups to improve EO. The first strategy is used to speed up the convergence rate and the second strategy promotes the algorithm to search for a better solution. These two kinds of communication strategies are used in the early and later iterations of PEO respectively. To check the optimization effect of… More >

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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 >

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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 >

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2024

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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 >

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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 >

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