Journal of Intelligent & Fuzzy Systems - Volume 39, issue 1

Authors: Naeem, Khalid | Riaz, Muhammad | Afzal, Deeba

Article Type: Research Article

Abstract: The inspiration behind this article is to introduce the notions of fuzzy neutrosophic soft σ -algebra ( fns σ -algebra), fuzzy neutrosophic soft measure ( fns measure) and fns outer measure using the concepts of fuzzy sets, soft sets, neutrosophic sets and soft σ -algebra. A number of related results along with elaborative examples are also included. We render an algorithm based upon fns -mapping to deal with imprecise data utilizing mean proportional operator and employ it on multi-criteria group decision making (MCGDM) …problem to exhibit its efficacy. Show more

Keywords: 𝔉𝔑𝔖 σ-algebra, 𝔉𝔑𝔖-measure, 𝔉𝔑𝔖 outer measure, MCGDM

DOI: 10.3233/JIFS-191062

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 277-287, 2020

Authors: Zhang, Wenyu | Ding, Jiepin | Wang, Yan | Zhang, Shuai | Zhuang, Xiaoyu

Article Type: Research Article

Abstract: The flexible multi-task scheduling problem has been extensively investigated in manufacturing systems, and its objectives are often related to the quality of manufacturing services. However, energy-related objectives along with workload balance have rarely been considered. Thus, a novel bi-objective optimization model is proposed to achieve green manufacturing. The Pareto-based fitness evaluation is employed to make a trade-off between total energy consumption and workload balance. Intermediate buffers are also considered, making the model more practical and more complicated. To solve the proposed model, a new three-stage genetic algorithm (3S-GA) is presented. A Pareto-based adaptive population size method is proposed to maintain …the diversity of the population and ensure the convergence rate. To cope with the subtask sequencing complexity, a real-time sequence scheduling heuristic is explored to effectively initialize the subtask sequence to save the energy in manufacturing systems, which is designed by minimizing the standby time according to the laxity of subtasks. After a series of experimental designs based on the Taguchi method, a suitable parameter combination of the 3S-GA is utilized. Further, computational experiments based on five instances demonstrate that the 3S-GA outperforms other four baseline algorithms taken from the literature in solving the proposed bi-objective optimization model. Show more

Keywords: Flexible multi-task scheduling, Energy consumption, Intermediate buffer, Bi-objective optimization, Pareto, Genetic algorithm

DOI: 10.3233/JIFS-191072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 289-304, 2020

Authors: Zhao, Feng | Xie, Min | Liu, Hanqiang | Fan, Jiulun | Lan, Rong | Xie, Wen | Zheng, Yue

Article Type: Research Article

Abstract: Multilevel thresholding is one of the effective image segmentation methods. However, it faces three big challenges: (1) how to adaptively determine the number of multiple thresholds; (2) how to overcome the sensitivity to image noise; (3) how to perform multilevel thresholding under several segmentation requirements. In order to solve these problems, an adaptive multilevel thresholding algorithm based on multiobjective artificial bee colony optimization (AMT-MABCO) segmentation is presented for noisy image in this paper. To improve the robustness of AMT-MABCO to image noise, a line intercept histogram which considers both the intensity and coordinate information in the neighborhood of the pixels …is firstly utilized to define a novel between-class variance function as one fitness function. Then, an interval-valued fuzzy entropy function is constructed as another fitness function to deal with the blurred characteristic in images. AMT-MABCO tries to obtain a compromising multilevel thresholding result under these two segmentation requirements. To adaptively determine the number of thresholds, a grouping population initialization and evaluation strategies are proposed in AMT-MABCO. Furthermore, two novel search equations are constructed in AMT-MABCO to generate candidate solutions in the employed bees and onlookers phases, respectively. Experimental results show that AMT-MABCO outperforms state-of-the-art thresholding methods in noise robustness and segmentation performance. Show more

