Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Al Tahan, Madeline | Hoskova-Mayerova, Sarka | Davvaz, Bijan

Article Type: Research Article

Abstract: In recent years, fuzzy multisets have become a subject of great interest for researchers and have been widely applied to algebraic structures including groups, rings, and many other algebraic structures. In this paper, we introduce the algebraic structure of fuzzy multisets as fuzzy multi-subnear rings (multi-ideals) of near rings. In this regard, we define different operations on fuzzy multi-ideals of near rings and we generalize some results known for fuzzy ideals of near rings to fuzzy multi-ideals of near rings.

Keywords: Near ring, multiset, fuzzy multiset, fuzzy multi-ideal, fuzzy multi-subnear ring

DOI: 10.3233/JIFS-202914

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Lu, Zhen-Yu | Wang, Xiao-Kang | Wang, Jian-Qiang | Cheng, Peng-Fei | Li, Lin

Article Type: Research Article

Abstract: The wireless propagation model is important for accurate 5 G network deployment. However, the traditional wireless propagation model is faced with the problems of limited application scenarios, unstable prediction results and high marginal cost of improving accuracy. In order to solve these problems, this paper constructs new features from the original data from different angles, and uses the random forest model to select the core features, which are used to train the fusion model based on the linear weighted summation of regression models such as KNN, LightGBM, and Bagging. After training, the final fusion model is obtained, it solves the …problems faced by traditional wireless propagation models. The results and analysis show that the fusion model outperforms the traditional wireless propagation models and the single models that constitutes the fusion model in terms of prediction accuracy and stability, and is not limited by scenarios and easy to deploy. Show more

Keywords: 5 G, wireless propagation model, feature engineering, fusion model

DOI: 10.3233/JIFS-202388

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Talafha, Mohammad | Alkouri, Abd Ulazeez | Alqaraleh, Sahar | Zureigat, Hamzeh | Aljarrah, Anas

Article Type: Research Article

Abstract: Decision-makers (DMs) usually face many obstacles to give the right decision, multiplicity of them highlights a problem to represent a set of potential values to assign a collective membership degree of an object to a set for several DM’s opinions. However, a hesitant fuzzy set (HFS) deals with such problems. The complexity appears in DM’s opinion which can be changed for the same object but with different times/phases. Each of them has a set of potential values in different times/phases of an object. In this paper, the periodicity of hesitant fuzzy information is studied and applied by extending the range …of HFS from [0, 1] to the unit disk in the complex plane to provide more ability for illustrating the full meaning of information to overcome the obstacles in decision making in the mathematical model. Moreover, the advantage of CHFS is that the amplitude and phase terms of CHFSs can represent hesitant fuzzy information, some basic operations on CHFS are also presented and we study its properties, in addition, several aggregation operators under CHFS are introduced, also, the relation between CHFS and complex intuitionistic fuzzy sets (CIFS) are presented. Finally, an efficient algorithm with a consistent process and an application in multiple attributes decision-making (MADM) problems are presented to show the effectiveness of the presented approach by using CHFS aggregation operators. Show more

Keywords: Hesitant fuzzy set, complex fuzzy sets, complex intuitionistic fuzzy sets, complex hesitant fuzzy sets, CIF aggregation operators, CHF aggregation operators

DOI: 10.3233/JIFS-211156

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-29, 2021

Authors: Ergun, Halime

Article Type: Research Article

Abstract: Fiber and vessel structures located in the cross-section are anatomical features that play an important role in identifying tree species. In order to determine the microscopic anatomical structure of these cell types, each cell must be accurately segmented. In this study, a segmentation method is proposed for wood cell images based on deep convolutional neural networks. The network, which was developed by combining two-stage CNN structures, was trained using the Adam optimization algorithm. For evaluation, the method was compared with SegNet and U-Net architectures, trained with the same dataset. The losses in these models trained were compared using IoU (Intersection …over Union), accuracy, and BF-score measurements on the test data. The automatic identification of the cells in the wood images obtained using a microscope will provide a fast, inexpensive, and reliable tool for those working in this field. Show more

Keywords: Image segmentation, fiber-vessel, microscopic wood cells, deep convolutional neural networks

DOI: 10.3233/JIFS-211386

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhang, Nian | Pan, Qian | Wei, Guiwu

Article Type: Research Article

Abstract: In order to effectively solve the decision-making problems with the diversity of evaluation information, the dynamics of research objects, the limitations of subjective authorization, and the irrational behavior of decision-makers, this paper extends catastrophe progression method to solve hybrid multiple attribute decision-making problems based on regret theory. Firstly, some basic theories are introduced. Secondly, the original catastrophe progression method is extended by using the regret theory, which is employed to solve the multiple attribute decision-making problems with hybrid evaluation information. Finally, a real-life case study of selecting a fresh cold chain logistics service provider is used to verify the practicality …and effectiveness of the proposed method, and a comparative analysis with the TOPSIS method and the sensitivity of the regret avoidance coefficient is analyzed in this article. Show more

Keywords: Regret theory, catastrophe progression, hybrid multi-attribute decision-making

DOI: 10.3233/JIFS-210515

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Zhou, Lexin | Yang, Wenzhong | Wang, Ting | Wu, Yongzhi

Article Type: Research Article

Abstract: Aspect-based sentiment analysis (ABSA) contains three subtasks, namely aspect term extraction, opinion term extraction and aspect-level sentiment classification. In order to make full use of the relationship between the three subtasks, some recent studies have successfully tried to use a unified framework to solve the problem of aspect-based sentiment analysis. However, these studies have not yet integrated domain knowledge into the model. Inspired by the post-training task, we propose a joint model (RACL-BERT-PT). This model combines the pre-training model BERT-PT with domain knowledge and the unified joint training framework RACL. The experimental results show that our model has achieved better …results than previous experiments on three public data. Show more

Keywords: Aspect-based sentiment analysis, post-training, domain knowledge

DOI: 10.3233/JIFS-210632

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Priyadarshi, Ankur | Saha, Sujan Kumar

Article Type: Research Article

Abstract: In this paper, we present our effort on the development of a Maithili Named Entity Recognition (NER) system. Maithili is one of the official languages of India, with around 50 million native speakers. Although various NER systems have been developed in several Indian languages, we did not find any openly available NER resource or system in Maithili. For the development, we manually annotated a Maithili NER corpus containing around 200K words. We prepared a baseline classifier using Conditional Random Fields (CRF). Then we ran many experiments using various recurrent neural networks (RNN). We collected larger raw corpus to obtain better …word embedding and character embedding. In our experiments, we found, neural models are better than CRF; a CRF layer is effective for the prediction of the final output in the RNN models; character embedding is effective in Maithili language. We also investigated the effectiveness of gazetteer lists in neural models. We prepared a few gazetteer lists from various web resources and used those in the neural models. The incorporation of the gazetteer layer caused performance improvement. The final system achieved an f-measure of 91.6% with 94.9% precision and 88.53% recall. Show more

Keywords: Named entity recognition, Maithili language, corpus annotation, LSTM model, gazetteer lists

DOI: 10.3233/JIFS-210051

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Zhou, Weibin | Chen, Tao | Huang, Huafang | Sheng, Chang | Wang, Yangfeng | Wang, Yang | Zhang, Daqiang

Article Type: Research Article

Abstract: Iris segmentation is one of the most important steps in iris recognition. The current iris segmentation network is based on convolutional neural network (CNN). Among these methods, there are still problems with the segmentation networks such as high complexity, insufficient accuracy, etc. To solve these problems, an improved low complexity DenseUnet is proposed to this paper based on U-net for acquiring a high-accuracy iris segmentation network. In this network, the improvements are as follows: (1) Design a dense block module that contains five convolutional layers and all convolutions are dilated convolutions aimed at enhancing feature extraction; (2) Except for the …last convolutional layer, all convolutional layers output feature maps are set to the number 64, and this operation is to reduce the amounts of parameters without affecting the segmentation accuracy; (3) The solution proposed to this paper has low complexity and provides the possibility for the deployment of portable mobile devices. DenseUnet is used on the dataset of IITD, CASIA V4.0 and UBIRIS V2.0 during the experimental stage. The results of the experiments have shown that the iris segmentation network proposed in this paper has a better performance than existing algorithms. Show more

