Journal of Intelligent & Fuzzy Systems - Volume 39, issue 3

Authors: Moreno, Jenny | Sánchez, Juan | Espitia, Helbert

Article Type: Research Article

Abstract: Floods are a climatic phenomena that affect different regions worldwide and that produces both human and material losses; for example in 2017, six of the worst floods were the cause of 3.273 deaths worldwide. In Colombia, the strong winter wave presented between 2010 and 2011, caused 1,374 deaths and 1,016 missing persons. The main river in Colombia is the Magdalena, which provides great benefits to the country but is also susceptible to flooding. This article presents a proposal to optimize a fuzzy system to prevent flooding in homes adjacent to areas of risk to the Magdalena River. The method used …is based on evolutionary algorithms to perform a global search, including a gradient-based algorithm to improve the solution obtained. The best result achieved was the Mean Square Error (MSE) of 7, 83E  - 05. As a conclusion, it is needed to employ optimization methods for the adjustment of parameters of the fuzzy system when considering that the sets and the rules are systematically obtained. Show more

Keywords: Artificial intelligence, fuzzy model, magdalena river, flood control, climate variability, genetic algorithms, particle swarm

DOI: 10.3233/JIFS-200486

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4533-4546, 2020

Authors: Yu, Wen | Vega, Francisco

Article Type: Research Article

Abstract:  The data driven black-box or gray-box models like neural networks and fuzzy systems have some disadvantages, such as the high and uncertain dimensions and complex learning process. In this paper, we combine the Takagi-Sugeno fuzzy model with long-short term memory cells to overcome these disadvantages. This novel model takes the advantages of the interpretability of the fuzzy system and the good approximation ability of the long-short term memory cell. We propose a fast and stable learning algorithm for this model. Comparisons with others similar black-box and grey-box models are made, in order to observe the advantages of the proposal.

Keywords: LSTM, fuzzy neural networks, nonlinear system identification

DOI: 10.3233/JIFS-200491

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4547-4556, 2020

Authors: Türk, Abdullah | Özkök, Murat

Article Type: Research Article

Abstract: The shipyard facility location selection (FLS) decision is a critical process that involves conflicting, qualitative, and quantitative criteria. Multi-Attribute Decision Making (MADM) methods are used as a powerful tool to overcome this complex problem. Today, using these methods in an integrated way, more accurate, efficient, and systematic results are obtained in solving complex issues such as FLS, which contains an uncertain structure. This paper proposes a framework for the weighting of criteria and ranking potential feasible locations (alternatives) using the combination of fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) methods. …While fuzzy AHP determines the importance values of the criteria by pairwise comparisons, fuzzy TOPSIS prioritizes the alternatives using the relative weights obtained with Fuzzy AHP. The integration of these two techniques provides a robust approach considering the results obtained for the shipyard FLS decision. The applicability of the proposed method is expressed in Turkey by a case study of the shipyard FLS decision. Show more

Keywords: Shipyard, location selection, fuzzy AHP, fuzzy TOPSIS

DOI: 10.3233/JIFS-200522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4557-4576, 2020

Authors: Liu, Zhenhua | Zhang, Mengting | Li, Yupeng | Chu, Xuening

Article Type: Research Article

Abstract: The evolution of the product family is the essential driving force for the development of a complex product. Only customer satisfaction is emphasized in the traditional module configuration methods, which is not beneficial for product family evolution that is due to non-customer factors such as the emergence of new technology. In this study, the intuitionistic fuzzy number is employed to quantify the degree of correlation between each module and configuration targets, namely customer satisfaction and the degree of evolution of the product family, respectively. The bi-objective integer programming model is constructed by maximizing the degree of customer satisfaction and product …family evolution. An improved Pareto ant colony optimization (P-ACO) is designed to solve this model and subsequently the Pareto frontier is obtained. The radar chart is adopted to represent the performance of each configuration scheme in the Pareto frontier. The feasibility and effectiveness of the proposed method are expounded by a case study and result comparison, showing that this method can provide a more competitive product configuration scheme to customers in the future market. Show more

Keywords: Product family evolution, complex products, module configuration, customer requirements, P-ACO

DOI: 10.3233/JIFS-200527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4577-4595, 2020

Authors: Rajpoot, Vikram | Mannepalli, Praveen Kumar | Choubey, Shruti Bhargava | Sohoni, Parag | Chaturvedi, Prashant