Keywords: Image segmentation, multi-objective optimization, artificial bee colony, multilevel thresholding, interval-valued fuzzy information

DOI: 10.3233/JIFS-191083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 305-323, 2020

Authors: Yang, RouJian | Jin, LeSheng | Paternain, Daniel | Yager, Ronald R. | Mesiar, Radko | Bustince, Humberto

Article Type: Research Article

Abstract: In decision making, very often the data collected are with different extents of uncertainty. The recently introduced concept, Basic Uncertain Information (BUI), serves as one ideal information representation to well model involved uncertainties with different extents. This study discusses some methods of BUI aggregation by proposing some uncertainty transformations for them. Based on some previously obtained results, we at first define IOWA operator with poset valued input vector and inducing vector. The work then defines the concept of uncertain system, on which we can further introduce the multi-layer uncertainty transformation for BUI. Subsequently, we formally introduce MUT_IOWA aggregation procedure, which …has good potential to more and wider application areas. A numerical example is also offered along with some simple usage of it in decision making. Show more

Keywords: Aggregation function, BUI aggregation, decision making, evaluation, OWA operators, uncertain decision making

DOI: 10.3233/JIFS-191106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 325-332, 2020

Authors: Yu, Shujuan | Liu, Danlei | Zhu, Wenfeng | Zhang, Yun | Zhao, Shengmei

Article Type: Research Article

Abstract: Text classification is a fundamental task in Nature Language Processing(NLP). However, with the challenge of complex semantic information, how to extract useful features becomes a critical issue. Different from other traditional methods, we propose a new model based on two parallel RNNs architecture, which captures context information through LSTM and GRU respectively and simultaneously. Motivated by the siamese network, our proposed architecture generates attention matrix through calculating similarity between the parallel captured context information, which ensures the effectiveness of extracted features and further improves classification results. We evaluate our proposed model on six text classification tasks. The result of experiments …shows that the ABLGCNN model proposed in this paper has the faster convergence speed and the higher precision than other models. Show more

Keywords: Long short term memory, gated recurrent unit, convolutional neural network, attention mechanism, text classification

DOI: 10.3233/JIFS-191171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 333-340, 2020

Authors: Patil, Varsha H. | Bhavsar, Swati A. | Patil, Aboli H.

Article Type: Research Article

Abstract: Digital Bibliography and Library Project dataset is a collection of bibliographic records of computer science publications of various authors and co-authors. It contains approximately 1.5 million bibliographic records. An algorithm for an author’s information retrieval is developed to retrieve details of specific author publications and correlation among authors. Further performance of an author is measured with parameters like consistency, contribution factor, stability, cooperativeness, and solidity. The work presented is tested on the DBLP dataset. Experimental results clearly support the claim that it works efficiently for retrieving specific author-publication records and its analysis with respect to suggested parameters.

Keywords: Author, consistency, contribution factor, cooperativeness, DBLP, graph, publication stability, solidity

DOI: 10.3233/JIFS-191289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 341-353, 2020

Authors: Szeto, Pok Man | Parvin, Hamid | Mahmoudi, Mohammad Reza | Tuan, Bui Anh | Pho, Kim-Hung

Article Type: Research Article

Abstract: Features play an important role in image processing. But as not all features are comparable, relative features emerged. From the beginning, low-level features, extracted by experts, have been employed to create difficult models for learning the problem of relative attribute. Knowing these models are limited in generality of their applicability, deep learning models can be employed instead of them. A deep artificial neural network framework has been suggested for the task of relative attribute prediction in this article. The paper suggests to use a convolutional artificial neural network for learning the mentioned attributes through a peripheral auxiliary layer, called also …a ranking layer, which is able to learn how to rank the images. A suitable ranking cost function is used to train the whole network in an end-to-end manner. The suggested method through this paper is experimentally superior to the state of the art methods on some well-known benchmarks. The experimental results indicate that the proposed method is capable of learning the problem of relative attribute. Show more