Keywords: Iris segmentation, iris recognition, CNN, U-net, low complexity

DOI: 10.3233/JIFS-211396

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Chang, Jingfei | Lu, Yang | Xue, Ping | Wei, Xing | Wei, Zhen

Article Type: Research Article

Abstract: Deep convolutional neural network (CNN) is difficult to deploy to mobile and portable devices due to its large number of parameters and floating-point operations (FLOPs). To tackle this problem, we propose a novel channel pruning method. We use the modified squeeze-and-excitation blocks (MSEB) to measure the importance of the channels in the convolutional layers. The unimportant channels, including convolutional kernels related to them, are pruned directly, which greatly reduces the storage cost and the number of calculations. For ResNet with basic blocks, we propose an approach to consistently prune all residual blocks in the same stage to ensure that the …compact network structure is dimensionally correct. After pruning we retrain the compact network from scratch to restore the accuracy. Finally, we verify our method on CIFAR-10, CIFAR-100 and ILSVRC-2012. The results indicate that the performance of the compact network is better than the original network when the pruning rate is small. Even when the pruning amplitude is large, the accuracy can also be maintained or decreased slightly. On the CIFAR-100, when reducing the parameters and FLOPs up to 82% and 62% respectively, the accuracy of VGG-19 even improve by 0.54% after retraining. The source code is available at https://github.com/JingfeiChang/UCP . Show more

Keywords: Deep learning, convolutional neural network, network pruning, image classification

DOI: 10.3233/JIFS-202290

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Kaliraman, Bhawna | Duhan, Manoj

Article Type: Research Article

Abstract: Electroencephalogram (EEG) signals are essential in brain-computer interface systems. Nowadays, these signals are employed in various medical applications. In the past few years, EEG signals gain more attention in security systems to identify users, as these signals are unique for each individual. The current study explores deep learning frameworks for EEG-based user identification. Data from 107 users were considered for the study, which is acquired using 64 channels. Several experimental tests are performed over both convolutional neural network (CNN) and recurrent neural networks (RNN) using a 10-fold cross-validation process to check system effectiveness. In CNN, 1-D Convolutional layer is employed …for the processing of EEG signals. In RNN, LSTM and GRU are used to check system accuracy. For performance measure various metrices were considered such as accuracy, precision, recall and kappa score. Acquired results suggest that gated recurrent unit (GRU) outperforms other models in terms of accuracy and complexity both. GRU model has 91.2% accuracy and has three layers only, which reduces the model’s complexity. The training cost is also decreasing due to the low complexity of the model. Show more

Keywords: Convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), feed forward neural networks (FFNN), rectified linear units (ReLU), gated recurrent unit (GRU)

DOI: 10.3233/JIFS-202490

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Gao, Ting | Ma, Zhengming | Gao, Wenxu | Liu, Shuyu

Article Type: Research Article

Abstract: There are three contributions in this paper. (1) A tensor version of LLE (short for Local Linear Embedding algorithm) is deduced and presented. LLE is the most famous manifold learning algorithm. Since its proposal, various improvements to LLE have kept emerging without interruption. However, all these achievements are only suitable for vector data, not tensor data. The proposed tensor LLE can also be used a bridge for various improvements to LLE to transfer from vector data to tensor data. (2) A framework of tensor dimensionality reduction based on tensor mode product is proposed, in which the mode matrices can be …determined according to specific criteria. (3) A novel dimensionality reduction algorithm for tensor data based on LLE and mode product (LLEMP-TDR) is proposed, in which LLE is used as a criterion to determine the mode matrices. Benefiting from local LLE and global mode product, the proposed LLEMP-TDR can preserve both local and global features of high-dimensional tenser data during dimensionality reduction. The experimental results on data clustering and classification tasks demonstrate that our method performs better than 5 other related algorithms published recently in top academic journals. Show more

Keywords: Tensor decompostion, local linear embedding, mode product, dimensionality reduction

DOI: 10.3233/JIFS-202588

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Xu, Kai | Luo, Xinlin | Pang, Xinyu

Article Type: Research Article

Abstract: Based on the nonlinearity of energy consumption systems and the influence of multiple factors, this paper presents a nonlinear multivariable grey prediction model with parameter optimization and estimates the parameters and the approximate time response function of the model. Next, a genetic algorithm is applied to optimize the nonlinear terms of the novel model to seek the optimal parameters, and the modelling steps are outlined. Then, to assess the effectiveness of the novel model, this paper adopts Chinese oil, gas, coal and clean energy as research objects, and three classical grey forecasting models and one time series method are chosen …for comparison. The results indicate that the new model attains a high simulation and prediction accuracy, basically higher than that of the three grey prediction models and the time series method. Show more

Keywords: Grey prediction model, energy consumption, simulated annealing optimization, genetic algorithm

DOI: 10.3233/JIFS-210822

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Zixian, Zhang | Xuning, Liu | Zhixiang, Li | Hongqiang, Hu

Article Type: Research Article

Abstract: The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, …based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness. Show more

Keywords: Coal and gas outbursts, feature selection, Boruta, Apriori, Bayesian optimization, SVM

DOI: 10.3233/JIFS-210466

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Mohan, Prakash | Sundaram, Manikandan | Satpathy, Sambit | Das, Sanchali

Article Type: Research Article

Abstract: Techniques of data compression involve de-duplication of data that plays an important role in eliminating duplicate copies of information and has been widely employed in cloud storage to scale back the storage capacity and save information measure. A secure AES encryption de-duplication system for finding duplication with the meaning and store up it in the cloud. To protect the privacy of sensitive information whereas supporting de-duplication, The AES encryption technique and SHA-256 hashing algorithm have been utilized to encrypt the information before outsourcing. Pre-processing is completed and documents are compared and verified with the use of wordnet. Cosine similarity is …employed to see the similarity between both the documents and to perform this, a far economical VSM data structure is used. Wordnet hierarchical corpus is used to see syntax and semantics so that the identification of duplicates is done. NLTK provides a large vary of libraries and programs for symbolic and statistical natural language process (NLP) for the Python programming language that is used here for the unidentified words by cosine similarity. Within the previous strategies, cloud storage was used abundantly since similar files were allowed to store. By implementing our system, space for storing is reduced up to 85%. Since AES and SHA-256 are employed, it provides high security and efficiency. Show more

Keywords: Vector space Model, Wordnet, deduplication, cosine similarity, NLTK

DOI: 10.3233/JIFS-210038

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Song, Qinyu | Ni, Yaodong | Ralescu, Dan

Article Type: Research Article

Abstract: The customer demands of various products bring a challenge for manufacturers. They have to design customized products while maintaining economies of scale and low costs. In this paper, to address this challenge, four approaches are argued to help companies find out the optimal solutions of products’ performance and the maximum profit: (i) only platform modularity without component sharing (ii) only component sharing without platform modularity, (iii) using both platform modularity and component sharing to develop products, or iv) the products are developed individually from a given unshared components set. A theoretical model is proposed and the most profitable approach is …found to develop a whole new product family when uncertainty exists in the customer demand and economies of scale with pre-defined parameters. We find that, when consumers’ valuation is considered, the manufacturer may prefer to adopt platform or component sharing individually rather than combining them because the performance of high-end products using platform and component sharing strategies is worse than that using two strategies separately. If platform and component sharing are adopted, the high-end product is under designed, but the manufacturer can benefit from economies of scale. When economies of scale of the platform are greater than or equal to that of component sharing, the optimal performance level of low-end products under platform strategy is lower than that under component sharing strategy. Finally, the detailed numerical analysis provides support for the feasibility and effectiveness of the model. Show more

