Journal of Intelligent & Fuzzy Systems - Volume 39, issue 5

Authors: Zhang, Xianquan | Yang, Ju | Dong, Yu | Yu, Chunqiang | Tang, Zhenjun

Article Type: Research Article

Abstract: Most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, a robust data hiding with multiple backups and optimized reference matrix is proposed in this paper. Specifically, secret data is divided into a set of groups and multiple backups of each group data are generated according to the number of backups. The cover image is divided into several blocks. A reference matrix is constructed by four constraints to assist data hiding and data extraction. The proposed method aims to extract exactly at least one backup of each group data so that the correct backups …can construct the secret data well if the stego-image is corrupted. Experimental results show that the proposed algorithm is robust to cropping and noise attacks. Show more

Keywords: Data hiding, anti-cropping, anti-noise, multi-backup data, data security

DOI: 10.3233/JIFS-200089

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6965-6977, 2020

Authors: El Atik, Abd El Fattah A. | wahba, Ashgan S.

Article Type: Research Article

Abstract: Rough set theory is used in simple directed graphs to study nano topology. Adjacent vertices was used in digraphs only to define their neighborhoods. Four types of neighborhood systems for vertices are introduced in this article which depend on both adjacent vertices and associated edges. Additionally, the generalization of some notions presented by Pawlak and Lellis Thivagar and some of their properties are investigated. Finally, we present a new model of a blood circulation system of the human heart based on blood paths. Also, different kinds of topological separation axioms are presented and studied between vertices and edges of the …heart blood circulation model. Show more

Keywords: Graph theory, Rough sets, Nano topology, Human heart, Separation axioms

DOI: 10.3233/JIFS-200126

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6979-6992, 2020

Authors: Han, Lu | Su, Zhi | Lin, Jing

Article Type: Research Article

Abstract: Ever increasing ordinal variables are being collected by the Personal Credit Reference System in China, however this system suffers from analysis of this kind of data, which cannot be calculated by Euclidean distance. In this study, we put forward a hybrid KNN algorithm based on Sugeno measure, and we prove that the error of this algorithm is smaller than that of Euclidean distance, furthermore, we use real data obtained from the Personal Credit Reference System to perform experiments and get the user’s initial portrait. Through the comparisons with Kmeans algorithm and other different distance measures in KNN algorithm, we find …that the hybrid KNN algorithm is more suitable for clustering personal credit data. Show more

Keywords: Hybrid KNN clustering, personal credit reference system, Sugeno measure, user’s portrait

DOI: 10.3233/JIFS-200191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6993-7004, 2020

Authors: Zhu, Zhanlong | Liu, Yongjun | Wang, Yuan

Article Type: Research Article

Abstract: Adding spatial penalty to fuzzy C-means (FCM) model is an important way to reduce the influence of noise in image segmentation. However, these improved algorithms easily cause segmentation failures when the image has the characteristics of unequal cluster sizes. Besides, they often fall into local optimal solutions if the initial cluster centers are improper. This paper presents a noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search algorithm and improved fuzzy c-means to overcome the above defects. Firstly, each size of clusters is integrated into the objective function of noise detecting fuzzy c-means …algorithm (NDFCM), which can reduces the contribution of larger clusters to objective function and then the new membership degree and cluster centers are deduced. Secondly, a new expression called compactness, representing the pixel distribution of each cluster, is introduced into the iteration process of clustering. Thirdly, we use two- paths to seek the optimal solutions in each step of iteration: one path is produced by the chaotic crow search algorithm and the other is originated by gradient method. Furthermore, the better solutions of the two-paths go to next generation until the end of the iteration. Finally, the experiments on the synthetic and non–destructive testing (NDT) images show that the proposed algorithm behaves well in noise robustness and segmentation performance. Show more

Keywords: Image segmentation, fuzzy clustering, chaotic crow search algorithm, unequal cluster sizes

DOI: 10.3233/JIFS-200197

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7005-7020, 2020

Authors: Li, Feng

Article Type: Research Article

Abstract: Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests. The evaluation of our approach demonstrates the significant cost …savings with respect to the state-of-the-art approach not only on the real-world datasets as well as on synthetic uncertain graph databases. Show more