Keywords: Image processing, relative features, deep learning, deep features

DOI: 10.3233/JIFS-191292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 355-369, 2020

Authors: Xu, Shuzhen | Wang, Jin | Zhu, Qing

Article Type: Research Article

Abstract: Motivated by a widely studied computer vision task: image inpainting, we became interested in a less concerned problem image outpainting. By which, contents beyond the image boundaries may be extrapolated. In recent years, deep learning methods have achieved remarkable improvements in image inpainting, these techniques can be considered to be applied to image outpainting as solutions. However, many of these inpainting methods generate image blocks generally resulting in blur or smooth. Recently, hallucinating edges for the missing holes before completion has been proved to be a state-of-the-art image inpainting method. Refer to the aforementioned method, we propose a three-phase outpainting …model that consists of an edge generation phase, an image expansion phase and a refinement phase. In order to depict the edge lines more accurately, we adopt a comparatively effective focal loss for edge prediction. An optimization stage with a refinement network is also added since large portions outside the image need to be inferred, and discriminator in this stage works on a decreased patch size with a coarse-to-fine fashion. In addition, with recursive outpainting, an image could be expanded arbitrarily. Experiments show that an image can be effectively expanded by our method, and our outpainting method of predicting edges and then coloring is generally superior to other methods both quantitatively and qualitatively. Show more

Keywords: Outpainting, edge detector, generative adversarial network, focal loss

DOI: 10.3233/JIFS-191310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 371-381, 2020

Authors: Alkouri, Abd Ulzeez M. J. | Massa’deh, Morad Oqla | Ali, Mabruka

Article Type: Research Article

Abstract: Many factors with the perspective of bipolarity in the traditional Chinese food system “Yin and Yang food system” manipulate with types of food simultaneously to have a balanced body. This research studies the multiple attributes decision making (MADM) problem that measuring the “bipolarity of periodic” variation in bipolar information with an illustration example in order to find an optimal nutrition program for a person X . To convey this type of data to a mathematical formula and vice versa without losing the full meaning of human knowledge, we use bipolar fuzzy set in a complex geometry by extending the range …of bipolar fuzzy set to the realm of a complex number. This extension needs to be successful to study and introduce intensely a new mathematical structure called a bipolar complex fuzzy set (BCFS) with its properties. Ranges of values are extended to [0, 1] e iα [0,1]  and [-1, 0] e iα [-1,0] for both positive and negative membership functions, respectively, as a replacement for [-1, 0] × [0, 1] , as in the bipolar fuzzy set. The main benefit of BCFS that the amplitude and phase terms of BCFSs can convey bipolar fuzzy information. Moreover, the formal definition of BCF distance measure and illustration application are introduced. Some basic mathematical operations on BCFS are also proposed and study its properties with arithmetical examples. Show more

Keywords: Bipolar fuzzy set, bipolar complex fuzzy distance, bipolar fuzzy complement, union and intersection, fuzzy set, complex fuzzy set

DOI: 10.3233/JIFS-191350

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 383-397, 2020

Authors: Saplioglu, Kemal | Ozturk, Tulay Sugra Kucukerdem | Acar, Ramazan

Article Type: Research Article

Abstract: Due to recent increasing water demand, planning and projecting of water resources has resulted in increased costs. There-fore, it is important to obtain optimum results in project planning. In this study, dimensioning of open channels used espe-cially for irrigation purposes has been studied using a particle swarm optimization algorithm to investigate optimum base width, channel height, and slope angles. The results are summarized in graphs and tables. In the study, it was found that the optimum slope angle varied between 0 . 20⌣ 0 . 450 . Furthermore, it was found that increasing the slope angle significant-ly increased costs. Finally, the increase in flow …increases costs but the rate of increase diminishes. Show more

Keywords: Particle swarm optimization, open channels, dimension

DOI: 10.3233/JIFS-191355

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 399-405, 2020