Keywords: Platform, component sharing, uncertainty theory

DOI: 10.3233/JIFS-210957

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: de Lima, Byanca Porto | Marins, Fernando Augusto Silva | da Silva, Aneirson Francisco

Article Type: Research Article

Abstract: This paper presents a new hybrid decision-making support method (New Hesitant Fuzzy AHP-QFD-PROMETHEE II Method), which jointly uses the Analytic Hierarchy Process (AHP), the Quality Function Deployment (QFD) and the Preference Ranking Method for Enrichment Evaluation (PRO-METHEE II), as well as the Hesitant Fuzzy Linguistic Term Sets (HFLTS) to capture hesitation and aggregate divergent opinions from different experts. A real application of the new method to a packaging design selection problem for an automotive company is described, finding that AHP assisted in determining the importance of QFD’s customer requirements (CRs) and PROMETHEE II was used to select the best packaging …design. With this same problem, for the purpose of validating the proposed method, a comparative analysis was made with the use of the Hesitant Fuzzy AHP-QFD-TOPSIS method and also with the traditional AHP-QFD-PROMETHEE method, which makes it impossible to capture the hesitation of decision makers. The result showed similarity in the rankings of design alternatives found in the three methods application. The proposed method proved advantageous for solving problems that can generally be solved with the QFD House of Quality but have serious difficulties when decision makers have divergent opinions and hesitate in evaluating criteria and alternatives. Show more

Keywords: Decision making problem, hesitant fuzzy linguistic term sets, hesitant fuzzy, house of quality, AHP, PROMETHEE

DOI: 10.3233/JIFS-201739

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Wu, Meiqin | Hou, Xiaoqing | Fan, Jianping

Article Type: Research Article

Abstract: As an extended model of data envelopment analysis (DEA), cross-efficiency is utilized in various fields. It applies the relative evaluation values of homogeneous decision-making units (DMUs) in ranking completely. Due to the non-uniqueness of DEA optimal weights, and the cross-efficiency methods focus on the aggregation of cross-efficiency matrix values. There is a low correction between the final efficiency evaluation value and the value assigned to the criteria weight or the value of the criteria. This paper uses the modified aggressive cross-efficiency model to calculate the weights of the decision-making units (DMUs). Then according to the consistency of peer-evaluation criteria, the …entropy weight method is used to aggregate the index weights to generate a set of common weights, and a new Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is used to calculate the utility score of DMUs that can rank them completely. Finally, a numerical case of supplier selection is offered to illustrate the feasibility and effectiveness of the proposed method. Show more

Keywords: Cross-efficiency, group decision-making, criteria utility, MARCOS method

DOI: 10.3233/JIFS-210279

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Zhang, Jin | Gu, Fu | Ji, Yangjian | Guo, Jianfeng

Article Type: Research Article

Abstract: To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers’ titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view …history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers’ preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress. Show more

Keywords: Scientific and technological literature resources, personalized recommendation, deep learning, recommendation algorithm

DOI: 10.3233/JIFS-210043

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Atmaca, S. | Zorlutuna, I.

Article Type: Research Article

Abstract: In 2020, r-near topological spaces on Near Approximation Spaces were introduced by Atmaca [1 ]. In this study, we introduce the concept of continuity on r-near topological spaces and examine some properties of it.

Keywords: Near set, r-near topology, topology

DOI: 10.3233/JIFS-211017

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-5, 2021

Authors: Liu, Xiang-Min | Hu, Jian | Mwakapesa, Deborah Simon | Nanehkaran, Y.A. | Mao, Yi-Min | Zhang, Rui-Peng | Chen, Zhi-Gang

Article Type: Research Article

Abstract: Deep convolutional neural networks (DCNNs), with their complex network structure and powerful feature learning and feature expression capabilities, have been remarkable successes in many large-scale recognition tasks. However, with the expectation of memory overhead and response time, along with the increasing scale of data, DCNN faces three non-rival challenges in a big data environment: excessive network parameters, slow convergence, and inefficient parallelism. To tackle these three problems, this paper develops a deep convolutional neural networks optimization algorithm (PDCNNO) in the MapReduce framework. The proposed method first pruned the network to obtain a compressed network in order to effectively reduce redundant …parameters. Next, a conjugate gradient method based on modified secant equation (CGMSE) is developed in the Map phase to further accelerate the convergence of the network. Finally, a load balancing strategy based on regulate load rate (LBRLA) is proposed in the Reduce phase to quickly achieve equal grouping of data and thus improving the parallel performance of the system. We compared the PDCNNO algorithm with other algorithms on three datasets, including SVHN, EMNIST Digits, and ISLVRC2012. The experimental results show that our algorithm not only reduces the space and time overhead of network training but also obtains a well-performing speed-up ratio in a big data environment. Show more

Keywords: DCNN, MapReduce, network compression, conjugate gradient method, load balancing

DOI: 10.3233/JIFS-201790

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Venkataramanan, C. | Ramalingam, S. | Manikandan, A.

Article Type: Research Article

Abstract: Smart farming is one of the immense applications of Wireless Sensor Networks (WSN). Still, most of the researches have been focusing on precision agriculture using WSNs. In general, the nodes within the wireless sensor systems are self-configured. Based on the application requirement, gadgets within the region of interest collect data, prepare it, and send it to the recipient. The biggest impediments to these sensor systems are collision, restricted battery, and transmission capacity. Due to these characteristics, the node battery depletes earlier, when it starts working. Currently, agriculture depends on rain due to the lack of water resources and irrigation services. …The crop development depends totally on the factors of water, the climatic conditions of the soil, etc. In large-scale agriculture, it is exceptionally problematic to analyze all the parameters accurately throughout the growing field. In this article, high-precision architecture for large-scale agriculture has been proposed. An IoT (Internet of Things) enabled WSN has been built and installed in the respective areas to measure the physical quantities regularly. In addition, Lévy-Walk Bat (LWBA) algorithm has been proposed to optimize the collected data. The prediction accuracy of the collected data is evaluated by LWBA and then, it is compared with the existing optimization algorithms with different error solvers. It has provided the exact information regarding the whole landscape and it will help the farmers to irrigate precisely. Show more

Keywords: Data prediction, error minimization, IoT, regression, machine learning, optimization, smart agriculture, SVM, WSNs

DOI: 10.3233/JIFS-202953

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Wang, Weizhong | Ma, Yilin | Liu, Shuli

Article Type: Research Article

Abstract: Current risk prioritization approaches for FMEA models are insufficient to cope with risk analysis problem in which the self-confidence of expert’s judgment and the deviation among risk evaluation information are considered, simultaneously. Therefore, to remedy this limitation, this paper reports an extended risk prioritization approach by integrating the MULTIMOORA approach, Z-numbers and power weighted average (PWA) operator. Firstly, the Z-numbers with triangular fuzzy numbers are applied to reflect the self-confidence and uncertainty of expert’s judgment. Next, the PWA operator for Z-numbers (Z-PWA) with similarity measure is proposed to obtain the group risk evaluation matrix by considering the influence of the …deviation among risk evaluation information. Then, an extended version of MULTIMOORA method with developed entropy method is presented to calculate risk priority ranking order of each failure. Finally, the equipment failures in a certain oil and gas plant is utilized to test the extended risk prioritization approach for FMEA model. After that, the sensitivity and comparison studies are led to illustrate the availability and reliability of the proposed risk prioritization approach for FMEA based risk analysis problem. Show more

