International Journal of Computational Intelligence Systems
Volume 12, Issue 1, November 2018
1. Spontaneous Concept Learning with Deep Autoencoder
Pages: 1 - 12
In this study we investigate information processing in deep neural network models. We demonstrate that unsupervised training of autoencoder models of certain class can result in emergence of compact and structured internal representation of the input data space that can be correlated with higher level...
2. Germinal Center Optimization Algorithm
Carlos Villaseñor, Nancy Arana-Daniel, Alma Y. Alanis, Carlos López-Franco, Esteban A. Hernandez-Vargas
Pages: 13 - 27
Artificial immune systems are metaheuristic algorithms that mimic the adaptive capabilities of the immune system of vertebrates. Since the 1990s, they have become one of the main branches of computer intelligence. However, there are still many competitive processes in the biological phenomena that can...
3. Case-Based Decision Support System for Breast Cancer Management
Booma Devi Sekar, Jean-Baptiste Lamy, Nekane Larburu, Brigitte Séroussi, Gilles Guézennec, Jacques Bouaud, Naiara Muro, Hui Wang, Jun Liu
Pages: 28 - 38
Breast cancer is identified as the most common type of cancer in women worldwide with 1.6 million women around the world diagnosed every year. This prompts many active areas of research in identifying better ways to prevent, detect, and treat breast cancer. DESIREE is a European Union funded project,...
4. Deep Learning for Detection of Routing Attacks in the Internet of Things
Furkan Yusuf YAVUZ, Devrim ÜNAL, Ensar GÜL
Pages: 39 - 58
Cyber threats are a showstopper for Internet of Things (IoT) has recently been used at an industrial scale. Network layer attacks on IoT can cause significant disruptions and loss of information. Among such attacks, routing attacks are especially hard to defend against because of the ad-hoc nature of...
5. Optimization of quality measures in association rule mining: an empirical study
J. M. Luna, M. Ondra, H. M. Fardoun, S. Ventura
Pages: 59 - 78
In the association rule mining field many different quality measures have been proposed over time with the aim of quantifying the interestingness of each discovered rule. In evolutionary computation, many of these metrics have been used as functions to be optimized, but the selection of a set of suitable...
6. Using regression trees to predict citrus load balancing accuracy and costs
G. R. R. Bóbeda, E. F. Combarro, S. Mazza, L. I. Giménez, I. Díaz
Pages: 79 - 89
In order to define management and marketing strategies, farmers need adequate knowledge about future yield with the greatest possible accuracy and anticipation. In citrus orchards, greater variability and non-normality of yield distributions complicate the early estimation of fruit production. This study...
7. Fuzzy Rough Graph Theory with Applications
Muhammad Akram, Maham Arshad, Shumaiza
Pages: 90 - 107
Fuzzy rough set theory is a hybrid method that deals with vagueness and uncertainty emphasized in decision-making. In this research study, we apply the concept of fuzzy rough sets to graphs. We introduce the notion of fuzzy rough digraphs and describe some of their methods of construction. In particular,...
8. Multi-Scale Fuzzy Feature Selection Method applied to Wood Singularity Identification
Vincent BOMBARDIER, Laurent WENDLING
Pages: 108 - 122
A multi-scale feature selection method based on the Choquet Integral is presented in this paper. Usually, aggregation decision-making problems are well solved, relying on few decision rules associated to a small number of input parameters. However, many industrial applications require the use of numerous...
9. Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia.
Julio Suarez-Paez, Mayra Salcedo-Gonzalez, M. Esteve, J.A. Gómez, C. Palau, I. Pérez-Llopis
Pages: 123 - 130
This paper shows the implementation of a prototype of street theft detector using the deep learning technique R-CNN (Region-Based Convolutional Network), applied in the Command and Control Information System (C2IS) of National Police of Colombia, the prototype is implemented using three models of CNN...
10. An improved chaotic firefly algorithm for global numerical optimization
Ivona Brajević, Predrag Stanimirović
Pages: 131 - 148
Firefly algorithm (FA) is a prominent metaheuristc technique. It has been widely studied and hence there are a lot of modified FA variants proposed to solve hard optimization problems from various areas. In this paper an improved chaotic firefly algorithm (ICFA) is proposed for solving global optimization...
11. Differential Evolution and Local Search based Monarch Butterfly Optimization Algorithm with Applications
Xingyue Cui, Zhe Chen, Fuliang Yin
Pages: 149 - 163
Global optimization for nonlinear function is a challenging issue. In this paper, an improved monarch butterfly algorithm based on local search and differential evolution is proposed. Local search strategy is first embedded into original monarch butterfly optimization to enhance the searching capability....
12. Bayesian Deep Reinforcement Learning via Deep Kernel Learning
Junyu Xuan, Jie Lu, Zheng Yan, Guangquan Zhang
Pages: 164 - 171
Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems could benefit from RL, e.g., industrial robotics,...
