International Journal of Computational Intelligence Systems
Volume 13, Issue 1, 2020
1. Almost Automorphic Solutions to Cellular Neural Networks With Neutral Type Delays and Leakage Delays on Time Scales
Changjin Xu, Maoxin Liao, Peiluan Li, Zixin Liu
Pages: 1 - 11
In this paper, cellular neural networks (CNNs) with neutral type delays and time-varying leakage delays are investigated. By applying the existence of the exponential dichotomy of linear dynamic equations on time scales, a fixed point theorem and the theory of calculus on time scales, a set of sufficient...
2. Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
Jalal Sadoon Hameed Al-bayati, Burak Berk Üstündağ
Pages: 12 - 23
Apple leaf disease is the foremost factor that restricts apple yield and quality. Usually, much time is taken for disease detection with the existing diagnostic techniques; therefore, farmers frequently miss the best time for preventing and treating diseases. The detection of apple leaf diseases is a...
3. Personalized Tag Recommendation Based on Convolution Feature and Weighted Random Walk
Liu Zheng, Zhao Tianlong, Han Huijian, Zhang Caiming
Pages: 24 - 35
Automatic image semantic annotation is of great importance for image retrieval, therefore, this paper aims to recommend tags for social images according to user preferences. With the rapid development of the image-sharing community, such as Flickr, the image resources of the social network with rich...
4. Light Weight Proactive Padding Based Crypto Security System in Distributed Cloud Environment
N. Indira, S. Rukmanidevi, A.V. Kalpana
Pages: 36 - 43
The organization maintains various information in cloud which is a loosely coupled environment. However, the nature of cloud encourages the threats in different level. Among them the data security has been a keen issue being identified and challenges the service provider. To improve the data security...
5. New Ant Colony Optimization Algorithm for the Traveling Salesman Problem
Pages: 44 - 55
As one suitable optimization method implementing computational intelligence, ant colony optimization (ACO) can be used to solve the traveling salesman problem (TSP). However, traditional ACO has many shortcomings, including slow convergence and low efficiency. By enlarging the ants' search space...
6. Coreference Resolution Using Neural MCDM and Fuzzy Weighting Technique
Samira Hourali, Morteza Zahedi, Mansour Fateh
Pages: 56 - 65
Coreference resolution has been an active field of research in the past several decades and plays a vital role in many areas such as information extraction, document summarization, machine translation, and question answering systems. This paper presents a new coreference resolution approach by incorporating...
7. RunPool: A Dynamic Pooling Layer for Convolution Neural Network
Huang Jin Jie, Putra Wanda
Pages: 66 - 76
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image...
8. Multi-Class Skin Lesions Classification System Using Probability Map Based Region Growing and DCNN
T. Sreekesh Namboodiri, A. Jayachandran
Pages: 77 - 84
Background: Melanoma is a type of threatening pigmented skin lesion, and as of now is among the most hazardous existing diseases. Suitable automated diagnosis of skin lesions and Melanoma classification can extraordinarily enhance early identification of melanomas. Methods: However, classification...
9. A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
Shengdong Du, Tianrui Li, Xun Gong, Shi-Jinn Horng
Pages: 85 - 97
Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which can jointly and adaptively learn the spatial–temporal correlation features and long temporal interdependence...
10. Enhanced Particle Swarm Optimization Based on Reference Direction and Inverse Model for Optimization Problems
Wei Li, Yaochi Fan, Qingzheng Xu
Pages: 98 - 129
While particle swarm optimization (PSO) shows good performance for many optimization problems, the weakness in premature convergence and easy trapping into local optimum, due to the ignorance of the diversity information, has been gradually recognized. To improve the optimization performance of PSO,...
11. Optimisation of Group Consistency for Incomplete Uncertain Preference Relation
Xiujuan Ma, Zaiwu Gong, Weiwei Guo
Pages: 130 - 141
An incomplete uncertain preference relation (UPR) is typical in group decision making (GDM) for decision makers (DMs) to express preference over alternatives because of the information interaction barrier between people and decision making environment. Completing missing values can guarantee individual...
12. Examining the Impact of Artificial Intelligence (AI)-Assisted Social Media Marketing on the Performance of Small and Medium Enterprises: Toward Effective Business Management in the Saudi Arabian Context
Pages: 142 - 152
Purpose: To examine the impact of artificial intelligence-assisted social media marketing (AISMM)on the performance of start-up businesses of small and medium enterprises (SMEs) in Saudi Arabia. Design/methodology/approach: A survey technique was employed whereby primary and secondary data was collected,...
13. Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing
Xian-Jia Wang, Guan-Tian Zeng, Ke-Xin Zhang, Hai-Bo Chu, Zhen-Song Chen
Pages: 153 - 166
Based on a multi-class support vector machine, an urban real estate early warning model is constructed for the Beijing real estate market. The initial indicator system is established based on the historical development of Beijing's real estate market and the selection of real estate early warning...
14. Interval Subsethood Measures with Respect to Uncertainty for the Interval-Valued Fuzzy Setting
Barbara Pękala, Urszula Bentkowska, Mikel Sesma-Sara, Javier Fernandez, Julio Lafuente, Abdulrahman Altalhi, Maksymilian Knap, Humberto Bustince, Jesús M. Pintor
Pages: 167 - 177
In this paper, the problem of measuring the degree of subsethood in the interval-valued fuzzy setting is addressed. Taking into account the widths of the intervals, two types of interval subsethood measures are proposed. Additionally, their relation and main properties are studied. These developments...
15. DAMA: A Dynamic Classification of Multimodal Ambiguities
Patrizia Grifoni, Maria Chiara Caschera, Fernando Ferri
Pages: 178 - 192
Ambiguities represent uncertainty but also a fundamental item of discussion for who is interested in the interpretation of languages and it is actually functional for communicative purposes both in human–human communication and in human–machine interaction. This paper faces the need to address ambiguity...
16. A Single Machine Scheduling with Periodic Maintenance and Uncertain Processing Time
Jiayu Shen, Yuanguo Zhu
Pages: 193 - 200
A single machine scheduling problem with periodic maintenance is studied in this paper. Due to many uncertainties in reality, the processing time is recognized as an uncertain variable. The aim is to minimize the makespan at a confidence level. An uncertain chance-constrained programming model is developed...
17. On the Use of Conjunctors With a Neutral Element in the Modus Ponens Inequality
Ana Pradera, Sebastia Massanet, Daniel Ruiz, Joan Torrens
Pages: 201 - 211
The inference rule of Modus Ponens has been extensively investigated in the framework of approximate reasoning, especially for the case of t-norms. Recently, more general kinds of conjunctors have also been considered, like semi-copulas, copulas, and conjunctive uninorms. A common feature of all these...
18. Sparse Least Squares Support Vector Machine With Adaptive Kernel Parameters
Chaoyu Yang, Jie Yang, Jun Ma
Pages: 212 - 222
In this paper, we propose an efficient Least Squares Support Vector Machine (LS-SVM) training algorithm, which incorporates sparse representation and dictionary learning. First, we formalize the LS-SVM training as a sparse representation process. Second, kernel parameters are adjusted by optimizing their...
19. An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms
Ahmed Maghawry, Mohamed Kholief, Yasser Omar, Rania Hodhod
Pages: 223 - 233
The digital transformation revolution has been crawling toward almost all aspects of our lives. One form of the digital transformation revolution appears in the transformation of our routine everyday tasks into computer executable programs in the form of web, desktop and mobile applications. The vast...
20. Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices
Fong-Ching Yuan, Chao-Hui Lee, Chaochang Chiu
Pages: 234 - 246
Gold price prediction has long been a crucial and challenging research topic for gold investors. In conventional models, most scholars have used the historical gold price or economic indicators to forecast gold prices. The gold prices depend mainly on confidence in the current market. To reduce the time...
21. Feature Selection Based on a Novel Improved Tree Growth Algorithm
Changkang Zhong, Yu Chen, Jian Peng
Pages: 247 - 258
Feature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance. Tree growth algorithm (TGA) is a recent proposed population-based metaheuristic, which shows great power of...
22. Optimal Core Operation in Supply Chain Finance Ecosystem by Integrating the Fuzzy Algorithm and Hierarchical Framework
Pages: 259 - 274
Supply chain finance (SCF), which has the key concept of the delivery of credit, is a new type of financial service that can enhance the financial efficiency of a supply chain. Using the transaction records from the core operations (CO) of the members, financers can provide a higher level of cash flow...
23. Information Retrieval Based on Knowledge-Enhanced Word Embedding Through Dialog: A Case Study
Jin Ren, Hengsheng Wang, Tong Liu
Pages: 275 - 290
The aim of this paper is to provide a systematic route of information retrieval from a knowledge-based database (or domain knowledge) through a dialog system of natural language interaction. The application is about a comprehensive building at a university, with classrooms, laboratory rooms, meeting...
24. Multi-Objective Particle Swarm Optimization Algorithm for the Minimum Constraint Removal Problem
Bo Xu, Feng Zhou, Antonio Marcel Gates
Pages: 291 - 299
This paper proposes a multi-objective approach for the minimum constraint removal (MCR) A problem. First, a multi-objective model for MCR path planning is constructed. This model takes into account factors such as the minimum constraint set, the route length, and the cost. A multi-objective particle...
25. Research on National Pattern Reuse Design and Optimization Method Based on Improved Shape Grammar
Ning Ding, Jian Lv, Lai Hu
Pages: 300 - 309
Considering the low degree of abstraction of traditional shape grammar in national pattern reuse design, this paper proposes a method based on the combination of improved shape grammar and an optimization algorithm to reuse national patterns design. As an application example, we carried out research...
26. Hierarchical Bayesian Choice of Laplacian ARMA Models Based on Reversible Jump MCMC Computation
Pages: 310 - 317
An autoregressive moving average (ARMA) is a time series model that is applied in everyday life for pattern recognition and forecasting. The ARMA model contains a noise which is assumed to have a specific distribution. The noise is often considered to have a Gaussian distribution. However in applications,...
27. Applying Heuristic Algorithms to Solve Inter-hospital Hierarchical Allocation and Scheduling Problems of Medical Staff
Ping-Shun Chen, Wen-Tso Huang, Tsung-Huan Chiang, Gary Yu-Hsin Chen
Pages: 318 - 331
To address the inter-hospital hierarchical allocation and scheduling problems, this research used the pooling resource concept to allocate medical staff among hospital branches as well as determine their monthly schedules. This study proposed a two-stage strategy. The first stage proposed three heuristic...
28. An Extension of Social Network Group Decision-Making Based on TrustRank and Personas
Mei Cai, Yiming Wang, Zaiwu Gong
Pages: 332 - 340
With the development of social networking big data, social network group decision-making (SN-GDM) has been widely applied in many fields. This paper focuses on three main components: (1) the determination of the decision makers' (DMs) weights based on different social influence; (2) the anti-deception...
29. End-to-End Sequence Labeling via Convolutional Recurrent Neural Network with a Connectionist Temporal Classification Layer
Xiaohui Huang, Lisheng Qiao, Wentao Yu, Jing Li, Yanzhou Ma
Pages: 341 - 351
Sequence labeling is a common machine-learning task which not only needs the most likely prediction of label for a local input but also seeks the most suitable annotation for the whole input sequence. So it requires the model that is able to handle both the local spatial features and temporal-dependence...
30. NNIR: N-Non-Intersecting-Routing Algorithm for Multi-Path Resilient Routing in Telecommunications Applications
Lewis Veryard, Hani Hagras, Andrew Starkey, Anthony Conway, Gilbert Owusu
Pages: 352 - 365
In this paper, we will present a N-Non-Intersecting-Routing (NNIR) algorithm which is used to reduce the cost of resilient routing in telecommunications problems. Resilient Routing is the connections between two locations in a graph through the use of N completely independent routes. Resilient Routing...
31. A Multi-Criteria Group Decision-Making Approach Based on Improved BWM and MULTIMOORA with Normal Wiggly Hesitant Fuzzy Information
Chengxiu Yang, Qianzhe Wang, Weidong Peng, Jie Zhu
Pages: 366 - 381
Multi-criteria group decision-making (MCGDM) problems are widespread in real life. However, most existing methods, such as hesitant fuzzy set (HFS), hesitant fuzzy linguistic term set (HFLTS) and inter-valued hesitant fuzzy set (IVHFS) only consider the original evaluation data provided by experts but...
32. Optimal Walking Gait Generator for Biped Robot Using Modified Jaya Optimization Technique
Ho Pham Huy Anh, Tran Thien Huan
Pages: 382 - 399
This paper treats the optimization of the biped walking trajectory that can be used as a reference trajectory for control. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is derived for the specified positions of the hips and feet....
33. Blind Channel and Data Estimation Using Fuzzy Logic Empowered Cognitive and Social Information-Based Particle Swarm Optimization (PSO)
Muhammad Asadullah, Muhammad Adnan Khan, Sagheer Abbas, Tahir Alyas, Muhammad Asif Saleem, Areej Fatima
Pages: 400 - 408
Multiple Input Multiple Output (MIMO) is a technology used to improve the channel capacity of the wireless communication systems. Rapid increase in the number of users has led to data rate demand increased in growing modern wireless communication systems. To overcome this issue, MIMO is being used with...
34. m-Polar Picture Fuzzy Ideal of a BCK Algebra
Shovan Dogra, Madhumangal Pal
Pages: 409 - 420
In this paper, the notions of m-polar picture fuzzy subalgebra (PFSA), m-polar picture fuzzy ideal (PFI) and m-polar picture fuzzy implicative ideal (PFII) of BCK algebra are introduced and some related basic results are presented. A relation between m-polar PFI and m-polar PFII is established. It is...
35. Multi-Sine Cosine Algorithm for Solving Nonlinear Bilevel Programming Problems
Yousria Abo-Elnaga, M.A. El-Shorbagy
Pages: 421 - 432
In this paper, multi-sine cosine algorithm (MSCA) is presented to solve nonlinear bilevel programming problems (NBLPPs); where three different populations (completely separate from one another) of sine cosine algorithm (SCA) are used. The first population is used to solve the upper level problem, while...
36. A Two-Stage Multi-objective Programming Model to Improve the Reliability of Solution
Chenxia Jin, Fachao Li, Kaixin Feng, Yunfeng Guo
Pages: 433 - 443
Randomness is a common uncertainty encountered in practical multi-objectives decision-making. But it is always a challenge for decision-makers to process randomness in multi-objective programming problems. This paper takes the decision-making objectives as fuzzy events and aims to solve numerical multi-objective...