Keywords: Failure model and effect analysis, risk prioritization, Z-numbers, MULTIMOORA

DOI: 10.3233/JIFS-200678

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Senniappan, Gomathi | Umapathy, Prabha

Article Type: Research Article

Abstract:  Renewable energy presently occupies a prominent position in India’s overall energy generation scheme. In the midst of numerous alternative energy resources, solar energy is widely used as it persists in large volumes with varying specification ratings. It is suited for both stand-alone and grid-coupled application. The Maximum Power Tracking (MPPT) scheme has a profound effect on the operating efficiency of a Photovoltaic (PV) power plant. This paper proposes an inventive hybridized Human Psychology Optimization-Perturb and Observation (HPO-PO) MPPT approach for obtaining the optimal duty cycle of the boost converter to harvest global maxima from a grid-connected Total Cost Tied (TCT) …configured PV array of 4080 W. The suggested method provides enriched performance both in steady-state, as well as in rapid and randomly changing weather conditions. Comparison studies of various MPPT procedures, including Perturbation and Observation (PO), Artificial Bee Colony (ABC), and Human Psychology Optimization (HPO) in MATLAB environment, illustrate the usefulness of the evoked system in meeting its goals. The suggested MPPT procedure has offered enhanced activities in terms of voltage quality, maximum power tracking capability, and converter efficiency compared to other methods. The recommended hybridized MPPT approach is experimentally validated on a hardware set-up using a 16-bit dsPIC30F2010 Digital Signal Controller in enhancing the behavior of the grid-connected PV system. Show more

Keywords: Photovoltaic (PV) systems, MPPT, perturb and observation, artificial bee colony, human psychology optimization (HPO), hybrid HPO-PO

DOI: 10.3233/JIFS-210114

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2021

Authors: Luo, Damei | Li, Zhaowen | Qu, Liangdong

Article Type: Research Article

Abstract: An information system (IS) is an important mathematical tool for artificial intelligence. A fuzzy probabilistic information system (FPIS), the combination of some fuzzy relations in the same universe which satisfies the probability distribution, can be seen as an IS with fuzzy relations. A FPIS overcomes the shortcoming that rough set theory assumes elements in the universe with equal probability and leads to lose some useful information. This paper integrates the probability distribution into the fuzzy relations in a FPIS and studies its reduction. Firstly, the concept of a FPIS is introduced and its reduction is proposed. Then, the fuzzy relations …in a FPIS are divided into three categories (P -necessary, P -relatively necessary and P -unnecessary fuzzy relations) according to their importance. Next, entropy measurement for a FPIS is investigated. Moreover, some reduction algorithms are constructed. Finally, an example is given to verify the effectiveness of these proposed algorithms. Show more

Keywords: Fuzzy relation, FPIS, reduction, core, uncertainty, entropy, algorithm

DOI: 10.3233/JIFS-202783

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Riaz, Muhammad | Habib, Anam | Aslam, Muhammad

Article Type: Research Article

Abstract:  A cubic bipolar fuzzy set (CBFS) is a new approach in computational intelligence and decision-making under uncertainty. This model is the generalization of bipolar fuzzy sets to deal with two-sided contrasting features which can describe the information with a bipolar fuzzy number and an interval-valued bipolar fuzzy number simultaneously. In this paper, the Dombi’s operations are analyzed for information aggregation of cubic bipolar fuzzy numbers (CBFNs). The Dombi’s operations carry the advantage of more pliability and reliability due to the existence of their operational parameters. Owing to the pliable nature of Dombi’s operators, this research work introduces new aggregation operators …named as cubic bipolar fuzzy Dombi weighted averaging (CBFDWA) operator and cubic bipolar fuzzy Dombi ordered weighted averaging (CBFDOWA) operator with ℙ -order and ℝ -order, respectively. Additionally, this paper presents some significant characteristics of suggested operators including, idempotency, boundedness and monotonicity. Moreover, a robust multi-criteria decision making (MCDM) technique is developed by using ℙ -CBFDWA and ℝ -CBFDWA operators. Based on the suggested operators a practical application is demonstrated towards MCDM under uncertainty. The comparison analysis of suggested Dombi’s operators with existing operators is also given to discuss the rationality, efficiency and applicability of these operators. Show more

Keywords: Cubic bipolar fuzzy sets, Dombi’s operations, cubic bipolar fuzzy Dombi weighted averaging operator, cubic bipolar fuzzy Dombi ordered weighted averaging operator, ℙ-order and ℝ-order operations, multi-criteria decision making

DOI: 10.3233/JIFS-210667

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Kişi, Ömer

Article Type: Research Article

Abstract: Statistical convergence of sequences has been studied in neutrosophic normed spaces (NNS) by Kirişci and Şimşek [39 ]. Ideal convergence is more general than statistical convergence for sequences. This has motivated us to study the ideal convergence in NNS. In this paper, we study the concept of ideal convergence and ideal Cauchy for sequences in NNS.

Keywords: Neutrosophic normed spaced, ideal convergence, ideal Cauchy sequence, t-norm, t-conorm

DOI: 10.3233/JIFS-201568

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Xiao, Yanjun | Han, Furong | Ding, Yvheng | Liu, Weiling

Article Type: Research Article

Abstract: The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge …base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms. Show more

Keywords: Rapier loom, expert system, fault tree, fault diagnosis

DOI: 10.3233/JIFS-210741

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Aydın, Serhat | Yörükoğlu, Mehmet | Kabak, Mehmet

Article Type: Research Article

Abstract: The fourth party logistics (4PL) is an combiner that designs and implements the holistic supply chain solutions by using skills, knowledge, technology and resources of the service provider and its customer. A 4PL provider is also a technological service provider with eligible intellectual capital and the sufficient computer/software infrastructure. Defining the most appropriate 4PL service provider from the alternatives is not easy for companies, the solution can be addressed within the framework of the Multi-Criteria Decision Making (MCDM) problem, and subjective and uncertain data are required for this solution. “Fuzzy set theory” is a helpful tool for dealing with such …subjectivity and uncertainty. In recent times, extensions of fuzzy sets have been evolved to address and describe the subjectivities and uncertainties more widely. Neutrosophic sets are one of the extensions of fuzzy sets, and unlike other extensions, they use the independent indeterminacy-membership function, thereby extracting important information and improving the accuracy of the decision-making process. A neutronophic MCDM method was proposed for the assessment of 4PL providers’ performance. In the application part of the study, neutrosophic language scale was used by three experts to evaluate the performance of 4PL providers. Then the closeness coefficient of each alternative was computed and sequenced in descending order. We also presented a comparative analysis with neutrosphic TOPSIS method. The results determined that the proposed neutrosophic MCDM method could be used in the performance evaluation of 4PL providers and similar problems. Show more

Keywords: Supply chain management, 4PLs, multi criteria decision making, neutrosophic sets

DOI: 10.3233/JIFS-219209

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Kurt, Mahmut Mahir | Yazıcıoğlu, Osman

Article Type: Research Article

Abstract: Retail e-commerce has been growing over the years by attracting entrepreneurs’ attention. Increasing interest in retail e-commerce has affected competition and made it necessary to choose the right competition strategy. This study aims at selecting the right competition strategy to be successful in retail e-commerce under vague and imprecise conditions. AHP and TOPSIS methods are used under intuitionistic fuzzy environment, which allows decision-makers to reflect their hesitation in their judgements. After a literature review on retail, e-commerce, and competition strategy, the main criteria and sub-criteria of multicriteria retail e-commerce alternative selection are presented. Cost leadership, differentiation, and focusing strategies are …evaluated as competitive strategies, based on the opinions of experts collected through a questionnaire. The results of the study show that the strategies are ranked as differentiation, focus, and cost leadership strategies, respectively. Show more

Keywords: Decision making, intuitionistic fuzzy, competitive strategy, retail, e-commerce

