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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Zhong, Ying | Huang, Chenze | Li, Qi
Article Type: Research Article
Abstract: With the rapid growth of data scale, the problems of collaborative filtering recommendation algorithm are more and more obvious, such as data sparsity, cold start, scalability, and the change of user interest over time. About the existing problems, we introduce the fuzzy clustering and propose a collaborative filtering algorithm based on fuzzy C-means clustering. The algorithm performs fuzzy clustering on the item attribute information to make items belonging to different categories in different membership degree, increases the data density, effectively reduces the data sparsity, and solves the issue that the inaccuracy of similarity leads to the low recommendation accuracy. Meanwhile, …the algorithm introduces the time weight function. Different evaluation times give different time weight values, and recently evaluated items are more representative of the user current interest, so we give a higher weight value, and early evaluated items have less effect on the user current interest, thus the weight value are relatively lower. The experimental results show that our algorithm can effectively alleviate the data sparsity problem and time migration of users preferences, thus achieve better performance. Show more
Keywords: Recommender systems, collaborative filtering, data sparsity, interest migration
DOI: 10.3233/JIFS-212216
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 309-323, 2022
Authors: Xiao, Feng | Liu, Lu | Han, Jiayu | Guo, Degui | Wang, Shang | Cui, Hai | Peng, Tao
Article Type: Research Article
Abstract: Time series forecasting (TSF) is significant for many applications, therefore the exploration and study for this problem has been proceeding. With the advances of computing power, deep neural networks (DNNs) have shown powerful performance on many machine learning tasks when considerable amounts of data can be used. However, sufficient data may be unavailable in some scenarios, which leads to performance degradation or even not working of DNN-based models. In this paper, we focus on few-shot time series forecasting task and propose to employ meta-learning to alleviate the problems caused by insufficient training data. Therefore, we propose a meta-learning-based prediction mechanism …for few-shot time series forecasting task, which mainly consists of meta-training and meta-testing. The meta-training phase uses first-order model-agnostic meta-learning algorithm (MAML) as a core component to conduct cross-task training, and thus our method also inherits the advantages of the MAML, i.e., model-agnostic, in the sense that our method is compatible with any model trained with gradient descent. In the meta-testing phase, the DNN-based models are fine-tuned by the small number of time series data from an unseen task in the meta-training phase. We design two groups of comparison models to validate the effectiveness of our method. The first group, as the baseline models, is trained directly on specific time series dataset from target task. The second group, as comparison models, is trained by our proposed method. Also, we conduct data sensitivity study to validate the robustness of our method. The experimental results indicate the second group models outperform the first in different degrees in terms of prediction accuracy and convergence speed, and our method has strong robustness for forecast horizons and data scales. Show more
Keywords: Time series forecasting, meta-learning, few-shot learning
DOI: 10.3233/JIFS-212228
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 325-341, 2022
Authors: Liu, Shulin | Jiang, Rui
Article Type: Research Article
Abstract: In this paper, considering the traditional geometric operation laws and Pythagorean fuzzy information, a varies of new distance measures of Pythagorean fuzzy set are proposed, such as generalized Pythagorean fuzzy ordered geometric distance (GPFOWGD) measures and generalized Pythagorean fuzzy hybrid weighted geometric distance (GPFHWGD) measures. Besides, some special issues including Hamming distance, Euclidean distance and Hausdorff distance of these raised geometric distance measures are investigated. To testify the valid of these new presented distance measures, a decision-making model is built and illustrated by a mathematical calculation example to evaluate the service quality of sports clubs using Pythagorean fuzzy information. The …results display that the approach is uncomplicated, valid and simple to compute. The example illustrates that the method presented in the paper can be used to deal with problems of uncertainty such as dynamic multiple attribute decision making. Show more
Keywords: Multiple attribute decision making, Pythagorean fuzzy sets (PFSs), geometric distance measures, Pythagorean fuzzy geometric distance measures, service quality of sports clubs
DOI: 10.3233/JIFS-212229
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 343-354, 2022
Authors: Gondere, Mesay Samuel | Schmidt-Thieme, Lars | Sharma, Durga Prasad | Scholz, Randolf
Article Type: Research Article
Abstract: Handwritten digit recognition is one of the extensively studied areas in machine learning. Apart from the wider research on handwritten digit recognition on MNIST dataset, there are many other research works on various script recognition. However, it is not very common for multi-script digit recognition which encourages the development of robust and multipurpose systems. Additionally, working on multi-script digit recognition enables multi-task learning. It is evident that multi-task learning improves model performance through inductive transfer using the information contained in related tasks. Therefore, in this study multi-script handwritten digit recognition using multi-task learning is proposed to be investigated. As a …specific case of demonstrating the solution to the problem, Amharic handwritten character recognition is also experimentally tested. The handwritten digits of three scripts including Latin, Arabic, and Kannada are studied to show that multi-task models with a reformulation of the individual tasks have shown promising results. In this study, a novel approach of using the individual tasks predictions was proposed to help the classification performance. These research findings have outperformed the baseline and the conventional multi-task learning models. More importantly, it avoided the need for weighting the different losses of the tasks, which is one of the challenges in multi-task learning. Show more
Keywords: Multi-script, handwritten digit recognition, multi-task learning, Amharic handwritten character recognition
DOI: 10.3233/JIFS-212233
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 355-364, 2022
Authors: Meziane, Mohammed El-Amine
Article Type: Research Article
Abstract: The new wave of industry 4.0 has made battery-based automated guided vehicles (AGVs) an essential tool for material handling in manufacturing systems. However, many challenges related to battery management and machines and AGVs energy consumption. To handle these challenges an efficient battery management strategy is designed. The proposed approach supports multispeed operating modes for machines and AGVs, which offers a high flexibility to the manufacturing system. The aim of the proposed approach is to keep the minimal residual electric charge above the critical level, while enhancing the global performance of the manufacturing system. As a consequence, it increases the AGVs …production hours and guarantees batteries safety. The developed approach can bring economic benefits for industry 4.0, by increasing the productivity and avoiding AGVs batteries damage. Extended literature benchmark instances related to the manufacturing 4.0 are used to evaluate the efficiency of the suggested approach. Show more
Keywords: Automated guided vehicle, battery management, industry 4.0, sustainability, multi-speed operating mode
DOI: 10.3233/JIFS-212242
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 365-381, 2022
Authors: Hussain, Muhammad | Alotaibi, Fouziah | Qazi, Emad-ul-Haq | AboAlSamh, Hatim A.