37. A Novel Pythagorean Fuzzy LINMAP-Based Compromising Approach for Multiple Criteria Group Decision-Making with Preference Over Alternatives
Jih-Chang Wang, Ting-Yu Chen
Pages: 444 - 463
This paper presents a new compromising approach to multiple criteria group decision-making (MCGDM) for the treatment of uncertainty which is based on Pythagorean fuzzy (PF) sets. The present work intends to propose a novel linear programming technique for multidimensional analysis of preference (LINMAP)...
38. On Contradiction and Inclusion Using Functional Degrees
Nicolás Madrid, Manuel Ojeda-Aciego
Pages: 464 - 471
The notion of inclusion is a cornerstone in set theory and therefore, its generalization in fuzzy set theory is of great interest. The degree of f-inclusion is one generalization of such a notion that differs from others existing in the literature because the degree of inclusion is considered as a mapping...
39. Synchronization of Delayed Inertial Cohen–Grossberg Neural Networks Under Adaptive Feedback Controller
Qun Huang, Jinde Cao, Qingshan Liu
Pages: 472 - 478
This paper investigates the issue on adaptive synchronization of delayed inertial Cohen–Grossberg neural networks (ICGNNs). By adopting the method of variable transformation, the addressed model, which includes the so-called inertial term, is transformed into first-order differential equations. On the...
40. Online Streaming Feature Selection via Multi-Conditional Independence and Mutual Information Entropy†
Hongyi Wang, Dianlong You
Pages: 479 - 487
The goals of feature selection are to remove redundant and irrelevant features from high-dimensional data, extract the “optimal feature subset” of the original feature space to improve the classification accuracy, and reduce the time complexity. Traditional feature selection algorithms are based on static...
41. Supervised Filter Learning for Coronary Artery Vesselness Enhancement Diffusion in Coronary CT Angiography Images
Pages: 488 - 495
In medical imaging, vesselness diffusion is usually performed to enhance the vessel structures of interest and reduce background noises, before vessel segmentation and analysis. Numerous learning-based techniques have recently become very popular for coronary artery filtering due to their impressive...
42. Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing
Boonhatai Kruekaew, Warangkhana Kimpan
Pages: 496 - 510
This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous...
43. A Novel Cause-Effect Variable Analysis in Enterprise Architecture by Fuzzy Logic Techniques
C. Rubio-Manzano, Juan Carlos Díaz, D. Alfonso-Robaina, A. Malleuve, Jesús Medina
Pages: 511 - 523
In this paper, we present a new integration approach for managing Information Technology variables within enterprise architecture in an integrated way. Additionially, a novel method based on fuzzy logic for cause-effect variable analysis is proposed as a useful support decision-making tool for companies...
44. A Decomposition-Based Multiobjective Chemical Reaction Optimization Algorithm for Community Detection in Complex Networks
Hongye Li, Wei Gan
Pages: 524 - 537
Community detection structure is very important for understanding the organization of the complex networks. This problem is NP-hard, which is modeled as a seriously nonlinear optimization problem. Recently, different intelligence algorithm has shown promising results for this problem. The chemical reaction...
45. Bi-GRU Sentiment Classification for Chinese Based on Grammar Rules and BERT
Qiang Lu, Zhenfang Zhu, Fuyong Xu, Dianyuan Zhang, Wenqing Wu, Qiangqiang Guo
Pages: 538 - 548
Sentiment classification is a fundamental task in NLP, and its aim to predict the sentiment polarities of the given texts. Recent researches show great interest in modeling Chinese sentiment classification. However, the complexity of Chinese grammar makes the performance of the existing Chinese sentiment...
46. A Fuzzy Framework to Evaluate Players' Performance in Handball
Francisco P. Romero, Eusebio Angulo, Jesús Serrano-Guerrero, José A. Olivas
Pages: 549 - 558
The evaluation of the players' performance in sports teams is commonly based on the opinion of experts who do not always agree on the importance of the chosen indicators. This paper presents a novel approach based on fuzzy multi-criteria group decision-making tools for selecting those criteria that...
47. Vegetable Recognition and Classification Based on Improved VGG Deep Learning Network Model
Zhenbo Li, Fei Li, Ling Zhu, Jun Yue
Pages: 559 - 564
To improve the accuracy of automatic recognition and classification of vegetables, this paper presents a method of recognition and classification of vegetable image based on deep learning, using the open source deep learning framework of Caffe, the improved VGG network model was used to train the vegetable...
48. Solution to Resolve Cognitive Ambiguity in Interactive Customization of Product Shape
Dong Zeng, Zhuan Zhou, Maoen He, Chaogang Tang
Pages: 565 - 575
Interactive genetic algorithms have been used in a wide variety of applications and extensively developed to facilitate the personalization and customization of products for users. However, the ambiguity effect or cognitive ambiguity of users during the product customization process will affect the effects...
49. Multi-view Genetic Programming Learning to Obtain Interpretable Rule-Based Classifiers for Semi-supervised Contexts. Lessons Learnt
Carlos García-Martínez, Sebastián Ventura
Pages: 576 - 590
Multi-view learning analyzes the information from several perspectives and has largely been applied on semi-supervised contexts. It has not been extensively analyzed for inducing interpretable rule-based classifiers. We present a multi-view and grammar-based genetic programming model for inducing rules...
50. Machine Learning Techniques for the Detection of Inappropriate Erotic Content in Text
Gonzalo Molpeceres Barrientos, Rocío Alaiz-Rodríguez, Víctor González-Castro, Andrew C. Parnell
Pages: 591 - 603
Nowadays, children have access to Internet on a regular basis. Just like the real world, the Internet has many unsafe locations where kids may be exposed to inappropriate content in the form of obscene, aggressive, erotic or rude comments. In this work, we address the problem of detecting erotic/sexual...
51. Using Fuzzy Logic Algorithms and Growing Hierarchical Self-Organizing Maps to Define Efficient Security Inspection Strategies in a Container Terminal
Leonela Morales, Luis Onieva, Ventura Pérez, Pablo Cortés
Pages: 604 - 623
Maritime transport is one of the oldest methods of moving various types of goods, and it continues to have an important role in our modern society. More than 20 million containers are transported across the oceans daily. However, this form of transportation is constantly threatened by illegal operations,...
52. An Integrated Decision Framework for Group Decision-Making with Double Hierarchy Hesitant Fuzzy Linguistic Information and Unknown Weights
Raghunathan Krishankumar, Kattur Soundarapandian Ravichandran, Huchang Liao, Samarjit Kar
Pages: 624 - 637
As an attractive generalization to hesitant fuzzy linguistic term set, double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is used to represent complex linguistic expressions by providing rich and flexible context. Previous studies on DHHFLTS show that aggregation of preference information...
53. Technology State Control Based on Multi-source Heterogeneous Data Fusion in Manufacturing
Jie Yu, Shenggao Gu, Wei Zhang
Pages: 638 - 644
The technology state is determined not by a single data, but by multiple data sources that has a certain degree of uncertainty and ambiguity, and even be contradictory which is difficult to support decision-making effectively. In this paper, an efficient intelligent decision-making method based on multi-source...
54. Automatic Music Generator Using Recurrent Neural Network
Alexander Agung Santoso Gunawan, Ananda Phan Iman, Derwin Suhartono
Pages: 645 - 654
In this paper, we developed an automatic music generator with midi as the input file. This study uses long short-term memory (LSTM) and gated recurrent units (GRUs) network to build the generator and evaluator model. First, a midi file is converted into a midi matrix in midi encoding process. Then, each...