DOI: 10.3233/JIFS-202010

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Karimi, Saeed | Mirzamohammadi, Saeed | Pishvaee, MirSaman

Article Type: Research Article

Abstract: As a major concern of chief managers in each organization, project portfolio selection has a special place in their responsibilities. To assist managers in making decisions, applicable optimization models play an essential role in such processes. In this regard, this paper provides a stochastic optimization model for a project portfolio selection problem under different scenarios. Providing the novelty in the model along with making it closer to reality, the interdependency between revenue and cost of projects is considered. Due to the inherent uncertainty of parameters, the revenue and cost of each project, as well as contributed capital, follow triangular fuzzy …parameters. Contrary to the previous model, the appreciation of assets is considered in the proposed model as the other novelty of the proposed model. To tackle the uncertainty of parameters, a robust possibilistic approach is used, which has been first-ever devised in such problems. Being both optimistic and pessimistic approaches available for decision-makers, a new measure is introduced to make the model inclusive. Moreover, by considering the confidence level as both parameter and decision variables, the robust possibilistic programming approach is adopted to solve the proposed model. Using the new proposed measure, the optimal average value of robust model are obtained under different confidence level. Finally, solving the optimization model, the results are provided by implementing the realization for uncertain parameters, and regarding the obtained results, discussions are made to provide some insights to the managers. Show more

Keywords: Project portfolio selection, project interdependencies, possibilistic robustness, fuzzy uncertainty

DOI: 10.3233/JIFS-210144

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Zhang, Nan | Sheng, Yuhong | Zhang, Jing | Wang, Xiaoli

Article Type: Research Article

Abstract: In uncertainty theory, parameter estimation of uncertain differential equation is a very important research direction. The parameter estimation of multifactor uncertain differential equation needs to be solved. Multifactor uncertain differential equation is a differential equation driven by multiple Liu processes. The paper introduces two methods to solve the unknown parameters of the multifactor uncertain differential equation, they are the method of moment estimation and the method of least squares estimation. Several numerical examples are used to illustrate the proposed parameter estimation methods.

Keywords: Uncertainty theory, multifactor uncertain differential equation, parameter estimation

DOI: 10.3233/JIFS-202891

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Chen, Hongyu | Wang, Shengsheng

Article Type: Research Article

Abstract: Since the end of 2019, the COVID-19, which has swept across the world, has caused serious impacts on public health and economy. Although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for clinical diagnosis, it is very time-consuming and labor-intensive. At the same time, more and more people have doubted the sensitivity of RT-PCR. Therefore, Computed Tomography (CT) images are used as a substitute for RT-PCR. Powered by the research of the field of artificial intelligence, deep learning, which is a branch of machine learning, has made a great success on medical image segmentation. However, general full supervision methods …require pixel-level point-by-point annotations, which is very costly. In this paper, we put forward an image segmentation method based on weakly supervised learning for CT images of COVID-19, which can effectively segment the lung infection area and doesn’t require pixel-level labels. Our method is contrasted with another four weakly supervised learning methods in recent years, and the results have been significantly improved. Show more

Keywords: COVID-19, deep learning, weakly supervised learning, computed tomography, automated segmentation

DOI: 10.3233/JIFS-210569

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Zhang, Feng | Wu, Mingying | Hou, Xinting | Wang, Xinhe | Han, Cheng | Xu, Xiayu | Zhang, Leilei

Article Type: Research Article

Abstract: In order to improve the reliability and performance of landing gear retraction systems, this paper presents two importance analysis methods based on universal grey operation. According to the system principle and fault mechanisms, the fault tree of the retraction system was first established. The uncertainties of the bottom events were then described using the universal grey number to obtain the universal grey representation of the system failure probability. And compared with the traditional interval operation, the results show that universal grey operation can solve the problem of interval expansion with uncertainty. Importance analyses of the bottom events were then conducted …based on the probability importance and the key importance. By comparing the two important indices of the bottom events, the larger the value is, the higher the importance is. It was found that the occurrence of the bottom events “pipeline oil leakage,” “pump motor damage,” and “oil pollution” had the greatest impact on system failure probability, thus determining the key weak links affecting system failure and indicating the most effective targets for improvement. Show more

Keywords: Universal grey operation, interval operation, importance measure, fault tree, landing gear retraction system

DOI: 10.3233/JIFS-202248

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: TuncalıYaman, Tutku | Bilgiç, Emrah | Fevzi Esen, M.

Article Type: Research Article

Abstract: Injury severity in motor vehicle traffic accidents is determined by a number of factors including driver, vehicle, and environment. Airbag deployment, vehicle speed, manner of collusion, atmospheric and light conditions, degree of ejection of occupant’s body from the crash, the use of equipment or other forces to re-move occupants from the vehicle, model and type of vehicle have been considered as important risk factors affecting accident severity as well as driver-related conditions such as age, gender, seatbelt use, alcohol and drug involvement. In this study, we aim to identify important variables that contribute to injury severity in the traffic crashes. …A contemporary dataset is obtained from National Highway Traffic Safety Administration’s (NHTSA) Fatality Analysis Reporting System (FARS). To identify accident severity groups, we performed different clustering algorithms including fuzzy clustering. We then assessed the important factors affecting injury severity by using classification and regression trees (CRT). The results which would guide car manufacturers, policy makers and insurance companies indicate that the most important factor in defining injury severity is deployment of air-bag, followed by extrication, ejection occurrences, and travel speed and alcohol involvement. Show more

Keywords: Traffic accidents, fuzzy clustering, data mining, injury severity, clustering, CRT

DOI: 10.3233/JIFS-219213

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Sergi, Duygu | Sari, Irem Ucal | Senapati, Tapan

Article Type: Research Article

Abstract: Capital budgeting requires dealing with high uncertainty from the unknown characteristics of cash flow, interest rate, and study period forecasts for future periods. Many fuzzy extensions of capital budgeting techniques have been proposed and used in a wide range of applications to deal with uncertainty. In this paper, a new fuzzy extension of the most used capital budgeting techniques is proposed. In this content, first interval-valued Fermatean fuzzy sets (IVFFSs) are defined, and the algebraic and aggregation operations are determined for interval-valued Fermatean fuzzy (IVFF) numbers. The formulations of IVFF net present value, IVFF equivalent uniform annual value, and IVFF …benefit-cost ratio (B/C) methods are generated. To validate the proposed methods, proposed formulations are illustrated with a hypothetical example, and the results are compared with classical fuzzy capital budgeting techniques. Show more

Keywords: Interval-valued Fermatean fuzzy sets, fuzzy engineering economics, fuzzy net present value, fuzzy equivalent uniform annual value, fuzzy benefit cost ratio

DOI: 10.3233/JIFS-219196

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Roy, Ranadip | Mukherjee, V. | Pratap Singh, Rudra

Article Type: Research Article

Abstract: This paper aims to investigate the application of Harris hawks optimization (HHO) optimization for the solution of model order reduction (MOR) problem of power system application. The proposed approach is implemented to determine the reduced order equivalent model of large-scale power system model. The obligations encountered by the higher-scale model like stability, calculative effort and problem into local optima can be enhanced by this propounded methodology. These yielded reduced models have been tried in addition to the existing algorithms and the obtained results are contrasted considering various technical parameters to accomplish its effectiveness, reliability and robustness of the adopted strategy. …Thus, superior performance of the proposed method is demonstrated. The validation of the methodology in terms of error index, time and frequency domain outputs, convergence curves with scalability of the approach are outlined in the investigation of the system. Show more

Keywords: Butterfly optimization algorithm, Harris hawks optimization, power system, reduced-order model, transfer function

DOI: 10.3233/JIFS-211132

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Zhu, Kun | Zhang, Shuai | Zhang, Wenyu | Zhang, Zhiqiang

