International Journal of Computational Intelligence Systems
Volume 12, Issue 2, 2019
Jialan Xie, Wanhui Wen, Guangyuan Liu, Yongtao Li
Pages: 453 - 459
Nap is an effective way to reduce daily-level fatigue after several hours of work. However, no alarm clock, which intelligently manages the nap duration with good autonomic nervous recovery (ANR) from fatigue, has been reported in literature. In this work, an intelligent biological alarm clock algorithm...
2. An IT2-Based Hybrid Decision-Making Model Using Hesitant Fuzzy Linguistic Term Sets for Selecting the Development Plan of Financial Economics
Hasan Dincer, Secil Senel Uzunkaya, Serhat Yüksel
Pages: 460 - 473
The novelty of the study is to propose a hybrid IT2 decision-making approach under the hesitant fuzzy linguistic sets for evaluating the criteria and alternatives. For this purpose, the dimensions and criteria are weighted with interval type-2 fuzzy DEMATEL and the economic development plans are ranked...
Yun Li, Tianrui Li, Javier Montero
Pages: 474 - 475
Eugenio Aguirre, Miguel García-Silvente
Pages: 476 - 484
Recent improvements in deep learning techniques applied to images allow the detection of people with a high success rate. However, other types of sensors, such as laser rangefinders, are still useful due to their wide field of vision and their ability to operate in different environments and lighting...
5. An Adaptive Control Method for Resource Provisioning with Resource Utilization Constraints in Cloud Computing
Siqian Gong, Beibei Yin, Zheng Zheng, Kai-yuan Cai
Pages: 485 - 497
Cloud computing enables users to purchase virtual resources on demand; therefore, the service requests change over time. Dynamic resource provisioning for cloud computing has become a key challenge. To reduce the associated costs, resource utilization must be improved, and ensure the Quality of Service...
6. A Novel Method Based on Extended Uncertain 2-tuple Linguistic Muirhead Mean Operators to MAGDM under Uncertain 2-Tuple Linguistic Environment
Yi Liu, Jun Liu, Ya Qin, Yang Xu
Pages: 498 - 512
The present work is focused on multi-attribute group decision-making (MAGDM) problems with the uncertain 2-tuple linguistic information (ULI2–tuple) based on new aggregation operators which can capture interrelationships of attributes by a parameter vector P. To begin with, we present some new uncertain...
Junhui Liu, Xindu Chen
Pages: 513 - 518
Aiming at the diversity of Nondominated Sorting Genetic Algorithm II (NSGA-II) in screening out nondominated solutions, a crowding distance elimination (CDE) method is proposed. Firstly, the crowding distance is calculated in the same level of nondominated solutions, and the solution of minimum crowding...
8. New Product Launching with Pricing, Free Replacement, Rework, and Warranty Policies via Genetic Algorithmic Approach
Vijay Kumar, Biswajit Sarkar, Alok Nath Sharma, Mandeep Mittal
Pages: 519 - 529
New products are appearing in the marketplace at an ever-increasing step. Their launching is either market driven, or technology driven. Pricing and warranty policies play a vital role in launching of a new product, consequently growth of a company. In this paper a decision model is proposed to determine...
9. An Efficient Modified Particle Swarm Optimization Algorithm for Solving Mixed-Integer Nonlinear Programming Problems
Ying Sun, Yuelin Gao
Pages: 530 - 543
This paper presents an efficient modified particle swarm optimization (EMPSO) algorithm for solving mixed-integer nonlinear programming problems. In the proposed algorithm, a new evolutionary strategies for the discrete variables is introduced, which can solve the problem that the evolutionary strategy...
10. Improved TODIM Method Based on Linguistic Neutrosophic Numbers for Multicriteria Group Decision-Making
Peide Liu, Xinli You
Pages: 544 - 556
The TODIM (an acronym in Portuguese for interactive multicriteria decision-making) method can consider the decision-makers’ (DMs’) psychological behavior. However, the classical TODIM method has been unable to address fuzzy information such as the linguistic neutrosophic number (LNN), which is an effective...
Fan Jia, Peide Liu
Pages: 557 - 570
Group decision-making can effectively deal with complex decision problems in reality and takes important research status in the field of decision-making. In recent years, three-way decision has been a hot topic in the field of uncertain decision-making, so the models of three-way decision under group...
Chi-I Hsu, Shelly P. J. Wu, Chaochang Chiu
Pages: 571 - 579
Successful blogs receive high ratings and generate marketing value. What factors contribute to the success of a blog and how to predict its success level are questions worth discussing. A hybrid swam intelligence approach is proposed in this study to predict blog success level. First, this study develops...