55. Incorporating Active Learning into Machine Learning Techniques for Sensory Evaluation of Food
Nhat-Vinh Lu, Roengchai Tansuchat, Takaya Yuizono, Van-Nam Huynh
Pages: 655 - 662
The sensory evaluation of food quality using a machine learning approach provides a means of measuring the quality of food products. Thus, this type of evaluation may assist in improving the composition of foods and encouraging the development of new food products. However, human intervention has been...
56. A Neural Network for Moore–Penrose Inverse of Time-Varying Complex-Valued Matrices
Yiyuan Chai, Haojin Li, Defeng Qiao, Sitian Qin, Jiqiang Feng
Pages: 663 - 671
The Moore–Penrose inverse of a matrix plays a very important role in practical applications. In general, it is not easy to immediately solve the Moore–Penrose inverse of a matrix, especially for solving the Moore–Penrose inverse of a complex-valued matrix in time-varying situations. To solve this problem...
57. A Novel Combinational ATP Based on Contradiction Separation for First-Order Logic
Jian Zhong, Yang Xu, Feng Cao
Pages: 672 - 680
At present, most of the first-order logic theorem provers use a binary-resolution method, which can effectively solve the general first-order logic problems to a certain extent. However, the cooperative processing ability of this method for multiple clauses is insufficient, and it is easy to cause rapid...
58. Quasi-Copulas, Copulas and Fuzzy Implicators
Radko Mesiar, Anna Kolesárová
Pages: 681 - 689
In this paper, we study relations between fuzzy implicators and some kinds of fuzzy conjunctors, in particular, quasi-copulas and copulas. We show that there is a one-to-one correspondence between the classes of all quasi-copulas and 1-Lipschitz fuzzy implicators. A similar relation holds for copulas...
59. A Novel Density Peaks Clustering Algorithm Based on Local Reachability Density
Hanqing Wang, Bin Zhou, Jianyong Zhang, Ruixue Cheng
Pages: 690 - 697
A novel clustering algorithm named local reachability density peaks clustering (LRDPC) which uses local reachability density to improve the performance of the density peaks clustering algorithm (DPC) is proposed in this paper. This algorithm enhances robustness by removing the cutoff distance dc which...
60. Individual Heterogeneous Learning with Global Centrality in Prisoner Dilemma Evolutionary Game on Complex Network
Zundong Zhang, Yifang Zhang, William Danziger
Pages: 698 - 705
The influence of individual heterogeneity on the evolutionary game has been studied extensively in recent years. Whereas many theoretical studies have found that the heterogeneous learning ability effects cooperation rate, the individual learning ability in networks is still not well understood. It is...
61. Using Recurrent Neural Networks for Part-of-Speech Tagging and Subject and Predicate Classification in a Sentence
David Muñoz-Valero, Luis Rodriguez-Benitez, Luis Jimenez-Linares, Juan Moreno-Garcia
Pages: 706 - 716
In natural language processing the use of deep learning techniques is very common. In this paper, a technique to identify the subject and predicate in a sentence is introduced. To achieve this, the proposed technique completes POS tagging identifying in a later stage the subject and the predicate in...
62. Dose Regulation Model of Norepinephrine Based on LSTM Network and Clustering Analysis in Sepsis
Jingming Liu, Minghui Gong, Wei Guo, Chunping Li, Hui Wang, Shuai Zhang, Christopher Nugent
Pages: 717 - 726
Sepsis is a life-threatening condition that arises when the body's response to infection causes injury to its own tissues and organs. Despite the advancement of medical diagnosis and treatment technologies, the morbidity and mortality of sepsis are still relatively high. In this paper, a two-layer...
63. Optimizing Production Mix Involving Linear Programming with Fuzzy Resources and Fuzzy Constraints
B.O. Onasanya, Y. Feng, Z. Wang, O.V. Samakin, S. Wu, X. Liu
Pages: 727 - 733
In this paper, Fuzzy Linear Programming (FLP) was used to model the production processes at a university-based bakery for optimal decisions in the daily productions of the bakery. Using the production data of five products from the bakery, a fuzzy linear programme was developed to help make decisions...
64. Weighted Nonnegative Matrix Factorization for Image Inpainting and Clustering
Xiangguang Dai, Nian Zhang, Keke Zhang, Jiang Xiong
Pages: 734 - 743
Conventional nonnegative matrix factorization and its variants cannot separate the noise data space into a clean space and learn an effective low-dimensional subspace from Salt and Pepper noise or Contiguous Occlusion. This paper proposes a weighted nonnegative matrix factorization (WNMF) to improve...
65. Fine-Grained Sentiment Analysis for Measuring Customer Satisfaction Using an Extended Set of Fuzzy Linguistic Hedges
Asad Khattak, Waqas Tariq Paracha, Muhammad Zubair Asghar, Nosheen Jillani, Umair Younis, Furqan Khan Saddozai, Ibrahim A. Hameed
Pages: 744 - 756
In recent years, the boom in social media sites such as Facebook and Twitter has brought people together for the sharing of opinions, sentiments, emotions, and experiences about products, events, politics, and other topics. In particular, sentiment-based applications are growing in popularity among individuals...
66. An Efficient Clustering Algorithm for Mixed Dataset of Postoperative Surgical Records
Hemant Petwal, Rinkle Rani
Pages: 757 - 770
In data mining, data clustering is a prevalent data analysis methodology that organizes unlabeled data points into distinct clusters based on a similarity measure. In recent years, several clustering algorithms found, dependent on a predefined number of clusters and centered around the dataset with either...
67. VGG16-T: A Novel Deep Convolutional Neural Network with Boosting to Identify Pathological Type of Lung Cancer in Early Stage by CT Images
Shanchen Pang, Fan Meng, Xun Wang, Jianmin Wang, Tao Song, Xingguang Wang, Xiaochun Cheng
Pages: 771 - 780
Lung cancer is known as the highest mortality rate cancer, which needs biopsy to determine its subtype for further treatment. Recently, deep learning has provided powerful tools in lung cancer diagnose and therapeutic regimen making. However, it is still a challenge to identify the pathological type...
68. An Efficient Evolutionary Metaheuristic for the Traveling Repairman (Minimum Latency) Problem
Boldizsár Tüű-Szabó, Péter Földesi, László T. Kóczy
Pages: 781 - 793
In this paper we revisit the memetic evolutionary family of metaheuristics, called Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), whose members combine Furuhashi's Bacterial Evolutionary Algorithm and various discrete local search techniques. These algorithms have proven to be efficient...
69. New Kind of MV-Modules
S. Saidi Goraghani, R.A. Borzooei, S.S. Ahn, Y.B. Jun
Pages: 794 - 801
In this paper, by considering the notion of MV-modules, which is the structure that naturally correspond to lu-modules over lu-rings, we investigate some properties of a new kind of MV-modules, that we introduced in Borzooei and Saidi Goraghani, Free MV-modules, J. Intell. Fuzzy Syst. 31 (2016), 151–161...
70. An Intelligent Traffic Light System Using Object Detection and Evolutionary Algorithm for Alleviating Traffic Congestion in Hong Kong
Sin-Chun Ng, Chok-Pang Kwok
Pages: 802 - 809
High traffic flow is a typical characteristic of a mobilized city with a high population. Efficient traffic management is a proper solution to reduce the stress and anxiety associated with driving or traveling. The road users can have better timing for traveling as they will not experience journey delays...
