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

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

Authors: Annie Nancy, G. | Ramakrishnan, Kalpana | Senthil Nathan, J.

Article Type: Research Article

Abstract: Pressure injury usually develop in the bony prominence of immobile bedridden subjects. Predicting pressure injuries based on the subjects’ physiological information will reduce the burden of the caretakers in adjusting the frequency of repositioning such subjects. Visual assessment, diagnostic, and prognostic approaches only provide pressure injury information after onset. Therefore, the objective of this unique modeling technique is to predict the internal alterations that take place in human tissues before the onset of pressure injuries. In this approach the bio-mechanical and bio-thermal properties was integrated to predict the internal changes of skin, fat, and muscle layers when subjects were self-loaded …continuously for one hour in the sacrum region. A change in temperature of all the layers, as well as the distribution of Von-Mises stress in these layers, was observed. The inflammation caused by the changes in the temperature and the stress was measured from the simulation model. Ultrasound measurements was also taken for the same subjects in the supine position in the sacral region, before and after one hour by applying a self-load. An identical change in the thickness of the above-mentioned layers due to thermal expansion was noticed. Hence this computational model is hypothesized to give identical thermal expansion in comparison with the ultrasound measurements. There was an agreement between the thermal expansion using the simulation technique and the ultrasound technique which was assessed through Bland-Altman analysis, with a 96% confidence interval. Show more

Keywords: Bio-thermal model, bio-mechanical model, sacrum, pressure injury, multi-physics coupling

DOI: 10.3233/JIFS-222485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5045-5057, 2023

Authors: Linhares, Luís Fernando | da Silva, Alisson Marques | Meireles, Magali Resende Gouvêa

Article Type: Research Article

Abstract: Private transport has become a viable and increasingly popular alternative to urban transportation. However, with this growth, an old and recurring problem becomes more latent: the relationship between passenger demands and taxi supply. This problem suggests the creation and use of techniques which make it possible to reduce the gap between the demand for taxi passengers and the effective contingent of vehicles needed to meet this demand. This work introduces a new approach to forecasting and classifying taxi passengers’ demands. The proposed approach uses historical data from taxi rides and meteorological data. The Kruskal-Wallis method identifies the most relevant variables, …and an evolving fuzzy system performs demand forecasting/classification. Five evolving systems are evaluated with our approach: Autonomous Learning Multi-Model (ALMMo), evolving Multivariable Gaussian Fuzzy System (eMG), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy (eFCE), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Multi-Innovations Recursive Weighted Least Squares (eFMI), and evolving Neo-Fuzzy Neuron (eNFN). In addition, computational experiments using real-world data were conducted to evaluate and compare the performance of the proposed approach. The results revealed that it obtained performance superior or comparable to state-of-the-art ones. Therefore, the experimental results suggest that the proposed approach is promising as an alternative for forecasting and classifying taxi passenger demand. Show more

Keywords: Taxi demand, forecasting, classification, evolving systems, fuzzy systems

DOI: 10.3233/JIFS-222115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5059-5084, 2023

Authors: Naqvi, Deeba R. | Sachdev, Geeta | Ahmad, Izhar

Article Type: Research Article

Abstract: Game theory has been successfully applied in a variety of domains to deal with competitive environments between individuals or groups. The matrix games involving fuzzy, interval fuzzy, and intuitionistic fuzzy numbers exclusively examine the numeric components of an issue. However, several researchers have also examined various extensions of conventional game theory, considering the ambiguous situations for payoffs and goals. In many real-life scenarios, qualitative information is often critical in expressing the payoffs of a matrix game. Thus, the present work contributes to the field of matrix games where the payoffs have been quantified via qualitative variables, termed interval-valued hesitant fuzzy …linguistic sets. The mathematical formulation and solution concept for matrix games involving interval-valued hesitant fuzzy linguistic numbers is designed by utilizing an aggregation operator supported by linguistic scale function and solving them by employing score function. Finally, the proposed approach is validated by applying it to electric vehicle sales. Show more

Keywords: Interval-valued, linguistic set, hesitant fuzzy set, matrix games, average aggregation operator, linguistic scale function, electric vehicles

