Journal of Intelligent & Fuzzy Systems - Volume 41, issue 2

Authors: Liu, Peide | Zhang, Pei

Article Type: Research Article

Abstract: A normal wiggly hesitant fuzzy set is a very useful tool to mine the potential uncertain information given by decision makers, which is considered as an extension of hesitant fuzzy set and can improve the effectiveness of decision making. Power average operator can relieve the impact on decision result of unreasonable data, and the generalized Maclaurin symmetric mean operator (GMSM) is an extension of Maclaurin symmetric mean operator with wider range of applications, which can consider the relationship among decision attributes. By integrating the advantages of them, in this paper, we develop the normal wiggly hesitant fuzzy power GMSM (NWHFPGMSM) …operator and its weighted form based on the distance measure of two normal wiggly hesitant fuzzy elements, then we further discuss their properties and some special cases. Thus, a new multi-attribute decision making method based on the NWHFPGMSM operator under normal wiggly hesitant fuzzy environment is proposed. Finally, we select some examples to illustrate the effectiveness and superiority of the proposed method in this paper through comparison and analysis with other methods. Show more

Keywords: Normal wiggly hesitant fuzzy set, power average operator, generalized maclaurin symmetric mean operator, multi-attribute decision making

DOI: 10.3233/JIFS-202112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3895-3920, 2021

Authors: Mohammady Talvar, Houshyar | Haj Seyyed Javadi, Hamid | Navidi, Hamidreza | Rezakhani, Afshin

Article Type: Research Article

Abstract: IoT-based network systems use a modern architecture called fog computing, In which data providing data services is economical with low latency. This paper tends to solve the challenge of resource allocation in fog computing. Solving the resource allocation challenge leads to increased profits, economic savings, and optimal computing systems use. Here resource allocation is improved by making use of the combined algorithm Nash equilibrium and auction. In the proposed method, each player is assigned a matrix. Each player matrix includes fog nodes (FNs), data service subscribers (DSSs), and data service operators (DSOs). Each player generates the best strategy based on …the other players strategy in all stages of the algorithm. The simulation results show that FNs profit in the combined Nash and Auction equilibrium algorithms is superior to the Stackelberg game algorithm. Show more

Keywords: Fog computing, resource allocation, IoT, nash equilibrium, auction algorithm

DOI: 10.3233/JIFS-202122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3921-3932, 2021

Authors: Adu, Kwabena | Yu, Yongbin | Cai, Jingye | Mensah, Patrick Kwabena | Owusu-Agyemang, Kwabena

Article Type: Research Article

Abstract: Convolutional neural networks (CNNs) for automatic classification and medical image diagnosis have recently displayed a remarkable performance. However, the CNNs fail to recognize original images rotated and oriented differently, limiting their performance. This paper presents a new capsule network (CapsNet) based framework known as the multi-lane atrous feature fusion capsule network (MLAF-CapsNet) for brain tumor type classification. The MLAF-CapsNet consists of atrous and CLAHE, where the atrous increases receptive fields and maintains spatial representation, whereas the CLAHE is used as a base layer that uses an improved adaptive histogram equalization (AHE) to enhance the input images. The proposed method is …evaluated using whole-brain tumor and segmented tumor datasets. The efficiency performance of the two datasets is explored and compared. The experimental results of the MLAF-CapsNet show better accuracies (93.40% and 96.60%) and precisions (94.21% and 96.55%) in feature extraction based on the original images from the two datasets than the traditional CapsNet (78.93% and 97.30%). Based on the two datasets’ augmentation, the proposed method achieved the best accuracy (98.48% and 98.82%) and precisions (98.88% and 98.58%) in extracting features compared to the traditional CapsNet. Our results indicate that the proposed method can successfully improve brain tumor classification problems and support radiologists in medical diagnostics. Show more

Keywords: Brain tumor classification, capsule networks, deep neural network, atrous convolution, dynamic routing

DOI: 10.3233/JIFS-202261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3933-3950, 2021