71. A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
Ivona Brajević, Predrag S. Stanimirović, Shuai Li, Xinwei Cao
Pages: 810 - 821
Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed...
72. Group Decision-Making Using Complex q-Rung Orthopair Fuzzy Bonferroni Mean
Peide Liu, Zeeshan Ali, Tahir Mahmood, Nasruddin Hassan
Pages: 822 - 851
Complex q-rung orthopair fuzzy set (CQROFS), as a modified notion of complex fuzzy set (CFS), is an important tool to cope with awkward and complicated information. CQROFS contains two functions which are called truth grade and falsity grade by the form of complex numbers belonging to unit disc in a...
73. An Intelligent and Automated Approach for Smart Minimarkets
Talal A. Edwan, Ashraf Tahat, Sara Hammouri, Leen Hashem, Leen Da'boul
Pages: 852 - 863
This paper presents the design and implementation of a smart and safe minimarket prototype for deployment in busy smart cities to mitigate the overhead of shopping experience. The prototype allows customers to remotely access and browse the available products at the minimarket using a special smart-phone...
74. An Integer Cat Swarm Optimization Approach for Energy and Throughput Efficient MPSoC Design
Shahid Ali Murtza, Ayaz Ahmad, Muhammad Yasir Qadri, Nadia N. Qadri, Majed Alhaisoni, Sajid Baloch
Pages: 864 - 874
Modern multicore architectures have an ability to allocate optimum system resources for a specific application to have improved energy and throughput balance. The system resources can be optimized automatically by using optimization algorithms. State-of-the-art using optimization algorithm in the field...
75. Fuzzy Load Forecast with Optimized Parametric Adjustment Using Jaya Optimization Algorithm
Ho Pham Huy Anh
Pages: 875 - 892
This paper proposes an advanced fuzzy load forecast method optimized by modified Jaya optimization (MJO) algorithm. MATLAB® platform is used to implement the proposed hybrid Fuzzy-MJO load forecasting algorithm and to verify the outperforming features of a Jaya technique over a fuzzy load forecast model....
76. An Improved Parameter Control Based on a Fuzzy System for Gravitational Search Algorithm
Yu Xianrui, Yu Xiaobing, Li Chenliang, Chen Hong
Pages: 893 - 903
Recently, a kind of heuristic optimization algorithm named gravitational search algorithm (GSA) has been rapidly developed. In GSA, there are two main parameters that control the search process, namely, the number of applied agents (Kbest) and the gravity constant (G). To balance exploration and exploitation,...
77. A Human-Machine Language Dictionary
Fei Liu, Shirin Akther Khanam, Yi-Ping Phoebe Chen
Pages: 904 - 913
In this paper, we propose a framework for building a human-machine language dictionary. Given a concept/word, an application can extract the definition of the concept from the dictionary, and consequently “understand” its meaning. In the dictionary, a concept is defined through its relations with other...
78. Accuracy Improvement of Autonomous Straight Take-off, Flying Forward, and Landing of a Drone with Deep Reinforcement Learning
Che-Cheng Chang, Jichiang Tsai, Peng-Chen Lu, Chuan-An Lai
Pages: 914 - 919
Nowadays, drones are expected to be used in several engineering and safety applications both indoors and outdoors, e.g., exploration, rescue, sport, entertainment, and convenience. Among those applications, it is important to make a drone capable of flying autonomously to carry out an inspection patrol....
79. A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems
M.A. Farag, M.A. El-Shorbagy, A.A. Mousa, I.M. El-Desoky
Pages: 920 - 940
The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cosine algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm...
80. Whale Optimization for Wavelet-Based Unsupervised Medical Image Segmentation: Application to CT and MR Images
Thavavel Vaiyapuri, Haya Alaskar
Pages: 941 - 953
Image segmentation plays crucial role in medical image analysis and forms the basis for clinical diagnosis and patient's treatment planning. But the large variation in organ shapes, inhomogeneous intensities, poor contrast, organic nature of textures and complex boundaries in medical images makes...
81. Group-Like Uninorms
Pages: 954 - 965
Uninorms play a prominent role both in the theory and the applications of aggregations and fuzzy logic. In this paper a class of uninorms, called group-like uninorms will be introduced and a complete structural description will be given for a large subclass of them. First, the four versions of a general...
82. Kernels of Residuated Maps as Complete Congruences in Lattices
Branimir Šešelja, Andreja Tepavčević
Pages: 966 - 973
In a context of lattice-valued functions (also called lattice-valued fuzzy sets), where the codomain is a complete lattice L, an equivalence relation defined on L by the equality of related cuts is investigated. It is known that this relation is a complete congruence on the join-semilattice reduct of...
83. Teaching Explainable Artificial Intelligence to High School Students
Jose M. Alonso
Pages: 974 - 987
Artificial Intelligence (AI) is part of our everyday life and has become one of the most outstanding and strategic technologies. Explainable AI (XAI) is expected to endow intelligent systems with fairness, accountability, transparency and explanation ability when interacting with humans. This paper describes...
84. Classical and Fuzzy Two-Layered Modal Logics for Uncertainty: Translations and Proof-Theory
Paolo Baldi, Petr Cintula, Carles Noguera
Pages: 988 - 1001
This paper is a contribution to the study of two distinct kinds of logics for modelling uncertainty. Both approaches use logics with a two-layered modal syntax, but while one employs classical logic on both levels and infinitely-many multimodal operators, the other involves a suitable system of fuzzy...
85. Coreference Resolution Using Semantic Features and Fully Connected Neural Network in the Persian Language
Hossein Sahlani, Maryam Hourali, Behrouz Minaei-Bidgoli
Pages: 1002 - 1013
Coreference resolution is one of the most critical issues in various applications of natural language processing, such as machine translation, sentiment analysis, summarization, etc. In the process of coreference resolution, in this paper, a fully connected neural network approach has been adopted to...
86. Computation of Support and Confidence for Interval-Valued Fuzzy Association Rules
Michal Burda, Viktor Pavliska, Petra Murinová
Pages: 1014 - 1026
The aim of this paper is to provide an algorithm for the computation of support and confidence of the association rules on interval-valued fuzzy sets. Each element of the interval-valued fuzzy set has a membership degree defined as an interval. In other words, the membership intervals may be interpreted...
87. Automated Recognition of Hand Grasps Using Electromyography Signal Based on LWT and DTCWT of Wavelet Energy
A. Haiter Lenin, S. Mary Vasanthi, T. Jayasree
Pages: 1027 - 1035
This paper presents a novel framework that automatically classifies hand grasps using Electromyogram (EMG) signals based on advanced Wavelet Transform (WT). This method is motivated by the observation that there lies a unique correlation between different samples of the signal at various frequency levels...
88. Climbing the Hill with ILP to Grow Patterns in Fuzzy Tensors
Lucas Maciel, Jônatas Alves, Vinicius Fernandes dos Santos, Loïc Cerf
Pages: 1036 - 1047
Fuzzy tensors encode to what extent n-ary predicates are satisfied. The disjunctive box cluster model is a regression model where sub-tensors are explanatory variables for the values in the fuzzy tensor. In this article, locally optimal patterns for that model, with high areas times squared densities,...