13. A Novel Method Based on Fuzzy Tensor Technique for Interval-Valued Intuitionistic Fuzzy Decision-Making with High-Dimension Data
Shengyue Deng, Jianzhou Liu, Jintao Tan, Lixin Zhou
Pages: 580 - 596
To solve the interval-valued intuitionistic fuzzy decision-making problems with high-dimension data, the fuzzy matrix is extended to the fuzzy tensor in this paper. Based on the constructed tensor definition, we propose the generalized interval-valued intuitionistic fuzzy weighted averaging (GIIFWA)...
Pages: 597 - 612
Artificial Bee Colony (ABC) algorithm inspired by the intelligent source search and consumption characteristics of the real honey bees is one of the most powerful optimization techniques. Although the existing behaviours of the honey bees in standard ABC algorithm are capable of producing optimal or...
Wei-Ming Huang, Tzung-Pei Hong, Ming-Chao Chiang, Jerry Chun-Wei Lin
Pages: 613 - 626
In the field of fuzzy utility mining, the characteristics of transaction time have been a widely studied topic in data mining. However, using a single-conditional threshold for all items does not suffice to reflect the true properties of items. This paper, therefore, proposes a multi-conditional minimum...
Rajeev Kumar, Mohammad Zarour, Mamdouh Alenezi, Alka Agrawal, Raees Ahmad Khan
Pages: 627 - 642
It is critical to develop secure software with long-term performance and capability to withstand and forestall the growing competition in the software development industry. To enhance the potential of Confidentiality, Integrity, and Availability (CIA), a mechanism is required to built in and secure the...
Gangyi Hu, Jin Peng, Weili Kou
Pages: 643 - 648
It proposes a pseudo-random number generation algorithm based on cellular neural networks. This method uses the hyper-chaos characteristics of the cellular neural networks and sets the appropriate parameters to generate the pseudo-random number. The experimental results show that, compared with other...
18. GMDH2: Binary Classification via GMDH-Type Neural Network Algorithms—R Package and Web-Based Tool
Osman Dag, Erdem Karabulut, Reha Alpar
Pages: 649 - 660
Group method of data handling (GMDH)-type neural network algorithms are the self-organizing algorithms for modeling complex systems. GMDH algorithms are used for different objectives; examples include regression, classification, clustering, forecasting, and so on. In this paper, we present GMDH2 package...
Juan M. Alberola, Elena del Val, Angelo Costa, Paulo Novais, Vicente Julián
Pages: 661 - 675
Different studies have shown the benefits of a cooperative activities programme for the elderly. Members of a group with similar abilities or disabilities are often encouraged by having the opportunity to share their experiences, knowledge, or opinions. Nevertheless, when caregivers try to plan specific...
20. A Real-Coded Optimal Sensor Deployment Scheme for Wireless Sensor Networks Based on the Social Spider Optimization Algorithm
Fernando Fausto, Erik Cuevas, Oscar Maciel-Castillo, Bernardo Morales-Castañeda
Pages: 676 - 696
Wireless sensor networks (WSNs) involves a set of wireless sensor nodes located within a region of interest (ROI) to acquire and/or transmit specific information from their surroundings. A common problem in the operation of WSNs is sensor coverage, which is related to the distribution of sensor nodes...
Haisheng Li, Yanping Zheng, Xiaoqun Wu, Qiang Cai
Pages: 697 - 705
Generative adversarial network (GANs) has significant progress in 3D model generation and reconstruction recently years. GANs can generate 3D models by sampling from uniform noise distribution. But they generate randomly and are often not easy to control. To address this problem, we add the class information...
Julie Thomas, K. Indhira, V. M. Chandrasekaran
Pages: 706 - 712
The concept of T-normed fuzzy TM-subalgebras is introduced by applying the notion of t-norm to fuzzy TM-algebra and its properties are investigated. The ideas based on minimum t-norm are generalized to all widely accepted t-norms in a fuzzy TM-subalgebra.The characteristics of an idempotent T-normed...
23. Development of a Textile Coding Tag for the Traceability in Textile Supply Chain by Using Pattern Recognition and Robust Deep Learning
Kaichen Wang, Vijay Kumar, Xianyi Zeng, Ludovic Koehl, Xuyuan Tao, Yan Chen
Pages: 713 - 722
The traceability is of paramount importance and considered as a prerequisite for businesses for long-term functioning in today's global supply chain. The implementation of traceability can create visibility by the systematic recall of information related to all processes and logistics movement....
24. Attention Pooling-Based Bidirectional Gated Recurrent Units Model for Sentimental Classification
Dejun Zhang, Mingbo Hong, Lu Zou, Fei Han, Fazhi He, Zhigang Tu, Yafeng Ren
Pages: 723 - 732
Recurrent neural network (RNN) is one of the most popular architectures for addressing variable sequence text, and it shows outstanding results in many natural language processing (NLP) tasks and remarkable performance in capturing long-term dependencies. Many models have achieved excellent results based...