13. Multi-adjoint based group decision-making under an intuitionistic fuzzy information system
Meishe Liang, Jusheng Mi, Tao Feng, Bin Xie
Pages: 172 - 182
The construction of belief intervals is crucial for decision-making in multi-attribute group information integration. Based on multi-adjoint and evidence theory, an approach to multi-criteria group decision-making(MCGDM) in intuitionistic fuzzy information system is proposed. First, the upper and lower...
14. Living Face Verification via Multi-CNNs
Peiqin Li, Jianbin Xie, Wei Yan, Zhen Li, Gangyao Kuang
Pages: 183 - 189
In face verification applications, precision rate and identifying liveness are two key factors. Traditional methods usually recognize global faces and can not gain good enough results when the faces are captured from different ages, or there are some interference factors, such as facial shade, etc. Besides,...
15. The Control of Greenhouses Based on Fuzzy Logic Using Wireless Sensor Networks
Özlem Alpay, Ebubekir Erdem
Pages: 190 - 203
Greenhouses cannot be easily controlled because their climate parameters are interrelated. This study contributes to increasing the quality and yield of greenhouses by saving time, energy, light and water consumption via measuring and controlling the climate parameters that are effective in forming climate...
16. Design of Fuzzy Controllers for Embedded Systems With JFML
J.M. Soto-Hidalgo, A. Vitiello, J.M. Alonso, G. Acampora, J. Alcala-Fdez
Pages: 204 - 214
Fuzzy rule-based systems (FRBSs) have been successfully applied to a wide range of real-world problems. However, they suffer from some design issues related to the difficulty to implement them on different hardware platforms without additional efforts. To bridge this gap, recently, the IEEE Computational...
17. Optimized Differential Evolution Algorithm for Software Testing
Xiaodong Gou, Tingting Huang, Shunkun Yang, Mengxuan Su, Fuping Zeng
Pages: 215 - 226
Differential evolution (DE) algorithms for software testing usually exhibited limited performance and stability owing to possible premature-convergence-related aging during evolution processes. This paper proposes a new framework comprising an antiaging mechanism, that is, a rebirth strategy with partial...
18. A Novel Comparative Linguistic Distance Measure Based on Hesitant Fuzzy Linguistic Term Sets and Its Application in Group Decision-Making
Mei Cai, Yiming Wang, Zaiwu Gong, Guo Wei
Pages: 227 - 237
The linguistic approaches are required in order to assess qualitative aspects of many real problems. In most of these problems, decision makers only adopt single and very simple terms which would not reflect exactly what the experts mean for many intricate applications. Frequently, the assessments of...
19. Localization of a Mobile Device with Sensor Using a Cascade Artificial Neural Network-Based Fingerprint Algorithm
Ebubekir Erdem, Taner Tuncer, Resul Doğan
Pages: 238 - 249
One of the important functions of sensor networks is that they collect data from the physical environment and transmit them to a center for processing. The location from which the collected data is obtained is crucial in many applications, such as search and rescue, disaster relief, and target tracking....
20. A Hybrid Learnt Clause Evaluation Algorithm for SAT Problem
Guanfeng Wu, Qingshan Chen, Yang Xu, Xingxing He
Pages: 250 - 258
It is of great theoretical and practical significance to develop the efficient SAT solvers due to its important applications in hardware and software verifications and so on, and learnt clauses play the crucial role in state of the art SAT solvers. In order to effectively manage learnt clauses, avoid...
21. Linguistic Modeling and Synthesis of Heterogeneous Energy Consumption Time Series Sets
Sergio Martínez-Municio, Luis Rodríguez-Benítez, Ester Castillo-Herrera, Juan Giralt-Muiña, Luis Jiménez-Linares
Pages: 259 - 272
Thanks to the presence of sensors and the boom in technologies typical of the Internet of things, we can now monitor and record the energy consumption of buildings over time. By effectively analyzing these data to capture consumption patterns, significant reductions in consumption can be achieved and...
22. Detection of Land in Marine Images
Pages: 273 - 281
In order to navigate safely at sea, contemporary marine vehicles, both surface and underwater, are equipped with a variety of different sensors. One of these sensors is typically a video camera, a device that is becoming increasingly popular on the decks of both ships and smaller marine vehicles. A compact...
23. An Empirical Study for Enhanced Software Defect Prediction Using a Learning-Based Framework
Kamal Bashir, Tianrui Li, Chubato Wondaferaw Yohannese
Pages: 282 - 298
The object of software defect prediction (SDP) is to identify defect-prone modules. This is achieved through constructing prediction models using datasets obtained by mining software historical depositories. However, data mined from these depositories are often associated with high dimensionality, class...
24. A Methodology to Refine Labels in Web Search Results Clustering
Zaher Salah, Ahmad Aloqaily, Malak Al-Hassan, Abdel-Rahman Al-Ghuwairi
Pages: 299 - 310
Information retrieval systems like web search engines can be used to meet the user’s information needs by searching and retrieving the relevant documents that match the user’s query. Firstly, the query is inputted to the web search engine and assumed to be a good representative for the user’s intention...