DOI: 10.3233/JIFS-222466

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5085-5105, 2023

Authors: Gullapelly, Aparna | Banik, Barnali Gupta

Article Type: Research Article

Abstract: Multi-object tracking (MOT) is essential for solving the majority of computer vision issues related to crowd analytics. In an MOT system designing object detection and association are the two main steps. Every frame of the video stream is examined to find the desired objects in the first step. Their trajectories are determined in the second step by comparing the detected objects in the current frame to those in the previous frame. Less missing detections are made possible by an object detection system with high accuracy, which results in fewer segmented tracks. We propose a new deep learning-based model for improving …the performance of object detection and object tracking in this research. First, object detection is performed by using the adaptive Mask-RCNN model. After that, the ResNet-50 model is used to extract more reliable and significant features of the objects. Then the effective adaptive feature channel selection method is employed for selecting feature channels to determine the final response map. Finally, an adaptive combination kernel correlation filter is used for multiple object tracking. Extensive experiments were conducted on large object-tracking databases like MOT-20 and KITTI-MOTS. According to the experimental results, the proposed tracker performs better than other cutting-edge trackers when faced with various problems. The experimental simulation is carried out in python. The overall success rate and precision of the proposed algorithm are 95.36% and 93.27%. Show more

Keywords: Computer vision, surveillance, tracking, correlation filters, holistic samples

DOI: 10.3233/JIFS-223516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5107-5121, 2023

Authors: Nalini Joseph, L. | Anand, R.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219330 .

DOI: 10.3233/JIFS-223018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5123-5135, 2023

Authors: Sudhagar, D. | ArokiaRenjit, J.

Article Type: Research Article

Abstract: Many real-time applications, including some emerging ones, rely on high-dimensional feature datasets. For simplifying the high-dimensional data, the various models are available by using the different feature optimization techniques, clustering and classification techniques. Even though the high-dimensional data is not handled effectively due to the increase in the number of features and the huge volume of data availability. In particular, the high-dimensional medical data needs to be handled effectively to predict diseases quickly. For this purpose, we propose a new Internet of Things and Fuzzy-aware e-healthcare system for predicting various diseases such as heart, diabetes, and cancer diseases effectively. The …proposed system uses a newly proposed Intelligent Mahalanobis distance aware Fuzzy Weighted K-Means Clustering Algorithm (IMFWKCA) for grouping the high dimensional data and also applies a newly proposed Moth-Flame Optimization Tuned Temporal Convolutional Neural Network (MFO-TCNN) for predicting the diseases effectively. The experiments have been done by using the UCI Repository Machine Learning datasets and live streaming patient records for evaluating the proposed e-healthcare system and have proved as better than others by achieving better performance in terms of precision, recall, f-measure, and prediction accuracy. Show more

Keywords: Feature optimization, clustering, e-healthcare system, high dimensional data, internet of things

DOI: 10.3233/JIFS-220629

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5137-5150, 2023

Authors: Saini, Munish | Adebayo, Sulaimon Oyeniyi | Singh, Harnoor | Singh, Harpreet | Sharma, Suchita

Article Type: Research Article

Abstract: The United Nations prescribed the Sustainable Development Goals (SDGs) to various nations to provide enduring answers to widespread problems and to give long-lasting solutions to common issues being faced across the globe. SDG 5 in particular was aimed at minimizing gender inequality by employing 9 targets and 14 indicators. The indicators serve as a yardstick to measure the progress of each of the 9 targets. This research takes an in-depth look at the perspectives of SDG 5 –Gender Inequalities, its targets, and indicators. Furthermore, explanatory data analysis and numerical association rule mining alongside QuantMiner are applied to the generated Indian …datasets on SDG 5 to extract patterns and associations among the fourteen indicators of SDG 5. The association rule mining carried out on the indicators reveals the pattern of association among these indicators. Legal provision for women and the rate of crimes against women have a perfect association of 100% while the association between legal provision for women and women who have experienced physical violence stands at 80%. The full relationships of all the 14 indicators are discussed extensively in the result and discussion section. Overall, it is established that these indicators are interdependent. This will make it easier for academics, the general public, and governmental and non-governmental organizations to understand the trends and form informed opinions on issues relating to gender inequality and SDG 5. Show more

Keywords: Sustainable development goals (SDG), gender equality, indicators, numerical association rule mining, knowledge extraction

DOI: 10.3233/JIFS-222384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5151-5162, 2023

Authors: Karthikeyan, N. | Gugan, I. | Kavitha, M.S. | Karthik, S.

