Journal of Intelligent & Fuzzy Systems - Volume 44, issue 3

Authors: Karthiga, S. | Abirami, A.M.

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Abstract: This article has been retracted.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-220408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4257-4272, 2023

Authors: Tang, Chao | Tang, Yong | Zeng, Zhuolin | Zhang, Linghao | Xiang, Siyu

Article Type: Research Article

Abstract: Because the traditional methods do not select the best feature collection in feature analysis, the accuracy and effectiveness of user feature clustering are not high, and the accuracy of user feature classification is not high. Therefore, this paper proposes a customer feature analysis method based on power consumption feature selection and behavior portrait of different people. The optimal feature set is obtained according to the maximum correlation and minimum redundancy criterion, and the user portrait task is described. The spatial feature domain classification method is used to classify the user portrait information, and the user label database is constructed according …to the classification results. The AP clustering algorithm is used to cluster the power user portrait information and complete the customer feature analysis. Experimental results show that this method effectively improves the accuracy and effectiveness of user feature clustering, and the accuracy of user feature classification is high, indicating that the application effect is good. Show more

Keywords: Power consumption characteristics, behavioral portraits, customer characteristics, AP clustering algorithm, information classification

DOI: 10.3233/JIFS-220615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4273-4283, 2023

Authors: Yin, Ming | Zhou, Pan | Xu, Taige | Jiang, Jijiao

Article Type: Research Article

Abstract: Requirements are important in software development. Ambiguous requirements cause inconsistent understanding by developers, which leads to rework, delayed delivery, and other problems, and may even have devastating effects on the project. A large number of requirements text written in natural language are not concise, intuitive, and accurate. This condition increases the workload of designers and the difficulty of their tasks. An effective solution for the aforementioned problems is to extract actors and use cases from the requirement texts. This study proposes a model for extracting actors and using cases automatically, which combines bi-directional long short-term memory (BiLSTM) and conditional random …fields. BiLSTM is used to capture the contextual information of the texts, and CRF is used to calculate the tag transfer score and determine the most accurate tag sequence, which aims to extract actors and use cases. Results show that the accuracy of extraction is significantly improved compared with the baseline method, which verifies the effectiveness of the proposed method in extracting actors and use cases. Show more

Keywords: BiLSTM-CRF, software requirements, actors and use cases, context

DOI: 10.3233/JIFS-221094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4285-4299, 2023

Authors: Park, Choonkil | Rehman, Noor | Ali, Abbas

Article Type: Research Article

Abstract: The q -rung orthopair fuzzy sets accommodate more uncertainties than the Pythagorean fuzzy sets and hence their applications are much extensive. Under the q -rung orthopair fuzzy set, the objective of this paper is to develop new types of q -rung orthopair fuzzy lower and upper approximations by applying the tolerance degree on the similarity between two objects. After employing tolerance degree based q -rung orthopair fuzzy rough set approach to it any times, we can get only the six different sets at most. That is to say, every rough set in a universe can be approximated by only six …sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Furthermore, we propose tolerance degree based multi granulation optimistic/pessimistic q -rung orthopair fuzzy rough sets and investigate some of their properties. Another main contribution of this paper is to disclose the ideas of different kinds of approximations called approximate precision, rough degree, approximate quality and their mutual relationship. Finally a technique is devloped to rank the alternatives in a q -rung orthopair fuzzy information system based on similarity relation. We find that the proposed method/technique is more efficient when compared with other existing techniques. Show more

Keywords: q-rung orthopair fuzzy set, fuzzy rough set, similarity relation, tolerance classes, multigranulation q-rung orthopair fuzzy rough sets

DOI: 10.3233/JIFS-221249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4301-4321, 2023

Authors: Apinaya Prethi, K.N. | Sangeetha, M. | Nithya, S.

Article Type: Research Article

Abstract: Due to decentralized infrastructure in modern Internet-of-Things (IoT), the tasks should be shared around the edge devices via network resources and traffic prioritizations, which weaken the information interoperability. To solve this issue, a Minimized upgrading batch Virtual Machine (VM) Scheduling and Bandwidth Planning (MSBP) was adopted to reduce the amount of batches needed to complete the system-scale upgrade and allocate the bandwidth for VM migration matrices. But, the suboptimal use of VM and possible loss of tasks may provide inadequate Resource Allocation (RA). Hence, this article proposes an MSBP with the Priority-based Task Scheduling (MSBP-PTS) algorithm to allocate the tasks …in a prioritized way and maximize the profit by deciding which request must handle by the edge itself or send to the cloud server. At first, every incoming request in its nearest fog server is allocated and processed by the priority scheduling unit. Few requests are reallocated to other fog servers when there is an inadequate resource accessible for providing the request within its time limit. Then, the request is sent to the cloud if the fog node doesn’t have adequate resources, which reduces the response time. However, the MSBP is the heuristics solution which is complex and does not ensure the global best solutions. Therefore, the MSBP-PTS is improved by adopting an Optimization of RA (MSBP-PTS-ORA) algorithm, which utilizes the Krill Herd (KH) optimization instead of heuristic solutions for RA. The simulation outcomes also demonstrate that the MSBP-PTS-ORA achieve a sustainable network more effectively than other traditional algorithms. Show more

Keywords: Internet-of-things, edge devices, resource allocation, priority levels, task scheduling, krill herd optimization

DOI: 10.3233/JIFS-221430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4323-4334, 2023

Authors: Wu, Nengkai | Jia, Dongyao | Zhang, Chuanwang | Li, Ziqi

Article Type: Research Article

Abstract: Cervical cancer is one of the most common causes of death in women in the world, and early screening is an effective means of diagnosis and treatment, which can greatly improve the survival rate. Cervical cell classification model is an effective means to assist screening. However, the existing single model, including CNNs and machine learning methods, still has shortcomings such as unclear feature meaning, low accuracy and insufficient supervision. To solve the shortcomings of a single model, a novel framework based on strong feature Convolutional Neural Networks (CNN)-Lagrangian Support Vector Machine (LSVM) model is proposed for the accurate classification of …cervical cells. Strong features extracted by hybrid methods are fused with the abstract ones from hidden layers of LeNet-5, then the fused features are processed with dimension reduction and fed into the LSVM classifier optimized by Adaboost for classification. Proposed model is evaluated using the augmented Herlev and private dataset with the metrics including accuracy (Acc) , sensitivity (Sn) , and specificity (Sp) , which outperformed the baselines and state-of-the-art approaches with the Acc of 99.5% and 94.2% in 2&7-class classification, respectively. Show more

Keywords: Cervical cancer, strong feature, convolutional neural networks (CNN), lagrangian support vector machine (LSVM), cancer cell classification

DOI: 10.3233/JIFS-221604

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4335-4355, 2023

Authors: Liu, Zhiyong | Jin, Ying | Bao, Hong | Zhao, Yong

Article Type: Research Article

Abstract: A novel for an integrated fault and states estimator was proposed for the generalized linear discrete-time system with disturbances. The proposed scheme was based on a Self-Organizing fuzzy Luenberger system to estimate the states and approximate the fault information simultaneously for the generalized linear discrete-time system. For this purpose, a generalized linear discrete-time system with disturbances was transformed into an equivalent standard state-space system with disturbances and faults. Then, the faults and disturbances of generalized linear discrete-time system can be separated with the coordinate transform, meanwhile the Self-Organizing fuzzy Luenberger estimator was designed to obtain the accurate fault information. Based …on the obtained fault information, the fault detection experiments were performed, and the fault feature parameters required for fault isolation were determined. Finally, the proposed strategy was applied for a direct current motor to demonstrate the effectiveness of the proposed approach. Show more

Keywords: Self-organizing fuzzy Luenberger method, coordinate transform, faults and disturbances separation, fault detection, generalized linear discrete time system