Article Type: Research Article
Abstract: The face is a dominant biometric for recognizing a person. However, face recognition becomes challenging when there are severe changes in lighting conditions, i.e., illumination variations, which have been shown to have a more severe effect on recognition performance than the inherent differences between individuals. Most of the existing methods for tackling the problem of illumination variation assume that illumination lies in the large-scale component of a facial image; as such, the large-scale component is discarded, and features are extracted from small-scale components. Recently, it has been shown that large-scale component is also important; in addition, small-scale component contains detrimental …noise features. Keeping this in view, we introduce a method for illumination invariant face recognition that exploits large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using ContourletDS. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model; we examined three widely employed CNN models: VGG-16, GoogLeNet, and ResNet152. To reduce the dimensions of local and global features and fuse them, we employ linear discriminant analysis (LDA). Finally, ridge regression is used for recognition. The method was evaluated on three benchmark datasets; it achieved accuracies of 99.7%, 100%, and 79.76% on Extended Yale B, AR, and M-PIE, respectively. The comparison reveals that it outperforms the state-of-the-art methods. Show more
Keywords: Face recognition, deep learning, convolutional neural network (CNN)
DOI: 10.3233/JIFS-212254
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 383-396, 2022
Authors: Zhang, Xuewu | Jing, Wenfeng
Article Type: Research Article
Abstract: Overhead contact systems are important power supply equipments for electrified railway locomotives. If there are faults in the equipment, it will threaten the safety of train operation. Dropper faults and foreign matter are two common types of Overhead contact systems faults, the fault regions of which occupy a relatively small area in the whole image, so it is difficult to detect the faults accurately by capturing detailed features using original Faster R-CNN. To solve the above problem, an improved Faster R-CNN based on convolutional neural networks, named multi-view Faster R-CNN, is proposed in order to extract more details of the …fault regions by combining deep and shallow feature maps. Experiments on the images collected from the Lanzhou-Xinjiang Railway line show that the feature fusion can significantly enhance the mean average precision. The precision of our model on the test set was 89.53%, increasing by 3.5%, 9.65%, and 5.8% compared with YOLOv3, SSD, and Faster R-CNN, and the detection accuracies of our model for unforced droppers and foreign matter were 88.17% and 90.60%, respectively, under a recall rate of almost 1.0. Because the multi-view feature fusion model in our method can flexibly detect faults with various sizes, it has an important application value in the fault detection of an overhead contact system. Show more
Keywords: Overhead contact system, dropper, fault detection, multi-view Faster R-CNN
DOI: 10.3233/JIFS-212257
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 397-407, 2022
Authors: Wang, Jianhua | Zhu, Kai | Peng, Yongtao | Zhu, Kang
Article Type: Research Article
Abstract: Due to the fact that the real manufacturing processes are often constrained by many kinds of resources and the trend that the energy consumption of factories is regulated more and more strictly, this paper studies the energy-efficient multi-resource flexible job shop scheduling problem (EE-MRFJSP). The goal is to minimize the energy consumption and completion time for all of the jobs’ production. Firstly, a general mathematic model for EE-MRFJSP is set up, in which the unit energy consumptions of the main resource’s different states are varied, and a constraint formula to ensure no crossover working periods for any resource is included. …Then, a non-dominated sorting teaching-learning-based optimization(NSTLBO) algorithm is proposed to solving the problem, the details of NSTLBO include the real encoding method, Giffler Thompson rule for decoding, non-dominated sorting rule to rank the pareto sets and crowding distance of solution for maintaining the population’s diversity, and the traditional two evolving stages: teacher education and student mutual study. Finally, comparative experiments are made based on some new designed instances, and the results verify our proposed NSTLBO algorithm can effectively solve the EE-MMFJSP, and has obvious advantages by comparing with NSGA-II, NRGA, and MOPSO. Show more
Keywords: Scheduling, energy-efficient, multi-resource constraint, flexible job shop, NSTLBO
DOI: 10.3233/JIFS-212258
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 409-423, 2022
Authors: Li, Mingxia | Chen, Kebing | Liu, Baoxiang
Article Type: Research Article
Abstract: The substitutability between products or the intensity of market competition is the key parameter affecting the supplier’s pricing decision. However, the parameter cannot be accurately measured in real life. This paper provides a method based on prior information to solve this issue. First, compared to classical concept lattice theory, the interval concept lattice theory can deal with uncertain information more accurately. It is used to extract the objects within the interval parameters [α , β ], and then interval concepts and lattice structure are built. Second, based on the interval concepts and lattice structure, the association rule mining algorithm is …designed to further extract the association rules under different interval parameters. Third, to obtain the effective association degree between two objects, the rule optimization algorithm is put forward by comparing the update of rules. Finally, the association degree can indirectly reflect the substitutability between products. Then the price of a new product can be determined. Our paper provides some implication on pricing for suppliers in competitive supply chain. Show more
Keywords: Pricing decision, formal context, interval concept lattice structure, optimization and mining of association rule
DOI: 10.3233/JIFS-212265
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 425-435, 2022
Authors: Srifi, Mehdi | Oussous, Ahmed | Ait Lahcen, Ayoub | Mouline, Salma
Article Type: Research Article
Abstract: In the era of big data, recommender systems (RSs) have become growing essential tools. They represent important machine learning solutions that mainly contribute to keeping users engaged with personalized content in e-platforms. Several RSs have been proposed in the literature, and most of them have focused on English content. However, for content in other languages like Arabic, very restricted works have been done to develop RSs. In recent times, the Arabic content on the Web has increased significantly because of the growing number of Arabic web users. This highlights the need for building RSs for Arabic content. To better handle …this challenge, we decided to provide the research community with a novel deep learning (DL)-based RS devoted to Arabic content. The main goal of the proposed RS is to predict user preferences from textual reviews written in the Arabic language. This is achieved by combining two independent DL techniques into one system: a convolutional neural network (CNN)-text processor for representing users and items; and a neural network, in particular, a multi-layer perceptron (MLP) to estimate interactions between user-item pairs. Extensive experiments on four large-scale Arabic datasets demonstrate that our proposed system can achieve better prediction accuracy than other state-of-the-art alternatives. Notably, it improves the MSE between 0.84% and 16.96%, and the MAE between 0.14% and 13.71%. This work is the first attempt designed to deal with a large volume of data in the Arabic context, opening up new research possibilities for future developments of Arabic RSs. Show more
Keywords: Arabic, recommender systems, user reviews, natural language processing, deep learning
DOI: 10.3233/JIFS-212274
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 437-449, 2022
Authors: Jia, Zhifu | Liu, Xinsheng
Article Type: Research Article
Abstract: Uncertain delay differential system is an important mathematical model. Stability is a basic problem of uncertain delay differential system. Delay and uncertain interference often lead to changes in the stability of the system. Establishing the judgment of the stability of uncertain delay differential system conditions is very important. Based on the strong Lipschitz condition, the judgment of p -th moment stability for uncertain delay differential equations (UDDEs) has been investigated. Actually, the strong Lipschitz condition is assumed that it only relates to the current state, it is difficult to be employed to determine the stability in p -th moment for …the UDDEs. In this paper, we consider two kinds of new Lipschitz conditions containing the current state and the past state, which are more weaker than the strong Lipschitz condition. Meanwhile, new sufficient theorems and corollaries under the new Lipschitz conditions as the tools to judge the p -th moment stability for the UDDEs are proved. Some examples explain the rationality of the corresponding theorems and corollaries. Show more