Article Type: Research Article

Abstract: The drastic advancements in the field of Information Technology make it possible to analyze, manage and handle large-scale environment data and spatial information acquired from diverse sources. Nevertheless, this process is a more challenging task where the data accessibility has been performed in an unstructured, varied, and incomplete manner. The appropriate extraction of information from diverse data sources is crucial for evaluating natural disaster management. Therefore, an effective framework is required to acquire essential information in a structured and accessible manner. This research concentrates on modeling an efficient ontology-based evaluation framework to facilitate the queries based on the flood disaster …location. It offers a reasoning framework with spatial and feature patterns to respond to the generated query. To be specific, the data is acquired from the urban flood disaster environmental condition to perform data analysis hierarchically and semantically. Finally, data evaluation can be accomplished by data visualization and correlation patterns to respond to higher-level queries. The proposed ontology-based evaluation framework has been simulated using the MATLAB environment. The result exposes that the proposed framework obtains superior significance over the existing frameworks with a lesser average query response time of 7 seconds. Show more

Keywords: Flood disaster management, ontology framework, spatial information, data pre-processing

DOI: 10.3233/JIFS-223000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5163-5178, 2023

Authors: Yang, Hai-Long | Ren, Huan-Huan

Article Type: Research Article

Abstract: In this paper, we focus on the three-way decision model on incomplete single-valued neutrosophic information tables. Firstly, we define the minimum and maximum similarity measures between single-valued neutrosophic numbers (SVNNs) which may contain unknown values. On this basis, the notion of θ-weak similarity measure is given. Then, we introduce the conception of an incomplete single-valued neutrosophic information table (ISVNIT). For an incomplete single-valued neutrosophic information table, a new similarity relation is proposed based on the θ-weak similarity measure. Some properties are also studied. By using Bayesian decision theory and this similarity relation, we construct a three-way decision model on an …ISVNIT. Finally, an example of choosing product service providers is explored to illustrate the rationality and feasibility of the proposed model. We also discuss the influence of parameters in the model on decision results. Show more

Keywords: Three-way decision, single-valued neutrosophic number, incomplete single-valued neutrosophic information table, similarity measure

DOI: 10.3233/JIFS-221942

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5179-5193, 2023

Authors: Lei, Fan | Cai, Qiang | Wei, Guiwu | Mo, Zhiwen | Guo, Yanfeng

Article Type: Research Article

Abstract: The emergence of new energy electric vehicles (NEEV) can effectively reduce vehicle fuel consumption and alleviate the contradiction between fuel supply and demand. It has made great contributions to improving the atmospheric environment and promoting the development of environmental protection. However, the insufficient number of new energy electric vehicle charging stations (NEEVCSs) and unreasonable coverage areas have become obstacles to the large-scale promotion of new energy electric vehicles. Therefore, we build a multi-attribute decision making (MADM) model based on probabilistic double hierarchy linguistic weight Maclaurin symmetric mean (PDHLWMSM) operator and a MADM model based on probabilistic double hierarchy linguistic weight …power Maclaurin symmetric mean (PDHLWPMSM) operator to select the best charging station construction point from multiple alternative sites. In addition, the model constructed in this paper is compared with the existing MADM models to verify the scientificity of the model proposed in this paper. Show more

Keywords: Multiple attribute decision making (MADM), probabilistic double hierarchy linguistic term set (PDHLTS), PDHLWMSM operator, PDHLWPMSM operator, new energy electric vehicle charging station (NEEVS)

DOI: 10.3233/JIFS-221979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5195-5216, 2023

Authors: Zhang, Zhaojun | Sun, Rui | Xu, Tao | Lu, Jiawei

Article Type: Research Article

Abstract: When the shuffled frog leaping algorithm (SFLA) is used to solve the robot path planning problem in obstacle environment, the quality of the initial solution is not high, and the algorithm is easy to fall into local optimization. Herein, an improved SFLA named ISFLA combined with genetic algorithm is proposed. By introducing selection, crossover and mutation operators in genetic algorithm, the ISFLA not only improves the solution quality of the SFLA, but also accelerates its convergence speed. Moreover, the ISFLA also proposes a location update strategy based on the central frog, which makes full use of the global information to …avoid the algorithm falling into local optimization. By comparing ISFLA with other algorithms including SFLA in the map environment of different obstacles, it is confirmed that ISFLA can effectively improve the minimum path optimization and robustness in the simulation experiments of mobile robots. Show more