25. Evaluate the Effectiveness of Multiobjective Evolutionary Algorithms by Box Plots and Fuzzy TOPSIS
Xiaobing Yu, Chenliang Li, Hong Chen, Xianrui Yu
Pages: 733 - 743
Now, there are a lot of multiobjective evolutionary algorithms (MOEAs) available and these MOEAs argue that they are competitive. In fact, these results are generally unfair and unfaithful. In order to make fair comparison, comprehensive performance index system is established. The weights among the...
David Alfonso, Angeles Manjarrés, Simon Pickin
Pages: 744 - 760
We propose a semi-automatic method for the generation of educational-competency maps from repositories of multiple-choice question responses, using Bayesian structural learning and data-mining techniques. We tested our method on a large repository of responses to multiple-choice exam questions from an...
J. Francisco Figueroa-Perez, Juan C. Leyva-Lopez, Luis C. Santillan, Edgar O. Pérez Contreras, Pedro J. Sánchez
Pages: 761 - 774
Product design is an important phase of the new product development process and one of the most crucial decisions in marketing. In the latest two decades, a significant number of marketing decision support systems (MDSSs) for automating new product design activities have been reported in the literature...
Jorge de Andrés-Sánchez, Laura González-Vila Puchades
Pages: 775 - 794
The Lee–Carter model is a useful dynamic stochastic model to represent the evolution of central mortality rates throughout time. This model only considers the uncertainty about the coefficient related to the mortality trend over time but not to the age-dependent coefficients. This paper proposes a fuzzy-random...
29. A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search
Miguel Leon, Ning Xiong, Daniel Molina, Francisco Herrera
Pages: 795 - 808
Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been...
Jiasheng Zeng, Zhaowen Li, Meng Liu, Shimin Liao
Pages: 809 - 821
An incomplete interval-valued information system (IIVIS) is an information system (IS) in which the information values are interval numbers with missing values. This article researches information structures in an IIVIS. First, information structures in an IIVIS are obtained. In addition, the dependence...
Yi Yang, Yan Song
Pages: 822 - 832
MicroRNA regulatory module (MRM) plays an important role in the study of microRNA synergism. To detect MRMs, researchers have developed a number of related methods in the preceding decades. However, some existing methods are stochastic or specific to a certain situation. In this paper, we presented a...
Ángel Riesgo, Pedro Alonso, Irene Díaz, Susana Montes
Pages: 833 - 841
Fuzzy multisets represent a particularly challenging generalization of the concept of fuzzy sets. The membership degrees of fuzzy multisets are given by multisets in 0,1 rather than single values. Mathematically, they can be also seen as a generalization of the hesitant fuzzy sets. But in this general...
33. A Novel Approach to Fuzzy Cognitive Map Based on Hesitant Fuzzy Sets for Modeling Risk Impact on Electric Power System
Xiaodi Liu, Zengwen Wang, Shitao Zhang, Jiashu Liu
Pages: 842 - 854
Electric power industry has been undergoing enormous transformations. Therefore, it is necessary to improve the security of electric power system and the decision capacity in the emergency process. As a complicated system, electric power system is affected by many factors, the reasoning of which can...
34. Matrix-Based Approaches for Updating Approximations in Multigranulation Rough Set While Adding and Deleting Attributes
Peiqiu Yu, Jinjin Li, Hongkun Wang, Guoping Lin
Pages: 855 - 872
With advanced technology in medicine and biology, data sets containing information could be huge and complex that sometimes are difficult to handle. Dynamic computing is an efficient approach to solve some problems. Since multigranulation rough sets were proposed, many algorithms have been designed for...
Yuhong Liu, Shuying Liu, Cuiran Li, Danfeng Yang
Pages: 873 - 880
Compressed sensing theory is widely used in image and video signal processing because of its low coding complexity, resource saving, and strong anti-interference ability. Although the compression sensing theory solves the problems brought by the traditional signal processing methods to a certain extent,...
36. Multi-Attribute Decision-Making Method Based on Prospect Theory in Heterogeneous Information Environment and Its Application in Typhoon Disaster Assessment
Ruipu Tan, Wende Zhang, Lehua Yang, Shengqun Chen
Pages: 881 - 896
Aiming at the decision-making problem in heterogeneous information environment and considering the influence of decision makers' psychological behavior on decision-making results, this paper proposes a multi-attribute decision-making method based on prospect theory in heterogeneous information environment....
S. D. Lalitha, K. K. Thyagharajan
Pages: 903 - 913
Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this...
Xiuli Zhang, Chunming Ji
Pages: 897 - 902
Roughness is a comprehensive assessment indicator of pavement performance. Prediction of pavement roughness exhibits great difficulties by using traditional methods such as mechanistic-empirical method and regression method. Considering the fact that the value of international roughness index (IRI) varies...