Article Type: Research Article

Abstract: Image enhancement (IE) is a common thing we use to get better results from previous imagery. This image enhancement is not only used by us, but it is implemented in many fields. Such as implementation in the military field, medical field, legal field, industry field, entertainment field, and much more. The main use of IE in each field is to get clear information. Pedestrian detection is an essential way of support in current traffic management. Traditional pedestrian detection error & miss detection rates are high owing to irregular lighting, dim tunnel atmosphere, and blurred controlled picture, making subsequent identifying hard. …A rapid image enhancement (FIE) algorithm founded on picture model restriction is therefore suggested in this document and reduced to the pedestrian region of interest (ROI) in the pavement close the road under highway tunnel (HT) scene. First, the technique used to assess the local atmospheric light (LAL) by combining global atmospheric light (GAL) and partitioned atmospheric light (AL). Second, the transmission is predicted to be founded on the plan obtained as of the image model’s constraints. The third is for balancing tunnel illumination, the technique utilizes steady instead of illumination. Lastly, the picture of the tunnel is improved by the picture model. Moreover, we propose a narrowing region approach for improving the overall computing performance, due to the real-time requirements of the algorithm. Taking account of the highway tunnel features, which are a blurred scene and difficult to identify from the context, we use a multi-function integration approach to detect the enhanced image. We described a novel filter in this article that is commonly used in computer vision & graphics. Guided algorithm filter is MATLAB simulated. Results of the experimental and comparative assessment indicate that the suggested technique can quickly and efficiently enhance the picture of the tunnel and highly enhance the impact of pedestrian detection. Show more

Keywords: Image enhancement, transmission, atmospheric light, pedestrian detection, constraint of imaging model

DOI: 10.3233/JIFS-200551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4597-4616, 2020

Authors: Gao, Chengrui | Liu, Feiqiang | Yan, Hua

Article Type: Research Article

Abstract: Infrared and visible image fusion refers to the technology that merges the visual details of visible images and thermal feature information of infrared images; it has been extensively adopted in numerous image processing fields. In this study, a dual-tree complex wavelet transform (DTCWT) and convolutional sparse representation (CSR)-based image fusion method was proposed. In the proposed method, the infrared images and visible images were first decomposed by dual-tree complex wavelet transform to characterize their high-frequency bands and low-frequency band. Subsequently, the high-frequency bands were enhanced by guided filtering (GF), while the low-frequency band was merged through convolutional sparse representation and …choose-max strategy. Lastly, the fused images were reconstructed by inverse DTCWT. In the experiment, the objective and subjective comparisons with other typical methods proved the advantage of the proposed method. To be specific, the results achieved using the proposed method were more consistent with the human vision system and contained more texture detail information. Show more

Keywords: image fusion, dual-tree complex wavelet transform, convolutional sparse representation, guided filter

DOI: 10.3233/JIFS-200554

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4617-4629, 2020

Authors: Zhou, Xiao-Yu | Wang, Xiao-Kang | Wang, Jian-qiang | Li, Jun-Bo | Li, Lin

Article Type: Research Article

Abstract: With the rapid growth of the global population and economy, energy consumption and demad are increasing sharply. As an essential renewable energy, biomass energy can promote the reform of energy production and consumption. Considering the characteristics of long investment cycle and large investment scale of agroforestry biomass power generation (AFBPG) projects, this study establishes a decision support framework for risk ranking of AFBPG project under picture fuzzy environment. The proposed framework considers not only the fuzziness and uncertainty of decision-making problems but also the decision-makers’ (DMs) psychological behavior. First, given the integrity of information representation, DMs provide risk assessment information …expressed with picture fuzzy numbers, and then gives the distance of the picture fuzzy set (PFS) to maximize the PFS information. Second, the entropy weight method is used to compute the objective weight. Third, the VIKOR (Vlse Kriterijumska Optimizacija I Kompromisno Resenje ) – TODIM (an acronym in Portuguese for an interactive multi-criteria decision making) method is suggested for ranking risk factors, which reflects the behavioral psychology of DMs. Moreover, the proposed evaluation model is successfully applied in a practical case. The results show that the model is valid for ranking risk factors under picture fuzzy environment. Last but not least, comparison and sensitivity analysis are implemented to verify the effectiveness and applicability of the proposed method and some suggestions for practical application are put forward. Show more

Keywords: Multi-criteria decision-making, picture fuzzy set, agroforestry biomass power generation project, risk ranking, VIKOR, TODIM

DOI: 10.3233/JIFS-200575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4631-4650, 2020

