Journal of Intelligent & Fuzzy Systems - Volume 43, issue 4

Authors: Lian, Wenwu

Article Type: Research Article

Abstract: The uncertainty of information plays an important role in practical applications. Uncertainty measurement (UM) can help us in disclosing the substantive characteristics of information. Probabilistic set-valued data is an important class of data in machine learning. UM for probabilistic set-valued data is worth studying. This paper measures the uncertainty of a probability set-valued information system (PSVIS) by means of its information structures based on Gaussian kernel method. According to Bhattacharyya distance, the distance between objects in each subsystem of a PSVIS is first built. Then, the fuzzy T cos -equivalence relations in a PSVIS by using Gaussian kernel method …are obtained. Next, information structures in a PSVIS are defined. Moreover, dependence between information structures is investigated by using the inclusion degree. As an application for the information structures, UM in a PSVIS is investigated. Finally, to evaluate the performance of the investigated measures, effectiveness analysis is performed from dispersion analysis, correlation analysis, and analysis of variance and post-hoc test. Show more

Keywords: GrC, PSVIS, Gaussian kernel method, Bhattacharyya distance, Information structure, Uncertainty, Measurement

DOI: 10.3233/JIFS-210460

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4645-4668, 2022

Authors: Dhurkari, Ram Kumar

Article Type: Research Article

Abstract: The Analytic Hierarchy Process (AHP) is a popular Multi-Criteria Decision Making (MCDM) method. The workability of AHP made it suitable for solving complicated and elusive decision problems that subsequently led to its widespread applications in highly diverse fields. However, AHP has also received criticisms on various fronts, one of which is the rank reversal problem. When a replica of an existing alternative is introduced in the Multi-Criteria Decision (MCD) setting, it sometimes causes rank order reversal among alternatives. However, the addition of a replica of an alternative in the MCD setting is not limited to the rank reversal problem, but …it also affects the inconsistency measure computed for the decision-maker (DM). An empirical study was conducted using AHP to measure the changes in the inconsistency of the DM on a well-defined and familiar MCD problem. The results indicate that when a replica is added to a pair-wise comparison matrix, the inconsistency of the DM reduces. It is found that there are two sources of inconstancy in a pair-wise preference matrix. One is intransitivity and another is the limitation of the 1–9 ratio scale. It is found that an inconsistency up to 50% is purely because of limitations of the ratio scale and higher inconsistencies are purely because of intransitivity in preferences defined by the DM. Therefore, the DMs should review and revise their preferences when their inconsistency exceeds 50%. This 50% threshold is also useful in deciding whether to apply a prediction algorithm to identify near consistent matrices. If the inconsistency of a matrix is above 50%, the prediction algorithms used to improve the consistency cannot be applied on the original inconsistent matrix because the source of inconsistency is intransitivity which means that the DM either does not have complete information about the problem or has not attended to the problem carefully. Show more

Keywords: Analytic Hierarchy Process, inconsistency, transitivity

DOI: 10.3233/JIFS-212041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4669-4679, 2022

Authors: Chakaravarthy, Sankar | Chandran, Kalaivani | Mariappan, Saravanan | Ramalingam, Sujatha

Article Type: Research Article

Abstract: Transport network is the backbone of economy. Every path has some positive and negative attributes such as transportation cost, road condition, traveling time etc., These attribute values are taken as fuzzy membership value with either positive or negative sign when modeling the transport network as signed fuzzy graph. The stability of these type of signed fuzzy graphs are discussed with the help of vulnerability parameters and edge integrity. In this paper, we have introduced complete signed fuzzy graph, signed fuzzy star graph, complement of a signed fuzzy graph, union of two signed fuzzy graph, join of two signed fuzzy graph …and cartesian product of two signed fuzzy graphs. For some standard signed fuzzy graph edge integrity value is calculated. Further this concept is applied in supply chain network with three layers, to study its stability and optimum path. Show more

Keywords: Vulnerability parameters, edge integrity, signed fuzzy graph

DOI: 10.3233/JIFS-220314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4681-4690, 2022