Keywords: Robot path planning, shuffled frog leaping algorithm, genetic algorithm, location update strategy

DOI: 10.3233/JIFS-222213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5217-5229, 2023

Authors: Sundarakumar, M.R. | Mahadevan, G. | Natchadalingam, R. | Karthikeyan, G. | Ashok, J. | Manoharan, J. Samuel | Sathya, V. | Velmurugadass, P.

Article Type: Research Article

Abstract: In the modern era, digital data processing with a huge volume of data from the repository is challenging due to various data formats and the extraction techniques available. The accuracy levels and speed of the data processing on larger networks using modern tools have limitations for getting quick results. The major problem of data extraction on the repository is finding the data location and the dynamic changes in the existing data. Even though many researchers created different tools with algorithms for processing those data from the warehouse, it has not given accurate results and gives low latency. This output is …due to a larger network of batch processing. The performance of the database scalability has to be tuned with the powerful distributed framework and programming languages for the latest real-time applications to process the huge datasets over the network. Data processing has been done in big data analytics using the modern tools HADOOP and SPARK effectively. Moreover, a recent programming language such as Python will provide solutions with the concepts of map reduction and erasure coding. But it has some challenges and limitations on a huge dataset at network clusters. This review paper deals with Hadoop and Spark features also their challenges and limitations over different criteria such as file size, file formats, and scheduling techniques. In this paper, a detailed survey of the challenges and limitations that occurred during the processing phase in big data analytics was discussed and provided solutions to that by selecting the languages and techniques using modern tools. This paper gives solutions to the research people who are working in big data analytics, for improving the speed of data processing with a proper algorithm over digital data in huge repositories. Show more

Keywords: HADOOP, SPARK, scalability, batch processing, big-data

DOI: 10.3233/JIFS-223295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5231-5255, 2023

Authors: Saranya, N. | Srinivasan, K. | Pravin Kumar, S.K.

Article Type: Research Article

Abstract: Ripeness of the fruit is significant in agriculture since it affects the fruit’s quality and sales. Manually determining the fruit’s ripeness has various drawbacks, including the fact that it consumes time, needs a lot of work, and occasionally results in errors. One of the crucial areas of the economies of nations is the agricultural sector. However, the manual approach is still occasionally used to assess the maturity of fruit. Fruit ripeness could be automatically categorized by the advancement of computer vision and machine learning technology. The Convolutional Neural Network (CNN) is used in this work is to classify the different …ripeness stages of banana fruit. The four stages of banana ripeness are unripe, mid-ripe, ripe, and overripe. Proposed method uses a fuzzy-based convolutional neural network with tunicate swarm algorithm. The proposed model outperforms cutting-edge computer vision-based algorithms in both coarse and perfectly acceptable classification of maturation phases. The experimental results using images of bananas at various stages of ripening, achieves overall accuracy of 96.9%. Show more

Keywords: Banana, ripening stages, convolutional neural network, fuzzy logic, and tunicate swarm algorithm

DOI: 10.3233/JIFS-221841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5257-5273, 2023

Authors: Wang, Peng | Lu, Shaojun | Cheng, Hao | Liu, Lin | Pei, Feng

Article Type: Research Article

Abstract: The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. …Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved Moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm. Show more

Keywords: Project scheduling, multi-skill, preemption, moth-flame optimization algorithm, ship maintenance

DOI: 10.3233/JIFS-221994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5275-5294, 2023

Authors: Wu, Jiali | Sheng, Yuhong

Article Type: Research Article

Abstract: Uncertain data envelopment analysis (DEA) model make an estimate of the efficiency of decision making unit (DMU) under data uncertainty. The current research on uncertain DEA model is only based on sectional data to calculate DMU’s static efficiency for the DMU’s set in the same period. From this article, we attempt to combine Malmquist productivity index and uncertain DEA model (the uncertain DEA-Malmquist productivity index model) to calculate the dynamic change of DMU’s efficiency over time. Additionally, the impact of technical factors and scale factors on DMU’s efficiency can be further explored and the Malmquist productivity index will be decomposed …into pure technical efficiency change, scale efficiency change and technical change. Finally, the article uses the model to analyze the provincial environmental efficiency from 2014 to 2016 in China. Show more