Authors: Patro, Sunkuru Gopal Krishna | Mishra, Brojo Kishore | Panda, Sanjaya Kumar | Kumar, Raghvendra | Long, Hoang Viet | Tuan, Tran Manh

Article Type: Research Article

Abstract: A recommender system (RS) delivers personalized suggestions on products based on the interest of a particular user. Content-based filtering (CBF) and collaborative filtering (CF) schemes have been previously used for this task. However, the main challenge in RS is cold start problem (CSP). This originates once a new user joins the system which makes the recommendation task tedious due to the shortage of information (clickstream, dwell time, rating, etc.) regarding the user’s interest. Therefore, CBF and CF are combined together by developing a knowledge-based preference learning (KBPL) system. This system considers the demographic data that includes gender, occupation, and age …for the recommendation task. Initially, the dataset is clustered using the self-organizing map (SOM) technique, then the high dimensional data is decomposed by higher-order singular value decomposition (HOSVD) and finally, Adaptive neuro-fuzzy inference system (ANFIS) predicts the output. For the big dataset, SOM is a robust clustering method and the similarities among the users can be easily observed by grid clustering. The HOSVD extracts the required information from the available data set to find the user similarity by decomposing the dataset in lower dimensions. ANFIS uses IF-THEN rules to recommend similar product to the new users. The proposed KBPL system is evaluated with the Black Friday dataset and the obtained error value is compared with the existing CF and CBF techniques. The proposed KBPL system has obtained root mean squared error (RMSE) of 0.71%, mean absolute error (MAE) of 0.54%, and mean absolute percentage error (MAPE) of 37%. Overall, the outcome of the comparative analysis shows minimum error and better performance in terms of precision, recall, and f-measure for the proposed KBPL system compared to the existing techniques and therefore more suitable for accurately recommending the products for the new users. Show more

Keywords: Clustering, ANFIS, cold start: Data decomposition, prediction, recommendation

DOI: 10.3233/JIFS-200595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4651-4665, 2020

Authors: Fan, Changxing | Fan, En | Chen, Jihong | Ye, Jun | Zhou, Kang

Article Type: Research Article

Abstract: Port as an irreplaceable important node in the process of logistics is a special form of the integrated logistics system, which completes the basic logistics service and value-added services in the global supply chain logistics system. At present, the port logistics service has become an important breakthrough in the competition of ports, the improvement of port logistics competitiveness has great influence on the development of port and port city and even the area economic development. Analyzing from the port logistics competitiveness, this paper establishes a comprehensive evaluation index system and proposes a single-value neutrosophic cosine measure method to evaluate the …port logistics competitiveness of five sample ports, and gets the score sorting of the logistics competitiveness of these five ports. This method as a helpful tool is clear and easy for port logistics competitiveness evaluation during actual application. Show more

Keywords: Single-valued neutrosophic set (SVNS), port logistics competitiveness, cosine measure, single-value neutrosophic cosine measure method

DOI: 10.3233/JIFS-200598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4667-4675, 2020

Authors: Ding, Weimin | Wu, Shengli

Article Type: Research Article

Abstract: Stacking is one of the major types of ensemble learning techniques in which a set of base classifiers contributes their outputs to the meta-level classifier, and the meta-level classifier combines them so as to produce more accurate classifications. In this paper, we propose a new stacking algorithm that defines the cross-entropy as the loss function for the classification problem. The training process is conducted by using a neural network with the stochastic gradient descent technique. One major characteristic of our method is its treatment of each meta instance as a whole with one optimization model, which is different from some …other stacking methods such as stacking with multi-response linear regression and stacking with multi-response model trees. In these methods each meta instance is divided into a set of sub-instances. Multiple models apply to those sub-instances and each for a class label. There is no connection between different models. It is very likely that our treatment is a better choice for finding suitable weights. Experiments with 22 data sets from the UCI machine learning repository show that the proposed stacking approach performs well. It outperforms all three base classifiers, several state-of-the-art stacking algorithms, and some other representative ensemble learning methods on average. Show more

Keywords: Ensemble learning, stacking, cross entropy, gradient descent

DOI: 10.3233/JIFS-200600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4677-4688, 2020