Authors: Wang, Guan | Wang, Jie-Sheng | Wang, Hong-Yu | Liu, Jia-Xu

Article Type: Research Article

Abstract: Fuzzy clustering is an important research field in pattern recognition, machine learning and image processing. The fuzzy C-means (FCM) clustering algorithm is one of the most common fuzzy clustering algorithms. However, it requires a given number of clusters in advance for accurate clustering of data sets, so it is necessary to put forward a better clustering validity index to verify the clustering results. This paper presents a ratio component-wise design method of clustering validity function based on FCM clustering method. By permutation and combination of six clustering validity components representing different meanings in the form of ratio, 49 different clustering …validity functions are formed. Then, these functions are verified experimentally under six kinds of UCI data sets, and a clustering validity function with the simplest structure and the best classification effect is selected by comparison. Finally, this function is compared with seven traditional clustering validity functions on eight UCI data sets. The simulation results show that the proposed validity function can better verify the classification results and determine the optimal clustering number of different data sets. Show more

Keywords: Data mining, fuzzy c-means clustering algorithm, clustering validity function, ratio component-wise design

DOI: 10.3233/JIFS-213481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4691-4707, 2022

Authors: Gökalp, Yaşar | Yüksel, Serhat | Dinçer, Hasan

Article Type: Research Article

Abstract: This study aims to create a strategy for reducing energy costs in hospitals to ensure the sustainability of health services. In this framework, a novel hybrid decision making approach is generated based on golden cut-oriented bipolar and q-rung orthopair fuzzy sets (q-ROFs). Firstly, balanced scorecard (BSC)-based criteria are evaluated by using multi stepwise weight assessment ratio analysis (M-SWARA) approach. Secondly, alternatives are examined with the help of technique for order preference by similarity to ideal solution (TOPSIS) technique. The novelty of this study is to find critical factors that affect the energy costs of health institutions with an original fuzzy …decision-making model. This proposed model has also some superiorities by comparing with previous models in the literature. First, SWARA method is improved, and this technique is generated with the name of M-SWARA. Hence, the relationship between the criteria can be examined owing to this issue. Additionally, golden cut is taken into consideration to compute the degrees in bipolar q-ROFSs to achieve more accurate results. These two issues have an important impact on the originality of the proposed model. The findings demonstrate that consciousness level of employees has the highest weight with respect to the energy costs in hospitals. Additionally, the type of energy used also plays a significant role for this issue. Thus, renewable energy sources should be considered in meeting the energy needs of hospitals. Although the installation costs of these energy types are higher, it will be possible to significantly reduce energy costs in the long run. Show more

Keywords: q-rung orthopair fuzzy sets, M-SWARA, bipolar fuzzy sets, golden cut, SWARA, TOPSIS

DOI: 10.3233/JIFS-220126

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4709-4722, 2022

Authors: Zhou, Xiaoguang | He, Xin | Huang, Xiaoxia

Article Type: Research Article

Abstract: Traditionally, the return on investment has been described as either a random variable or a fuzzy variable, while this paper discusses the uncertain portfolio selection in which each security return is assumed to be an uncertain variable. To better optimize the return and risk of a portfolio, we propose two models: uncertain minimax mean-variance (UM-EV) model and uncertain minimax mean-semivariance (UM-SVE) model. The crisp equivalents of the UM-EV model that regard the security return as a normal and linear uncertain variable are derived, and the optimization problem is solved using linear programming. For the UM-SVE model, the crisp equivalent of …a zigzag uncertain variable is introduced, and the optimization solution is calculated using hybrid intelligent algorithm. Finally, the effectiveness of the proposed models is illustrated using numerical examples. Show more

Keywords: Uncertain theory, minimax model, portfolio selection, mean-variance model, mean-semivariance model

DOI: 10.3233/JIFS-211766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4723-4740, 2022

Authors: Fathabadi, Fatemeh Rashidi | Grantner, Janos L. | Shebrain, Saad A. | Abdel-Qader, Ikhlas

Article Type: Research Article

Abstract: Recent developments in deep learning can be used in skill assessments for laparoscopic surgeons. In Minimally Invasive Surgery (MIS), surgeons should acquire many skills before carrying out a real operation. The Laparoscopic Surgical Box-Trainer allows surgery residents to train on specific skills that are not traditionally taught to them. This study aims to automatically detect the tips of laparoscopic instruments, localize a point, evaluate the detection accuracy to provide valuable assessment and expedite the development of surgery skills and assess the trainees’ performance using a Multi-Input-Single-Output Fuzzy Logic Supervisor system. The output of the fuzzy logic assessment is the performance …evaluation for the surgeon, and it is quantified in percentages. Based on the experimental results, the trained SSD Mobilenet V2 FPN can identify each instrument at a score of 70% fidelity. On the other hand, the trained SSD ResNet50 V1 FPN can detect each instrument at the score of 90% fidelity, in each location within a region of interest, and determine their relative distance with over 65% and 80% reliability, respectively. This method can be applied in different types of laparoscopic tooltip detection. Because there were a few instances when the detection failed, and the system was designed to generate pass-fail assessment, we recommend improving the measurement algorithm and the performance assessment by adding a camera to the system and measuring the distance from multiple perspectives. Show more