DOI: 10.3233/JIFS-222890

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4357-4370, 2023

Authors: Wu, Xiu-Yun | Niu, Yu-Jie | Zhang, Hui-Min

Article Type: Research Article

Abstract: In this paper, the notion of M -fuzzifying convex remotehood systems is introduced and characterizations of M -fuzzifying convex spaces are obtained. Further, notions of remote mappings and M -fuzzifying convex quasi-uniformities are introduced. It is proved that M -fuzzifying convex quasi-uniform space and M -fuzzifying convex space are mutually induced. In order to discuss categorical relationships among M -fuzzifying convex quasi-uniform spaces, M -fuzzifying convex remotehood spaces and M -fuzzifying convex spaces, notions of M -fuzzifying quasi-uniformizable convex structures and M -fuzzifying quasi-uniformizable convex remotehood spaces are presented. It is proved that the category of M -fuzzifying quasi-uniformizable convex …spaces and the category of M -fuzzifying quasi-uniformizable convex remotehood spaces can be embedded into the category of M -fuzzifying convex quasi-uniform spaces as subcategories. Show more

Keywords: M-fuzzifying convex space, M-fuzzifying convex remotehood space, M-fuzzifying quasi-uniformizable convex space, M-fuzzifying convex quasi-uniform space, M-fuzzifying quasi-uniform preserving mapping

DOI: 10.3233/JIFS-223036

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4371-4382, 2023

Authors: Liu, Shengyao | Lin, Jiaoqing | Xu, Xinrui

Article Type: Research Article

Abstract: The construction industry is the basic industry of the country. With the rapid development of the economy, the construction industry has grown rapidly and the competition in the construction market has become more intense. The competition in the construction market is not only between individual enterprises, but also between the whole supply chain that provides products. Therefore, it is imperative to introduce the idea of supply chain management, strengthen the cooperation with suppliers and improve competitiveness. Supplier evaluation and selection is one of the first issues to be solved for the development of supply chain management. The selection and application …of building material suppliers is a classic multiple attribute decision making (MADM). In this paper, the intuitionistic fuzzy sets (IFSs) and Hamacher operations is introduced and the induced intuitionistic fuzzy Hamacher power ordered weighted average (I-IFHPOWA) operator is built. Meanwhile, the properties of built operator are analyzed. Then, the I-IFHPOWA operator is applied to solve the MADM under IFSs. Finally, an example for building material supplier selection is utilized to proof this built model. Show more

Keywords: Multiple attribute decision making (MADM), intuitionistic fuzzy sets (IFSs), I-OWA operator, I-IFHPOWA operator, building material suppliers

DOI: 10.3233/JIFS-221437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4383-4395, 2023

Authors: Jagadish Kumar, N. | Balasubramanian, C.

Article Type: Research Article

Abstract: In a cloud computing system, resources can be accessed at a minimal cost whenever users raise request needs. The primary goal of cloud computing is to provide cost-efficiency of service scheduling to clients fast while using the least number of resources. Cloud Service Provisioning (CSP) can match consumer needs with minimal use of resources. There are several metaheuristic optimization algorithms have been developed in the field of CSP resource minimization and adequate computing resources are required to ensure client satisfaction. However, it performs poorly under a variety of practical constraints, including a vast amount of user data, smart filtering to …boost user search, and slow service delivery. In this regard, propose a Black Widow Optimization (BWO) algorithm that reduces cloud service costs while ensuring that all resources are devoted only to end-user needs. It is a nature-inspired metaheuristic algorithm that involved a multi-criterion correlation that is used to identify the relationship between user requirements and available services and thereby, it is defined as an MS-BWO algorithm. Thus finds the most efficient virtual space allocation in a cloud environment. It uses a service provisioning dataset with metrics like energy usage, bandwidth utilization rate, computational cost, and memory consumption. In terms of data performance, the proposed MS-BWO outperforms exceed than other existing state-of-art-algorithms including Work-load aware Autonomic Resource Management Scheme(WARMS), Fuzzy Clustering Load balancer(FCL), Agent-based Automated Service Composition (A2SC) and Load Balancing Resource Clustering (LBRC), and an autonomic approach for resource provisioning (AARP ) Show more

Keywords: Cloud service provisioning, Resource utilization, Virtual machine, Metaheuristic black widow optimization

DOI: 10.3233/JIFS-222048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4397-4417, 2023

Authors: Ali, Jawad | Ali, Jawad | Naeem, Muhammad | Mahmood, Waqas

Article Type: Research Article

Abstract: The q-rung picture linguistic set (q-RPLS) is an effective tool for managing complex and unpredictable information by changing the parameter ‘q’ regarding hesitancy degree. In this article, we devise some generalized operational laws of q-RPLS in terms of the Archimedean t-norm and t-conorm. Based on the proposed generalized operations, we define two types of generalized aggregation operators, namely the q-rung picture linguistic averaging operator and the q-rung picture linguistic geometric operator, and study their relevant characteristics in-depth. With a view toward applications, we discuss certain specific cases of the proposed generalized aggregation operators with a range of parameter values. Furthermore, …we explore q-rung picture linguistic distance measure and its required axioms. Then we put forward a technique for q-RPLSs based on the proposed aggregation operators and distance measure to solve multi-attribute decision-making (MADM) challenges with unknown weight information. At last, a practical example is presented to demonstrate the suggested approaches’ viability and to perform the sensitivity and comparison analysis. Show more

Keywords: q-rung-Picture linguistic fuzzy set, generalized operations, generalized aggregation operators, entropy, decision-making

DOI: 10.3233/JIFS-222292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4419-4443, 2023

Authors: Umamaheswari, K.M. | Muthu kumaran, A.M.J.

Article Type: Research Article

Abstract: Cloud technology has raised significant prominence providing a unique market economic approach for resolving large-scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on-demand to meet a variety of user QoS standards. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. One of the most important and difficult non-deterministic polynomial-hard challenges in cloud technology is task scheduling. Therefore, in this paper, an efficient task scheduling approach is developed. To achieve …this objective, a hybrid genetic algorithm with particle swarm optimization (HGPSO) algorithm is presented. The scheduling is performed based on the multi-objective function; the function is designed based on three parameters such as makespan, cost, and resource utilization. The proper scheduling system should minimize the makespan and cost while maximizing resource utilization. The proposed algorithm is implemented using WorkflowSim and tested with arbitrary task graphs in a simulated setting. The results obtained reveal that the proposed HGPSO algorithm outperformed all available scheduling algorithms that are compared across a range of experimental setups. Show more

Keywords: Cloud computing, HGPSO, workflow, task scheduling, makespan, resource utilization, multi-objective function, and fitness

DOI: 10.3233/JIFS-222842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4445-4458, 2023

Authors: Garg, Harish | Kahraman, Cengiz | Ali, Zeeshan | Mahmood, Tahir

Article Type: Research Article

Abstract: Complex Pythagorean fuzzy set (CPFS) is a massive influential principle for managing ambiguity and inconsistent information in genuine life dilemmas. To determine the relationship among any number of attributes, the Hamy mean (HM) operators based on interaction operational laws are very dominant and massive flexible to manage awkward and problematic information. This study aims to combine the complex Pythagorean fuzzy (CPF) information with interaction HM operators to initiate the CPF interaction HM (CPFIHM) operator, CPF interaction weighted HM (CPFIWHM) operator, CPF interaction dual HM (CPFIDHM) operator, CPF interaction weighted dual HM (CPFIWDHM) operator and their powerful properties. Additionally, a decision-making …strategy for determining the security threats in the computer is elaborated under the interaction of HM operators based on the CPF setting. Numerous examples are illustrated with the help of presented operators to determine the consistency and flexibility of the investigated operators. Finally, with the help of sensitivity analysis, advantages, and geometrical representation, the supremacy, and efficiency of the presented works are also elaborated. Show more

Keywords: Complex pythagorean fuzzy sets, interaction hamy mean operators, interaction dual hamy mean operators, security threats in computers

DOI: 10.3233/JIFS-220947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4459-4479, 2023

Authors: Neena Raj, N.R. | Shreelekshmi, R.