Keywords: Liu process, stability in p-th moment, new Lipschitz conditions, uncertain delay differential equations
DOI: 10.3233/JIFS-212288
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 451-461, 2022
Authors: Shuang, Chen | Tao, Ren | Yuntai, Ding
Article Type: Research Article
Abstract: Automatic Text Summarization(ATS) is distinctly beneficial due to a vast amount of textual data and time-consuming manual summarization. In order to enhance ATS for single document in huge datasets, a new extractive graph framework - text extractive SUMmarization framework based on EDge information with COreference resolution EDCOSUM is proposed in this paper that relies on coreference resolution, adding edge information in word-level graph and a sentence-ranking strategy. EDCOSUM combines the graph-based and statistical-based extractive summarization methods. It is a general method for any document (not limited to a specific domain). Moreover, two ranking strategies(sentence and LSA ranking strategy) are proposed …for sentence selection. A set of extensive experiments on CNN/Daily Mail and NEWSROOM are conducted for investigating the proposed method. The widely used automatic evaluation tool: Recall-Oriented Understudy for Gisting Evaluation(ROUGE) is utilized to evaluate EDCOSUM. Compared to the state-of-the-art ATS methods, EDCOSUM achieves a competitive result by improvements of over the highest scores in the literature for metrics ROUGE-1, ROUGE-2 and ROUGE-L respectively. Show more
Keywords: Text extractive summarization, Graph theory, Coreference resolution, Word-level graph, Ranking strategy
DOI: 10.3233/JIFS-212289
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 463-475, 2022
Authors: Heim, Isaac | Fonseca, Daniel J. | Houser, Rick | Cook, Ryan | O’Donnell, John
Article Type: Research Article
Abstract: This paper deals with the design and development of a novel approach, centered on the creation and development of a fuzzy controller to analyze electroencephalogram (EEG) data. The fuzzy controller makes use of the functions associated with the different regions of the brain to correlate multiple Brodmann areas to several outputs, where a normal analysis would associate only one region to one output. This controller was designed to quickly adapt to any data imported into it. The current implemented framework supports a math study. The math subjects’ outputs were attuned to their related study which involved transcranial direct current stimulation …(tDCS), which is a form of neurostimulation. Anode affinity, cathode affinity, calculation, memory, and decision making were the outputs focused on for the math study. This task is best suited to a fuzzy controller since interactions between Brodmann areas can be analyzed and the contributions of each area accounted for by indicating which regions have stronger and weaker effects on any given output. Show more
Keywords: Neurological activation, transcranial direct current stimulation, EEG, fuzzy controller, math enhancement
DOI: 10.3233/JIFS-212315
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 477-494, 2022
Authors: Wang, Ya-na | Zhou, Guo-hua
Article Type: Research Article
Abstract: The aim of this paper is to investigate pricing and production decisions of a monopoly firm that operates a co-product technology with two grades. A novel mathematical model that embeds a utility-maximizing customer choice model is developed to solve this problem. The closed-form expressions for the optimal solutions are derived and the results suggest that the distribution of customer valuations, yield rate and demand uncertainties have a vital influence on the firm’s optimal prices and profits. We then extend our study by allowing stockout-based substitution where a customer may be willing to purchase a substitute if his most preferred product …is not available but the substitute provides him with non-negative utility. The results indicate that disregarding stockout-based substitution (i) results in severe supply-demand mismatches for the product line in two directions; (ii) leads to higher or lower profit margins for both products; (iii) may not cause profit loss when the prices of both products are exogenous; however, this result does not hold when the prices are endogenous. Show more
Keywords: Random yield, utility-maximizing customers, customer substitution, product line
DOI: 10.3233/JIFS-212317
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 495-507, 2022
Authors: Zhang, Qiang | Liu, Jianping | Hu, Junhua | Yao, Zhihai | Yang, Jian
Article Type: Research Article
Abstract: With the increasing complexity of decision making (DM) problems, powerful mathematical tools are needed to represent and process fuzzy and uncertain DM information, and Pythagorean fuzzy set (PFS) is such a mathematical tool. PFS has been successfully applied in the field of fuzzy multiple criteria decision making (MCDM). Correlation coefficient is an information measure of PFS, and plays an important role in the application of PFS. At present, there is a problem that the existing correlation coefficients cannot moderately measure the correlation degree between PFSs, so this paper proposes the new correlation coefficients of PFS. The TODIM method has been …proved to be effective in dealing with MCDM problems that consider the psychological behavior of decision makers. This paper extends the TODIM method with the new correlation coefficients of PFS, and the extended TODIM method is called Pythagorean fuzzy CC-TODIM method. By numerical examples, it is verified that the new correlation coefficients of PFS are more reasonable and valid. By case analysis, it is verified that the Pythagorean fuzzy CC-TODIM method can effectively solve the MCDM problems, and the Pythagorean fuzzy CC-TODIM method based on the new correlation coefficients is more accurate and reliable. Show more
Keywords: Pythagorean fuzzy set, correlation coefficient, TODIM method, multiple criteria decision making
DOI: 10.3233/JIFS-212323
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 509-523, 2022
Authors: Wang, Huidong | Zhang, Yao | Bai, Chuanzheng
Article Type: Research Article
Abstract: As an effective tool for three-way decisions (3WD) problems, decision-theoretic rough sets (DTRSs) have raised increasing attention recently. In view of the advantages of q-rung orthopair uncertain linguistic variables (q-ROULVs) in depicting uncertain information, a new DTRSs model based on q-ROULVs is proposed to solve three-way group decision-making (3WGDM) problems. Firstly, the loss function of DTRSs is depicted by q-ROULVs and a q-rung orthopair uncertain linguistic DTRSs model is constructed subsequently. Secondly, to aggregate different experts’ evaluation results on loss function in group decision-making (GDM) scenario, the q-rung orthopair uncertain linguistic geometric Heronian mean (q-ROULGHM) operator and the q-rung orthopair …uncertain linguistic weighted geometric Heronian mean (q-ROULWGHM) operator are presented. Related properties of the proposed operators are investigated. Thirdly, to compare the expected loss of each alternative, a new score function of q-ROULVs is defined and the corresponding decision rules for 3WGDM are deduced. Finally, an illustrative example of venture capital in high-tech projects is provided to verify the rationality and effectiveness of our method. The influence of different conditional probabilities and parameter values on decision results is comprehensively discussed. Show more
Keywords: q-rung orthopair uncertain linguistic variable, decision-theoretic rough set, three-way group decision-making, geometric Heronian mean operator
DOI: 10.3233/JIFS-212327
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 525-544, 2022
Authors: Kumar, Arvind | Sodhi, Sartaj Singh
Article Type: Research Article
Abstract: We increase the power of the Artificial Neural Networks with the help of the Activation Function (AF). The tansig and logsig are widely used AF. But there is still requires some improvement in the AF. So, in this paper, we have proposed a NewSigmoid AF in the neural network. NewSigmoid is also as powerful as tansig and logsig. In multiple cases, the NewSigmoid function gives a better or equivalent performance as compared with both these AF. Like these AF, NewSigmoid is also a smooth S-shape, bounded, continuously differentiable, and zero-centered function. Therefore the NewSigmoid is also suitable for solving non-linear …problems. We have tested this AF on iris, cancer, glass, chemical, bodyfat, wine, and ovarian datasets. We use Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), and Bayesian Regularization (BR) algorithms during the optimization of the neural network. Maximum 100% accuracy in the iris dataset while using LM, and BR; 99.9% accuracy in the cancer dataset using BR; 100% accuracy in the glass dataset using BR; 100% accuracy in the chemical and bodyfat dataset using SCG, LM, and BR; 100% accuracy in the wine dataset using LM, and BR; and 99.1% accuracy in the ovarian dataset using BR has been found while working with multilayer neural networks. The NewSigmoid also achieves 100% training and validation accuracy on the mathework-cap image dataset using SCG. Show more
Keywords: Logsigmoid, tanigmoid, neural network, activation function, multilayer network.
DOI: 10.3233/JIFS-212333
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 545-559, 2022
Authors: Anoop, P.S. | Sugumaran, V.