89. Novel Optimization Based Hybrid Self-Organizing Map Classifiers for Iris Image Recognition
J. Jenkin Winston, Gul Fatma Turker, Utku Kose, D. Jude Hemanth
Pages: 1048 - 1058
The concern over security in all fields has intensified over the years. The prefatory phase of providing security begins with authentication to provide access. In many scenarios, this authentication is provided by biometric systems. Moreover, the threat of pandemic has made the people to think of hygienic...
90. Comparison of Recent Metaheuristic Algorithms for Shape Detection in Images
Erik Cuevas, Angel Trujillo, Mario A. Navarro, Primitivo Diaz
Pages: 1059 - 1071
Shape recognition in images represents one of the complex and hard-solving problems in computer vision due to its nonlinear, stochastic and incomplete nature. Classical image processing techniques have been normally used to solve this problem. Alternatively, shape recognition has also been conducted...
91. Specific Types of q-Rung Picture Fuzzy Yager Aggregation Operators for Decision-Making
Peide Liu, Gulfam Shahzadi, Muhammad Akram
Pages: 1072 - 1091
q-rung picture fuzzy sets can handle complex fuzzy and impression information by changing a parameter q based on the different hesitation degree, and Yager operator is a useful aggregation technology that can control the uncertainty of valuating data from some experts and thus get intensive information...
92. A Heuristic and ANN based Classification Model for Early Screening of Cervical Cancer
S. Priya, N. K. Karthikeyan
Pages: 1092 - 1100
Cervical cancer is one of the most leading causes of mortality among women worldwide. This deadly disease could be prevented by vaccines and easily cured if detected at an early stage. Various researchers focus on providing methods for unambiguous results of screening tests for early diagnosis of cervical...
93. Scalable Real-Time Attributes Responsive Extreme Learning Machine
Hongbo Wang, Yuejuan Yao, Xi Liu, Xuyan Tu
Pages: 1101 - 1108
Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, and ease of implementation. Its many applications, such as regression, binary and multiclass classification, acquired better results. However, when some attributes of the dataset...
94. Framework of Computational Intelligence-Enhanced Knowledge Base Construction: Methodology and A Case of Gene-Related Cardiovascular Disease
Yi Zhang, Mengjia Wu, Hua Lin, Steven Tipper, Mark Grosser, Guangquan Zhang, Jie Lu
Pages: 1109 - 1119
Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques...
95. Sustainable Supplier Selection Based on Regret Theory and QUALIFLEX Method
Limei Liu, Zhongli Bin, Biao Shi, Wenzhi Cao
Pages: 1120 - 1133
Sustainable supplier selection is the essential core of sustainable supply chain management, which can directly influence the manufacturer's performance and can enormously enhance the manufacturer's competitiveness in the international market. However, most of the previous studies concerning...
96. A Novel Two-Stage DEA Model in Fuzzy Environment: Application to Industrial Workshops Performance Measurement
M. R. Soltani, S. A. Edalatpanah, F. Movahhedi Sobhani, S. E. Najafi
Pages: 1134 - 1152
One of the paramount mathematical methods to compute the general performance of organizations is data envelopment analysis (DEA). Nevertheless, in some cases, the decision-making units (DMUs) have middle values. Furthermore, the conventional DEA models have been originally formulated solely for crisp...
97. YOLOv3: Face Detection in Complex Environments
Lin Zheng Chun, Li Dian, Jiang Yun Zhi, Wang Jing, Chao Zhang
Pages: 1153 - 1160
Face detection has been well studied for many years. However, the problem of face detection in complex environments is still being studied. In complex environments, faces is often blocked and blurred. This article proposes applying YOLOv3 to face detection problems in complex environments. First, we...
98. A Search-Based Test Data Generation Method for Concurrent Programs
Seyed Mohsen Mirhosseini, Hassan Haghighi
Pages: 1161 - 1175
Concurrent programs are being widely adopted in development of multi-core and many-core processors. However, these types of programs present some features such as concurrency, communication and synchronization which make their testing more challenging than sequential programs. Search-based techniques,...
99. Group Decision Algorithm for Aged Healthcare Product Purchase Under q-Rung Picture Normal Fuzzy Environment Using Heronian Mean Operator
Zaoli Yang, Xin Li, Harish Garg, Rui Peng, Shaomin Wu, Lucheng Huang
Pages: 1176 - 1197
With the intensification of the aging, the health issue of the elderly is arousing public concern increasingly. Various healthcare products for the elderly are emerging from the market, thus how to select suitable aged healthcare product is critical to the well-being of the elderly. In the literature,...
100. Flow Measurement of Natural Gas in Pipeline Based on 1D-Convolutional Neural Network
Pages: 1198 - 1206
Time-difference method is a vitally significant algorithm for measuring natural gas flow with ultrasonic gas flowmeter. The key of this algorithm is to accurately measure the arrival time of ultrasonic signal. However, it is difficult to determine the feature points corresponding to the arrival time...
101. Predicting Cards Using a Fuzzy Multiset Clustering of Decks
Alexander Dockhorn, Rudolf Kruse
Pages: 1207 - 1217
Search-based agents have shown to perform well in many game-based applications. In the context of partially-observable scenarios agent's require the state to be fully determinized. Especially in case of collectible cards games, the sheer number of decks constructed by players hinder an agent to...
102. Tolerance Rough Set-Based Bag-of-Words Model for Document Representation
Dong Qiu, Haihuan Jiang, Ruiteng Yan
Pages: 1218 - 1226
Document representation is one of the foundations of natural language processing. The bag-of-words (BoW) model, as the representative of document representation models, is a method with the properties of simplicity and validity. However, the traditional BoW model has the drawbacks of sparsity and lacking...
103. Bid Evaluation for Major Construction Projects Under Large-Scale Group Decision-Making Environment and Characterized Expertise Levels
Lu Xiao, Zhen-Song Chen, Xuan Zhang, Jian-Peng Chang, Witold Pedrycz, Kwai-Sang Chin
Pages: 1227 - 1242
Rapid growth and development of civil engineering in recent years inspire building enterprises to concentrate on construction contractor selection for achieving more construction quality and lower construction cost. The existing studies generally regard the process of selecting the best contractor as...
104. A Formal Concept Analysis Approach to Cooperative Conversational Recommendation
Pablo Cordero, Manuel Enciso, Ángel Mora, Manuel Ojeda-Aciego, Carlos Rossi
Pages: 1243 - 1252
We focus on the development of a method to guide the choice of a set of users in an environment where the number of features describing the items is high and user interaction becomes laborious. Using the framework of formal concept analysis, particularly the notion of implication between attributes,...
105. Discovering Potential Partners via Projection-Based Link Prediction in the Supply Chain Network
Zhi-Gang Lu, Qian Chen
Pages: 1253 - 1264
As reserving a certain number of potential partners plays a significant role in alleviating existing partners' collaborative interruption risks, we investigate the process of discovering potential partners to improve the supply chain network's resilience. Most of the existing research confines...
106. Cubic Graphs and Their Application to a Traffic Flow Problem
G. Muhiuddin, M. Mohseni Takallo, Y. B. Jun, R. A. Borzooei
Pages: 1265 - 1280
A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we introduce the concept of cubic graph, which is different from the notion of cubic graph in S. Rashid, N. Yaqoob, M. Akram, M. Gulistan,...