Article Type: Research Article

Abstract: Accurate taxi demand forecasting is significant to estimate the change of demand to further make informed decisions. Although deep learning methods have been widely applied for taxi demand forecasting, they neglect the complexity of taxi demand data and the impact of event occurrences, making it hard to effectively model the taxi demand in highly dynamic areas (e.g., areas with frequent event occurrences). Therefore, to achieve accurate and stable taxi demand forecasting in highly dynamic areas, a novel hybrid deep learning model is proposed in this study. First, to reduce the complexity of taxi demand time series, the seasonal-trend decomposition procedures …based on loess is employed to decompose the time series into three simpler components (i.e., seasonal, trend, and remainder components). Then, different forecasting methods are adopted to handle different components to obtain robust forecasting results. Moreover, considering the instability and nonlinearity of the remainder component, this study proposed to fuse the event features (in particular, text data) to capture the unusual fluctuation patterns of remainder component and solve its extreme value problem. Finally, genetic algorithm is applied to determine the optimal weights for integrating the forecasting results of three components to obtain the final taxi demand. The experimental results demonstrate the better accuracy and reliability of the proposed model compared with other baseline forecasting models. Show more

Keywords: Taxi demand forecasting, deep learning, seasonal-trend decomposition procedures based on loess, data fusion, text data, genetic algorithm

DOI: 10.3233/JIFS-210657

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Singla, Namarta | Kaur, Parmpreet | Gupta, Umesh Chandra

Article Type: Research Article

Abstract: In the word of uncertain competitive situations everything is in the state of flux. Under such situations knowing the exact outcomes of mixed strategies adopted by a player is nearly impossible. It is highly rational to assume that no two experts will project the similar fuzzy payoffs for mix of strategies used. Aggregation of expert’s judgement becomes utmost important before solving such competitive situations. Considering this the present paper proposes a method to solve intuitionistic fuzzy game problems by using aggregation operators on payoff judgments of more than one expert. The proposed method significantly adds to the existing literature by …overcoming the limitation of Li’s existing method that considers only one expert’s opinion for solving intuitionistic fuzzy game problems. Illustrative example has been given for showing the superiority of the proposed method. Show more

Keywords: Aggregation operators, intuitionistic fuzzy sets, fuzzy matrix game

DOI: 10.3233/JIFS-202232

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhang, Jing | Sheng, Yuhong | Wang, Xiaoli

Article Type: Research Article

Abstract: Parameter estimation of high-order uncertain differential equations is an inevitable problem in practice. In this paper, the equivalent equations of high-order uncertain differential equations are obtained by transformation, and the parameters of the first-order uncertain differential equation including Liu process are estimated. Based on the least squares estimation method, this paper proposes a means to minimize the residual sum of squares to obtain an estimate of the parameters in the drift term, and make the noise sum of squares equal to the residual sum of squares to obtain an estimate of the parameters in the diffusion term. In addition, some …numerical examples are given to illustrate the proposed method. Finally, applications of the high-order uncertain spring vibration equations verify the viability of our method. Show more

Keywords: Uncertainty theory, high-order uncertain differential equation, parameter estimation, least squares estimation

DOI: 10.3233/JIFS-202522

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Goraghani, Simin Saidi | Ali Borzooei, Rajab | Ahn, Sun Shin

Article Type: Research Article

Abstract: In recent years, A. Di Nola et al. studied the notions of MV -semiring and semimodules and investigated related results [9, 10, 12, 26 ]. Now in this paper, by using an MV -semiring and an MV -algebra, we introduce the new definition of MV -semimodule, study basic properties and find some examples. Then we study A -ideals on MV -semimodules and Q -ideals on MV -semirings, and by using them, we study the quotient structures of MV -semimodule. Finally, we present the notions of prime A -ideal, torsion free MV -semimodule and annihilator on MV -semimodule and we study …the relations among them. Show more

Keywords: MV-semiring, MV-algebra, MV-module, MV-semimodule, Q-ideal, prime A-ideal, 06D35, 16Y60

DOI: 10.3233/JIFS-211130

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Amsaprabhaa, M. | Nancy Jane, Y. | Khanna Nehemiah, H.

Article Type: Research Article

Abstract: Due to the COVID-19 pandemic, countries across the globe has enforced lockdown restrictions that influence the people’s socio-economic lifecycle. The objective of this paper is to predict the communal emotion of people from different locations during the COVID-19 lockdown. The proposed work aims in developing a deep spatio-temporal analysis framework of geo-tagged tweets to predict the emotions of different topics based on location. An optimized Latent Dirichlet Allocation (LDA) approach is presented for finding the optimal hyper-parameters using grid search. A multi-class emotion classification model is then built via a Recurrent Neural Network (RNN) to predict emotions for each topic …based on locations. The proposed work is experimented with the twitter streaming API dataset. The experimental results prove that the presented LDA model-using grid search along with the RNN model for emotion classification outperforms the other state of art methods with an improved accuracy of 94.6%. Show more

Keywords: Topic modeling, latent dirichlet allocation, grid search, multi-class emotion classification, recurrent neural network

DOI: 10.3233/JIFS-210544

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Zhao, Shan | Li, Zhao

Article Type: Research Article

Abstract: The interpolation functions of interval type-2 fuzzy systems and their universal approximation are investigated in this paper. Two types of fuzzification methods are designed to construct the antecedents and consequents of the type-2 inference rules. Then the properties of the fuzzy operator and the type-reduction algorithm are used to integrate all parts of the fuzzy system. Interpolation functions of interval type-2 fuzzy systems, which are proved to be universal approximators, are obtained based on three models, namely single input and single output, double inputs and single output, and multiple inputs and single output. The proposed approach is applied to approximate …experiments of dynamic systems so as to evaluate the system performance. The system parameters are optimized by the QPSO algorithm. Experimental results for several data sets are given to show the approximation performances of the proposed interpolation functions are better than those of the interpolation function of the classical type-1 fuzzy system. Show more

Keywords: Interval type-2 fuzzy system, interval type-2 fuzzy set, interpolation function, universal approximation

DOI: 10.3233/JIFS-210435

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Duan, Huiming | Huang, Jiangbo | Wang, Siqi | He, Chenglin

Article Type: Research Article

Abstract: The stock market is an important embodiment of a national economy and financial activities and has an important impact on a country, enterprises and individuals. Stock forecasting can allow investment institutions and investors to understand the trend of the stock market in advance, which is a challenging and meaningful study. First, through the impulse phenomenon of the stock market, this paper discusses the problem of stock price prediction with delay, and the impulse delay differential equation is established. Second, according to the difference between the differential and the difference, the nonlinear delay grey prediction model is established. Next, the model …parameters are estimated and the solving steps are obtained. The nonlinear parameters and delay time are optimized by the particle swarm optimization algorithm. Finally, the new model is applied to the prediction of the Shanghai stock market and the Shenzhen stock market closing indexes; the results show that the new model can effectively predict stock prices, which is much better than the existing four grey models and a time series model. Show more

Keywords: Stock price, impulse delay, grey model, forecasting

DOI: 10.3233/JIFS-210726

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Sharma, Sudeep | Padhy, Prabin K.