Article Type: Research Article
Abstract: Tyre condition monitoring system (TCMS) is an emerging electronic safety system present in most of the new generation vehicles. It is intended to warn the driver when wheels are unbalanced or inflation pressure in a tyre goes below a certain recommended value. In India only limited vehicles having this critical safety system installed because it is highly expensive. The existing TCMS monitor only the tyre pressure using direct or indirect methods. In recent days the indirect TCMS using vibration signals and machine learning techniques are gaining momentum. This paper reports the effect of wheel balancing on a proposed affordable indirect …TCMS. The present study goes one step forward to include the wheel balancing in the TCMS, thus increasing the scope of TCMS. The study was carried out using vibration signal data acquired from a low cost accelerometer placed on the wheel hub. The features required were taken out from the acquired data with the help of statistical feature extraction techniques. Features selection and feature classification were processed with J48 decision tree algorithm. The effect of wheel balancing in classification accuracy is clearly explained. Show more
Keywords: Tyre condition monitoring system, machine learning, statistical features, TCMS, effect of balancing, J48 decision tree
DOI: 10.3233/JIFS-212336
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 561-573, 2022
Authors: Zhou, Qin | Su, Zuqiang | Liu, Lanhui | Hu, Xiaolin | Yu, Jianhang
Article Type: Research Article
Abstract: This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each …working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions. Show more
Keywords: Rolling bearing, fault diagnosis, transfer learning, subdomain adaptation
DOI: 10.3233/JIFS-212343
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 575-585, 2022
Authors: Hao, Bing | Zhang, Tianwei
Article Type: Research Article
Abstract: Exponential Euler differences for semi-linear differential equations of first order have got rapid development in the past few years and a variety of exponential Euler difference methods have become very significant researching topics. In allusion to fuzzy genetic regulatory networks of fractional order, this paper firstly establishes a novel difference method called Mittag-Leffler Euler difference, which includes the exponential Euler difference. In the second place, the existence of a unique global bounded solution and equilibrium point, global exponential stability and synchronization of the derived difference models are investigated. Compared with the classical fractional Euler differences, fuzzy Mittag-Leffler discrete-time genetic regulatory …networks can better depict and retain the dynamic characteristics of the corresponding continuous-time models. What’s more important is that it starts a new avenue for studying discrete-time fractional-order systems and a set of theories and methods is constructed in studying Mittag-Leffler discrete models. Show more
Keywords: Genetic regulatory, Caputo, Mittag-Leffler Euler difference, exponential stability, exponential synchronization
DOI: 10.3233/JIFS-212361
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 587-613, 2022
Authors: Li, Huipeng | Lu, Lin | Yang, Liguo
Article Type: Research Article
Abstract: The rapid development of the Internet has accelerated the expansion of e-commerce sacle of fresh agricultural products. The actual audience of smart logistics distribution of fresh agricultural products is customers, and customers enjoy the process and results of distribution services. However, the current research mainly selects indicators from the aspects of enterprise performance, cost and technical level based on the perspective of managers and technicians, which make it difficult to truly reflect customers’ feelings in the evaluation results. At the same time, the evaluation methods mainly focus on the comprehensive evaluation method and fuzzy evaluation method. These evaluation methods are …greatly affected by subjective factors in the evaluation grade distribution, and the assignment is often relatively complete and inaccurate. To solve these problems, this paper constructs the evaluation index system of intelligent logistics distribution of fresh agricultural products from the perspective of customers, so that the selection of indicators is more in line with the real wishes of customers. And we use the extension function to construct the correlation function for multi-level extension evaluation to ensure the accuracy of the evaluation results. Taking X logistics enterprise as an example, this paper verifies the scientificity of the evaluation index system of intelligent logistics distribution of fresh agricultural products through empirical research, which has reference significance for further improving the intelligent logistics distribution of fresh agricultural products. Show more
Keywords: Smart logistics distribution, fresh agricultural products, customer perspective, extenics
DOI: 10.3233/JIFS-212362
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 615-626, 2022
Authors: Kaliappan, Manikandan | Manimegalai Govindan, Sumithra | Kuppusamy, Mohana Sundaram
Article Type: Research Article
Abstract: Cardio vascular disease threatens human life with higher mortality rate. Therefore it is quite important to monitor. An arrhythmia is an abnormal heart beat and rhythm which causes the disease. The best tool to find the heart rhythm of heart is Electro Cardiogram (ECG) which provides information about the different types of arrhythmias. This paper aims at proposing an automatic framework by employing multi-domain features to classify ECG signals. Proposed work uses optimum method of feature selection to improvise the efficiency of the classification process. A hybrid optimization algorithm is used for feature selection and proposed to optimize the parameters …of the existing Support Vector Machine (SVM) classifier. Proposed hybrid optimization algorithm was developed using Particle Swarm Optimization (PSO) and Migration Modified Biogeography Based Optimization (MMBBO) algorithm. Algorithm provides an improved solution to the optimizing the parameters of ECG signals. Results are evaluated by implementing in MATLAB software and the performance is justified with comparative analysis. The proposed framework enhances the process of automatic prediction of various arrhythmias or rhythm abnormalities which performs in gaining better accuracy. For data sets, the average classification accuracy of this method is 97.89%. This result is an improvement of 4–5% over the comparison of other methods. Show more
Keywords: Heart disease, arrhythmia, feature selection, hybrid optimization algorithm, classification, particle swarm optimization
DOI: 10.3233/JIFS-212373
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 627-642, 2022
Authors: Li, Jun | Wei, Lixin | Wen, Yintang | Liu, Xiaoguang | Wang, Hongrui
Article Type: Research Article
Abstract: With the continuous development of sensor and computer technology, human-computer interaction technology is also improving. Gesture recognition has become a research hotspot in human-computer interaction, sign language recognition, rehabilitation training, and sports medicine. This paper proposed a method of hand gestures recognition which extracts the time domain and frequency domain features from surface electromyography (sEMG) by using an improved multi-channels convolutional neural network (IMC-CNN). The 10 most commonly used hand gestures are recognized by using the spectral features of sEMG signals which is the input of the IMC-CNN model. Firstly, the third-order Butterworth low-pass filter and high-pass filter are used …to denoise the sEMG signal. Secondly, effective sEMG signal segment from denoised signal is applied. Thirdly, the spectrogram features of different channels’ sEMG signals are merged into a comprehensive improved spectrogram feature which is used as the input of IMC-CNN to classify the hand gestures. Finally, the recognition accuracy of IMC-CNN model, three single channel CNN of IMC-CNN model, SVM, LDA, LCNN and EMGNET are compared. The experiment was carried out on the same dataset and the same computer. The experimental results showed that the recognition accuracy, sensitivity and accuracy of the proposed model reached 97.5%, 97.25% and 96.25% respectively. The proposed method not only has high average recognition accuracy on MYO collected dataset, but also has high average recognition accuracy on NinaPro DB5 dataset. Overall, the proposed model has more advantages in accuracy and efficiency than that of the comparison models. Show more
Keywords: Hand gesture recognition, sEMG, spectrogram feature, multi-channels, convolutional neural network
DOI: 10.3233/JIFS-212390
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 643-656, 2022
Authors: Qendraj, Daniela Halidini | Xhafaj, Evgjeni | Thanasi, Teuta
Article Type: Research Article
Abstract: Learning Management Systems is a challenge of implementing information technology (IT) in the higher educational field. This paper introduces a framework for assessing an LMS by integrating partial last squares-structural equation modeling (PLS-SEM) and fuzzy analytic hierarchic process with Z-numbers (Fuzzy Z-AHP). The objective is to propose the combination of the two approaches via results of PLS-SEM for the construction of the decision matrix for Fuzzy Z-AHP. The PLS-SEM method was used firstly to evaluate the conceptual model Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and extracting the significant connections between the independent constructs and the behavioral …intention to use an LMS. Secondly is adapted the Fuzzy Z-AHP method to rank the independent significant constructs initializing from the PLS-SEM results. Using a questionnaire survey, the study sampled 530 users of LMS in 4 Albanian universities as respondents. To the best of our knowledge this paper is among the first that combines PLS-SEM with Fuzzy Z-AHP for the UTAUT2 model while using an LMS. This combination showed that the most important construct of UTAUT2 affecting behavioral intention to use an LMS was habit. This study assist the decision makers and policy makers to provide the means to obtain better managerial conclusions for the improvement and progress of an LMS. Show more
Keywords: Google classroom, UTAUT2, PLS-SEM, Fuzzy Z-AHP, behavioral intention
DOI: 10.3233/JIFS-212396
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 657-669, 2022
Authors: Periasamy, Madhumathi | Kaliannan, Thenmalar
Article Type: Research Article
Abstract: Distributed Generating (DG) units, Energy Storage Systems (ESS), Distributed Reactive Sources (DRS), and resilient loads make up the microgrid (MG), which can operate in both connected and isolated modes. Because the amount of power generated by Renewable Energy Sources (RES) such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES) is unpredictable, it becomes difficult for MGs planners to make judgments. In this article, the uncertainties caused by RES are resolved through the successful application of a hybrid optimization approach and the integration of hybrid DGs. The Teaching Learning Algorithm (TLA) is used in this study to determine the …best site for DGs and reconfiguration, and heuristic fuzzy has been merged with TLA to handle multi-objectives such as total generation and emission cost minimization, and bus voltage deviation. In addition, the impact of replacing RES with hybrid DGs on RES performance is investigated. The ideal structures are determined by solving four different scenarios with the suggested approach, allowing DSO to make decisions with greater flexibility. The proposed technique is validated using a benchmark IEEE 33 bus system that has been converted into a microgrid. WES, PVES, and hybrid DGs are validated using a 24-hour daily load pattern with 24-hour load dispatching characteristic behaviors. Show more
Keywords: Renewable energy sources, radial distribution system, wind energy systems, photovoltaic energy systems, teaching learning algorithm
DOI: 10.3233/JIFS-212397
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 671-686, 2022
Authors: Ajilisa, O.A. | Jagathy Raj, V.P. | Sabu, M.K.