25. A Validation Approach for Ontology-Based Real-time DBMS
Wided Ben Abid, Mohamed Ben Ahmed Mhiri, Malek Ben Salem, Emna Bouazizi, Faiez Gargouri
Pages: 311 - 317
Real-time DBMS (DataBase Management Systems) are an appropriate storage system under real-time constraints. However real-time DBMS do not implement inference or reasoning mechanisms. Ontologies on the other hand allow these mechanisms by creating formal representations of concepts, properties and relationships...
26. Assessment of Healthcare Waste Treatment Alternatives Using an Integrated Decision Support Framework
Akshay Hinduja, Manju Pandey
Pages: 318 - 333
Healthcare waste (HCW) management has become a major environmental and public-health concern especially in developing countries, and therefore, it has been receiving increasing attention from both industrial practitioners and researcher in recent years. Selection of the optimal treatment technology for...
27. A New Rewarding Mechanism for Branching Heuristic in SAT Solvers
Wenjing Chang, Yang Xu, Shuwei Chen
Pages: 334 - 341
Decision heuristic strategy can be viewed as one of the most central features of state-of-the-art conflict-driven clause-learning SAT solvers. Variable state independent decaying sum (VSIDS) still is the dominant branching heuristics because of its low cost. VSIDS consists of a rewarding mechanism for...
28. A New Algorithm of Mining High Utility Sequential Pattern in Streaming Data
Huijun Tang, Yangguang Liu, Le Wang
Pages: 342 - 350
High utility sequential pattern (HUSP) mining has emerged as a novel topic in data mining, its computational complexity increases compared to frequent sequences mining and high utility itemsets mining. A number of algorithms have been proposed to solve such problem, but they mainly focus on mining HUSP...
29. Multimodal Emotion Recognition Method Based on Convolutional Auto-Encoder
Jian Zhou, Xianwei Wei, Chunling Cheng, Qidong Yang, Qun Li
Pages: 351 - 358
Emotion recognition is of great significance to computational intelligence systems. In order to improve the accuracy of emotion recognition, electroencephalogram (EEG) signals and external physiological (EP) signals are adopted due to their perfect performance in reflecting the slight variations of emotions,...
30. Further Complete Solutions to Four Open Problems on Filter of Logical Algebras
Wei Wang, Pengxi Yang, Yang Xu
Pages: 359 - 366
This paper focuses on the investigation of filters of pseudo BCK-algebra and BL-algebra, important and popular generic commutative and non-commutative logical algebras. By characterizing Boolean filter and implicative filter in pseudo BCK-algebra, the essential equivalent relation between these two filters...
31. A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
Pablo A. Flores-Vidal, Guillermo Villarino, Daniel Gómez, Javier Montero
Pages: 367 - 378
Traditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtained...
32. Fusion of Measures for Image Segmentation Evaluation
Macmillan Simfukwe, Bo Peng, Tianrui Li
Pages: 379 - 386
Image segmentation is an important task in image processing. However, no universally accepted quality scheme exists for evaluating the performance of various segmentation algorithms or just different parameterizations of the same algorithm. In this paper, an extension of a fusion-based framework for...
33. Enactment of Ensemble Learning for Review Spam Detection on Selected Features
Faisal Khurshid, Yan Zhu, Zhuang Xu, Mushtaq Ahmad, Muqeet Ahmad
Pages: 387 - 394
In the ongoing era of flourishing e-commerce, people prefer online purchasing products and services to save time. These online purchase decisions are mostly influenced by the reviews/opinions of others who already have experienced them. Malicious users use this experience sharing to promote or degrade...
34. A New Distance-Based Consensus Reaching Model for Multi-Attribute Group Decision-Making with Linguistic Distribution Assessments
Pages: 395 - 409
This paper proposes a novel consensus reaching model for multi-attribute group decision making (MAGDM) with information represented by means of linguistic distribution assessments. Firstly, some drawbacks of the existing distance measures for linguistic distribution assessments are analyzed by using...
35. A Pythagorean Fuzzy TOPSIS Method Based on Novel Correlation Measures and Its Application to Multiple Criteria Decision Analysis of Inpatient Stroke Rehabilitation
Yu-Li Lin, Lun-Hui Ho, Shu-Ling Yeh, Ting-Yu Chen
Pages: 410 - 425
The complex nature of the realistic decision-making process requires the use of Pythagorean fuzzy (PF) sets which have been shown to be a highly promising tool capable of solving highly vague and imprecise problems. Multiple criteria decision analysis (MCDA) methods within the PF environment are very...
36. CPO: A Crow Particle Optimization Algorithm
Ko-Wei Huang, Ze-Xue Wu
Pages: 426 - 435
Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently,...
37. AEkNN: An AutoEncoder kNN–Based Classifier With Built-in Dimensionality Reduction
Francisco J. Pulgar, Francisco Charte, Antonio J. Rivera, María J. del Jesus
Pages: 436 - 452
High dimensionality tends to be a challenge for most machine learning tasks, including classification. There are different classification methodologies, of which instance-based learning is one. One of the best known members of this family is the k-nearest neighbors (kNNs) algorithm. Its strategy relies...