Keywords: Uncertainty theory, uncertain DEA model, malmquist productivity index, decision making unit

DOI: 10.3233/JIFS-222109

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5295-5308, 2023

Authors: Wang, Encheng | Mao, Zichen | Wang, Jie | Lin, Daming

Article Type: Research Article

Abstract: Wind power is widely used in industry, meteorology, shipping and so on. Accurate measurement of wind parameters is the key to improve the efficiency of wind power application. But at present, wind parameters are largely measured by different devices based on time difference method, which is easily influnced by enviromental noise. Beam-forming algorithm can improve the ability to resist environmental noise and the accuracy of hardware itself. Therefore, the beam-forming algorithm can be used to measure wind parameters in the high noise environment. However, the efficiency of the algorithm depends on how to search for spectral peak. In this paper, …a three-dimensional wind measurement method with chaotic-sequence improved genetic-particle swarm optimization algorithm is proposed to improve the waveform searching efficiency of beamforming algorithm. It first searches for rough target wind parameters globally, and then searches for precise target wind parameters locally. Through simulation verification, the proposed algorithm can measure the wind parameters after 0.087s under the condition of system error of 50dB and environmental noise of 20dB, the accuracy of wind speed is 0.5%, the accuracy of wind direction is 1%, and the accuracy of pitch angle is 0.5%. Compared with the wind measurement by traversal method, the proposed algorithm can improve the wind measurement efficiency by about 20 times, and has similar or even better measurement results.. And by comparing with other algorithms, the advantages of this algorithm are verified. Show more

Keywords: Three-dimensional wind measurement, beam-forming algorithm, chaotic sequence, genetic algorithm, particle swarm algorithm

DOI: 10.3233/JIFS-223378

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5309-5320, 2023

Authors: Gang, Wang | Ling, Song Jin | Yin, Feng Jia | Yan, Jia Dong | Yan, Zhao

Article Type: Research Article

Abstract: In this study, a novel hybrid metaheuristic model was developed to forecast the undrained soil shear (USS ) property from cone penetration test (CPT ) data (data from bore log sample from 70 different sites in Louisiana). This algorithm produced with the integration of grey wolf optimization (GWO ) and multilayer perceptron neural network (MLP ), named GWO  - MLP , where different numbers of hidden layers were tested (1 to 4). The duty of optimization algorithm was to determine the optimal number of neurons in each hidden layer. To this objective, the system comprised five inputs entitled sleeve friction, cone …tip persistence, liquid limit, plastic limitation, too much weight, and USS as outcome. The developed models for forecasting the USS of soil show the proposed best models have R2 at 0.9134 and 0.9236 in the training and predicting stage. Although the total ranking score of GWO-MLP2 and GWO-MLP4 is equal, the OBJ value shows that GWO-MLP4 has better performance than GWO-MLP2. In this case, considering the time of model running and a greater number of hidden layers suggests that GWO-MLP2 could be most appropriate. Therefore, the GWO-MLP3 model outperforms other GWO-MLP networks in the training and testing phase. Show more

Keywords: CPT, undrained shear strength of soil, estimation, grey wolf optimization, multilayer perceptron neural network

DOI: 10.3233/JIFS-221058

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5321-5332, 2023

Authors: Huang, Yuexin | Yu, Suihuai | Chu, Jianjie | Su, Zhaojing | Zhu, Yaokang | Wang, Hanyu | Wang, Mengcheng | Fan, Hao

Article Type: Research Article

Abstract: Design knowledge is critical to creating ideas in the conceptual design stage of product development for innovation. Fragmentary design data, massive multidisciplinary knowledge call for the development of a novel knowledge acquisition approach for conceptual product design. This study proposes a Design Knowledge Graph-aided (DKG-aided) conceptual product design approach for knowledge acquisition and design process improvement. The DKG framework uses a deep-learning algorithm to discover design-related knowledge from massive fragmentary data and constructs a knowledge graph for conceptual product design. The joint entity and relation extraction model is proposed to automatically extract design knowledge from massive unstructured data. The feasibility …and high accuracy of the proposed design knowledge extraction model were demonstrated with experimental comparisons and the validation of the DKG in the case study of conceptual product design inspired by massive real data of porcelain. Show more