107. A Deng-Entropy-Based Evidential Reasoning Approach for Multi-expert Multi-criterion Decision-Making with Uncertainty
Huchang Liao, Zhongyuan Ren, Ran Fang
Pages: 1281 - 1294
The evidential reasoning (ER) approach has been widely applied to aggregate evaluation information in multi-expert multi-criterion decision-making (MEMCDM) problems with uncertainties. However, the comprehensive results derived by the ER approach remain uncertain. In this study, we propose a Deng-entropy-based...
108. Novel Cross-Entropy Based on Multi-attribute Group Decision-Making with Unknown Experts' Weights Under Interval-Valued Intuitionistic Fuzzy Environment
Yonghong Li, Yali Cheng, Qiong Mou, Sidong Xian
Pages: 1295 - 1304
This paper studies the multi-attribute group decision-making problems with unknown experts' weights under interval-valued intuitionistic fuzzy environment. First, in order to provide more flexibilities for decision-makers in actual decision-making problems, a novel cross-entropy measure with parameter...
109. Who Is the Designer? ARC-100 Database and Benchmark on Architecture Classification
Yen-Chang Huang, Shih-Yuan Wang, Sze-Teng Liong, Chieh-En Huang, Yi-Chen Hsieh, Hsiang-Yu Wang, Wen-Hung Lin, Y. S. Gan
Pages: 1305 - 1314
Architecture is about evolution, there exist many types of architectural styles that depend on the geography, traditions, and culture of the particular regions. An architectural designer may have a similar preference in creating the new architectural building, which can be easily recognized from the...
110. Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning
Xiangyin Zhang, Shuang Xia, Xiuzhi Li
Pages: 1315 - 1331
As a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields. To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper....
111. Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization
Shui-Hua Wang, Xiaosheng Wu, Yu-Dong Zhang, Chaosheng Tang, Xin Zhang
Pages: 1332 - 1344
Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT) is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation...
112. Evolutionary Multimodal Optimization Based on Bi-Population and Multi-Mutation Differential Evolution
Wei Li, Yaochi Fan, Qingzheng Xu
Pages: 1345 - 1367
The most critical issue of multimodal evolutionary algorithms (EAs) is to find multiple distinct global optimal solutions in a run. EAs have been considered as suitable tools for multimodal optimization because of their population-based structure. However, EAs tend to converge toward one of the optimal...
113. Corrigendum to “An Approach for Evolving Transformation Sequences Using Hybrid Genetic Algorithms” [International Journal of Computational Intelligence Systems Vol. 13(1), 2020, pp. 223–233]
Ahmed Maghawry, Mohamed Kholief, Yasser Omar, Rania Hodhod
Pages: 1368 - 1368
114. Clustering-Based Monarch Butterfly Optimization for Constrained Optimization
Sibo Huang, Han Cui, Xiaohui Wei, Zhaoquan Cai
Pages: 1369 - 1392
Monarch butterfly optimization (MBO) algorithm is a newly-developed metaheuristic approach that has shown striking performance on several benchmark problems. In order to enhance the performance of MBO, many scholars proposed various strategies for benchmark evaluation and practical applications. As an...
115. Woodland Labeling in Chenzhou, China, via Deep Learning Approach
Wei Wang, Yujing Yang, Ji Li, Yongle Hu, Yanhong Luo, Xin Wang
Pages: 1393 - 1403
In order to complete the task of the woodland census in Chenzhou, China, this paper carries out a remote sensing survey on the terrain of this area to produce a data set, and used deep learning methods to label the woodland. There are two main improvements in our paper: Firstly, this paper comparatively...
116. Deep Learning and Higher Degree F-Transforms: Interpretable Kernels Before and After Learning
Vojtech Molek, Irina Perfilieva
Pages: 1404 - 1414
One of the current trends in the deep neural network technology consists in allowing a man–machine interaction and providing an explanation of network design and learning principles. In this direction, an experience with fuzzy systems is of great support. We propose our insight that is based on the particular...
117. Fine-Tuning the Fuzziness of Strong Fuzzy Partitions through PSO
Ciro Castiello, Corrado Mencar
Pages: 1415 - 1428
We study the influence of fuzziness of trapezoidal fuzzy sets in the strong fuzzy partitions (SFPs) that constitute the database of a fuzzy rule-based classifier. To this end, we develop a particular representation of the trapezoidal fuzzy sets that is based on the concept of cuts, which are the cross-points...
118. Multi-Criteria Group Decision-Making Using Spherical Fuzzy Prioritized Weighted Aggregation Operators
Muhammad Akram, Samirah Alsulami, Ayesha Khan, Faruk Karaaslan
Pages: 1429 - 1446
Spherical fuzzy sets, originally proposed by F.K. Gündogdu, C. Kahraman, Spherical fuzzy sets and spherical fuzzy TOPSIS method, J. Intell. Fuzzy Syst. 36 (2019), 337–352, can handle the information of type: yes, no, abstain and refusal, owing to the feature of broad space of admissible triplets. This...
119. A Novel Particle Swarm Optimization Approach to Support Decision-Making in the Multi-Round of an Auction by Game Theory
Trinh Ngoc Bao, Quyet-Thang Huynh, Xuan-Thang Nguyen, Gia Nhu Nguyen, Dac-Nhuong Le
Pages: 1447 - 1463
In this paper, game-theoretic optimization by particle swarm optimization (PSO) is used to determine the Nash equilibrium value, in order to resolve the confusion in choosing appropriate bidders in multi-round procurement. To this end, we introduce an approach that proposes (i) a game-theoretic model...
120. On Relationship between L-valued Approximation Spaces and L-valued Transformation Systems
Sutapa Mahato, S.P. Tiwari
Pages: 1464 - 1472
The objective of this paper is to establish the relationship between L-valued approximation spaces and L-valued transformation systems. We show that for each L-valued upper/lower fuzzy transformation system there exist an L-valued reflexive approximation space and vice versa. In between, we study the...
121. Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model
Yan Gao, Changwei Lv, Zhengjiang Wu
Pages: 1473 - 1482
Neighborhood rough set is a powerful tool to deal with continuous value information systems. Graphics processing unit (GPU) computing can efficiently accelerate the calculation of the attribute reduction and approximation sets based on matrix. In this paper, we rewrite neighborhood approximation sets...
122. Contextualizing Support Vector Machine Predictions
Marcelo Loor, Guy De Tré
Pages: 1483 - 1497
Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to. Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use...
123. Uncertain Random Optimization Models Based on System Reliability
Qinqin Xu, Yuanguo Zhu
Pages: 1498 - 1506
The reliability of a dynamic system is not constant under uncertain random environments due to the interaction of internal and external factors. The existing researches have shown that some complex systems may suffer from dependent failure processes which arising from hard failure and soft failure. In...
124. Cancer Cell Detection through Histological Nuclei Images Applying the Hybrid Combination of Artificial Bee Colony and Particle Swarm Optimization Algorithms
Faozia Ali Alsarori, Hilal Kaya, Javad Rahebi, Daniela E. Popescu, D. Jude Hemanth
Pages: 1507 - 1516
Cancer is a fatal disease that is continuously growing in the developed countries. It is also considered as a main global human health problem. Based on several studies, which have been conducted so far, we found out that Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm has never...