Article Type: Research Article
Abstract: Thyroid nodule segmentation is an indispensable part of the computer-aided diagnosis of thyroid nodules from ultrasound images. However, it remains challenging to segment the nodules from ultrasound images due to low contrast, high noise, diverse appearance, and complex thyroid nodules structure. So, it requires high clinical experience and expertise for proper detection of nodules. To alleviate the doctor’s tremendous effort in the diagnosis stage, we utilized several convolutional neural network architectures based on Encoder-Decoder architecture, U-Net architecture, Res-UNet architecture. To handle the complexity of the residual blocks, we also proposed three hybrid Res-UNet architectures by reducing the number of residual …connections. The experimental analysis of the segmentation models proves the viability of residual learning in the U-Net architecture. Hybrid models which use minimum residual connections provide efficient segmentation frameworks similar to Res-UNet architecture with a minimum computational requirement. The experimental results indicate that all the segmentation models based on residual learning and U-Net can accurately delineate nodules without human intervention. This model helps to reduce dependencies on operators and acts as a decision tool for the radiologist. Show more
Keywords: Semantic segmentation, thyroid nodules, ultrasound images, U-Net, residual learning
DOI: 10.3233/JIFS-212398
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 687-705, 2022
Authors: Alagu, Matheswaran | Selladurai, Ravindran | Chelladurai, Chinnadurrai
Article Type: Research Article
Abstract: The electric vehicle market has surged the consideration of charging station requirements in the commercial and residential areas of the urban regions. The addition of charging stations at the existing power network introduces a greater challenge on voltage stability and losses. The effect of the charging station can be addressed through the optimal integration of Distributed Generation (DG) units into the network. The improper placement of DG units can jeopardize the network stability. These issues are addressed by optimal placement of DG units and charging stations in the network to improve voltage, reduce transmission loss and maximize the charging station …capacity. Here the objectives are considered as a multi-objective problem and solved using an enhanced Ant-lion optimization algorithm. The proposed method is implemented and tested over IEEE – 33, 69 and 94 radial bus system in MATLAB R2020a version. In IEEE – 33 bus system, the total loss reduction of 67.63% and the minimum voltage of 0.981 is attained with 2909.2 kW of DG and 1770.7 kW of charging station. The voltage stability index is improved to 0.92. The efficacy of the proposed method is evaluated through comparison with existing methods such as Genetic Algorithm (GA) with VRP method, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO). It is evident that the proposed method gives improved performance than other methods. Show more
Keywords: Charging stations, distributed power generation, optimization, renewable energy sources, smart grids
DOI: 10.3233/JIFS-212401
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 707-719, 2022
Authors: Borza, Mojtaba | Rambely, Azmin Sham
Article Type: Research Article
Abstract: Finding efficient solutions for the multi-objective linear fractional programming problem (MOLFPP) is a challenging issue in optimization because more than one target has to be taken into account. For the problem, we face the concept of efficient solutions which is an infinite set especially when the objectives are in conflict. Since a classical method generally comes out with only one efficient solution, thus introducing new efficient approaches is helpful and beneficial for the decision makers to make their decisions according to more possibilities. In this paper, we aim to consider the MOLFPP with fuzzy coefficients (FMOLFPP) where the concept of …α - cuts is utilized so as to transform the fuzzy numbers into closed intervals and rank the fuzzy numbers as well. Consequently, the fuzzy problem is changed into an interval valued multi-objective linear fractional programming problem (IV-MOLFPP). Subsequently, the IV-MOLFPP is further changed into linear programming problems (LPPs) using a parametric approach, weighted sum and max-min methods. It is demonstrated that the solution obtained is at least a weakly ɛ - efficient solution, where the value of ɛ helps a decision maker (DM) to make his decision appropriately i.e. DMs chose more likely the solutions with the lowest value of ɛ. Numerical examples are solved to illustrate the method and comparison are made to show the accuracy, and the reliability of the proposed solutions. Show more
Keywords: Efficient solution, weighted sum approach, parametric approach, fuzzy numbers, interval arithmetic
DOI: 10.3233/JIFS-212403
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 721-734, 2022
Authors: Adisusilo, Anang Kukuh | Wahyuningtyas, Emmy | Saurina, Nia | Radi,
Article Type: Research Article
Abstract: Soil Tillage serious game designed as a training media has been researched based on the plowing forces using polynomial functions. However, the learning process is rare; hence the players in Serious Games (SG) are less engaged and tend to be more static in their games. The effects of vertical cutting angle, plowshare depth, and motor speed affect the soil plowing force in soil tillage. Therefore it is expected that a plow force model with a learning function will generate more actual conditions, engage the player and eventually affect the player’s behavior. The serious game design uses a Hierarchical Finite State …Machine (HFSM) in this study. HFSM state is motor speed, vertical cutting angle, and plowing depth. The learning function is based on Neural Network (NN), with a multilayer feed-forward neural network (FFNN) is chosen to estimate plowing forces. The Levenberg-Marquardt algorithm is used by NN to approach second-order training speed without computing the Hessian matrix and is the fastest backpropagation algorithm. The result of the research is a plowing force model values closer to the actual by giving players feedback as they learn. In the transition, HFSM has a feedback value to the initial state, which is helpful as part of measuring one game cycle that is run, thus providing a learning experience in a serious game. Show more
Keywords: Neural network, plowing forces, serious game, soil tillage, HFSM
DOI: 10.3233/JIFS-212419
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 735-744, 2022
Authors: Al-Sharqi, Faisal | Ahmad, Abd Ghafur | Al-Quran, Ashraf
Article Type: Research Article
Abstract: Interval complex neutrosophic soft sets (I-CNSSs) refers to interval neutrosophic soft sets (I-NSSs) featuring three two-dimensional independent membership functions accordingly (falsity, indeterminacy, as well as uncertainty interval). A relation is a tool that helps in describing consistency and agreement between objects. Throughout this paper, we insert and discuss the interval complex neutrosophic soft relation (simply denoted by I-CNSR), a novel soft computing technique used to examine the interaction degree among corresponding models known as I-CNSSs. We present the definition of the Cartesian product of I-CNSSs followed by the definition of I-CNSR. Furthermore, the definitions and some theorems and properties related …to the composition, inverse, and complement of I-CNSR are provided. The notions of symmetric, reflexive, transitive, and equivalent of I-CNSRs are proposed, and the algebraic properties of these concepts are verified. Furthermore, we demonstrate the relevance of our notion to real-world situations by offering a suggested method for solving a decision-making issue in the field of economics. Ultimately, an analysis is made between the current relationships and the proposed model to determine the model’s significance. Show more
Keywords: Complex neutrosophic set, complex neutrosophic relation, decision-making, interval neutrosophic set, interval complex neutrosophic soft set
DOI: 10.3233/JIFS-212422
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 745-771, 2022
Authors: Pushpa, B.R. | Shobha Rani, N.
Article Type: Research Article
Abstract: Low resolution mobile photographed images pose a complex set of research challenges as compared to non-mobile captured images, which really is a significant issue these days. For non-mobile captured and high-resolution photos, current plant recognition systems are the best solution providers. This study proposes the identification and extraction of leaf regions from complex backgrounds to meet the automatic recognition needs of a variety of mobile phone users. Additionally multiple factors complicate the leaf region extraction from complex backgrounds such as varying background patterns, clutters, varying leaf shape/size and varying illumination due to volatile weather conditions. In this paper, a simple …and efficient method for leaf extraction from complex background of mobile photographed low resolution images is proposed based on color channel thresholding and morphological operations. A self-built database of 5000 mobile photographed images in realistic environments is adapted for experimentations. Experiments were conducted on various resolution categories, and it was discovered that the proposed model has an average dice similarity measure of 99.5 percent for successful extraction of the leaf region in 13MP mobile photographed images. Furthermore, our comparative investigation reveals that the suggested model outperforms both traditional and state-of-the-art techniques. Show more
Keywords: Leaf extraction, color thresholding, morphological operations, realistic backgrounds, mobile camera images, gradient image analysis
DOI: 10.3233/JIFS-212451
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 773-789, 2022
Authors: Sreejith, S. | Subramanian, R. | Karthik, S.
