Journal of Intelligent & Fuzzy Systems - Volume 40, issue 3

Authors: Abu-Saleem, M.

Article Type: Research Article

Abstract: The main aim of this article is to present neutrosophic folding and neutrosophic retractions on a single-valued neutrosophic graph Ğ from the viewpoint of geometry and topology. For this reason, we use a sequence of neutrosophic transformations on Ğ to obtain a new single-valued neutrosophic graph G ˇ 1 which contains different parameters under new conditions. We deduce the isometric neutrosophic folding on neutrosophic spheres and neutrosophic torii. Also, we determine the relationship between the limit neutrosophic folding and the limit of neutrosophic retraction on Ğ. Theorems regulating these relations are attained.

Keywords: Single valued neutrosophic graph, neutrosophic folding, neutrosophic retraction, 51H20, 57N10, 57M05, 14F35, 20F34

DOI: 10.3233/JIFS-201957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5207-5213, 2021

Authors: Feng, Rui | Huang, Cheng-Chen | Luo, Kun | Zheng, Hui-Jun

Article Type: Research Article

Abstract: The West Lake of Hangzhou, a world famous landscape and cultural symbol of China, suffered from severe air quality degradation in January 2015. In this work, Random Forest (RF) and Recurrent Neural Networks (RNN) are used to analyze and predict air pollutants on the central island of the West Lake. We quantitatively demonstrate that the PM2.5 and PM10 were chiefly associated by the ups and downs of the gaseous air pollutants (SO2 , NO2 and CO). Compared with the gaseous air pollutants, meteorological circumstances and regional transport played trivial roles in shaping PM. The predominant meteorological factor …for SO2 , NO2 and surface O3 was dew-point deficit. The proportion of sulfate in PM10 was higher than that in PM2.5 . CO was strongly positively linked with PM. We discover that machine learning can accurately predict daily average wintertime SO2 , NO2 , PM2.5 and PM10 , casting new light on the forecast and early warning of the high episodes of air pollutants in the future. Show more

Keywords: Random forest, recurrent neural network, air pollutants prediction

DOI: 10.3233/JIFS-201964

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5215-5223, 2021

Authors: Elmuogy, Samir | Hikal, Noha A. | Hassan, Esraa

Article Type: Research Article

Abstract: Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID-19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s …life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training, 524 validation, 524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 in terms of accuracy, precision, recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool. Show more

Keywords: Deep learning, CNN, COVID-19 dataset, automatic classification, CT scan

DOI: 10.3233/JIFS-201985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5225-5238, 2021

Authors: Lin, Rongde | Li, Jinjin | Chen, Dongxiao | Huang, Jianxin | Chen, Yingsheng

Article Type: Research Article

Abstract: Fuzzy covering rough set model is a popular and important theoretical tool for computation of uncertainty, and provides an effective approach for attribute reduction. However, attribute reductions derived directly from fuzzy lower or upper approximations actually still occupy large of redundant information, which leads to a lower ratio of attribute-reduced. This paper introduces a kind of parametric observation sets on the approximations, and further proposes so called parametric observational-consistency, which is applied to attribute reduction in fuzzy multi-covering decision systems. Then the related discernibility matrix is developed to provide a way of attribute reduction. In addition, for multiple observational parameters, …this article also introduces a recursive method to gradually construct the multiple discernibility matrix by composing the refined discernibility matrix and incremental discernibility matrix based on previous ones. In such case, an attribute reduction algorithm is proposed. Finally, experiments are used to demonstrate the feasibility and effectiveness of our proposed method. Show more

Keywords: Attribute reduction, fuzzy discernibility matrix, fuzzy multi-covering systems, incremental discernibility matrix, observational consistency, refined discernibility matrix

DOI: 10.3233/JIFS-201998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5239-5253, 2021

Authors: Qi, Ping | Shu, Hong | Zhu, Qiang

Article Type: Research Article

Abstract: Computation offloading is a key computing paradigm used in mobile edge computing. The principle of computation offloading is to leverage powerful infrastructures to augment the computing capability of less powerful devices. However, the most existing computation offloading algorithms assume that the mobile device is not moving, and these algorithms do not take into account the reliability of task execution. In this paper, we firstly present the formalized description of the workflow, the wireless signal, the wisdom medical scenario and the moving path. Then, inspired by the Bayesian cognitive model, a trust evaluation model is presented to reduce the probability of …failure for task execution based on the reliable behaviors of multiply computation resources. According to the location and the velocity of the mobile device, the execution time and the energy consumption model based on the moving path are constructed, task deferred execution and task migration are introduced to guarantee the service continuity. On this basis, considering the whole scheduling process from a global viewpoint, the genetic algorithm is used to solve the energy consumption optimization problem with the constraint of response time. Experimental results show that the proposed algorithm optimizes the workflow under the mobile edge environment by increasing 20.4% of successful execution probability and decreasing 21.5% of energy consumption compared with traditional optimization algorithms. Show more

Keywords: Edge computing, computation offloading, trust evaluation model, energy consumption

DOI: 10.3233/JIFS-202025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5255-5273, 2021