Authors: Geng, Kaifeng | Ye, Chunming

Article Type: Research Article

Abstract: Facing the worsening environmental problems, green manufacturing and sustainable development have attracted much attention. Aiming at the energy-efficient distributed re-entrant hybrid flow shop scheduling problem considering the customer order constraints (EDORHFSP) under Time-of-Use (TOU) electricity price, a mathematical model is established to minimize the maximum completion time and total consumption energy cost. In the study, some customer orders require production in multiple factories and jobs belonging to the same customer order must be processed in one factory. Firstly, a memetic algorithm (MA) was proposed to solve the problem. To improve the performance of the algorithm, encoding and decoding methods, energy …cost saving procedure, three heuristic rules about the population initialization and some neighborhood search methods are designed. Then, Taguchi method is adopted to research the influence of parameters setting. Lastly, numerical experiments demonstrate the effectiveness and superiority of MA for the EDORHFSP. Show more

Keywords: Energy-efficient, memetic algorithm, Time-of-Use electricity price, distributed re-entrant hybrid flow shop scheduling, customer order constraints

DOI: 10.3233/JIFS-202963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3951-3971, 2021

Authors: Chen, Lifang | Wei, Mengru

Article Type: Research Article

Abstract: With the popularity of depth sensors and 3D scanners, 3D point cloud has developed rapidly. 3D scene understanding based on deep learning has become a research hotspot. However, many existing networks failed to fully consider the local structures of point clouds, limiting their abilities to exploit the complicated relationships between points. In this paper, we propose Enriching Local Features Network (ELF-Net), which enriches local features of point clouds. We propose Local Points Encoding Module (LPEM) and Feature Concatenate Module (FCM) in our network. Specifically, LPEM is designed to encode the information of eight orientations and 3D coordinate information of local …points. We stack the encoding units to achieve multi-scale representation, which is conducive to obtaining robustness and capturing details of the network. In Set Abstraction (SA) module, we apply farthest point sampling (FPS) method to sample the initial points and ball query method is used to group the neighboring points within a radius. FCM is designed to update the representations of local points by applying graph attention mechanism in local regions, which aims to enrich neighboring point feature representations. Finally, our network also proposes a new multivariate loss function, which combines the Center Loss function and Cross Entropy loss function to act on the classification branch. Experimental results show the effectiveness of our proposed network on ModelNet40 (achieves 92.35% accuracy), ScanNet (achieves 85.46% accuracy) and S3DIS (achieves 86.4% accuracy) datasets. Show more

Keywords: Point cloud classification and segmentation, local points encoding module, feature concatenate module, multivariate loss function

DOI: 10.3233/JIFS-210065

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3973-3983, 2021

Authors: Wan, Quan | Wu, Lin | Yu, Zhengtao

Article Type: Research Article

Abstract: Initial results of neural architecture search (NAS) in natural language processing (NLP) have been achieved, but the search space of most NAS methods is based on the simplest recurrent cell and thus does not consider the modeling of long sequences. The remote information tends to disappear gradually when the input sequence is long, resulting in poor model performance. In this paper, we present an approach based on dual cells to search for a better-performing network architecture. We construct a search space that is more compatible with language modeling tasks by adding an information storage cell inside the search cell, so …that we can make better use of the remote information of the sequence and improve the performance of the model. The language model searched by our method achieves better results than those of the baseline method on the Penn Treebank data set and WikiText-2 data set. Show more

Keywords: Neural architecture search, natural language processing, recurrent neural network

DOI: 10.3233/JIFS-210207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3985-3992, 2021