Article Type: Research Article

Abstract: This paper presents a secure image authentication scheme for tamper localization and recovery at pixel level. The proposed scheme encrypts the watermark comprising tamper localization code and self-recovery code using chaotic sequence to ensure security. This scheme uses pixel to block conversion technique for ensuring lossless recovery of the original image from an untampered watermarked image. For enhancing the localization accuracy, a multilevel tamper localization strategy is used. The experimental results show that the proposed scheme generates watermarked images with minimal information loss and can withstand copy-move, image splicing, content removal, vector quantization, collage and content only attacks. This scheme …has better security, better tamper localization accuracy and better recovered image quality under extensive tampering and takes less computation time in comparison to the state-of-the-art schemes. Show more

Keywords: Chaotic sequence, fragile watermarking, image authentication, image recovery, tamper localization

DOI: 10.3233/JIFS-221245

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4481-4493, 2023

Authors: Shi, Jianzhong

Article Type: Research Article

Abstract: Fuzzy clustering has been widely applied in T-S fuzzy model identification for nonlinear systems, however, tradition type-1 fuzzy clustering algorithms can’t deal with uncertainties in real world, an improved interval type-2 fuzzy c-regression model (IT2-FCRM) clustering is proposed for T-S fuzzy model identification in this paper. The improved IT2-FCRM adapts a new objective function, which makes the boundary of clustering more clearly and reduces the influence of outliers or noisy data on clustering results. The premise parameters of T-S fuzzy model are upper and lower hyperplanes obtained by improved IT2-FCRM, and the upper and lower hyperplanes are used to build …hyper-plane-shaped type-2 Gaussian membership function. Compared with the hyper-sphere-shaped membership function of tradition IT2-FCRM, the hyper-plane-shaped membership function is more coincided with point to plane sample distance described by FCRM clustering. The simulation results of several benchmark problems and a real bed temperature in circulating fluidized bed plant show that the identification algorithm has higher accuracy. Show more

Keywords: Fuzzy identification, interval type-2 fuzzy c-regression model, fuzzy clustering, T-S fuzzy model, orthogonal least squares

DOI: 10.3233/JIFS-221434

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4495-4507, 2023

Authors: Bakhshi, M. | Ahn, S. S. | Jun, Y. B. | Xin, X. L. | Borzooei, R. A.

Article Type: Research Article

Abstract: We study the lattice structure of fuzzy A-ideals in an mv-module M (fai (M), symbolically) and show that it is a complete Heyting lattice and so the set of its pseudocomplements forms a Boolean algebra. In the sequel, the properties of fuzzy congruences in an mv-module are investigated and using them some structural theorems are stated and proved. Finally, it is proved that fai (M) can be embedded into the lattice of fuzzy congruences.

Keywords: mv-module, fuzzy A-ideal, fuzzy congruence, distributive lattice

DOI: 10.3233/JIFS-221552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4509-4519, 2023

Authors: Li, Yufei | Hu, Nanyan | Ye, Yicheng | Wu, Menglong

Article Type: Research Article

Abstract: In order to solve the problem of underground goafs, particularly in light of the importance ranking of evaluation indices being more subjective and catastrophe progression values being large and too concentrated in the catastrophe progression method, the importance of multiple indices is ranked by the maximizing deviation method. An S-shaped curve is used to establish a regression function to improve the value of catastrophe progression method. First, three first-level evaluation indices and eight second-level evaluation indices are selected to establish an index system for risk evaluation of the underground goaf. Next, based on the principle of catastrophe progression method, an …improved catastrophe model for its risk evaluation is established. Finally, sample training and verification are performed based on the improved evaluation model. The evaluation results show that the improved catastrophe progression method objectively ranks the importance of the evaluation indices of each layer, which improves the credibility of the evaluation results. The evaluation results are consistent with the actual geological data and detection results, which verifies the validity and accuracy of the evaluation model. However, only 87.5% of the risk levels obtained by the fuzzy comprehensive evaluation method are completely consistent with the improved catastrophe progression method, and the ranking error of risk value within one rank also accounted for 87.5%. Therefore, the results calculated by the improved catastrophe progression method are more accurate. The numerical gap of the improved catastrophe progression values becomes larger, from [0.796, 0.969] to [0.275, 0.691], which is 2.405 times of the interval difference of the catastrophe progression values before the improvement, which makes the numerical distribution of the catastrophe progression values more scientific and reasonable, with a higher resolution level. Therefore, it is reasonable and feasible to use the improved catastrophe progression method for the risk evaluation of the underground goaf, which can provide a certain theoretical basis and engineering guidance for underground goaf disaster control and management. Show more

Keywords: Catastrophe progression method, maximizing deviation method, regression model, underground goaf, risk evaluation, catastrophe model

DOI: 10.3233/JIFS-222094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4521-4536, 2023

Authors: Bai, Wenhui | Zhang, Chao | Zhai, Yanhui | Sangaiah, Arun Kumar

Article Type: Research Article

Abstract: Water quality inspection (WQI) is one of the primary ways to ensure the safe utilization of water resources, and complicated data modeling, fusion and analysis play a significant role in seeking the resource with the best water quality. Nevertheless, the challenges of missing data, relatively large differences in decision results and bounded rationality owned by decision-makers (DMs) in terms of WQI still exist nowadays. Thus, from the aspect of stable and behavioral decision-making in multi-granularity incomplete intuitionistic fuzzy information systems (MG-IIFISs), the paper investigates a comprehensive multi-attribute group decision-making (MAGDM) approach for the application of WQI. First, the concept of …MG-IIFISs is built by modeling MAGDM problems with intuitionistic fuzzy numbers (IFNs), then a new transformation scheme is constructed for transforming MG-IIFISs into multi-granularity intuitionistic fuzzy information systems (MG-IFISs) based on the similarity principle. Second, three types of multigranulation intuitionistic fuzzy probabilistic rough sets (MG IF PRSs) are developed by referring to the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) method. Afterwards, attribute weights are objectively calculated based on the best-worst method (BWM), and a new stable and behavioral MAGDM approach is constructed by means of the TODIM (an acronym in Portuguese for interactive multi-criteria decision-making) method. At last, a case study in the setting of WQI is conducted with the support of a UCI data set, and sensitivity analysis, comparative analysis and experimental analysis are investigated to display the validity of the proposed approach. In general, the proposed approach improves the stability of decision results via MULTIMOORA and BWM, and also fully considers the bounded rationality of DMs’ psychological behaviors from the aspect of the TODIM method, which has certain advantages in the community of MAGDM studies. Show more

Keywords: Granular computing, rough set, MULTIMOORA, incomplete intuitionistic fuzzy information system, water quality inspection

DOI: 10.3233/JIFS-222385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4537-4556, 2023

Authors: Kaur, Kamalpreet | Gupta, Asha

Article Type: Research Article

Abstract: The present paper proposes a novel version of inducing nano topology by using new kinds of approximation operators via two ideals with respect to a general binary relation. This approach improves the accuracy of the approximation quite significantly. These newly defined approximations constitute the generalized version of rough sets defined by Pawlak in 1982. A comparison is drawn between the suggested technique and the already existing ones to demonstrate the significance of the proposed ideology. In addition, the standard notion of nano topology, based on an equivalence relation is generalized to the binary relation, which can have a broader scope …when applied to intelligent systems. Also, the significance of this approach is demonstrated by an example where an algorithm is given to find the key factors responsible for the profit of a company along with the comparison to the previous notions. Likewise, the proposed algorithm can be used in all fields of science to simplify complex information systems in extracting useful data by finding the core. Show more

Keywords: Nano topology, rough sets, ideals, bi-ideal approximation, core

DOI: 10.3233/JIFS-222958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4557-4567, 2023