Article Type: Research Article

Abstract:  The combination of machine learning and artificial intelligent has already proved its potential in achieving remarkable results for modeling unknown systems. These techniques commonly use enough data samples to train and optimize their architectures. In the present era, with the availability of enough storage and computation power, the machine learning based data-driven system modeling approaches are getting popular as they do not interrupt the normal system operations and work solely on collected data. This work proposes a data-driven parametric neural network technique for modeling time-delayed systems, which is demanding but challenging area of research and comes under nonlinear optimization problem. …The key contribution of this work is the inclusion of an extended B-polynomial into the network structure for estimating time-delayed first and second order system models. These type of models extensively used for addressing simulations, predictions, controlling and monitoring related issues. Also, an adaptive learning based convergence of the proposed algorithm is proved with the help of the Lyapunov stability theory. The proposed algorithm compared with existing techniques on some well-known example problems. A real practical system plant is also included for validating the proposed concept. Show more

Keywords: Identification, estimation, modeling, adaptive learning, time delays, neural networks, lyapunov theory, intelligent systems

DOI: 10.3233/JIFS-210580

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Ling, Jie | Xiong, Su | Luo, Yu

Article Type: Research Article

Abstract: Uniform Resource Location (URL) is the network unified resource location system that specifies the location and access method of resources on the Internet. At present, malicious URL has become one of the main means of network attack. How to detect malicious URL timely and accurately has become an engaging research topic. The recent proposed deep learning-based detection models can achieve high accuracy in simulations, but several problems are exposed when they are used in real applications. These models need a balanced labeled dataset for training, while collecting large numbers of the latest labeled URL samples is difficult due to the …rapid generation of URL in the real application environment. In addition, in most randomly collected datasets, the number of benign URL samples and malicious URL samples is extremely unbalanced, as malicious URL samples are often rare. This paper proposes a semi-supervised learning malicious URL detection method based on generative adversarial network (GAN) to solve the above two problems. By utilizing the unlabeled URLs for model training in a semi-supervised way, the requirement of large numbers of labeled samples is weakened. And the imbalance problem can be relieved with the synthetic malicious URL generated by adversarial learning. Experimental results show that the proposed method outperforms the classic SVM and LSTM based methods. Specially, the proposed method can obtain high accuracy with insufficient labeled samples and unbalanced dataset. e.g., the proposed method can achieve 87.8% /91.9% detection accuracy when the number of labeled samples is reduced to 20% /40% of that of conventional methods. Show more

Keywords: Malicious URL detection, network security, deep learning, semi-supervised learning

DOI: 10.3233/JIFS-210212

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Yang, Han | Qin, Keyun

Article Type: Research Article

Abstract: The theory of three-way concept analysis has been developed into an effective tool for data analysis and knowledge discovery. In this paper, we propose neutrosophic three-way concept lattice by combining neutrosophic set with three-way concept analysis and present an approach for conflict analysis by using neutrosophic three-way concept lattice. Firstly, we propose the notion of neutrosophic formal context, in which the relationships between objects and attributes are expressed by neutrosophic numbers. Three pairs of concept derivation operators are proposed. The basic properties of object-induced and attribute-induced neutrosophic three-way concept lattices are examined. Secondly, we divide the neutrosophic formal context into …three classical formal contexts and propose the notions of the candidate neutrosophic three-way concepts and the redundant neutrosophic three-way concepts. Two approaches of constructing object-induced (attribute-induced) neutrosophic three-way concept lattices are presented by using candidate, redundant and original neutrosophic three-way concepts respectively. Finally, we apply the neutrosophic formal concept analysis to the conflict analysis and put forward the corresponding optimal strategy and the calculation method of the alliance. Show more

Keywords: Three-way concept analysis, neutrosophic set, neutrosophic three-way concept lattice, conflict analysis

DOI: 10.3233/JIFS-210481

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Yılmaz, Melike | Atan, Tankut

Article Type: Research Article

Abstract: As the Covid-19 pandemic also proved, access to health care plays a crucial role in our lives. Public officials, managers and investors should consider many criteria such as public infrastructure, environment, accessibility and demand for selecting the most appropriate site for a new hospital. Thus, the hospital site selection problem is a critical multi-criteria decision-making (MCDM) problem. This paper is a case study for İstanbul where a recent MCDM methodology, the fuzzy Evaluation based on Distance from Average Solution (EDAS) method, is applied to this problem for the first time. We used a comprehensive set of five main criteria and …17 sub-criteria found in the relevant literature regarding hospital site selection. These criteria were evaluated by three decision-makers to choose the hospital site from three districts. The recommendation of the fuzzy EDAS method was then compared to the outcome of a frequently used fuzzy MCDM method. The methods resulted in different site recommendations. Show more

Keywords: Hospital site selection, health care planning, multi-criteria decision making, fuzzy EDAS, fuzzy TOPSIS

DOI: 10.3233/JIFS-201757

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Ullah, Kifayat | Ali, Zeeshan | Mahmood, Tahir | Garg, Harish | Chinram, Ronnason

Article Type: Research Article

Abstract: T-spherical fuzzy set (TSFS) is a generalized version of the spherical fuzzy set (SFS) and picture fuzzy set (PFS) to manage awkward and unpredictable information in realistic decision issues. TSFS deals with yes, abstinence, no, and refusal type of fuzzy information. This manuscript aims to observe the drawbacks of some existing dice similarity measures (DSMs) and to propose some new DSMs in the environment of TSFSs. The validation of the new DSMs is proved. The defined DSMs are further extended to introduce some generalized DSMs (GDSMs) and their special cases are studied. Additionally, the TOPSIS method using the entropy measures …(EMs) based on TSFSs is also explored and verified with the help of some examples. The proposed new GDSMs and TOPSIS method are applied to the problem of building material recognition, medical diagnosis, clustering, and the results obtained are investigated. A comparison of the new theory is established where the advancement of the proposed DSMs is elaborated under some conditions. The advantages of the new DSMs and the drawbacks of the previous DSMs of IFSs, PyFSs, and PFSs have been studied because of their applicability. The article is comprehensively summarized, and some possible future directions are stated. Show more

Keywords: Information measures, medical diagnosis, pattern recognition, T-spherical fuzzy set, TOPSIS method

DOI: 10.3233/JIFS-210402

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Farwa, Shabieh | Kamran, Muhammad | Sarwar, Sundas | Kazmi, Maedah | Ahmad, Hijaz | Gepreel, Khaled A.

Article Type: Research Article

Abstract:  In this work, bipolar fuzzy parameterized sets in conjunction with soft sets are studied. This research focuses on the application of bipolar fuzzy parameterized soft sets (BFPSS ), arising from association of bipolar fuzzy parameterized sets with soft sets. Some useful operations and fundamental properties of BFPSS are presented. Mainly, we aim to design a novel and comparatively labour-saving algorithm to see the efficacy of BFPSS . We discuss an application of our algorithm in pharmaceutical decision making problem based on effectiveness and harmfulness of certain drugs. However, the algorithm is equally applicable in other decision making environments as …well where BFPSS arise. By preserving the structural implication of BFPSS , we compare our technique with a most recent algorithm to prove the significance of our method. Show more

Keywords: Fuzzy sets, bipolar fuzzy sets, soft sets, bipolar fuzzy parameterized soft sets, decision making

DOI: 10.3233/JIFS-202685

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Haktanır, Elif | Kahraman, Cengiz

Article Type: Research Article

Abstract: Process capability analysis (PCA) is a tool for measuring a process’s ability to meet specification limits (SLs), which the customers define. Process capability indices (PCIs) are used for establishing a relationship between SLs and the considered process’s ability to meet these limits as an index. PCA compares the output of a process with the SLs through these capability indices. If the customers’ needs contain vague or imprecise terms, the classical methods are inadequate to solve the problem. In such cases, the information can be processed by the fuzzy set theory. Recently, ordinary fuzzy sets have been extended to several new …types of fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, and spherical fuzzy sets. In this paper, a new extension of intuitionistic fuzzy sets, which is called penthagorean fuzzy sets, is proposed, and penthagorean fuzzy PCIs are developed. The design of production processes for COVID-19 has gained tremendous importance today. Surgical mask production and design have been chosen as the application area of the penthagorean fuzzy PCIs developed in this paper. PCA of the two machines used in surgical mask production has been handled under the penthagorean fuzzy environment. Show more

Keywords: Process capability analysis, process capability indices, penthagorean fuzzy sets, surgical mask, COVID-19

DOI: 10.3233/JIFS-219205

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Unal, Yagmur | Temur, Gul T.