Authors: Sun, Zhe | Zou, Jiayang | He, Defeng | Man, Zhihong | Zheng, Jinchuan

Article Type: Research Article

Abstract: Due to the complex driving conditions confronted by an autonomous vehicle, it is significant for the vehicle to possess a robust control system to achieve effective collision-avoidance performance. This paper proposes a neural network-based adaptive integral terminal sliding mode (NNAITSM) control scheme for the collision-avoidance steering control of an autonomous vehicle. In order to describe the vehicle’s lateral dynamics and path tracking characteristics, a two-degrees-of-freedom (2DOF) dynamic model and a kinematic model are adopted. Then, an NNAITSM controller is designed, where a radial basis function neural network (RBFNN) scheme is utilized to online approximate the optimal upper bound of lumped …system uncertainties such that prior knowledge about the uncertainties is not required. The stability of the control system is proved via Lyapunov, and the selection guideline of control parameters is provided. Last, Matlab-Carsim co-simulations are executed to test the performance of the designed controller under different road conditions and vehicle velocities. Simulation results show that compared with conventional sliding mode (CSM) and nonsingular terminal sliding mode (NTSM) control, the proposed NNAITSM control scheme owns evident superiority in not only higher tracking precision but also stronger robustness against various road surfaces and vehicle velocities. Show more

Keywords: Autonomous vehicle, neural networks, sliding mode control, vehicle dynamics and control

DOI: 10.3233/JIFS-200625

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4689-4702, 2020

Authors: Min, Qu | Yu, Chen | Xing-Fu, Xiong | Jiang, Wu

Article Type: Research Article

Abstract: A comprehensive evaluation indicator system is the basis of car sharing systems (CSS) evaluation. The purpose of this study is to introduce the principles and methods of indicator selection for CSS, and to identify indicators for evaluating car sharing systems due to the reason that the importance of indicators can never be overestimated in CSS evaluation. A framework to identify indicators for evaluating CSS is proposed with four steps. First of all, the structure for indicator selection is established with application of AHP method. Secondly, adequacy check and redundancy check are carried out to ensure the structure is adequate and …redundant. Thirdly, underlying individual indicators are proposed according to questionnaires. Fourthly, to ensure the necessity, identification, and feasibility of indicators, we conduct N-I-F check. We carry out a case study of CSS evaluation indicators to validate the proposed framework from four dimensions: economic, environmental, systematic, and social. The proposed framework is quantitative and it is helpful in CSS evaluation to identify proper indicators and find out the best CSS option. Show more

Keywords: Car sharing systems, indicator selection, evaluation

DOI: 10.3233/JIFS-200646

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4703-4721, 2020

Authors: Azzam, A. A. | Khalil, Ahmed Mostafa | Li, Sheng-Gang

Article Type: Research Article

Abstract: It is known that mathematical statics, mathematical modeling, and differential equations are used to give an in-depth understanding of many medical problems. On the edge of the information revolution, minimal structures show some qualitative properties issues that are difficult to deal with it, such as quality of education, nutrition, etc. The aim of this paper is to discuss two medical applications and show that a minimal structure space is suitable for analyzing several real-life problems. Then, the accuracy of the decision-making and attributes reduction of the medical information system are explained and obtained. Furthermore, we introduce a comparison between our …approach and Pawlak’s approach to find accuracy for decision-making. Finally, the accuracy of decision-making via a variable precision model is improved. Show more

Keywords: Minimal structure, upper approximation, lower approximation, reduction of attributes, accuracy

DOI: 10.3233/JIFS-200651

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4723-4730, 2020

Authors: Deepika, T. | Prakash, P. | Dhanya, N.M.

Article Type: Research Article

Abstract: By leveraging the performance of small and medium-scale data centers (SMSDCs), which are involved in high-performance computing, data centers are central to the current modern industrial business world. Extensive enhancements in the SMSDC infrastructure comprise a diverse set of connected devices that disseminate resources to the end users. The high certainty workloads of end users and over resource provisioning result in high power consumption in SMSDCs, which are pivotal factors contributing to high carbon footprints from SMSDCs. The excessive emission of CO 2 is higher in SMSDCs compared with that of hyperscale data centers (HSDCs). An exorbitant amount of …electricity is utilized by 8.6 million data centers worldwide, and is expected to increase by up to 13% in 2030. The power requirement of an SMSDC domain is expected to be 5% of the global power production. However, the power consumption of SMSDCs changes annually. To aid SMSDCs, machine learning prediction is deployed. Literature review indicates that many studies have focused on the recurring issues of HSDCs rather than those of SMSDC. Herein, a regressive predictive analysis, i.e., multi-output random forest regressor, is proposed to forecast the resource usage and power utilization of virtual machines. These prediction results in diminishes the power utilization of SMSDC whilst reduces the CO 2 emission from SMSDC. The obtained result shows that the proposed approach yields better predictions than other single-output prediction methods for future resource demand from end users. Show more