Authors: Arulaalan, M. | Aparna, K. | Nair, Vicky | Banala, Rajesh

Article Type: Research Article

Abstract: It is difficult for underwater archaeologists to recover the fine details of a captured image on the seabed when the image quality worsens due to the presence of more noisy artefacts, a mismatched device colour map, and a blurry image. To resolve this problem, we present a machine learning-based image restoration model (ML-IRM) for improving the visual quality of underwater images that have been deteriorated. Using this model, a home-made bowl set-up is created in which a different liquid concentration is used to replicate seabed water variation, and an object is dipped, or a video is played behind the bowl …to recognise the object texture captured image in high-resolution for training the image restoration model is proposed. Gaussian and bidirectional pre-processing filters are used to both the high and low frequency components of the training image, respectively. To improve the clarity of the high-frequency channel background, soft-thresholding decreases the presence of distracting artefacts. On the other hand, the ML-IRM model can effectively keep the object textures on a low frequency channel. Experiment findings show that the proposed ML-IRM model improves the quality of seabed images, eliminates colour mismatches, and allows for more detailed information extraction. Blue shadow, green shadow, hazy, and low light test samples are randomly selected from all five datasets including U45 [1 ], EUVP [2 ], DUIE [3 ], UIEB [4 ], UM-ImageNet [5 ], and the proposed model. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are computed for each condition separately. We list the values of PSNR (at 16.99 dB, 15.96 dB, 18.09 dB, 15.67 dB, 9.39 dB, 17.98 dB, 19.32 dB, 14.27 dB, 12.07 dB, and 25.47 dB) and SSIM (at 0.52, 0.57, 0.33, 0.47, 0.44, and 0.23, respectively. Similarly, it demonstrates that the proposed ML-IRM achieves a satisfactory result in terms of colour correction and contrast adjustment when applied to the problem of improving underwater images captured in low light. To do so, high-resolution images were captured in two low-light conditions (after 6 p.m. and again at 6 a.m.) for the training image datasets, and the results of their observations were compared to those of other existing state-of-the-art-methods. Show more

Keywords: ML-IRM, image denoising, different low-lighting conditions, Gaussian and bidirectional filters, high and low frequency channel

DOI: 10.3233/JIFS-223310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4569-4591, 2023

Authors: He, Xiaoxu

Article Type: Research Article

Abstract: In clinical practice, segmenting polyps from colonoscopy images plays an important role in the diagnosis and treatment of colorectal cancer since it provides valuable information. However, accurate polyp segmentation is full of changes due to the following reasons: (1) the small training datasets with a limited number of samples and the lack of data variability; (2) the same type of polyps with a variation in texture, size, and color; (3) the weak boundary between a polyp and its surrounding mucosa. To address these challenges, we propose a novel robust deep neural network based on data augmentation, called Robust Multi-center Multi-resolution …Unet (RMMSUNet), for the polyp segmentation task. Data augmentation and Multi-center training are both utilized to increase the amount and diversity of training dataset. The new multi-resolution blocks make up for the lack of fine-grained information in U-Net, and ensures the generation of more accurate pixel-level segmentation prediction graphs. Region-based refinement is added as the post-processing for the network output, to correct some wrongly predicted pixels and further refine the segmentation results. Quantitative and qualitative evaluations on the challenging polyp dataset show that our RMMSUNet improves the segmentation accuracy significantly, when comparing to other SOTA algorithms. Show more

Keywords: Image segmentation, colon cancer, U-Net, polyp segmentation, data augmentation

DOI: 10.3233/JIFS-223340

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4593-4604, 2023

Authors: Ramamurthy, Priyadarshini | Nandagopal, Malarvizhi

Article Type: Research Article

Abstract: Fog computing enables the data analysis done nearer to the place of data generated, which makes a very short response time. Trust is essential for the effective performance of the fog nodes to overcome uncertainty, vulnerability, and risk. To enhance the trusted connection in fog computing, blockchain technology is adopted as a solution. Inclusion of blockchain in fog environment ensures consistency and security among the fog nodes. Each fog node mines every transaction and stores them as block creating a chain of blocks. In this proposed work, the adaption of blockchain technology is designed as a suitable solution for establishing …trusted security between fog nodes and for which a qualitative assessment is done. Show more

Keywords: Fog computing, fog nodes, blockchain, trust, data security, ethereum blockchain, smart contract

DOI: 10.3233/JIFS-222014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4605-4612, 2023

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

Article Type: Research Article

Abstract: In this study, a new fuzzy decision-making model is created to evaluate whether the solar panels are efficient to minimize energy costs of the hospitals. The weights of the criteria are calculated by considering T-Spherical fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Moreover, for the purpose of measuring the coherency of the findings, analysis results are also calculated for different t values. Additionally, by making improvements to some criticisms to the classical DEMATEL method, a new technique is created by the name of TOP-DEMATEL while integrating some steps of technique for order preference by similarity to ideal solution (TOPSIS) …to the DEMATEL technique. The main novelty of this study is that it is analyzed whether the solar panels are effective in reducing the costs of hospitals with an original decision-making model. It is concluded that generating own energy in the long run is the most crucial item according to both T-Spherical fuzzy DEMATEL and TOP-DEMATEL methods. The analysis results are quite similar for different t values. This situation gives information about the coherency and reliability of the findings. This situation gives information that the solar panels should be taken into consideration for the hospitals because they will minimize energy dependency of the hospitals. On the other side, the results of T-Spherical fuzzy TOP-DEMATEL indicate that the high initial investment cost is the second most critical factor in this respect. This finding is quite different by comparing with the results of T-Spherical fuzzy TOP-DEMATEL. Hence, it is seen that cost effectiveness should also be taken into consideration for the decision of generating the solar panels in the hospitals. Show more

Keywords: T-Spherical fuzzy sets, TOP-DEMATEL, solar energy, health industry

DOI: 10.3233/JIFS-222968

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4613-4625, 2023

Authors: Mathina Kani, Mohamed Ali Jinna | Parvathy, Meenakshi Sundaram | Maajitha Banu, Samsammal | Abdul Kareem, Mohamed Saleem

Article Type: Research Article

Abstract: In this article, a methodological approach to classifying malignant melanoma in dermoscopy images is presented. Early treatment of skin cancer increases the patient’s survival rate. The classification of melanoma skin cancer in the early stages is decided by dermatologists to treat the patient appropriately. Dermatologists need more time to diagnose affected skin lesions due to high resemblance between melanoma and benign. In this paper, a deep learning based Computer-Aided Diagnosis (CAD) system is developed to accurately classify skin lesions with a high classification rate. A new architecture has been framed to classify the skin lesion diseases using the Inception v3 …model as a baseline architecture. The extracted features from the Inception Net are then flattened and are given to the DenseNet block to extracts more fine grained features of the lesion disease. The International Skin Imaging Collaboration (ISIC) archive datasets contains 3307 dermoscopy images which includes both benign and malignant skin images. The dataset images are trained using the proposed architecture with the learning rate of 0.0001, batch size 64 using various optimizer. The performance of the proposed model has also been evaluated using confusion matrix and ROC-AUC curves. The experimental results show that the proposed model attains a highest accuracy rate of 91.29 % compared to other state-of-the-art methods like ResNet, VGG-16, DenseNet, MobileNet. A confusion matrix and ROC curve are used to evaluate the performance analysis of skin images. The classification accuracy, sensitivity, specificity, testing accuracy, and AUC values were obtained at 90.33%, 82.87%, 91.29%, 87.12%, and 87.40%. Show more

Keywords: Image processing, deep learning, feature extraction, image classification, Inception v3 model, computer aided diagnosis

DOI: 10.3233/JIFS-221386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4627-4641, 2023

Authors: Liang, Shaohui | Wei, Botao

Article Type: Research Article

Abstract: Teaching-learning-based optimization algorithm (TLBO) is a swarm intelligence optimization algorithm that simulates classroom teaching phenomenon. In order to solve the problem that TLBO algorithm is easy to fall into local optimum and has poor stability, an improved teaching-learning-based optimization algorithm based on fusion difference mutation (IDMTLBO) is proposed. Firstly, adaptive teaching factors are introduced. Secondly, in the teaching stage, each student studies according to the gap between himself and the teacher, which improves the convergence speed and convergence accuracy of the algorithm. Finally, in the learning stage, students are divided into two levels according to their learning level, and two …students are randomly selected to improve the iterative equation in the learning stage with the difference mutation strategy, It improves the disadvantage that the algorithm is easy to fall into local optimum. Numerical experiments show that the convergence speed and convergence accuracy of the algorithm are obviously better than TLBO algorithm, DMTLBO algorithm, DSTLBO algorithm. Show more

Keywords: Teaching-learning-based optimization, adaptive teaching factors, the improved teaching stage, learning stages, differential mutation

DOI: 10.3233/JIFS-221019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4643-4651, 2023

Authors: Fathima Perveen, P. A. | John, Sunil Jacob | Kamacı, Hüseyin | Baiju, T.