Authors: Lun, Xiangmin | Yu, Zhenglin | Wang, Fang | Chen, Tao | Hou, Yimin

Article Type: Research Article

Abstract: In order to develop an efficient brain-computer interface system, the brain activity measured by electroencephalography needs to be accurately decoded. In this paper, a motor imagery classification approach is proposed, combining virtual electrodes on the cortex layer with a convolutional neural network; this can effectively improve the decoding performance of the brain-computer interface system. A three layer (cortex, skull, and scalp) head volume conduction model was established by using the symmetric boundary element method to map the scalp signal to the cortex area. Nine pairs of virtual electrodes were created on the cortex layer, and the features of the time …and frequency sequence from the virtual electrodes were extracted by performing time-frequency analysis. Finally, the convolutional neural network was used to classify motor imagery tasks. The results show that the proposed approach is convergent in both the training model and the test model. Based on the Physionet motor imagery database, the averaged accuracy can reach 98.32% for a single subject, while the averaged values of accuracy, Kappa, precision, recall, and F1-score on the group-wise are 96.23%, 94.83%, 96.21%, 96.13%, and 96.14%, respectively. Based on the High Gamma database, the averaged accuracy has achieved 96.37% and 91.21% at the subject and group levels, respectively. Moreover, this approach is superior to those of other studies on the same database, which suggests robustness and adaptability to individual variability. Show more

Keywords: Brain-computer interface, Electroencephalography, motor imagery, convolutional neural network

DOI: 10.3233/JIFS-202046

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5275-5288, 2021

Authors: Liu, Jin | Xie, Jinsheng | Ahmadzade, Hamed | Farahikia, Mehran

Article Type: Research Article

Abstract: Entropy is a measure for characterizing indeterminacy of a random variable or an uncertain variable with respect to probability theory and uncertainty theory, respectively. In order to characterize indeterminacy of uncertain variables, the concept of exponential entropy for uncertain variables is proposed. For computing the exponential entropy for uncertain variables, a formula is derived via inverse uncertainty distribution. As an application of exponential entropy, portfolio selection problems for uncertain returns are optimized via exponential entropy-mean models. For better understanding, several examples are provided.

Keywords: Uncertain variable, uncertainty theory, exponential entropy, inverse uncertainty distribution, portfolio selection

DOI: 10.3233/JIFS-202073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5289-5293, 2021

Authors: Li, Yufeng | Jiang, HaiTian | Lu, Jiyong | Li, Xiaozhong | Sun, Zhiwei | Li, Min

Article Type: Research Article

Abstract: Many classical clustering algorithms have been fitted into MapReduce, which provides a novel solution for clustering big data. However, several iterations are required to reach an acceptable result in most of the algorithms. For each iteration, a new MapReduce job must be executed to load the dataset into main memory, which results in high I/O overhead and poor efficiency. BIRCH algorithm stores only the statistical information of objects with CF entries and CF tree to cluster big data, but with the increase of the tree nodes, the main memory will be insufficient to contain more objects. Hence, BIRCH has to …reduce the tree, which will degrade the clustering quality and decelerate the whole execution efficiency. To deal with the problem, BIRCH was fitted into MapReduce called MR-BIRCH in this paper. In contrast to a great number of MapReduce-based algorithms, MR-BIRCH loads dataset only once, and the dataset is processed parallel in several machines. The complexity and scalability were analyzed to evaluate the quality of MR-BIRCH, and MR-BIRCH was compared with Python sklearn BIRCH and Apache Mahout k-means on real-world and synthetic datasets. Experimental results show, most of the time, MR-BIRCH was better or equal to sklearn BIRCH, and it was competitive to Mahout k-means. Show more

Keywords: Clustering, BIRCH, k-means, MapReduce, Hadoop

DOI: 10.3233/JIFS-202079

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5295-5305, 2021

Authors: Hu, Chaofang | Zhang, Yuting

Article Type: Research Article

Abstract:  An interactive α -satisfactory method via relaxed order of desirable α -satisfactory degrees is proposed for multi-objective optimization with fuzzy parameters and linguistic preference in this paper. Fuzzy parameters existing in objectives and constraints of multi-objective optimization are defined as fuzzy numbers and α -level set is used to build the feasible domain of parameters. On the basis, the original problem with fuzzy parameters is transformed into multi-objective optimization with fuzzy goals. Linguistic preference of decision-maker is modelled by the relaxed order of desirable α -satisfactory degrees of all the objectives. In order to achieve a compromise between optimization and …preference, the multi-objective optimization problem is divided into two single-objective sub-problems: the preliminary optimization and the linguistic preference optimization. A preferred solution can be found by parameter adjustment of inner-outer loop. The minimum stable relaxation algorithm of parameter is developed for calculating the relaxation bound of maximum desirable satisfaction difference. The M-α -Pareto optimality of solution is guaranteed by the test model. The effectiveness, flexibility and sensitivity of the proposed method are well demonstrated by numerical example and application example to heat conduction system. Show more

Keywords: Multi-objective optimization, linguistic preference, fuzzy parameter, satisfactory degree

DOI: 10.3233/JIFS-202114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5307-5322, 2021

Authors: Konstantakopoulos, Grigorios D. | Gayialis, Sotiris P. | Kechagias, Evripidis P. | Papadopoulos, Georgios A. | Tatsiopoulos, Ilias P.

Article Type: Research Article

Abstract: Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of …them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm’s efficiency. The results indicate that the algorithm’s solutions have small deviation from the best-published and some non-dominated solutions are also obtained. Show more

Keywords: Vehicle routing problem, time windows, simultaneous pickups and deliveries, multiobjective optimization, evolutionary algorithm, logistics

DOI: 10.3233/JIFS-202129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5323-5336, 2021