Keywords: Cloud computing, virtual machine, power consumption prediction, machine learning

DOI: 10.3233/JIFS-200653

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4731-4747, 2020

Authors: Yang, LiGuo | Li, Chaoling | Lu, Lin | Guo, Tai

Article Type: Research Article

Abstract: The marine economy has become a growth point for the regional economy. International resource exchange is mainly achieved through marine transportation. Ports play a vital role in marine transportation and also play an important role in some large-scale international rescue activities. When an emergency occurs in the port, the port emergency logistics system has an important impact on the collection and distribution of materials, which can effectively reduce the negative impact and economic loss caused by the emergency. Through in-depth analysis of the emergency logistics system, design the comprehensive evaluation system of the port emergency logistics distribution system, and based …on the characteristics of the grey, fuzzy and difficult to quantify the influencing factors of the emergency logistics distribution system, apply the analytic hierarchy process and grey system theory to establish the gray level comprehensive evaluation model of the port emergency logistics distribution system. In this model, the analytic hierarchy process is used to determine the index weight, and then the grey theory is used to comprehensively evaluate the emergency logistics distribution system. Finally, the port is used as a case to verify the practicability and effectiveness of the model. Show more

Keywords: Port emergency logistics, grey AHP, evaluating indicator

DOI: 10.3233/JIFS-200674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4749-4761, 2020

Authors: Akram, Muhammad | Peng, Xindong | Al-Kenani, Ahmad N. | Sattar, Aqsa

Article Type: Research Article

Abstract: Complex Pythagorean fuzzy (CPF), a worthwhile generalization of Pythagorean fuzzy set, is a powerful tool to deal with two-dimensional or periodic information. In this paper, we develop two prioritized aggregation operators (AOs) under CPF environment, namely, complex Pythagorean fuzzy prioritized weighted averaging (CPFPWA) operator and complex Pythagorean fuzzy prioritized weighted geometric (CPFPWG) operator. We consider the prioritization relationship among criteria and decision makers (DMs) to make our result more accurate as in real decision making (DM) problems, the criteria and DMs have different priority level. Further, we discuss remarkable properties of our proposed AOs. Moreover, we promote the evolution of …MCDM problem by investigating an algorithm in CPF environment with its flow chart. Finally, to check the superiority and validity of proposed operators, we compare the computed results with the different existing techniques. Show more

Keywords: Complex pythagorean fuzzy sets, prioritized aggregation operators, decision making

DOI: 10.3233/JIFS-200684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4763-4783, 2020

Authors: Do Xuan, Cho | Dao, Mai Hoang | Nguyen, Hoa Dinh

Article Type: Research Article

Abstract: Advanced Persistent Threat (APT) attacks are a form of malicious, intentionally and clearly targeted attack. This attack technique is growing in both the number of recorded attacks and the extent of its dangers to organizations, businesses and governments. Therefore, the task of detecting and warning APT attacks in the real system is very necessary today. One of the most effective approaches to APT attack detection is to apply machine learning or deep learning to analyze network traffic. There have been a number of studies and recommendations to analyze network traffic into network flows and then combine with some classification or …clustering methods to look for signs of APT attacks. In particular, recent studies often apply machine learning algorithms to spot the present of APT attacks based on network flow. In this paper, a new method based on deep learning to detect APT attacks using network flow is proposed. Accordingly, in our research, network traffic is analyzed into IP-based network flows, then the IP information is reconstructed from flow, and finally deep learning models are used to extract features for detecting APT attack IPs from other IPs. Additionally, a combined deep learning model using Bidirectional Long Short-Term Memory (BiLSTM) and Graph Convolutional Networks (GCN) is introduced. The new detection model is evaluated and compared with some traditional machine learning models, i.e. Multi-layer perceptron (MLP) and single GCN models, in the experiments. Experimental results show that BiLSTM-GCN model has the best performance in all evaluation scores. This not only shows that deep learning application on flow network analysis to detect APT attacks is a good decision but also suggests a new direction for network intrusion detection techniques based on deep learning. Show more

Keywords: Advanced persistent threat, APT attack detection, network traffic, flow, bidirectional long short term memory, graph convolutional networks

DOI: 10.3233/JIFS-200694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4785-4801, 2020

Authors: Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4803-4805, 2020

Authors: Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 4807-4810, 2020