Article Type: Research Article

Abstract: Picture fuzzy sets are a direct extension of fuzzy sets and intuitionistic fuzzy sets, recently developed as a mathematical tool for solving uncertainty-related problems. In this paper, a novel similarity measure and corresponding weighted similarity measure between two picture fuzzy sets are proposed after indicating some disadvantages of the current similarity measures of picture fuzzy sets through some exemplary numerical examples. Also, some of their basic properties are discussed. Further, a picture fuzzy decision making algorithm based on the similarity aggregation method is constructed and then applied to the decision making problem. It is also used to deal with a …medical diagnosis problem to detect which disease a patient may be suffering from. Finally, the effectiveness of the proposed similarity measure is demonstrated by making comparison with the present picture fuzzy similarity measures. Show more

Keywords: Fuzzy sets, picture fuzzy sets, similarity measure, medical diagnosis, decision making

DOI: 10.3233/JIFS-222334

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4653-4665, 2023

Authors: Wang, Chaofeng

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-212862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4667-4679, 2023

Authors: Elavarasan, B. | Muhiuddin, G. | Porselvi, K. | Jun, Y. B.

Article Type: Research Article

Abstract: Many uncertainties arise in real-world problems, making them impossible to solve using conventional approaches. Researchers all over the world have developed new mathematical theories like fuzzy set theory and rough set theory to better understand the uncertainties that occur in various fields. Soft set theory, which was recently introduced, offers a novel approach to real-world problem solving by removing the need to set the membership function. This is helpful in resolving a variety of issues, and much progress is being made these days. Recently, Jun introduced the concept of a hybrid structure, which blends the concepts of a fuzzy set …as well as a soft set. In this paper, we define the hybrid k -sum and hybrid k -product of k -ideals of semiring and investigate their properties. We illustrate with an example that the hybrid sum and hybrid product of two k-ideals are not always hybrid ideals. We also describe semiring regularity constraints in terms of hybrid k -ideal structures. Show more

Keywords: Semiring, hybrid structure, ideal, hybrid product, k-ideal, hybrid k-product, hybrid ideals, hybrid k-ideals

DOI: 10.3233/JIFS-222335

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4681-4691, 2023

Authors: Fu, Xue | Zhu, Liangkuan | Wu, Bowen | Wang, Jingyu | Zhao, Xiaohan | Ryspayev, Arystan

Article Type: Research Article

Abstract: To improve the traditional image segmentation, an efficient multilevel thresholding segmentation method based on improved Chimp Optimization Algorithm (IChOA) is developed in this paper. Kapur entropy is utilized as the objective function. The best threshold values for RGB images’ three channels are found using IChOA. Meanwhile, several strategies are introduced including population initialization strategy combining with Gaussian chaos and opposition-based learning, the position update mechanism of particle swarm algorithm (PSO), the Gaussian-Cauchy mutation and the adaptive nonlinear strategy. These methods enable the IChOA to raise the diversity of the population and enhance both the exploration and exploitation. Additionally, the search …ability, accuracy and stability of IChOA have been significantly enhanced. To prove the superiority of the IChOA based multilevel thresholding segmentation method, a comparison experiment is conducted between IChOA and 5 six meta-heuristic algorithms using 12 test functions, which fully demonstrate that IChOA can obtain high-quality solutions and almost does not suffer from premature convergence. Furthermore, by using 10 standard test images the IChOA-based multilevel thresholding image segmentation method is compared with other peers and evaluated the segmentation results using 5 evaluation indicators with the average fitness value, PSNR, SSIM, FSIM and computational time. The experimental results reveal that the presented IChOA-based multilevel thresholding image segmentation method has tremendous potential to be utilized as an image segmentation method for color images because it can be an effective swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of color images. Show more

Keywords: Multi-threshold color image segmentation, chimp optimization algorithm, particle swarm algorithm, self-adaptive strategy, Kapur’s entropy

DOI: 10.3233/JIFS-223224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4693-4715, 2023

Authors: Perumal, T. Sudarson Rama | Jegatheesan, A. | Jayachandran, A.

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-220308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4717-4732, 2023

Authors: Aarthi, S. | Shanmugasundari, M.

Article Type: Research Article

Abstract: This paper presents boundless capacity, one server’s fuzzy and intuitionistic fuzzy queuing models. This study’s primary objective is to demonstrate and compare the performance of a single server queuing model with infinite capacity using fuzzy queuing theory and intuitionistic fuzzy queuing theory. This article demonstrates that intuitionistic fuzzy theory performs better when solving queuing problems. Furthermore, by integrating fuzzy queuing models into an intuitionistic fuzzy framework, their relevance in authentic situations is augmented. The fuzzy queuing theory model’s performance measurements are delivered as a range of values, but the intuitionistic fuzzy queuing theory model offers a broad array of values. …In this context, the arrival and the service rates are both triangular (TFN) and intuitionistic triangular fuzzy numbers (TIFN). An assessment is performed to determine the evaluation criteria, employing a design protocol in which the fuzzy values are taken as-is without being turned into crisp values. As a result, in an ambiguous environment, we can use the proposed approach to pick scientific findings. In this study, we are using the TFN in an intuitionistic fuzzy environment, compensating for the degree of stability and denial so that the sum of both virtues is never higher than one. We proffered many non-normal arithmetic techniques for this sort of fuzzified integer. The envisaged compositions are intuitive and concise, as they evolved by utilising canonical algebraic mathematics. In real-world situations, this tactic is simple and straightforward to enact. The nearest interval number is then used to round a TIFN. The key advantage of this strategy is that it allows us to quickly solve a constrained unrestrained optimization model with TIFN coefficients using a multi-section heuristic. The prevailing methodologies and initiatives are destined to be relevant to different types of updated decision-making obstacles in focusing on economic equity, funding, presidency, and environmental sciences, which will be the focus of our future research. And two numerical problems are solved to showcase the sustainability of the suggested technique. In this queuing model, we predict a variety of components, including prospective queue length, expected system length, and sojourn time in both the queue and the system. The statistical analysis reveals that the quantified performance indicators of the intuitionistic fuzzy queuing model agree well with the performance measurements of the fuzzy queuing model. Even though the average correlation between the two concepts is nearly equivalent, TIFN provides a more extensive range of possibilities than TFN. Despite the fact that fuzzy set theory is used to contend with unpredictability in decision-making circumstances, it only relates to the extent of membership and lacks a model for reluctance. The fact that each asset’s affirmation and deprivation levels are comprehended is the special feature of intuitionistic fuzzy sets. As a consequence, it becomes more meticulous, suitable, and generalizable. Show more

Keywords: Intuitionistic fuzzy queue, intuitionistic fuzzy arithmetic, sojourn time, triangular intuitionistic fuzzy numbers

DOI: 10.3233/JIFS-221367

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4733-4746, 2023

Authors: Lavanya, G. | Velammal, B.L. | Kulothungan, K.