125. Numerical Solution for Fuzzy Initial Value Problems via Interactive Arithmetic: Application to Chemical Reactions
Vinícius F. Wasques, Estevão Esmi, Laécio C. Barros, Peter Sussner
Pages: 1517 - 1529
This paper studies numerical solutions for fuzzy initial value problems, where the initial conditions are given by interactive fuzzy numbers. The fuzzy solution is given by a numerical method that employs the arithmetic of interactive fuzzy numbers and yields a fuzzy number at each instant of time. The...
126. Fuzzy Type Relations and Transformation Operators Defined by Monads
Pages: 1530 - 1538
Using the theory of monads in categories and the theory of monadic relations, the concept of general transformation operator defined by a monadic relation is introduced. It is proven that a number of standard relations used in categories of fuzzy structures are monadic relations for monads defined in...
127. APOLLO: A Fuzzy Multi-criteria Group Decision-Making Tool in Support of Climate Policy
Álvaro Labella, Konstantinos Koasidis, Alexandros Nikas, Apostolos Arsenopoulos, Haris Doukas
Pages: 1539 - 1553
Multi-criteria decision-making is a daily process in everyday life, in which different alternatives are evaluated over a set of conflicting criteria. Decision-making is becoming increasingly complex, and the apparition of uncertainty and vagueness is inevitable, especially when related to sustainability...
128. A New Hybrid and Ensemble Gene Selection Approach with an Enhanced Genetic Algorithm for Classification of Microarray Gene Expression Values on Leukemia Cancer
Mehmet Bilen, Ali H. Işik, Tuncay Yiğit
Pages: 1554 - 1566
Leukemia cancer, like other types of cancer, is a deadly health condition that threatens the lives of many people around the world. Micro array data are used extensively to reveal the gene-cancer as well as gene–gene relationships of Leukemia cancer due to the fact that it allows the expression value...
129. Graphical Analysis of the Progression of Atrial Arrhythmia Using Recurrent Neural Networks
Nahuel Costa, Jesús Fernández, Inés Couso, Luciano Sánchez
Pages: 1567 - 1577
Pacemaker logs are used to predict the progression of paroxysmal cardiac arrhythmia to permanent atrial fibrillation by means of different deep learning algorithms. Recurrent Neural Networks are trained on data produced by a generative model. The activations of the different nets are displayed in a graphical...
130. Sequential Prediction of Glycosylated Hemoglobin Based on Long Short-Term Memory with Self-Attention Mechanism
Xiaojia Wang, Wenqing Gong, Keyu Zhu, Lushi Yao, Shanshan Zhang, Weiqun Xu, Yuxiang Guan
Pages: 1578 - 1589
Type 2 diabetes mellitus (T2DM) has been identified as one of the most challenging chronic diseases to manage. In recent years, the incidence of T2DM has increased, which has seriously endangered people’s health and life quality. Glycosylated hemoglobin (HbA1c) is the gold standard clinical indicator...
131. Application of Fuzzy C-Mean Clustering Based on Multi-Polar Fuzzy Entropy Improvement in Dynamic Truck Scale Cheating Recognition
Zhenyu Lu, Xianyun Huang
Pages: 1590 - 1597
In the big data background, the uncertainty of data is increasingly apparent. Multi-polar fuzzy feature of data has been more popularly used by the research community for the purpose of the classification of weighing cheating in dynamic truck scale characteristic and the clustering problem of multi-polar...
132. A Learning-Based Framework for Identifying MicroRNA Regulatory Module
Pages: 1598 - 1607
Accurate identification of microRNA regulatory modules can give insights to understand microRNA synergistical regulatory mechanism. However, the identification accuracy suffers from incomplete biological data. In this paper, we proposed a learning-based framework called MicroRNA regulatory module dentification...
133. An Evolutionary Self-organizing Cost-Sensitive Radial Basis Function Neural Network to Deal with Imbalanced Data in Medical Diagnosis
Jia-Chao Wu, Jiang Shen, Man Xu, Fu-Sheng Liu
Pages: 1608 - 1618
Class imbalance is a common issue in medical diagnosis. Although standard radial basis function neural network (RBF-NN) has achieved remarkably high performance on balanced data, its ability to classify imbalanced data is still limited. So far as we know, cost-sensitive learning is an advanced imbalanced...
134. Information Structures in an Ordered Information System Under Granular Computing View and Their Optimal Selection Based on Uncertainty Measures
Yini Wang, Sichun Wang, Hongxiang Tang
Pages: 1619 - 1635
Information structures (i-structures) in an ordered information system (OIS) are mathematical structures of the information granules (i-granules) granulated from the data set of this OIS. This article investigates i-structures in an OIS with granular computing (GrC) view, i.e., i-structures in an OIS...
135. Multiple Bipolar Fuzzy Measures: An Application to Community Detection Problems for Networks with Additional Information
Inmaculada Gutiérrez, Daniel Gómez, Javier Castro, Rosa Espínola
Pages: 1636 - 1649
In this paper we introduce the concept of multiple bipolar fuzzy measures as a generalization of a bipolar fuzzy measure. We also propose a new definition of a group, which is based on the multidimensional bipolar fuzzy relations of its elements. Taking into account this information, we provide a novel...
136. Flexible Bootstrap for Fuzzy Data Based on the Canonical Representation
Przemyslaw Grzegorzewski, Olgierd Hryniewicz, Maciej Romaniuk
Pages: 1650 - 1662
Several new resampling methods for generating bootstrap samples of fuzzy numbers are proposed. To avoid undesired repetitions in the secondary samples we do not draw randomly directly observations from the primary samples but construct them allowing for some modifications in their membership functions,...
137. A New Approach for Low-Dimensional Constrained Engineering Design Optimization Using Design and Analysis of Simulation Experiments
Amir Parnianifard, Ratchatin Chancharoen, Gridsada Phanomchoeng, Lunchakorn Wuttisittikulkij
Pages: 1663 - 1678
The number of function evaluations in many industrial applications of simulation-based optimization problems is strictly limited. Therefore, only little analytical information on objective and constraint functions is available. This paper presents an adaptive algorithm called the Surrogate-Based Constrained...
138. A New Aggregation Operator Based on Uninorms in L*-Fuzzy Set
Minxia Luo, Yue Zhang, Bei Liu
Pages: 1679 - 1686
In practical applications, some existing multi-attribute decision-making methods based on the L∗ fuzzy set theory suffer from a lot of shortcomings, namely, incorrect choice preference orders of alternatives are obtained in some cases. In this paper, we construct a new aggregation operator based on uninorms...
139. Simpful: A User-Friendly Python Library for Fuzzy Logic
Simone Spolaor, Caro Fuchs, Paolo Cazzaniga, Uzay Kaymak, Daniela Besozzi, Marco S. Nobile
Pages: 1687 - 1698
Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data. In this work we propose...
140. Link Prediction in Social Networks by Neutrosophic Graph
Rupkumar Mahapatra, Sovan Samanta, Madhumangal Pal, Qin Xin
Pages: 1699 - 1713
The computation of link prediction is one of the most important tasks on a social network. Several methods are available in the literature to predict links in networks and RSM index is one of them. The RSM index is applicable in the fuzzy environment and it does not incorporate the notion of falsity...
141. Tree-Based Contrast Subspace Mining for Categorical Data
Florence Sia, Rayner Alfred, Yuto Lim
Pages: 1714 - 1722
Mining contrast subspace has emerged to find subspaces where a particular queried object is most similar to the target class against the non-target class in a two-class data set. It is important to discover those subspaces, which are known as contrast subspaces, in many real-life applications. Tree-Based...