39. Dynamic Knowledge Update Using Three-Way Decisions in Dominance-Based Rough Sets Approach While the Object Set Varies
Lei Wang, Min Li, Jun Ye, Xiang Yu, Ziqi Wang, Shaobo Deng
Pages: 914 - 928
Dominance-based rough set approach is the extension of classical Pawlak rough set theories and methodologies, in which the information with preference-ordered relation on the domain of attribute value is fully considered. In the dominance-based information system, upper and lower approximations will...
Sakshi Hooda, Suman Mann
Pages: 929 - 936
Real world problems for prediction usually try to predict rare occurrences. Application of standard classification algorithm is biased toward against these rare events, due to this data imbalance. Typical approaches to solve this data imbalance involve oversampling these “rare events” or under sampling...
German Rodriguez-Bermudez, Alejandro Lopez-Belchi, Arnaud Girault
Pages: 937 - 946
The purpose of a brain–computer interface (BCI) is the recording of brain signals to translate them into commands. This work proposes new naturalistic and intuitive motor imagery (MI) BCI task for aircraft Pilots for a BCI System, and explore if they take advantage of their previous motor experience...
Senquan Yang, Haifeng Su, Pu Li, Songxi Hu, Jinru Chen
Pages: 947 - 954
Effective channel order determination is an important problem in convolutive blind channel identification. The classical techniques are based on information theoretic criteria, which show a great potentiality to estimate the effective channel order. However, these methods are just effective for the overdetermined...
43. A Novel Distance Measure for Pythagorean Fuzzy Sets and its Applications to the Technique for Order Preference by Similarity to Ideal Solutions
Fang Zhou, Ting-Yu Chen
Pages: 955 - 969
Ever since the introduction of Pythagorean fuzzy (PF) sets, many scholars have focused on solving multicriteria decision-making (MCDM) problems with PF information. The technique for order preference by similarity to ideal solutions (TOPSIS) is a well-known and effective method for MCDM problems. The...
Alejandro Ramos, Jose M. Alonso, Ehud Reiter, Kees van Deemter, Albert Gatt
Pages: 970 - 983
We describe an applied methodology to build fuzzy models of geographical expressions, which are meant to be used for natural language generation purposes. Our approach encompasses a language grounding task within the development of an actual data-to-text system for the generation of textual descriptions...
45. Heterogeneous Interrelationships among Attributes in Multi-Attribute Decision-Making: An Empirical Analysis
Zhen-Song Chen, Xuan Zhang, Rosa M. Rodríguez, Xian-Jia Wang, Kwai-Sang Chin
Pages: 984 - 997
Tremendous effort has been exerted over the past few decades to construct multi-attribute decision functions with the capacity to model heterogeneous interrelationships among attributes. In this paper, we report an empirical study aiming to test whether or not considering interrelationships among attributes...
46. Constructing Novel Operational Laws and Information Measures for Proportional Hesitant Fuzzy Linguistic Term Sets with Extension to PHFL-VIKOR for Group Decision Making
Qiang Yang, Yan-Lai Li, Kwai-Sang Chin
Pages: 998 - 1018
To obtain reliable results in a qualitative multi-attribute group decision-making (MAGDM) problem, how to retain the evaluation information as much as possible and how to determine the reasonable weights of experts and attributes are two important issues. Proportional hesitant fuzzy linguistic term set...
Xin Zhou, Jianmin Pang
Pages: 1019 - 1028
Due to the seriousness of the network security situation, as a low-cost, high-efficiency email attack method, it is increasingly favored by attackers. Most of these attack vectors were embedded in email attachments and exploit vulnerabilities in Adobe and Office software. Among these attack samples,...
48. Revisiting the Role of Hesitant Multiplicative Preference Relations in Group Decision Making With Novel Consistency Improving and Consensus Reaching Processes
Rui Wang, Bin Shuai, Zhen-Song Chen, Kwai-Sang Chin, Jiang-Hong Zhu
Pages: 1029 - 1046
In recent years, hesitant multiplicative preference relation (HMPR) has been a powerful means to represent the evaluation information of decision makers during the pairwise comparison concerning alternatives. As the important parts of group decision making (GDM) issues with HMPRs, the consistency improving...
49. A Novel Hybrid Autoregressive Integrated Moving Average and Artificial Neural Network Model for Cassava Export Forecasting
Warut Pannakkong, Van-Nam Huynh, Songsak Sriboonchitta
Pages: 1047 - 1061
This paper proposes a novel hybrid forecasting model combining autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) with incorporating moving average and the annual seasonal index for Thailand's cassava export (i.e., native starch, modified starch, and sago). The...
50. Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients
Yanzhen Wang, Xuefeng Yan
Pages: 1062 - 1074
The key factors required to establish a precise soft sensor model for industrial processes include selection of variables affecting vital indicators from a large number of online measurement variables and elimination of the effects of unrelated disturbance variables. How to compress redundant information...