Article Type: Research Article

Abstract: A network of real time devices that can sense and transmit the information from the deployed environment by using multi hop communication is called as Wireless Sensor Network (WSNs). Despite the rapid advancement of WSN, where an increasing number of physical devices so called as sensors nodes are connected with each other, providing the improved security with optimized energy consumption during data transmission, communication and computation remains huge challenge. In wireless sensor networks, numerous sensor nodes are deployed in the physical environment to sense and collect the required information from the given environment. The sensed information is needed to be …transmitted from the nodes to the control station in an energy efficient manner. Data aggregation is one kind of techniques which will optimize the energy usage in wireless sensor networks during the data transmission. In data aggregation, the unnecessary data is removed which will significantly reduce energy of the nodes during data transmission. However, collected data during the data aggregation should be completely protected and there are various threats that can be launched by the intruders to carry out unauthorised data access and can cause threat to the integrity of the network. Therefore, ensuring data security during the data aggregation process is very important and essential for the security of the network. In this paper, a Secure Cluster based Data Aggregation Protocol (SCDAP) have been proposed to provide better security through secure authentication and verification process, and to reduce overall energy consumption of the network by implementing secure clustering process to eliminate the redundant data in the network. Moreover, the proposed system is more efficient in generating public and private keys for effective and secure data transmission and verification process. The proposed system is experimentally tested in NS-3 tool and proves that the proposed system reduces high energy consumption, computational and communicational cost, end-to-end delay and improves the packet delivery ratio. Moreover, the proposed system provides better security in the network when compared to other existing systems during the data aggregation. Show more

Keywords: Wireless networks, data aggregation, energy optimization, efficient authentication, key generation

DOI: 10.3233/JIFS-223256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4747-4757, 2023

Authors: Alabdaly, Ammar A. | El-Sayed, Wagdy G. | Hassan, Yasser F.

Article Type: Research Article

Abstract: The task of cell segmentation in microscope images is difficult and popular. In recent years, deep learning-based techniques have made incredible progress in medical and microscopy image segmentation applications. In this paper, we propose a novel deep learning approach called Residual-Atrous MultiResUnet with Channel Attention Mechanism (RAMRU-CAM) for cell segmentation, which combines MultiResUnet architecture with Channel Attention Mechanism (CAM) and Residual-Atrous connections. The Residual-Atrous path mitigates the semantic gap between the encoder and decoder stages and manages the spatial dimension of feature maps. Furthermore, the Channel Attention Mechanism (CAM) blocks are used in the decoder stages to better maintain the …spatial details before concatenating the feature maps from the encoder phases to the decoder phases. We evaluated our proposed model on the PhC-C2DH-U373 and Fluo-N2DH-GOWT1 datasets. The experimental results show that our proposed model outperforms recent variants of the U-Net model and the state-of-the-art approaches. We have demonstrated how our model can segment cells precisely while using fewer parameters and low computational complexity. Show more

Keywords: Cell segmentation, convolutional neural network, deep neural networks, medical image segmentation, u-net

DOI: 10.3233/JIFS-222631

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4759-4777, 2023

Authors: Sun, Jiaqi | Wang, Pingxin | Yu, Hualong | Yang, Xibei

Article Type: Research Article

Abstract: Essentially, the problem solving of attribute reduction can be regarded as a process of reduct searching which will be terminated if a pre-defined restriction is achieved. Presently, among a variety of searching strategies, meta-heuristic searching has been widely accepted. Nevertheless, it should be emphasized that the iterative procedures in most meta-heuristic algorithms rely heavily on the random generation of initial population, such a type of generation is naturally associated with the limitations of inferior stability and performance. Therefore, a constraint score guidance is proposed before carrying out meta-heuristic searching and then a novel framework to seek out reduct is developed. …Firstly, for each attribute and each label in data, the index called local constraint score is calculated. Secondly, the qualified attributes are identified by those constraint scores, which consist of the foundation of initial population. Finally, the meta-heuristic searching can be further employed to achieve the required restriction in attribute reduction. Note that most existing meta-heuristic searchings and popular measures (evaluate the significance of attributes) can be embedded into our framework. Comprehensive experiments over 20 public datasets clearly validated the effectiveness of our framework: it is beneficial to reduct with superior stabilities, and the derived reduct may further contribute to the improvement of classification performance. Show more

Keywords: Attribute reduction, constraint score, meta-heuristic searching, rough set

DOI: 10.3233/JIFS-222832

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4779-4800, 2023

Authors: Zhao, Xiaofang | Li, Faming | Chen, Biao | Li, Xiaofei | Lu, Shasha

Article Type: Research Article

Abstract: Examining the properties of High-Performance Concrete (HPC) has been a big challenge due to the highly heterogeneous relationships and coherence among several constituents. The employment of silica fume and fly ash as eco-friendly components in mixtures benefits the concrete to improve its physical features. Although machine learning approaches are utilized broadly in many studies solitarily to estimate the mechanical features of concrete, causing to reduce accuracy and lift the cost and complexities of computational networks. Consequently, current research aims to develop a Radial Basis Function Neural Network (RBFNN) integrating with optimization algorithms in order to precisely model the mechanical characteristics …of HPC mixtures including compressive strength (CS) and slump (SL). Feeding the dataset of HPC samples to hybrid models will result to reproduce the given CS and SL factors simultaneously. The results of the models showed that the maximum rate of correlation between estimated values and measured ones was obtained at 98.3% while the minimum rate of RMSE was calculated at 3.684 mm (and MPa) in the testing phase. Employing such soft-oriented approaches has been benefiting us to reduce costs and increase the result accuracy. Show more

Keywords: High-performance concrete, radial basis function, neural network, compressive strength, slump, whale optimization algorithm

DOI: 10.3233/JIFS-222805

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4801-4815, 2023

Authors: Li, Junxia

Article Type: Research Article

Abstract: In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic Graphical Modeling model based on multivariate normal hypothesis is constructed, and the relationship between the Graphical Gaussian model and the linear regression model is determined. The parameters of the multi-dimensional stochastic model are estimated by using the parameter estimation algorithm of the multi-dimensional stochastic model containing intermediate variables. According to the parameter estimation results of the multi-dimensional stochastic model, the structural change point estimation results of the multi-dimensional …stochastic model are obtained by using the accurate estimation algorithm of the structural change point based on the MLE identification local drift time. The experimental results show that the proposed algorithm has higher estimation accuracy of structural change points than the control algorithms, which shows that it can effectively estimate the structural change points of multi-dimensional random models and has higher practicability. Show more

Keywords: Multidimensional, stochastic model, structure, change point, accurate, estimation algorithm

DOI: 10.3233/JIFS-222821

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4817-4829, 2023

Authors: Nayana, P.G. | Iyer, Radha Rajamani

Article Type: Research Article

Abstract: Generalized Mycielskians are triangle-free networks with a large chromatic number, having a number of desirable characteristics like fast multi-path communication, high fault tolerance, reliable resource sharing, etc. Graph invariants like domination number and secure domination number can be used to protect the network by monitoring each node or moving to a failing node. A dominating set of a graph G is a subset of its vertex set, which can monitor every other vertex of the graph, and γ (G ), the domination number of G , is the least cardinality among all dominating sets of G . A secure dominating …set S of G is a dominating set with an additional property that, for each vertex u not in S there is a vertex v in S adjacent to u such that, the swapped set (S  - {v }) ∪ {u } is dominating. γs  (G ) is the secure domination number of G , which is the minimum cardinality among all secure dominating sets of G . In this paper, we analyzed γ and γs of the generalized Mycielskian (μm ) for path graphs P n and cycle graphs C n , varying both n and m , and used these results to obtain bounds for general graphs G . Show more

Keywords: Generalized Mycielskian, domination number, secure domination number

DOI: 10.3233/JIFS-223326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4831-4841, 2023

Authors: Lizhu, Yue | Liwei, Yao

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-213294

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4843-4852, 2023

Authors: Swapna, S. | Premila, T.R. | Janaki, N. | Kirubakaran, D.