Keywords: Conceptual product design, design knowledge graph, deep learning, knowledge acquisition, joint entity and relation extraction

DOI: 10.3233/JIFS-223100

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5333-5355, 2023

Authors: Yi, Tian | Li, Mingbo | Lei, Deming

Article Type: Research Article

Abstract: Unrelated parallel machine scheduling problem (UPMSP) with additional resources and UPMSP with learning effect have attracted some attention; however, UPMSP with additional resources and learning effect is seldom studied and meta-heuristics for UPMSP hardly possess reinforcement learning as new optimization mechanism. In this study, a shuffled frog-leaping algorithm with Q-learning (QSFLA) is presented to solve UPMSP with one additional resource and learning effect. A new solution presentation is presented. Two populations are obtained by division. A Q-learning algorithm is constructed to dynamically decide search operator and search times. It has 12 states depicted by population quality evaluation, four actions defined …as search operators, a new reward function and a new action selection. Extensive experiments are conducted. Computational results demonstrate that QSFLA has promising advantages for the considered UPMSP. Show more

Keywords: parallel machine scheduling, additional resource, learning effect, shuffled frog-leaping algorithm, reinforcement learning

DOI: 10.3233/JIFS-213473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5357-5375, 2023

Authors: Zhang, Min | Wang, Jie-Sheng | Liu, Yu | Wang, Min | Li, Xu-Dong | Guo, Fu-Jun

Article Type: Research Article

Abstract: In most data mining tasks, feature selection is an essential preprocessing stage. Henry’s Gas Solubility Optimization (HGSO) algorithm is a physical heuristic algorithm based on Henry’s law, which simulates the process of gas solubility in liquid with temperature. In this paper, an improved Henry’s Gas Solubility Optimization based on stochastic fractal search (SFS-HGSO) is proposed for feature selection and engineering optimization. Three stochastic fractal strategies based on Gaussian walk, Lévy flight and Brownian motion are adopted respectively, and the diffusion is based on the high-quality solutions obtained by the original algorithm. Individuals with different fitness are assigned different energies, and …the number of diffusing individuals is determined according to individual energy. This strategy increases the diversity of search strategies and enhances the ability of local search. It greatly improves the shortcomings of the original HGSO position updating method is single and the convergence speed is slow. This algorithm is used to solve the problem of feature selection, and KNN classifier is used to evaluate the effectiveness of selected features. In order to verify the performance of the proposed feature selection method, 20 standard UCI benchmark datasets are used, and the performance is compared with other swarm intelligence optimization algorithms, such as WOA, HHO and HBA. The algorithm is also applied to the solution of benchmark function. Experimental results show that these three improved strategies can effectively improve the performance of HGSO algorithm, and achieve excellent results in feature selection and engineering optimization problems. Show more

Keywords: Henry’s gas solubility optimization, stochastic fractal search, feature selection, benchmark function

DOI: 10.3233/JIFS-221036

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5377-5406, 2023

Authors: Sebastin Suresh, S. | Prabhu, V. | Parthasarathy, V.

Article Type: Research Article

Abstract: The Internet of Things (IoT) enabled wireless sensor network (WSN) is now widely employed in various sectors like smart city and vehicle transportation for their expanded capabilities such as data storage, access, and monitoring. The use of smart sensors that continuously collect data from the smart environment makes these possible. Furthermore, these facilitate the easy access of stored data over a secure IoT-gateway for mobile users. This device mobility that allows shifting to multiple locations, makes it challenging to route data across many access points. In this regard, it induces packet loss and improper node selection, which could result in …connection failure and network unreliability. This study proposes a new data routing protocol called as Fuzzy Logic Nodes Distributed Clustering for Energy-Efficient Fault Tolerance (F-NDC-EEFT). It can be deployed on any network platform, including mobile and non-mobile nodes. It considers performance metrics such as delivery rate, withstand node aliveness, communication delay, and energy efficiency to find an optimized path for the better performance of IoT enabled WSNs. The clustering approach is applied to the instant data load, which divides it into the distinct node groups. When proposed algorithm is tested alongside existing routing protocols for performance, it is found to save energy, minimize the number of connection failures, boost the throughput, and increase the network’s lifetime. Show more