51. Using Fuzzy Sets in Surgical Treatment Selection and Homogenizing Stratification of Patients with Significant Chronic Ischemic Mitral Regurgitation
Natalia Nikolova, Plamen Panayotov, Daniela Panayotova, Snejana Ivanova, Kiril Tenekedjiev
Pages: 1075 - 1090
We present three (main one and two auxiliary) fuzzy algorithms to stratify observations in homogenous classes. These algorithms modify, upgrade and fuzzify crisp algorithms from our earlier works on a medical case study to select the most appropriate surgical treatment for patients with ischemic heart...
52. Green Supplier Selection Based on Dombi Prioritized Bonferroni Mean Operator with Single-Valued Triangular Neutrosophic Sets
Jianping Fan, Xuefei Jia, Meiqin Wu
Pages: 1091 - 1101
The choice of green suppliers involves a large amount of inaccurate, incomplete, and inconsistent information, and the single-valued triangular Neutrosophic number that is an extension of the single-valued Neutrosophic number can effectively handle such problems. Considering the advantages of the single-valued...
Yunxia Zhang, Degen Huang, Wei Gao, Vassilis G. Kaburlasos
Pages: 1102 - 1112
In order to deal with the decision making problem including some linguistic values uncertainty information, we propose an approach for decision making with linguistic weighted and unavoidable incomparable ranking based on Linguistic-valued lattice implication algebra (LV-LIA). The properties of binary...
54. Reduce Cost Smart Power Management System by Utilize Single Board Computer Artificial Neural Networks for Smart Systems
Sudad J. Ashaj, Ergun Erçelebi
Pages: 1113 - 1120
The plan and usage of a smart power management system for household and buildings that control numerous electrical appliances in real time have been reported in this work. The system is based on using artificial intelligence with low-cost single board computer in order to design a smart power management...
Na Wang, Qingzheng Xu, Rong Fei, Jungang Yang, Lei Wang
Pages: 1121 - 1133
Multi-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for...
Lin Shaodan, Feng Chen, Chen Zhide
Pages: 1134 - 1143
Ship detection is a canonical problem in computer vision. Motivated by the observation that the major bottleneck of ship detection lies on the different scales of ship instances in images, we focus on improving the detection rate, especially for the small-sized ships which are relatively far from the...
57. Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization
Thieu Nguyen, Tu Nguyen, Binh Minh Nguyen, Giang Nguyen
Pages: 1144 - 1161
In this paper, a novel algorithm called opposition-based coral reefs optimization (OCRO) is introduced. The algorithm is built as an improvement for coral reefs optimization (CRO) using opposition-based learning (OBL). For efficient modeling as the main part of this work, a novel time series forecasting...
Manuel Parra-Royon, José M. Benítez
Pages: 1162 - 1172
Fuzzy systems have become widely accepted and applied in a host of domains such as control, electronics or mechanics. The software for construction of these systems has traditionally been exploited from tools, platforms and languages run on-premise computing infrastructure. On the other hand, rise and...
K. Ramya, Yuvaraja Teekaraman, K. A. Ramesh Kumar
Pages: 1173 - 1178
Energy security (ES) has great impact on power grids. Therefore it is important to have power security service (PSS). The PSS should be designed to handle interference and interruption attack in the grid. The interference and interruption attack in the grid is handled by incursion-detection system (IDS)....
60. Aggregating Interrelated Attributes in Multi-Attribute Decision-Making With ELICIT Information Based on Bonferroni Mean and Its Variants
Bapi Dutta, Álvaro Labella, Rosa M. Rodríguez, Luis Martínez
Pages: 1179 - 1196
In recent times, to improve the interpretability and accuracy of computing with words processes, a rich linguistic representation model has been developed and referred to as Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT). This model extends the definition of the comparative...
Josip Musić, Stanko Kružić, Ivo Stančić, Vladan Papić
Pages: 1197 - 1211
The paper proposes and analyses performance of a fuzzy-based mediator with showcase examples in robot navigation. The mediator receives outputs from two controllers and uses estimated collision probability for adapting the signal proportions in the final output. The approach was implemented and tested...
Meonghun Lee, Hyun Yoe
Pages: 1212 - 1220
The goal of biomimicry is to resolve problems by studying and mimicking the characteristics of organisms or design elements in nature. Although wireless sensor networks are used in various fields, they have limited network lifespan. Research has thus focused on observing and modeling the behavioral principles...
Quanquan Shao, Jie Hu, Weiming Wang, Yi Fang, Mingshuo Han, Jin Qi, Jin Ma
Pages: 1221 - 1231
Deep neural network-based end-to-end visuomotor control for robotic manipulation is becoming a hot issue of robotics field recently. One-hot vector is often used for multi-task situation in this framework. However, it is inflexible using one-hot vector to describe multiple tasks and transmit intentions...