Article Type: Research Article
Abstract: Ischemic stroke is a universal ailment that endangers the life of patients and makes them bedridden until death. Over a decade, doctors and radiologists have been dissecting patient status straightforwardly from the printouts of the slice images delivered by different diagnostic imaging modalities. Computed Tomography (CT) is a frequently used imaging strategy for therapeutic analysis and neuroanatomical investigations. The main objective of the paper is to develop a simple technique with less architectural complication and power consumption. The proposed work is to section the ischemic stroke lesion more efficiently from multi-succession CT images using patching the asymmetric region. The Hough …transform segment and extracts the features from the asymmetric region of the CT image and finally, the random forest is implemented to classify the unusual tissues from the CT image dependent on their pathological properties. RF classifier has been trained for different parts of the cerebrum for fragmenting the stroke lesion. The acquired outcomes produce better segmentation accuracy when compared with different strategies. The overall efficiency of the proposed method determines the Ischemic stroke with an accuracy of 95% with an RF classifier. Hence this method can be used in the segmentation process of stroke lesions. Show more
Keywords: Segmentation of ischemic stroke lesion, preprocessing, patching asymmetric region, Hough line symmetry axis, Random forest classifier
DOI: 10.3233/JIFS-212457
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 791-800, 2022
Authors: Senthil Vadivu, M. | Kavithaa, G.
Article Type: Research Article
Abstract: Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography …artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system. Show more
Keywords: Fetal ECG, generative adversarial networks (GAN), classification
DOI: 10.3233/JIFS-212465
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 801-811, 2022
Authors: Lin, Jiang | Jianjun, Zhu | Nanehkaran, Y.A.
Article Type: Research Article
Abstract: The problem of bilateral matching of teams and scientific and technical talents is studied in new R&D institutions with different forms of uncertain assessment information. A decision method is proposed based on a combination of grey correlation and cloud model. The method firstly applies interval grey numbers to characterize uncertain assessment score information and cloud models to characterize uncertain linguistic assessment information; secondly, the two different pieces of information are converted into grey correlation coefficients by applying grey correlation analysis methods to the assessment values, so as to solve indicator weights, and assemble assessment data based on indicator weights and …cloud models; finally, the bilateral matching model is constructed and the matching results are solved based on the cloud model data features and the dual objectives of maximum satisfaction and minimum uncertainty. The case analysis and method comparison show that the method is feasible and effective. Show more
Keywords: New R&D institutions, scientific and technical talents, evaluation, grey correlation, cloud model, bilateral matching
DOI: 10.3233/JIFS-212467
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 813-840, 2022
Authors: Muhiuddin, G. | Talebi, A. A. | Sadati, S. H. | Rashmanlou, Hossein
Article Type: Research Article
Abstract: The cubic set, introduced as a combination of a fuzzy set and an interval-valued fuzzy set, provided researchers with more flexibility than the previous two sets in dealing with complex and uncertain problems. Fuzzy graphs, based on this type of set, are among the emerging fuzzy graphs that have a great potential to model the surrounding phenomena. Consistent with the special role that cubic graphs play in decision-making and selecting superior options, dominating these graphs is of great importance and value. In this paper, we introduce the domination of the cubic graphs in terms of strong edges and examine their …properties. In addition, we examine domination in terms of independent sets and since many of the phenomena surrounding us are hybrid, we also discuss the domination concept on its fuzzy operations. Finally, we present an application of this graph on the subject of domination. Show more
Keywords: Cubic graph, dominating set, independent cubic set, cubic operations
DOI: 10.3233/JIFS-212534
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 841-857, 2022
Authors: Wei, Mingrun | Wang, Hongjuan | Cheng, Ru | Yu, Yue
Article Type: Research Article
Abstract: Clear images are generally desirable in high-level computer vision algorithms which are mostly deployed outdoors. However, affected by the changeable weather in the real world, images are inevitably contaminated by rain streaks. Deep convolutional neural networks (CNNs) have shown significant potential in rain streaks removal. The performance of most existing CNN-based deraining methods is often enhanced by stacking vanilla convolutional layers and some other methods use dilated convolution which can only model local pixel relations to provide the necessary but limited receptive field. Therefore, long-range contextual information is rarely considered for this specific task, thus, deraining a single image remains …challenging problem. To address the above problem, an effective residual deep attention network (RDANet) for single image rain removal is proposed. Specifically, we design a strong basic unit that contains dilated convolution, spatial and channel attention module (SCAM) simultaneously. As contextual information is very important for rain removal, the proposed basic unit can capture global long-distance dependencies among pixels in feature maps and model feature relations across channels. Compared with a single dilated convolution, the spatial and channel attention enhance the feature expression ability of the network. Moreover, some previous works have proven that the no-rain information in a rain image will be missing during deraining. To enrich the detailed information in the clean images, we present a residual feature processing group (RFPG) that contains several source skip connections to inject rainy shallow source information into each basic unit. In summary, our model can effectively handle complicated long rain streaks in spatial and the outputs of the network can retain most of the details of the original rain images. Experiments demonstrate the superiority of our RDANet over state-of-the-art methods in terms of both quantitative metrics and visual quality on both synthetic and real rainy images. Show more
Keywords: Single image deraining, convolutional neural network, spatial and channel attention, source skip connection
DOI: 10.3233/JIFS-212571
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 859-875, 2022
Authors: Zhang, Feng | Luo, Xiaoying | Li, Fengling | Li, Yun | Li, Yanbin | Zhang, Pengyu
Article Type: Research Article
Abstract: Although smart grids are characterized by self-healing, economy, high efficiency, and security, many hidden dangers exist in the development of smart grids due to a gradually expanding power grid and the continuous access of new energy to the power grid. Therefore, the development of smart grids, especially their reliability, security, and vulnerability, warrants further investigation. In this study, the vulnerability of smart grids is identified, and the vulnerability elements of smart grids are selected. Based on relevant theories, such as credibility and the combination of the credibility-based moment-generating function and the distortion function, a calculation model and framework of the …vulnerability index of a smart grid are constructed. An empirical analysis is also conducted. This study provides a scientific basis for analyzing the vulnerability of smart grids and suggesting reasonable preventive measures and auxiliary decision-making information for relevant planning, design, and operation personnel, which contributes to the sustainable and healthy development of smart grids. Show more
Keywords: Smart grids, vulnerability index, moment-generating function, distortion function
DOI: 10.3233/JIFS-212575
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 877-888, 2022
Authors: Li, Jie | Song, Li | Cao, Lianglin
Article Type: Research Article
Abstract: In this paper, to reduce the redundant attractions and incorrect directions of firefly algorithm (FA), a distance-guided selection approach (DSFA) is proposed, which consists of a distance-guided mechanism and selection strategy. Where the designed distance-guided mechanism reduces the attractions and plays as a classifier for global search and local search, the suggested selection strategy can avoid local search falling into traps, thereby increasing the probability of correct direction. With the good cooperation of these two approaches, DSFA obtains a good balance of exploration and exploitation. To confirm the performance of the proposed algorithm, excessive experiments are conducted on CEC2013 benchmark …functions, large-scale optimization problems CEC2008, and software defect prediction (SDP). In the comparison with the 5 advanced FA variants, DSFA provides the optimal solutions to most CEC2013 problems. Besides, when facing the problems of class imbalance and the dimensional explosion of datasets, DSFA greatly improves the performance of machine learning classifiers employed by SDP. It can be concluded that DSFA is an effective method for global continuous optimization problems. Show more
Keywords: Firefly algorithm, distance guided mechanism, selection strategy, global continuous optimization, software defect prediction
DOI: 10.3233/JIFS-212587
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 889-906, 2022