Authors: Mu, Tianshi | Lin, Kequan | Zhang, Huabing | Wang, Jian

Article Type: Research Article

Abstract: Deep learning is gaining significant traction in a wide range of areas. Whereas, recent studies have demonstrated that deep learning exhibits the fatal weakness on adversarial examples. Due to the black-box nature and un-transparency problem of deep learning, it is difficult to explain the reason for the existence of adversarial examples and also hard to defend against them. This study focuses on improving the adversarial robustness of convolutional neural networks. We first explore how adversarial examples behave inside the network through visualization. We find that adversarial examples produce perturbations in hidden activations, which forms an amplification effect to fool the …network. Motivated by this observation, we propose an approach, termed as sanitizing hidden activations, to help the network correctly recognize adversarial examples by eliminating or reducing the perturbations in hidden activations. To demonstrate the effectiveness of our approach, we conduct experiments on three widely used datasets: MNIST, CIFAR-10 and ImageNet, and also compare with state-of-the-art defense techniques. The experimental results show that our sanitizing approach is more generalized to defend against different kinds of attacks and can effectively improve the adversarial robustness of convolutional neural networks. Show more

Keywords: Adversarial examples, sanitizing hidden activations, adversarial robustness, convolutional neural networks

DOI: 10.3233/JIFS-210371

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3993-4003, 2021

Authors: Kazemi, Mohsen | Niknam, Taher | Bahmani-Firouzi, Bahman | Nafar, Mehdi

Article Type: Research Article

Abstract: This paper uses the coordinated energy management strategy for different sources and storages in the framework of flexible grid-connected energy hubs that participate in the day-ahead (DA) energy and reserve markets. In the base problem, this method maximizes the difference between the expected revenue of hubs gained by selling energy and reserve power in the proposed markets and the expected cost of lost flexibility (COLF). Also, it is subject to linearized optimal power flow (LOPF) equations in the electricity, gas and district heating systems, as well as hub constraints including different sources, storages and reserve models. This problem contains uncertainties …of load, market price, reserve requirement, renewable power and hub mobile storages parameters. Therefore, the hybrid stochastic/robust optimization (HSRO) is suitable to model these uncertain parameters and obtain robust capability for the hub to improve the system flexibility. Accordingly, the bounded uncertainty-based robust optimization (BURO) is used in this paper to model the uncertainty of hub mobile storages to achieve the hub robust potential in improving the system flexibility, and other uncertain parameters are modeled according to scenario-based stochastic programming (SBSP). Finally, the proposed strategy is implemented on a standard test system. The obtained numerical results confirm the capability of the suggested scheme in improving the economic status of sources and storages using the coordinated energy management strategy in the form of an energy hub, as well as enhancing economic conditions, operation, and flexibility of energy networks thanks to hubs for having access to optimal scheduling. Show more

Keywords: Coordinated energy management, cost of lost flexibility, energy and reserve market, flexible grid-connected energy hub, hybrid stochastic/robust optimization

DOI: 10.3233/JIFS-201284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4005-4020, 2021

Authors: Faragallah, Osama S. | Muhammed, Abdullah N. | Taha, Taha S. | Geweid, Gamal G.N.

Article Type: Research Article

Abstract: This paper presents a new approach to the multi-modal medical image fusion based on Principal Component Analysis (PCA) and Singular value decomposition (SVD).The main objective of the proposed approach is to facilitate its implementation on a hardware unit, so it works effectively at run time. To evaluate the presented approach, it was tested in fusing four different cases of a registered CT and MRI images. Eleven quality metrics (including Mutual Information and Universal Image Quality Index) were used in evaluating the fused image obtained by the proposed approach, and compare it with the images obtained by the other fusion approaches. …In experiments, the quality metrics shows that the fused image obtained by the presented approach has better quality result and it proved effective in medical image fusion especially in MRI and CT images. It also indicates that the paper approach had reduced the processing time and the memory required during the fusion process, and leads to very cheap and fast hardware implementation of the presented approach. Show more

Keywords: Image fusion, PCA, SVD, medical images, fusion

DOI: 10.3233/JIFS-202884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4021-4033, 2021

Authors: Gao, Jinding

Article Type: Research Article

Abstract: In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion …of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions. Show more

Keywords: Swarm intelligence optimization algorithm, population dynamics, environmental pollution, microbial control

DOI: 10.3233/JIFS-210127

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4035-4049, 2021