Zhenping Qiang, Libo He, Qinghui Zhang, Junqiu Li
Pages: 1232 - 1244
Semantic face inpainting from corrupted images is a challenging problem in computer vision and has many practical applications. Different from well-studied nature image inpainting, the face inpainting task often needs to fill pixels semantically into a missing region based on the available visual data....
Feng Cao, Yang Xu, Shuwei Chen, Jian Zhong, Guanfeng Wu
Pages: 1245 - 1254
Most of the advanced first-order logic automated theorem proving (ATP) systems adopt binary resolution methods as the core inference mechanism, where only two clauses are involved and a complementary pair of literals are eliminated during each deduction step. Recently, a novel multi-clause inference...
Xiao Yan, Li Dengfeng
Pages: 1255 - 1260
Presently, a conventional coalition structure can no more cover all the types of cooperative structures in practice, external cooperation between the coalitions also affects the payoff allocation between the participants. We propose a solution to solve the bargaining problem with level structure by defining...
K. G. Parthiban, S. Vijayachitra, R. Dhanapal
Pages: 1261 - 1269
Epilepsy can well be stated as a disorder of the central nervous systems (CNS) that brought about recurring seizures owing to chronic abnormal blasts of electrical discharge on the brain. Knowing if an individual is having a seizure and diagnosing the seizure type or epilepsy syndrome could be hard....
68. A Hybrid Global Optimization Algorithm Based on Particle Swarm Optimization and Gaussian Process
Yan Zhang, Hongyu Li, Enhe Bao, Lu Zhang, Aiping Yu
Pages: 1270 - 1281
The optimization problems and algorithms are the basics subfield in artificial intelligence, which is booming in the almost any industrial field. However, the computational cost is always the issue which hinders its applicability. This paper proposes a novel hybrid optimization algorithm for solving...
69. Artificial Neural Network to Model Managerial Timing Decision: Non-linear Evidence of Deviation from Target Leverage
Hafezali Iqbal Hussain, Fakarudin Kamarudin, Hassanudin Mohd Thas Thaker, Milad Abdelnabi Salem
Pages: 1282 - 1294
The current study highlights the utilization of a non-linear model to analyze an important decision-making process in the study of corporate finance where managers are deciding on the capital structure of a firm. This study compares the results from based on the unbalanced panel data multiple regression...
70. Economic Impact of Artificial Intelligence: New Look for the Macroeconomic Assessment in Asia-Pacific Region
Muhammad Haseeb, Sasmoko, Leonardus W. W. Mihardjo, Abid Rashid Gill, Kittisak Jermsittiparsert
Pages: 1295 - 1310
Objective To determine the impact of artificial intelligence (AI) on the selected economies in the Asia-Pacific region. Methods This secondary research collected data from macroeconomic and AI-specific data sets. The sources of data from which insights were gained included digital technology sectors...
71. Regional Input–Output Multiple Choice Goal Programming Model and Method for Industry Structure Optimization on Energy Conservation and GHG Emission Reduction in China
Ping Ping Lin, Deng Feng Li, Bin Qian Jiang, An Peng Wei, Gao Feng Yu
Pages: 1311 - 1322
To assess the potential of China's industrial restructuring on energy conservation and greenhouse gas (GHG) emission reduction in 2020, this study proposes an input–output multi-choice goal programming model and method. In this model, the goals include the maximization of gross domestic product...
72. II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems
László Barna Iantovics, Dimitris K. Iakovidis, Elena Nechita
Pages: 1323 - 1338
A novel accurate and robust metric called II-Learn for measuring the increase of intelligence of a system after a learning process is proposed. We define evolving learning systems, as systems that are able to make at least one measurable evolutionary step by learning. To prove the effectiveness of the...
Peng Wu, Jiaming Zhu, Ligang Zhou, Huayou Chen, Yu Chen
Pages: 1339 - 1352
Consistency and priority weights of preference relations are two important phases of decision-making process since the decision-making solutions are determined by them. Therefore, it is meaningful to investigate consistency and priority weights for preference relations. In this paper, consistency and...
74. A Biform Game Approach to Preventing Block Withholding Attack of Blockchain Based on Semi-CIS Value
Xiao-Li Du, Deng-Feng Li, Kai-Rong Liang
Pages: 1353 - 1360
In proof-of-work (PoW)-based blockchain network, the blockchain miners publish blocks by contributing computing power to solve crypto-puzzles. Due to the weak computing power of single miner, miners tend to join a mining pool and share the profits from the mining pool according to the contribution proportions...