Authors: Zeeshan, Muhammad | Khan, Madad | Iqbal, Sohail
Article Type: Research Article
Abstract: In this paper, we introduce the notion of amplitude interval-valued complex Pythagorean fuzzy sets (AIVCPFSs). The motivation for this extension is the utility of interval-valued complex fuzzy sets in membership and non-membership degree which can express the two dimensional ambiguous information as well as the interaction among any set of parameters when they are in the form of interval-valued. The principle of AIVCPFS is a mixture of the two separated theories such as interval-valued complex fuzzy set and complex Pythagorean fuzzy set which covers the truth grade (TG) and falsity grade (FG) in the form of the complex number whose …real part is the sub-interval of the unit interval. We discuss some set-theoretic operations and laws of the AIVCPFSs. We study some particular examples and basic results of these operations and laws. We use AIVCPFSs in signals and systems because its behavior is similar to a Fourier transform in certain cases. Moreover, we develop a new algorithm using AIVCPFSs for applications in signals and systems by which we identify a reference signal out of the large number of signals detected by a digital receiver. We use the inverse discrete Fourier transform for the membership and non-membership functions of AIVCPFSs for incoming signals and a reference signal. Thus a method for measuring the resembling values of two signals is provided by which we can identify the reference signal. Show more
Keywords: Amplitude interval-valued complex Pythagorean fuzzy set, Complex fuzzy set, Fuzzy set, Inverse discrete Fourier transform
DOI: 10.3233/JIFS-212615
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 907-925, 2022
Authors: Kanika, | Singla, Jimmy
Article Type: Research Article
Abstract: Since the introduction of online payment systems, people have started doing online transactions which has also led to the rise of fraudulent transactions causing loss of money to the users and created distrust in the usage of online payment systems. Hence, fraud detection systems are the need of the hour. Among the transactions occurring on daily basis, frauds are less in number as compared to the genuine transactions, so class imbalance naturally exists in fraud detection systems. In this research work, a novel framework for online transaction fraud detection system based on Deep Neural Network (DNN) has been proposed by …utilizing algorithm-level method capable to detect frauds from imbalanced data and to maintain the overall performance of the model as well. The proposed system optimizes the decision threshold by utilizing the validation data efficiently for deciding whether an incoming transaction is a Fraud or not. For demonstration of the efficiency of our proposed system, three class imbalanced publicly available datasets have been used. Proposed system has shown better performance than data-level method. The results produced by the proposed fraud detection system have also been compared with existing machine learning techniques-based fraud detection systems. The experimental results show that the deep learning-based fraud detection system is more efficient for detecting frauds from imbalanced datasets having large number of input features as compared to the machine learning models. Show more
Keywords: Deep learning, machine learning, fraud detection, imbalanced data, thresholding
DOI: 10.3233/JIFS-212616
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 927-937, 2022
Authors: Gupta, Bhavna | Kaur, Harmeet | Bedi, Punam
Article Type: Research Article
Abstract: A robust collaborative system of active products (a product is called active when its ownership does not get transferred from provider to requestor at the time of its usage) should have an in-built mechanism which can make entities (service provider(s) and requestor(s)) to decide with whom to collaborate. In the absence of such a mechanism, the system is bound to have high job failure rate, resulting in wastage of resources. This paper proposes a Trust based Multi-Agent Framework (TbMAF) for collaborative systems of active products which enable only trustworthy entities to collaborate, safeguarding both users’ sensitive applications and providers’ resources. …The trustworthiness of service provider(s) and requestor(s) is computed using Fuzzy Inference System (FIS) and Radial Basis Function Neural Network (RBFNN) methodologies, respectively. A prototype based on the proposed system has been tested using real time data of a collaborative system namely, EGEE (Enabling Grids for E-science). This paper finds evidence that the job failure rate is lower when collaborations take place only between trustworthy entities. Further, the proposed framework is found to be robust against malicious entities and can capture the evolving behavior of entities as well. Show more
Keywords: Trust, reputation, recommendation, active product, collaborative system
DOI: 10.3233/JIFS-212691
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 939-956, 2022
Authors: Shan, Chuanhui | Chen, Xiumei
Article Type: Research Article
Abstract: Because of the advantages of deep learning and information fusion technology, it has drawn much attention for researchers to combine them to achieve target recognition, positioning, and tracking. However, when the existing neural network process multichannel images (e.g., color images), multiple channels as a whole input into neural networks, which makes it hard for networks to fully learn information in R, G, and B channels of images. Therefore, it is not conducive to the final learning effect of the networks. To solve the problem, using different combinations of R, G, and B channels of color images for feature-level fusion, this …paper proposes three fusion types as “R/G/B”, “R+G/G+B/B+R”, and “R+G+B/R+G+B/R+G+B” multichannel concat-fusional convolutional neural networks. Experimental results show that multichannel concat-fusional convolutional neural networks with fusional types of “R+G/G+B/B+R” and “R+G+B/R+G+B/R+G+B” achieve better performance than the corresponding non-fusional convolutional neural networks on different datasets. It shows that networks with fusion types of “R+G/G+B/B+R” and “R+G+B/R+G+B/R+G+B” can learn more fully information of R, G, and B channels of color images and improve the learning performance of networks. Show more
Keywords: Information fusion technology, “R+G/G+B/B+R” fusional type, “R+G+B/R+G+B/R+G+B” fusional type, multichannel concat-fusional convolutional neural network, convolutional neural network
DOI: 10.3233/JIFS-212718
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 957-969, 2022
Authors: Poornima, R. | Elangovan, Mohanraj | Nagarajan, G.
Article Type: Research Article
Abstract: The evolving new and modern technologies raise the risks in the network which will be affected by several attacks and thus give rise to developing efficient network attack detection and classification methods. Here in this article for predicting and classifying the network attacks, the LSTM neural network with XGBoost is suggested in which the NSL-KDD dataset was utilized to train the LSTM in the study. In the beginning, the unnecessary data and the noisy data will be eliminated using the dataset and the feature subset with the most compelling features will be selected using the feature selection. By utilizing the …essential data, the proposed system will be trained and the training parameter values will be modified for maximizing the functionality of the proposed system. Then, the result of the proposed system will be evaluated with some of the existing machine learning and deep learning algorithms such as SVM, LR, RF, DNN, and CNN with the performance metrics like Accuracy, F1 score, Recall, and Precision. It was found that the proposed model outperforms better than the other algorithms as this model is trained with the most important features and due to this, the training time and overfitting of the learning model was reduced thereby increasing the model effectiveness Show more
Keywords: Deep learning, feature selection, LSTM, network attack, XGBoost
DOI: 10.3233/JIFS-212731
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 971-984, 2022
Authors: Yadav, Yadavendra | Chand, Satish | Sahoo, Ramesh Ch. | Sahoo, Biswa Mohan | Kumar, Somesh
Article Type: Research Article
Abstract: Machine learning and deep learning methods have become exponentially more accurate. These methods are now as precise as experts of respective fields, so it is being used in almost all areas of life. Nowadays, people have more faith in machines than men, so, in this vein, deep learning models with the concept of transfer learning of CNN are used to detect and classify diabetic retinopathy and its different stages. The backbone of various CNN-based models such as InceptionResNetV2, InceptionV3, Xception, MobileNetV2, VGG19, and DenceNet201 are used to classify this vision loss disease. In these base models, transfer learning has been …applied by adding some layers like batch normalization, dropout, and dense layers to make the model more effective and accurate for the given problem. The training of the resulting models has been done for the Kaggle retinopathy 2019 dataset with about 3662 fundus fluorescein angiography colored images. Performance of all six trained models have been measured on the test dataset in terms of precision, recall, F1 score, macro average, weighted average, confusion matrix, and accuracy. A confusion matrix is based on maximum class probability prediction that is the incapability of the confusion matrix. The ROC-AUC of different classes and the models are analyzed. ROC-AUC is based on the actual probability of different categories. The results obtained from this study show that InceptionResNetV2 is proven the best model for diabetic retinopathy detection and classification, among other models considered here. It can work accurately in case of less training data. Thus, this model may detect and classify diabetic retinopathy automatically and accurately at an early stage. So it would be beneficial for humans to reduce the effects of diabetes. As a result of this, the impact of diabetes on vision loss can be minimized, and that would be a blessing in the medical field. Show more