Article Type: Research Article

Abstract: This paper proposes a hybrid optimization system depending on optimal location for electric vehicles parking lot (PL) and capacitors on distribution system to maintain voltage profile with electricity loss. The proposed system is the consolidation of Seagull optimization algorithm (SGO) and salp swarm algorithm (SSA). The migration and attacking behaviors of seagull is empowered through SSA method. By this manner, the proposed hybrid optimization scheme is known as SGOS2 A method. Here, parking zone allocation with capacitor is considered to congestion management in conjunction through the compensation of reactive energy. So, one can optimally decide the size of automobile parking …space, SGOS2 A method is followed. Moreover, parking lot with capacitor allocation is considered to congestion control at the side of reactive power compensation. By this proper manipulate, the capacitors exact location, automobile parking space of electric vehicles on the grid, lessening of active with reactive power loss, voltage profile conversion is selected optimally. Besides, the proposed SGOS2 A scheme is activated on MATLAB/Simulink site, then the efficiency is examined with different techniques. The mean, median and standard deviation of the proposed approach achieves 1.0593, 1.0915 and 0.1050. Show more

Keywords: Electric vehicle, voltage and power loss, parking lot, seagull optimization algorithm, salp swarm algorithm

DOI: 10.3233/JIFS-220651

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4853-4868, 2023

Authors: Khatun, Mahafuja | Islam, Ridwan Arefin | Islam, Salekul

Article Type: Research Article

Abstract: In recent years, blockchain technology has been successfully used in many distributed environments where different stakeholders, who do not have any trust between them, interact with each other through a secured and transparent platform. The use of blockchain in healthcare insurance industry has not been studied methodically. In this study, we develop a blockchain based Secured and Automated Health Insurance Claim (B-SAHIC) processing system. First, we design an Entity-Relationship (ER) diagram identifying all actors with their respective data and relations between them. We also develop the business model and algorithms for all necessary steps. We implement the system in Hyperledger …Fabric and deploy a smart contract to implement these algorithms. We introduce CouchDB to store the OffChain data where we store World-State Database. B-SAHIC provides a web-based portal for all actors who interact with the blockchain. Privacy of clients’ claim-related data is ensured by encrypting treatment-related data with a new key that is derived uniquely for each new submission done of clients’ personal documents. We have also deployed Hyperledger Explorer, a user-friendly web application tool for monitoring the health state of each node participating on the blockchain network. We have studied the performance of B-SAHIC for two to six peer nodes. Moreover, our performance study shows that B-SAHIC is fast and scalable. In our study, the average query latency is decreased from 0.07 second (for two peer nodes) to 0.02 second (for six peer nodes) in case of 1300 queries per second while the average transaction latency remains unchanged (around 3.5 seconds) for 300 transactions per second. Moreover, B-SAHIC consumes minimum resources, around 350MB only for two peer nodes. We believe that the development process of this blockchain based platform can be applicable for the automation of other insurance industry too. Show more

Keywords: Health insurance, insurance claim processing, blockchain, hyperledger fabric, smart contract

DOI: 10.3233/JIFS-220690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4869-4890, 2023

Authors: Zhao, Dazhi | Hao, Yunquan | Li, Weibin | Tu, Zhe

Article Type: Research Article

Abstract:  Whether the exact amount of training data is enough for a specific task is an important question in machine learning, since it is always very expensive to label many data while insufficient data lead to underfitting. In this paper, the topic that what is the least amount of training data for a model is discussed from the perspective of sampling theorem. If the target function of supervised learning is taken as a multi-dimensional signal and the labeled data as samples, the training process can be regarded as the process of signal recovery. The main result is that the least amount …of training data for a bandlimited task signal corresponds to a sampling rate which is larger than the Nyquist rate. Some numerical experiments are carried out to show the comparison between the learning process and the signal recovery, which demonstrates our result. Based on the equivalence between supervised learning and signal recovery, some spectral methods can be used to reveal underlying mechanisms of various supervised learning models, especially those “black-box” neural networks. Show more

Keywords: Machine learning, sampling theorem, frequency principle, signal recovery, neural network, Gaussian process regression

DOI: 10.3233/JIFS-211024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4891-4906, 2023

Authors: Sathishkumar, B.R.

Article Type: Research Article

Abstract: Power dissipation at the network level to improve lifespan without degrading the bandwidth and collaboration is a fundamental impediment to effective spectral efficiency in wireless sensor networks (WSNs). This issue is made much more difficult. Wireless energy transfer (WET) for energizing remote sensor nodes gained interest. This research explores an FDD-based on-demand scenario with many relays where a transmitter is powered by direct and relayed links. A threshold is set for transmission energy & channel quality to decide whether the broadcasting can be efficient (for spectrum utilization) or the packet would not arrive at its destination. The network model offers …an energy-efficient scheduling strategy to decide whether to transmit information or not depending on the stored higher energy and network status. An energy-aware polling-based medium access control (MAC) mechanism, composite energy, and information first (CEDF) has also been developed to fine-tune packet delivery ratio by utilizing datagrams and energy packages to set polling prioritization. Computational simulations indicate that energy relayed and the recommended energy-efficient scheduled technique decrease the system’s active power losses supporting all theoretical predictions. Show more

Keywords: Polling, multi-relay, spectral efficiency, sensor network, MAC, data speed and power constraints

DOI: 10.3233/JIFS-223001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4907-4930, 2023

Authors: Sharma, Preeti | Gangadharappa, M.

Article Type: Research Article

Abstract: Anomalous event recognition has a complicated definition in the complex background due to the sparse occurrence of anomalies. In this paper, we form a framework for classifying multiple anomalies present in video frames that happen in a context such as the sudden moment of people in various directions and anomalous vehicles in the pedestrian park. An attention U-net model on video frames is utilized to create a binary segmented anomalous image that classifies each anomalous object in the video. White pixels indicate the anomaly, and black pixels serve as the background image. For better segmentation, we have assigned a border …to every anomalous object in a binary image. Further to distinguish each anomaly a watershed algorithm is utilized that develops multi-level gray image masks for every anomalous class. This forms a multi-class problem, where each anomalous instance is represented by a different gray color level. We use pixel values, Optical Intensity, entropy values, and Gaussian filter with sigma 5, and 7 to form a feature extraction module for training video images along with their multi-instance gray-level masks. Pixel-level localization and identification of unusual items are done using the feature vectors acquired from the feature extraction module and multi-class stack classifier model. The proposed methodology is evaluated on UCSD Ped1, Ped2 and UMN datasets that obtain pixel-level average accuracy results of 81.15%,87.26% and 82.67% respectively. Show more

Keywords: Anomaly detection, video surveillance, feature extraction, multi-class classification, classifier

DOI: 10.3233/JIFS-221925

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4931-4947, 2023

Authors: Zheng, Tingting | Chen, Hao | Yang, Xiyang

Article Type: Research Article

Abstract: The traditional Ordered Weighting Average (OWA) operator is suitable for aggregating numerical attributes. However, this method fails when the attribute values are given in a linguistic form. In this paper, a novel aggregating method named Entropy and Probability based Fuzzy Induced Ordered Weighted Averaging (EPFIOWA) is proposed for Gaussian-fuzzy-number-based linguistic attributes. A method is first designed to obtain a reasonable weighting vector based on probability distribution and maximal entropy. Such optimal weighting vectors can be obtained under any given level of optimism, and the symmetric properties of the proposed model are proven. The linguistic attributes of EPFIOWA are represented by …Gaussian fuzzy numbers because of their concise form and good operational properties. In particular, the arithmetic operations and distance measures of Gaussian fuzzy numbers required by EPFIOWA are given systematically. A novel method to obtain the order-inducing variables of linguistic attribute values is proposed in the EPFIOWA operators by calculating the distances between any Gaussian fuzzy number and a set of ordered grades. Finally, two numerical examples are used to illustrate the proposed approach, with evaluation results consistent with the observed situation. Show more

Keywords: Gaussian fuzzy numbers, induced ordered weighted averaging operators, order-inducing variables, probability distribution, maximal entropy

DOI: 10.3233/JIFS-222241

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4949-4962, 2023

Authors: Abdulrahim, Basiya K. | Sulaiman, Nejmaddin A. | Sadiq, Gulnar W.