Keywords: Uncertain graph, maximal frequent pattern, data mining

DOI: 10.3233/JIFS-200237

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7021-7033, 2020

Authors: Parsa, Navid | Bahmani-Firouzi, Bahman | Niknam, Taher

Article Type: Research Article

Abstract: Distribution automation is well recognized as an effective solution to enhance the reliability and efficiency of these grids in a timely manner. This paper introduces an effective probabilistic operation framework for the automated distribution networks (ADNs) incorporating the plug-in electric vehicles (PEVs) charging/discharging schemes in the presence of different renewable energy sources (RESs). To this end, this paper pursues four different strategic approaches. Firstly, an effective fuzzy based probabilistic method is proposed to model the forecast error in the wind and solar units well as the load demand through the cloud theory. Secondly, an appropriate framework is devised to model …the PEVs random behaviour considering their essential parameters such as the charging/discharging rate and arrival/departure time to/from the parking lots (PLs), the discharging level at driving mode on the road and the effects of battery degradation. As the third goal, an appropriate objective function which can consider automation indices including the social welfare and reliability is considered. Since the operation problem is a nonlinear continuous non-numerical problem, it requires an applicable and effective optimization algorithm which is regarded as the fourth goal of this paper. In this regard, a new θ -modified bat algorithm is introduced to find the optimal solution of the problem. The proposed model is simulated and examined on the IEEE 69-bus standard test system wherein results reveal the effectiveness and applicability of the proposed operation management framework. Show more

Keywords: Automated distribution networks, reliability, electric vehicles, renewable energy sources, optimization and operation management

DOI: 10.3233/JIFS-200246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7035-7051, 2020

Authors: Saini, Jagriti | Dutta, Maitreyee | Marques, Gonçalo

Article Type: Research Article

Abstract: Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% …of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%. Show more

Keywords: Indoor air quality, pollutants, prediction system, optimizers

DOI: 10.3233/JIFS-200259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7053-7069, 2020

Authors: Zhao, Ruirui | Luo, Minxia | Li, Shenggang

Article Type: Research Article

Abstract: The theory of single valued neutrosophic sets, which is a generalization of intuitionistic fuzzy sets, is more capable of dealing with inconsistent information in practice. In this paper, we propose reverse triple I method under single valued neutrosophic environment. Firstly, we give the definitions of single valued neutrosophic t-representation t-norms and single valued neutrosophic residual implications. Secondly, we develop a formula for calculating single valued neutrosophic residual implications. Then we propose reverse triple I method based on left-continuous single valued neutrosophic t-representation t-norms and its solutions. Lastly, we discuss the robustness of reverse triple I method based on the proposed …similarity measure. Show more

Keywords: Single valued neutrosophic sets, similarity measure, reverse triple I method

DOI: 10.3233/JIFS-200265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7071-7083, 2020

Authors: Liu, Shuqi | Shao, Mingwen | Liu, Xinping

Article Type: Research Article

Abstract: In recent years, deep neural networks have made significant progress in image classification, object detection and face recognition. However, they still have the problem of misclassification when facing adversarial examples. In order to address security issue and improve the robustness of the neural network, we propose a novel defense network based on generative adversarial network (GAN). The distribution of clean - and adversarial examples are matched to solve the mentioned problem. This guides the network to remove invisible noise accurately, and restore the adversarial example to a clean example to achieve the effect of defense. In addition, in order to …maintain the classification accuracy of clean examples and improve the fidelity of neural network, we input clean examples into proposed network for denoising. Our method can effectively remove the noise of the adversarial examples, so that the denoised adversarial examples can be correctly classified. In this paper, extensive experiments are conducted on five benchmark datasets, namely MNIST, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet. Moreover, six mainstream attack methods are adopted to test the robustness of our defense method including FGSM, PGD, MIM, JSMA, CW and Deep-Fool. Results show that our method has strong defensive capabilities against the tested attack methods, which confirms the effectiveness of the proposed method. Show more

Keywords: Deep neural network, generative adversarial network, adversarial example, defense

DOI: 10.3233/JIFS-200280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7085-7095, 2020

Authors: Nasr Saleh, Hayel | Imdad, Mohammad | Khan, Idrees | Hasanuzzaman, Md

Article Type: Research Article

Abstract: In the present article, inspired by the work of Jleli et al. [J. Inequal. Appl. 2014, 38 (2014)] and [J. Inequal. Appl. 2014, 439 (2014)] in metric spaces, we proposed a new class of contractive mappings termed as: fuzzy Θ f -contractive mappings by using an auxiliary function Θ f  : (0, 1) → (0, 1) satisfying suitable properties. This class has further been weakened by defining the class of fuzzy Θ f -weak contractive mappings to realize yet another class of contractive mappings. Thereafter, these two newly introduced classes of contractive mappings are utilized to establish some fixed point …theorems in M -complete fuzzy metric spaces (in the sense of George and Veeramani). In support of our newly obtained results, we provide some examples besides furnishing applications to dynamic programming. Show more

Keywords: Fixed point, fuzzy Θf-contractive mappings, fuzzy Θf-weak contractive mappings, fuzzy metric space, dynamic programming

DOI: 10.3233/JIFS-200319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7097-7106, 2020