Keywords: Diabetic retinopathy, nonproliferative, proliferative, maculopathy, transfer learning
DOI: 10.3233/JIFS-212771
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 985-999, 2022
Authors: Wong, Shi-Ting | Too, Chian-Wen | Yap, Wun-She | Khor, Kok-Chin
Article Type: Research Article
Abstract: With technological advancement, visual search has become an effective tool for searching important information by providing images. We propose a practical medical equipment recognition that can be used in visual search through deep transfer learning. We evaluated three deep learning models, i.e., VGG-16, ResNet-50, and Inception-v3, to recognise ten different classes of medical equipment. A data set consisting of 2,666 images had been collected and augmented to measure the models’ effectiveness. The models pre-trained with the ImageNet data set were transferred to the final models, and the last layers were replaced and trained with the collected data set. A grid …search method was then used to find the best combination of hyperparameters, such as optimiser, batch size, epoch number, dropout rate, and learning rate. We tested the models using photos captured using smartphones. The results showed that Inception-v3 outperformed the other two models with the highest accuracy of 0.9454. This is the first study that uses deep transfer learning for recognising medical equipment to our best knowledge. Such recognition technology can potentially be implemented in visual search for helping consumers to check the validity of medical equipment. Show more
Keywords: Medical equipment, object recognition, deep transfer learning
DOI: 10.3233/JIFS-212786
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1001-1010, 2022
Authors: Wei, Xiaolong | Huang, Xianglin | Yang, LiFang | Cao, Gang | Tao, Zhulin | Wang, Bing | An, Jing
Article Type: Research Article
Abstract: Structural models based on Attention can not only record the relationships between features’ position, but also can measure the importance of different features based on their weights. By establishing dynamically weighted parameters for choosing relevant and irrelevant features, the key information can be strengthened, and the irrelevant information can be weakened. Therefore, the efficiency of Deep Learning algorithms can be significantly elevated and improved. Although Transformer have been performed very well in many fields including Reinforcement Learning (RL). We tried to integrate Transformers into RL, however there are some challenge in this task. Especially, MARL (known as Multi-Agent Reinforcement Learning), …which can be recognized as a set of independent agents trying to adapt and learn through their way to reach the goal. In order to emphasize the relationship between each MDP decision in a certain time period, we applied the hierarchical coding method and validated the effectiveness of this method. This paper proposed a Hierarchical Transformer MADDPG based on recurrent neural network(RNN) which we call it Hierarchical RNNs-Based Transformers MADDPG(HRTMADDPG). It consists of a lower level encoder based on RNNs that encodes multiple step sizes in each time sequence, and it also consists of an upper sequence level encoder based on Transformer for learning the correlations between multiple sequences. Then we can capture the causal relationship between sub-time sequences and make HRTMADDPG more efficient. Show more
Keywords: MADDPG, Attention, RNN
DOI: 10.3233/JIFS-212795
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1011-1022, 2022
Authors: Elayaraja, P. | Kumarganesh, S. | Martin Sagayam, K. | Dang, Hien | Pomplun, Marc
Article Type: Research Article
Abstract: Cervical cancer can be cured if it is initially screened and giving timely treatment to the patients. This paper proposes an optimization technique for exposing and segmenting the cancer portion in cervical images using transform and windowing technique. The image processing steps are preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation involved in the proposed work. Initially, Gabor transform is enforced on the cervical test image to modify the pixels associated with the spatial domain into multi-resolution domain. Subsequently, the parameters of the multi-level features are extracted from the Gabor transformed cervical image. Then, the extracted features are optimized …using the Genetic Algorithm (GA), and the optimistic prominent part is classified by the Convolutional Neural Networks (CNN). Finally, the Finite Segmentation Algorithm (FSA) is used to detect and segment the cancer region in cervical images. The proposed GA based CNN classification method describes the effectual detection and classification of cervical cancer by the parameters such as sensitivity, specificity and accuracy. The experimental results are shown 99.37% of average sensitivity, 98.9% of average specificity and 99.21% of average accuracy, 97.8% of PPV, 91.8% of NPV, 96.8% of FPR and 90.4% of FNR. Show more
Keywords: Cervical cancer, Gabor, features, optimization, ANFIS, classification, Artificial Neural Network
DOI: 10.3233/JIFS-212871
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1023-1033, 2022
Authors: Bhatia, Tanveen Kaur | Kumar, Amit | Sharma, M.K. | Appadoo, S.S.
Article Type: Research Article
Abstract: To the best of author’s knowledge, only one approach is proposed in the literature to solve fuzzy linear fractional minimal cost flow problems (minimal cost flow problems in which each known arc cost is represented either by a triangular fuzzy number or a trapezoidal fuzzy number). In this paper, the mathematical incorrect assumptions, considered in the existing approach to solve fuzzy linear fractional minimal cost flow problems, are pointed out. Also, by generalizing an existing approach for solving fuzzy linear fractional programming problems, an approach (named as Mehar approach) is proposed to solve fuzzy linear fractional minimal cost flow problems. …Furthermore, two numerical examples are solved to illustrate the proposed Mehar approach. Show more
Keywords: Linear fractional minimal cost flow problem, triangular fuzzy numbers, trapezoidal fuzzy numbers, lexicographic approach
DOI: 10.3233/JIFS-212909
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1035-1051, 2022
Authors: Wang, Xiaohan | He, Zengyu | Wang, Pei | Zha, Xinmeng | Gong, Zimin
Article Type: Research Article
Abstract: Due to the limitation of positioning devices, there is a certain error between GPS positioning data and the real location on the map, and the positioning data needs to be processed to have better usability. For example, accurate location is needed for traffic flow control, automatic driving navigation, logistics tracking, etc. There are few studies specifically for circular road sections. In addition, many existing map matching methods based on Hidden Markov model (HMM) also have the problem that GPS points are easily to be matched to tangent or non-adjacent road sections at circular road sections. Therefore, the contextual voting map …matching method for circular road sections (STDV-matching) is proposed. The method proposes multiple subsequent point direction analysis methods based on STD-matching to determine entry into the circular section, and adds candidate section frequency voting analysis to reduce matching errors. The effectiveness of the proposed method is verified at the circular section by comparing it with three existing HMM methods through experiments using two real map and trajectory datasets. Show more
Keywords: Map matching, circular road, GPS, voting
DOI: 10.3233/JIFS-213054
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1053-1063, 2022
Authors: Lin, Yang | Ling, Yiqun | Yang, Zhe | Wang, Chunli | Li, Chuankun
Article Type: Research Article
Abstract: In the modern industrial process, a complete production process is achieved by requiring a variety of equipment to cooperate with each other. The abnormality in any equipment will have a large or small impact on process safety or product quality, resulting in increased risk. In recent years, many data-driven early-warning methods have been developed in academia. However, most of the methods need to be implemented on the support of normal and fault data. In order to overcome the problem, this paper establishes a new early-warning model based on negative selection algorithm (NSA) for centrifugal compressor unit without fault data. Firstly, …a nearest neighbor fixed boundary negative selection algorithm (NFB-NSA) is proposed by optimizing detector generation mechanism and matching rules for test samples. Secondly, the performance of NFB-NSA is tested by Iris dataset. The experimental results among NFB-NSA, V-detector, and other anomaly detection methods for Iris dataset shows that NFB-NSA can achieve the highest detection accuracy and the lowest false alarm rate in most cases. Finally, the early-warning of centrifugal compressor unit under normal samples is carried on by NFB-NSA in this paper. Validated by field data, NFB-NSA is demonstrated to be of excellent accuracy and robustness by results of experiments. Moreover, the influence of size of training sample on performance of NFB-NSA is obtained. Show more
Keywords: Early-warning, Centrifugal compressor unit, Fault data, NFB-NSA
DOI: 10.3233/JIFS-213075
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1065-1075, 2022
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