Article Type: Research Article

Abstract: This paper presents an efficient and straightforward methodology with less computational complexities to title the bi-level objective linear fractional programming problem with fuzzy interval coefficients (BILOLFPP with FIC). To construct the methodology, the concept of mean technique is utilized to tackle the fuzzy numbers in addition to adding to α = [mean  (a i ) , mean  (b i )] , i  = 1, …, n , then. Accordingly, the fuzzy programming issue is converted into a single objective linear fractional programming problem (SOLFPP with FIC) by the utilize of weight function. The fuzzy technique has significant structural transform metamorphosis during the …recent decades. Numerous to mention introduced have been undertaken to explanation fuzzy methodology for linear, non-linear programming issues. While, the previous finding that introduced have been conflicting, recent studies of competitive situations indicate that LFPP with fuzzy interval coefficients (LFPP with FIC) has an advantageous effect mostly on comparison situation. One of the suggestions which we found is interval approximations, closed interval approximation of sequential fuzzy number for resolving fuzzy number LFPP without changing it to a crisp issue. A new variant of modified simplex methodology is studied here just for resolving fuzzy number LFPP utilizing fuzzy arithmetic. Consequently, fuzzy representation of some important theories of fuzzy LFPP has been reproved. A fuzzy LFPP with FIC is worked out as numerical examples illustrate to the suggested methodology. On iterative processes, it decreases the overall processing time to explain, the modified simplex methodology for solving BILLFPP with FIC with out to crisp by taking numerical examples and compare with Nasseri, Verdegay and Mahmoudi methodology changing it to a crisp issue [9 ]. Show more

Keywords: Fuzzy number, FFLFPP, FFLFPP with fuzzy interval coefficients, FFBILLFPP with fuzzy interval coefficients, closed interval approximation, modified simplex methodology

DOI: 10.3233/JIFS-222519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4963-4973, 2023

Authors: Nasr, Asmaa M. | ElGhawalby, Hewayda | Mareay, R.

Article Type: Research Article

Abstract: In several empirical situations, a decision is needed to be made based on data that is captured in some information system. The problem occurs when the information system holds complex data or even too much data attributes. This leads to the need for reducing the number of attributes required to obtain a decision. In this paper, a novel attributes’ reduction method is presented; the proposed method is based on constructing a weighted pre-topology that represents the information system under consideration. In addition, some essential operations for the weighted pre-topological space are presented; as well as, a brief study of their …properties. Show more

Keywords: Fuzzy pretopological space, closure set, interior set, information system

DOI: 10.3233/JIFS-223077

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4975-4985, 2023

Authors: Chen, Chuen-Jyh | Huang, Chieh-Ni | Yang, Shih-Ming

Article Type: Research Article

Abstract: Weather forecasts are essential to aviation safety. Unreliable forecasts not only cause problems to pilots and air traffic controllers, but also lead to aviation accidents and incidents. This study develops a long short-term memory (LSTM) integrating both multiple linear regression and the Pearson’s correlation coefficients to improve forecasting. A numerical dataset of 10 weather features (sea pressure, temperature, dew point temperature, relative humidity, wind speed, wind direction, sunshine rate, global solar radiation, visible mean, and cloud amount) is applied on every calendar day in a year to train and validate the LSTM for temperature forecasting. It is shown that data …standardization is necessary to rescale the data to improve training convergence and reduce training time. In addition, feature selection by multiple linear regression and by Pearson’s correlation coefficients are shown effective to the forecast accuracy of the LSTM. By selecting only the sensitive features (sea pressure, dew point temperature, relative humidity and relative humidity), the temperature forecasting errors can be reduced from RMSE 4.0274 to 2.2215 and MAPE 23.0538% to 5.0069%. LSTM deep learning with data standardization and feature selection is effective in forecasting for aviation safety. Show more

Keywords: Deep learning, aviation weather, long short-term memory, weather forecasting

DOI: 10.3233/JIFS-223183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4987-4997, 2023

Authors: Chen, Xinzhang | Tian, Xiaoyan | Ye, Hongtu

Article Type: Research Article

Abstract: As the most popular art category in the contemporary art field, visual art is no longer limited to traditional art categories such as painting, sculpture and photography, but develops into more diverse forms of expression with the continuous iteration of science and technology. As the most cutting-edge and popular concept in the world today, the research, development and application of science and technology have attracted close attention from all walks of life, including management, economy, transportation, education and teaching. However, there is no in-depth and clear research between the concept of metaverse and the concept of metaverse in the art …field, especially in the visual art field. We believe that visual art creation under the background of the metaverse will be an important direction of art development in the future, and will also greatly promote the improvement of the visual presentation quality of the metaverse. Therefore, we focus our research in this study on the issue of visual art quality assessment and propose a theory and method for assessing the quality of visual art in a future-oriented metaverse. This method is based on the G1 entropy method in fuzzy mathematics. In our research, we have built a visual art field architecture based on the metaverse. Considering the difference between the traditional visual art evaluation index system and the index system after the introduction of the concept of the future metaverse, we have built a brand-new visual art quality evaluation index system facing the future metaverse. This indicator is composed of four first-class indicators and twelve second-class indicators. We combine the subjective and objective weighting G1 entropy method as the method basis for the quantitative calculation results of the indicator weight. On the basis of quantitative analysis, we propose three-point countermeasures for improving the visual art quality of the future metaverse. Our research makes up for the gap in the theory of visual art quality evaluation after the introduction of the concept of the future metaverse, innovates the analysis of new concepts and the improvement of old methods, builds a new scene of organic combination of new technology and traditional visual art, and provides a new idea for the improvement of visual art quality in the future at home and abroad, It can also provide experience and theoretical support for the academic topic of similar art quality evaluation research at home and abroad. Show more

Keywords: Visual arts, metaverse, field architecture, G1-entropy method, AHP method

DOI: 10.3233/JIFS-223351

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4999-5019, 2023

Authors: Yang, Run

Article Type: Research Article

Abstract: In the past, different useful extensions of fuzzy sets were established by the researchers to manage the vagueness and uncertainty in various practical problems. Usually, the real numbers are utilized to express the decision information, but it is noted that the description of attributes using picture fuzzy sets (PFSs) proves to be more appropriate. As a powerful decision tool, PFSs provides more decision information that requires the application of some specific situations more types of response of human ideas: yes, contain, no, reject. QUALIFLEX (qualitative flexible multiple criteria method), is one of the well-known outranking methods to solve the multiple …attribute group decision making (MAGDM) problems with crisp numbers. The QUALIFLEX method can perfectly address the complex MAGDM problems where a lot of attributes are utilized to assess a limited number of alternatives. The electronic music acoustic quality evaluation is a classical MAGDM. This paper proposes and utilizes the QUALIFLEX to develop the picture fuzzy QUALIFLEX(PF-QUALIFLEX) method for MAGDM. The current study is mainly devoted to explore and extend the measurement of alternatives and ranking according to the QUALIFLEX under the background of PFSs. Furthermore, an example to evaluate the electronic music acoustic quality is handled through the proposed method to substantiate the extended approach. Show more

Keywords: Multiple attribute group decision making (MAGDM), picture fuzzy sets (PFSs), the extended QUALIFLEX method, electronic music acoustic quality evaluation

DOI: 10.3233/JIFS-223377

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5021-5032, 2023

Authors: Fu, Chengcai | Lu, Fengli | Wu, Fan | Zhang, Guoying

Article Type: Research Article

Abstract: The estimation of gangue content is the main basis for intelligent top coal caving mining by computer vision, and the automatic segmentation of gangue is crucial to computer vision analysis. However, it is still a great challenge due to the degradation of images and the limitation of computing resources. In this paper, a hybrid connected attentional lightweight network (HALNet) with high speed, few parameters and high accuracy is proposed for gangue intelligent segmentation on the conveyor in the top-coal caving face. Firstly, we propose a deep separable dilation convolution block (DSDC) combining deep separable convolution and dilation convolution, which can …provide a larger receptive field to learn more information and reduce the size and computational cost of the model. Secondly, a bridging residual learning framework is designed as the basic unit of encoder and decoder to minimize the loss of semantic information in the process of feature extraction. An attention fusion block (AFB) with skip pathway is introduced to capture more representative and distinctive features through the fusion of high-level and low-level features. Finally, the proposed network is trained through the expanded dataset, and the gangue image segmentation results are obtained by pixel-by-pixel classification method. The experimental results show that the proposed HALNet reduces about 57 percentage parameters compared with U-Net, and achieves state-of-the art performance on dataset. Show more

Keywords: Gangue intelligent segmentation, the top-coal caving face, depthwise separable dilation convolution, attention mechanism

DOI: 10.3233/JIFS-213506

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5033-5044, 2023