Keywords: CH eligibility, energy efficiency, fuzzy modules, energy aware routing protocol, IoT enabled WSN

DOI: 10.3233/JIFS-221733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5407-5423, 2023

Authors: Tang, Chen | Yu, Qiancheng | Li, Xiaoning | Lu, Zekun | Yang, Yufan

Article Type: Research Article

Abstract: The stock market is a chaotic system, and stock forecasting has been the research focus. This paper proposes a multi-factor model based on DeepForest-CQP to make it more applicable to the stock domain. A t -test is used for selecting factors, and orthogonalization and heteroskedasticity tests are performed for the combined factors, which are particularly important in stock forecasting. DeepForest-CQP was combined with the multi-factor model to construct a stock selection model that can achieve higher returns. The obtained multi-factor quantitative stock selection model is used to study stock selection strategies, and simulated trading is used to evaluate the multi-factor …model and stock selection strategies and compare them with various machine learning multi-factor models. The experimental results show that the DeepForest-CQP-based multi-factor stock selection model achieves significant performance advantages in all backtesting metrics. Show more

Keywords: Multi-factor model, quantitative stock selection, machine learning, stock prediction, heteroskedasticity

DOI: 10.3233/JIFS-222328

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5425-5436, 2023

Authors: Chen, Zhixiang

Article Type: Research Article

Abstract: This paper modifies the original Teaching-Learning-based Optimization (TLBO) algorithm to present a novel Group-Individual Multi-Mode Cooperative Teaching-Learning-based Optimization (CTLBO) algorithm. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher-learner cooperation strategies in teaching and learning processes. In the preparation phase, teacher-learner interaction and teacher self-learning mechanism are applied. In the teaching phase, class-teaching and performance-based group-teaching operators are applied. In the learning phase, neighbor learning, student self-learning and team-learning strategies are mixed together to form three operators. Experiments indicate that CTLBO has significant improvement in accuracy and convergence ability compared with original …TLBO in solving large scale problems and outperforms other compared variants of TLBO in literature and other 9 meta-heuristic algorithms. A large-scale industrial engineering problem—warehouse materials inventory optimization problem is taken as application case, comparison results show that CTLBO can effectively solve the large-scale real problem with 1000 decision variables, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO, revealing that CTLBO can far outperform other algorithms. CTLBO is an excellent algorithm for solving large scale complex optimization issues. Show more

Keywords: Teaching-learning-based optimization, group-individual multi-mode cooperation, performance-based group teaching, teacher self-learning, team learning

DOI: 10.3233/JIFS-222516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5437-5465, 2023

Authors: Naresh Patel, K.M. | Ashoka, K. | Park, Choonkil | Shanmukha, M.C. | Azeem, Muhammad

Article Type: Research Article

Abstract: Diagnosis of human disease is a more difficult and complex process since it requires the consideration of various factors and symptoms to make a decision. Generally, the classification of diseases with fuzzy values is the most interesting topic because of accurate results. In this paper, we design a Bat-based Random Forest (BbRF) framework to enhance the performance of categorizing diseases with fuzzy values which also protect the privacy of the developed scheme. It involves pre-processing, attributes selection, fuzzy value generation, and classification. Additionally, the developed framework is implemented in Python tool and patient disease datasets are used for implementation. Moreover, …pre-processing remove the error and noise, attributes are selected based on the duration of diseases. Finally, classify the patient disease based on the generated fuzzy value. To prove the efficiency of the developed framework, attained results are compared with other existing techniques in terms of accuracy, sensitivity, specificity, F-measure, and precision. Show more

Keywords: Bat-based random forest, fuzzy value, optimization

DOI: 10.3233/JIFS-222749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5467-5479, 2023

Authors: Aramuthakannan, S. | Ramya Devi, M. | Lokesh, S. | Manimegalai, R.