Keywords: Deep learning, laparoscopic surgical box-trainer, laparoscopic surgical instrument detection, fuzzy logic-based performance assessment, minimally invasive surgery, CNN

DOI: 10.3233/JIFS-213243

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4741-4756, 2022

Authors: Pande, Sandeep Dwarkanath | Rathod, Suresh Baliram | Chetty, Manna Sheela Rani | Pathak, Shantanu | Jadhav, Pramod Pandurang | Godse, Sachin P.

Article Type: Research Article

Abstract: Due to the evolution in the digital domain limitless multimedia is generated daily. It creates a necessity of potential and appealing image resuscitation system. In this paper, a shape and texture-based image retrieval system is proposed that estimates the resemblances of each query image with the images stored in the repository in the form of shape and textural facets and retrieves the images within an expected range of resemblance. The proposed approach employs a statistical approach for image retrieval. The proposed approach takes into account discriminative features of the input image for generating the shape and texture descriptors that produce …outstanding results for image databases of restricted variety, which merely includes homogeneous patterns, this approach yielded satisfactory results. For texture images it uses the spatial gray level dependency matrix (SGLDM) and proposes an algorithm to compute the the inverse difference moment (IDM) as the optimal image representative feature. It further employs K-Nearest Neighbour (KNN) classifier for the classification and retrieval tasks. The proposed system outperforms the various other ultra-modern content-based image retrieval (CBIR) systems in many respects. Show more

Keywords: CBIR, shape, texture, fourier descriptors, IDM, retrieval, KNN

DOI: 10.3233/JIFS-213355

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4757-4768, 2022

Authors: An, Qing | Tang, Ruoli | Hu, Qiqi

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-213513

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4769-4787, 2022

Authors: Suriya, N. | Vijay Shankar, S.

Article Type: Research Article

Abstract: The usage of Electric vehicle (EVs) has been exponentially growing due to its focus on eco-friendly means of transport, distributed charging platform and user dictated supporting infrastructures. The EVs are charged by the charging stations which equipped with Electric Vehicle Supply Equipment (EVSE) that contains Internet enabled computers. These systems are considered to be more important for controlling the function such as charging electric vehicles, authorization and smart connection to the local power grid using different wireless technologies such as green WIFI, Bluetooth and even 5 G. The cyber-attacks such as DoS and DDoS attacks can violate integrity, confidentiality and availability …of the EVSE resources. Hence the intelligent Intrusion Detection System (IDS) is required to ensure the system for the robust and trustworthy deployment of EVSE resources. To meet the above challenge, this paper proposes new composite and intelligent system which contains the deep learning based IDS and high random chaotic generators to safeguard the data against the different cyber-attacks. The proposed IDS has been modelled based on Gated Recurrent Units (GRU) and counter measures are performed by adopting the Enhanced Chaotic Scroll attractor keys (ECSA). The contribution of this research paper is as follows: Novel Dataset Preparation for EVSE under different attack scenarios, Implementation of high accurate multi-objective accurate GRU based IDSs, Design of Enhanced Chaotic Countermeasure Encryption Schemes for the counterfeiting the attacks in Internet Enabled EVSE system. The extensive experimentation has been carried out into two important phases. In first phase algorithm centric metrics such as prediction accuracy, time of detection, whereas in second phase key centric metrics such as Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Key sensitivity and entropy are calculated and compared with the other existing methodologies. Results demonstrates that the proposed ensemble system has outperformed than the other methodologies and proves its strong place in designing the more secured Internet Enabled EVSE systems. Show more

Keywords: Cyber-attacks, gate current units, enhanced chaotic scroll attractors, npcr, uaci, entropy

DOI: 10.3233/JIFS-220310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4789-4801, 2022