75. EDAS Method for Multiple Attribute Group Decision Making with Probabilistic Uncertain Linguistic Information and Its Application to Green Supplier Selection
Yan He, Fan Lei, Guiwu Wei, Rui Wang, Jiang Wu, Cun Wei
Pages: 1361 - 1370
In order to adapt to the development of the new times, enterprises should not only care for the economic benefits, but also properly cope with environmental and social problems to achieve the integration of environmental, economic and social performance of sustainable development, so as to maximize the...
Hong-Yun Huang, Yan-Qing Lin, Qun Su, Xiao-Ting Gong, Ying-Ming Wang, Yang-Geng Fu
Pages: 1371 - 1381
The reasoning ability of the belief rule-based system is easy to be weakened by the quality of training instances, the inconsistency of rules and the values of parameters. This paper proposes an ensemble approach for extended belief rule-based systems to address this issue. The approach is based on the...
Raciel Yera, Manuel J. Barranco, Ahmad A. Alzahrani, Luis Martínez
Pages: 1382 - 1392
Recommender systems have played a relevant role in e-commerce for supporting online users to obtain suggestions about products that best fit their preferences and needs in overloaded search spaces. In such a context, several authors have proposed methods focused on removing the users' inconsistencies...
Peide Liu, Shufeng Cheng
Pages: 1393 - 1411
As a powerful extension to hesitant fuzzy sets (HFSs), dual hesitant fuzzy sets (DHFSs) have been closely watched by many scholars. The DHFSs can reflect the disagreement and hesitancy of decision-makers (DMs) flexibly and conveniently. However, all the evaluation values under the same membership degree...
Ahmed Saad Hussein, Tianrui Li, Chubato Wondaferaw Yohannese, Kamal Bashir
Pages: 1412 - 1422
Imbalance learning is a challenging task for most standard machine learning algorithms. The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing approach for handling imbalanced datasets, where the minority class is oversampled by producing synthetic examples in feature vector...
Chandadevi Giri, Sebastien Thomassey, Xianyi Zeng
Pages: 1423 - 1435
Growing use of social media such as Twitter, Instagram, Facebook, etc., by consumers leads to the vast repository of consumer generated data. Collecting and exploiting these data has been a great challenge for clothing industry. This paper aims to study the impact of Twitter on garment sales. In this...
81. Optimized Intelligent Design for Smart Systems Hybrid Beamforming and Power Adaptation Algorithms for Sensor Networks Decision-Making Approach
Ali Kamil Khiarullah, Ufuk Tureli, Didem Kivanc
Pages: 1436 - 1445
During last two decades, power adaptation and beamforming solutions have been proposed for multiple input multiple output (MIMO) Ad Hoc networks. Game theory based methods such as cooperative and non-cooperative joint beamforming and power control for the MIMO ad hoc systems consider the interference...
82. Application of Collaborative Filtering Algorithm in Mathematical Expressions of User Personalized Information Recommendation
Pages: 1446 - 1453
In order to solve the shortcomings of the existing mathematical expressions of the user's personalized recommendation search and to increase the accuracy of the user's personalized information recommendation, in this study, the collaborative filtering algorithm, the related theory of fuzzy...
83. Econometric Analysis of Disequilibrium Relations Between Internet Finance and Real Economy in China
Yixiao Li, Xin Jin, Wenwen Tian
Pages: 1454 - 1464
In this paper, we evaluate the development level of internet finance by focusing on three major virtual economies, internet “Baby” fund, internet financial credit and Shanghai Composite Index for the first time. We examine the disequilibrium relations between the development of both internet finance...
84. A Method to Multi-Attribute Group Decision-Making Problem with Complex q-Rung Orthopair Linguistic Information Based on Heronian Mean Operators
Peide Liu, Zeeshan Ali, Tahir Mahmood
Pages: 1465 - 1496
The notions of complex q-rung orthopair fuzzy sets (Cq-ROFSs) and linguistic sets (LSs) are two different concepts to deal with uncertain information in multi-attribute group decision-making (MAGDM) problems. The Heronain mean (HM) and geometric Heronain mean (GHM) operators are an effective tool used...
Muamer Kafadar, Zikrija Avdagic, Lejla Begic Fazlic
Pages: 1497 - 1511
There are many challenges in accurately measuring cigarette tar constituents. These include the need for standardized smoke generation methods related to unstable mixtures. In this research were developed algorithms using fusion of artificial intelligence methods to predict tar concentration. Outputs...
86. An Extreme Learning Machine and Gene Expression Programming-Based Hybrid Model for Daily Precipitation Prediction
Yuzhong Peng, Huasheng Zhao, Hao Zhang, Wenwei Li, Xiao Qin, Jianping Liao, Zhiping Liu, Jie Li
Pages: 1512 - 1525
Accurate daily precipitation prediction is crucially important. However, it is difficult to predict the precipitation accurately due to inherently complex meteorological factors and dynamic behavior of weather. Recently, considerable attention has been devoted in soft computing-based prediction approaches....