Article Type: Research Article

Abstract: The internet and social networks produce an increasing amount of data. There is a serious necessity for a recommendation system because exploring through the huge collection is time-consuming and difficult. In this study, a multi-modal classifier is introduced which makes use of the output from dual deep neural networks: GRU for text analysis and Faster R-CNN for image analysis. These two networks reduce overall complexity with minimal computational time while retaining accuracy. More precisely, the GRU network is utilized to process movie reviews and the Faster RCNN is used to recognize each frames of the movie trailers. Gated Recurrent Unit …(GRU) is a well-known variety of RNN that computes sequential data across recurrent structures. Faster RCNN is an enhanced version of Fast RCNN, it combines with the rectangular region proposals and with the features is extract by the ResNet-101. Initially, the trailer of the movie is manually splitted into frames and these frames are pre-processed using fuzzy elliptical filter for image analysis and the movie reviews are also tokenized for text analysis. The pre-processed text is taken as an input for GRU to classify offensive and non-offensive movies and the pre-processed images are taken as an input for Faster R-CNN to classify violence and non- violence movies based on the extracted features from the movie trailer. Afterwards, the four classified outputs are given as input for fuzzy decision-making unit for recommending best movies based on the Mamdani fuzzy inference system with gauss membership functions. The performance of the dual deep neural networks was evaluated using the specific parameters like specificity, precision, recall, accuracy and F1 score measures. The proposed GRU yields accuracy range of 97.73% for reviews and FRCNN yields the accuracy range of 98.42% for movie trailer. Show more

Keywords: Movie recommendation, deep learning, Mamdani fuzzy inference system, Gated Recurrent Unit, Faster R-CNN

DOI: 10.3233/JIFS-222970

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5481-5494, 2023

Authors: Sumo, Peter Davis | Ji, Xiaofen | Cai, Liling

Article Type: Research Article

Abstract: Due to the growing call to embrace environmentally responsible and sustainable business practices, textile reverse logistics (TRL) and recovery practices, such as reusing, remanufacturing, or recycling, are gaining prominence. Textile recycling companies can simultaneously obtain economic and environmental benefits via more efficient RL practices. However, a system for measuring these efficiencies is paramount, as it is impossible to run a reverse logistics system efficiently without the ability to measure its performance. Studies on performance measurement of TRL firms are completely lacking, and those of the general RL literature use manual systems that require longer time and participation of many workers …to complete. In this study, we develop a performance prediction model based on DEA and ANFIS. Data for the ANFIS were derived from the DEA computation. To enhance the model, PSO, GA, and Jaya algorithms were introduced to tweak the ANFIS parameters. Results from the ANFIS hybrid models reveal ANFIS-Jaya to have a better prediction accuracy with R2 of 0.9832 and 0.9851 in training and testing datasets, respectively. This study contributes to the RL performance management literature and the limited research on used clothing collection, textile recycling, and RL performance management measurement. Show more

Keywords: Textile, reverse logistics, DEA-ANFIS, recycling, Jaya algorithm

DOI: 10.3233/JIFS-223418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5495-5505, 2023

Authors: Wu, Qiyue | Yuan, Yinlong | Cheng, Yun | Ye, Tangdi

Article Type: Research Article

Abstract: Emotion recognition based on EEG (electroencephalogram) is one of the keys to improve communication between doctors and patients, which has attracted much more attention in recent years. While the traditional algorithms are generally based on using the original EEG sequence signal as input, they neglect the bad influence of noise that is difficult to remove and the great importance of shallow features for the recognition process. As a result, there is a difficulty in recognizing and analyzing emotions, as well as a stability error in traditional algorithms. To solve this problem, in this paper, a new method of EEG emotion …recognition based on 1D-DenseNet is proposed. Firstly, we extract the band energy and sample entropy of EEG signal to form a 1D vector instead of the original sequence signal to reduce noise interference. Secondly, we construct a 1D-Densenet model, which takes the above-mentioned 1D vector as the input, and then connects the shallow manual features of the input layer and the output of each convolution layer as the input of the next convolution layer. This model increases the influence proportion of shallow features and has good performance. To verify the effectiveness of this method, the MAHNOB-HCI and DEAP datasets are used for analysis and the average accuracy of emotion recognition reaches 90.02% and 93.51% respectively. To compare with the current research results, the new method proposed in this paper has better classification effect. Simple preprocessing and high recognition accuracy make it easy to be applied to real medical research. Show more

Keywords: EEG signals, emotion recognition, DenseNet, shallow features, feature fusion

DOI: 10.3233/JIFS-223456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5507-5518, 2023

Article Type: Correction

DOI: 10.3233/JIFS-219325

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 5519-5519, 2023