Yuvaraja Teekaraman, Pranesh Sthapit, Miheung Choe, Kiseon Kim
Pages: 1526 - 1536
The challenges faced in underwater communication systems are limited bandwidth, Energy consumption rate, larger propagation delay time, End-End Delay (E-ED), 3D topology, media access control, routing, resource utilization and power constraints. These issues and challenges are solved by means of deploying...
88. A Siamese Neural Network Application for Sales Forecasting of New Fashion Products Using Heterogeneous Data
Giuseppe Craparotta, Sébastien Thomassey, Amedeo Biolatti
Pages: 1537 - 1546
In the fashion market, the lack of historical sales data for new products imposes the use of methods based on Stock Keeping Unit (SKU) attributes. Recent works suggest the use of functional data analysis to assign the most accurate sales profiles to each item. An application of siamese neural networks...
89. GRA Method for Probabilistic Linguistic Multiple Attribute Group Decision Making with Incomplete Weight Information and Its Application to Waste Incineration Plants Location Problem
Fan Lei, Guiwu Wei, Jianping Lu, Jiang Wu, Cun Wei
Pages: 1547 - 1556
In this essay, we investigate the probabilistic linguistic multiple attribute group decision making (PL-MAGDM) with incomplete weight information. In this method, the linguistic representation developed recently is converted into probabilistic linguistic information. For deriving the weight information...
90. 2-Dimension Linguistic Bonferroni Mean Aggregation Operators and Their Application to Multiple Attribute Group Decision Making
Jianbin Zhao, Hua Zhu
Pages: 1557 - 1574
The aim of this paper is to provide a multiple attribute group decision making (MAGDM) method based on the 2-dimension linguistic weight Bonferroni mean aggregation (2DLWBMA) operator. Firstly, the new operations of 2-dimension linguistic variables are defined. Then, the 2-dimension linguistic Bonferroni...
91. Building an Artificial Neural Network with Backpropagation Algorithm to Determine Teacher Engagement Based on the Indonesian Teacher Engagement Index and Presenting the Data in a Web-Based GIS
Sasmoko Buddhtha, Christina Natasha, Edy Irwansyah, Widodo Budiharto
Pages: 1575 - 1584
Teacher engagement is a newly-emerged concept in the field of Indonesian teacher education. To support this concept, we designed an artificial neural network (ANN) using backpropagation, stochastic learning, and steepest gradient descent algorithms to determine teacher engagement based on the Indonesian...
92. Strategic Management of Organizational Knowledge and Employee's Awareness About Artificial Intelligence With Mediating Effect of Learning Climate
Mohammed Majdy M. Baslom, Shu Tong
Pages: 1585 - 1591
This study has aimed to examine the empirically relationship between strategic management of organizational knowledge and awareness about artificial intelligence (AI) through mediating effect of learning climate in service sector of Saudi Arabia. For better understanding, a structural questionnaire was...
Wang Wei, Jiang Yongbin, Luo Yanhong, Li Ji, Wang Xin, Zhang Tong
Pages: 1592 - 1601
In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence...
J. M. Luna, C. J. Carmona, A. M. García-Vico, M. J. del Jesus, S. Ventura
Pages: 1602 - 1612
To date, the subgroup discovery (SD) task has been considered in problems where a target variable is unequivocally described by a set of features, also known as instance. Nowadays, however, with the increasing interest in data storage, new data structures are being provided such as the multiple instance...
Zhihao Wang, Min Ren, Xiaoyan Tian, Xia Liang
Pages: 1613 - 1621
This article proposes a character-level neural language model (NLM) that is based on quantum theory. The input of the model is the character-level coding represented by the quantum semantic space model. Our model integrates a convolutional neural network (CNN) that is based on network-in-network (NIN)....
Mehmet Bilal Er, Ibrahim Berkan Aydilek
Pages: 1622 - 1634
Music has a great role and importance in human life since it has the ability to trigger or convey feelings. As recognizing music emotions is the subject of many studies conducted in many disciplines like science, psychology, musicology and art, it has attracted the attention of researchers as an up-to-date...
97. A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization
Saman Akbarzadeh, Selam Ahderom, Kamal Alameh
Pages: 1635 - 1648
Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications. Finding the best network and optimizing its hyperparameters for a specific application...
98. Evaluation on the Efficiency of Water-Energy-Food Nexus Based on Data Envelopment Analysis (DEA) and Malmquist in Different Regions of China
Tianming Zhang, Yejun Xu
Pages: 1649 - 1659
Water-energy-food (W-E-F) nexus is an essential element for human survival, as well as the basis for the sustainable development of regional economy and ecological environment. Water resource is an important input element of energy production and food production, which makes the W-E-F nexus become closer...