Article Type: Research Article

Abstract: In today’s highly competitive business market, the issue of sustainable supplier selection attracts great attention due to increased awareness of both social and environmental issues. The power of companies in the supply chain is determined not only by their own performance, but also by the power of other actors in the chain. As a field of study, the selection of sustainable suppliers remains its popularity because it requires developing a systematic procedure that addresses conflicting quantifiable and non-quantifiable factors simultaneously. In this study, one of the advanced fuzzy approaches called Spherical Fuzzy Sets (SFS) has been used to prioritize criteria …that affect sustainable supplier selection. The proposed methodology combines the Spherical Fuzzy Sets and the analytic hierarchy process (AHP) and takes into account four main criteria: economic, quality, social and environmental criteria. In order to prioritize the main and sub criteria, questionnaires were conducted with three experts who have valuable experience in this field of study. After then, for the selection of the best sustainable supplier, a real selection problem in an international company was handled. The figured out ranking of the criteria and supplier alternatives can be used as a guide by the researchers and industrial experts who are responsible for sustainable supplier selection. To measure how much unit increase in weights assigned to key criteria affects supplier selection, a sensitivity analysis was carried out. It was noticed that the selection procedure for the selected company is not highly sensitive to changes. Show more

Keywords: Analytic hierarchy process (AHP), spherical fuzzy sets, sustainable supplier selection

DOI: 10.3233/JIFS-219214

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Seker, Sukran

Article Type: Research Article

Abstract: The glass manufacturing includes operations, such as batch forming using raw materials, melting, forming, annealing, quality check and package. Due to risky processes in glass manufacturing, significant health hazards for workers are present in the glass industry. Risk assessment is effective way to prevent accidents and protect workers from serious accidents during glass manufacturing. To assess health hazards associated with glass manufacturing, in this study Risk Matrix and The Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method are integrated under Interval-Valued Intuitionistic Fuzzy (IVIF) environment to prioritize risk factors and suggest required preventive and protective measures. …Suggested preventive and protective measures provide technical, economic and environmental challenges for glass manufacturing firms. Once the importance weight of risk parameters in Risk Matrix’ are determined, the risk factors are assessed performing IVIF-TOPSIS method during glass manufacturing. In order to verify the validity and stability of the proposed risk assessment model, sensitivity and comparative analysis are accomplished at the end of the study. Show more

Keywords: Risk assessment, uncertainty, risk matrix, IVIF-TOPSIS

DOI: 10.3233/JIFS-219210

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Huang, Danyang | Zhou, Zhiheng | Deng, Ming | Li, Zhihao

Article Type: Research Article

Abstract: Detecting vehicle at night is critical to both assistant driving systems and autonomous driving systems. In this paper, we propose a deep network scheme assisted by light information with good generalization to detect vehicle at night. Our approach is divided into two branches, the object stream and the pixel stream. The object stream generates a batch of bounding boxes, and the pixel stream utilizes the vehicle light information to calibrate the bounding boxes of the object stream. In the object stream, we propose a new structure, Direction Attention Pooling (DAP), to improve the accuracy of the prior boxes. DAP leads …into attention mechanism. The feature maps obtained from backbone network is divided into two branches. One branch obtains direction perception information through IRNN layer, and the other branch learns attention weights. The weights are multiplied with the direction perception features in an element-wise manner. In the pixel stream, we propose a corner localization algorithm based on Bayes to get more accurate corners with the vehicle light pixels. The locations of the corners are considered as a discrete random variable. When the mask of the object is known, solving the probability distribution of the corner of the object. The corners with the highest probability is the correct corner. On the nighttime vehicle detection datasets CHUK and SYSU, our method achieves the accuracy of 97.2% and 96.86%, which outperforms other state-of-the-art method by at least 0.31% and 0.34%. Show more

Keywords: Nighttime vehicle detection, advanced driver-assistance systems, attention mechanism, deep learning

DOI: 10.3233/JIFS-202676

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Qiu, Liqing | Yang, Zhongqi | Zhu, Shiwei | Gu, Chunmei | Tian, Xiangbo

Article Type: Research Article

Abstract: Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node …not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy. Show more

Keywords: Social network, influence maximization, local influence, heuristic algorithm

DOI: 10.3233/JIFS-210379

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Trisal, Sushil Kumar | Kaul, Ajay

Article Type: Research Article

Abstract: Stress has become a household word which generates emotional distress, physical diseases, dysfunction and social ills. An abundant evidence is present in the literature that makes the stress research and theory high profile and important for physiological, psychological and social health. It can be legitimately said that due to the advent of social media, it has opened up inputs for the exploration of stress. The social media has become very prominent as it has touched daily lives. It has changed the way we are looking at the things, it has changed the life style, it has changed the way we …are consuming the information. It has created a bridge of trust among the people of different professional’s. Social media has become undeniably a global phenomenon in the last decade or so, since the founding of social media sites like Twitter and Facebook. It is of significant importance to detect and manage the stress from theses interactions at early stage otherwise it wreaks havoc on your emotional equilibrium and your physical health. It narrows your ability to think clearly, function effectively and enjoy life. In this work our endeavor is that to present a novel method to detect the different stress levels from the social media interactions using fuzzy and factor graph methods. A correlation analysis between stressed, non-stressed and emotion tweets is carried out for social engagement correlation and behavior correlation analysis of the social media users. The proposed method performs better when results are compared with the other state of art machine learning methods. Show more

Keywords: Stress, social engagement, correlation, psychological stress, machine learning, social media

DOI: 10.3233/JIFS-202035

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Xu, Haiyan | Chang, Yuqing | Zhao, Yong | Wang, Fuli

Article Type: Research Article

Abstract: Accurate and stable wind speed forecasting is an essential means to ensure the safe and stable operation of wind power integration. Therefore, a new hybrid model was proposed to improve wind speed forecasting performance, consisting of data pre-processing, model forecasting, and error correction (EC). The specific modeling process is as follows: (a) A wind speed series was decomposed into a series of subseries with different frequencies utilizing the ensemble empirical mode decomposition (EEMD) method. Afterward, various subseries were divided into high-frequency components, intermediate-frequency components, and low-frequency components based on their sample entropies (SE). (b) Three frequency components were forecast by …separately employing the hybrid model of convolutional neural network and long short-term memory network (CNN-LSTM), long short-term memory network (LSTM), and Elman neural network. (c) Subsequently, an error sequence was further forecast using CNN-LSTM. (d) Finally, three actual datasets were used to forecast the multi-step wind speed, and the forecasting performance of the proposed model was verified. The test results show that the forecasting performance of the proposed model is better than the other 13 models in three actual datasets. Show more

Keywords: Ensemble empirical mode decomposition, long short-term memory network, elman neural network, error correction

DOI: 10.3233/JIFS-210779

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2021

Authors: Neffati, Syrine | Ben Abdellafou, Khaoula | Aljuhani, Ahmad | Taouali, Okba

Article Type: Research Article

Abstract: The development of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems in the past decade has led to a remarkable advance in biomedical applications and devices. Particularly, CAM and CAD systems are employed in medical engineering, robotic surgery, clinical medicine, dentistry and other biomedical areas. Hence, the accuracy and precision of the CAD and CAM systems are extremely important for proper treatment. This work suggests a new CAD system for brain image classification by analyzing Magnetic Resonance Images (MRIs) of the brain. Firstly, we use the proposed Downsized Rank Kernel Partial Least Squares (DR-KPLS) as a feature extraction technique. Then, …we perform the classification using Support Vector Machines (SVM) and we validate with a k-fold cross validation approach. Further, we utilize the Tabu search metaheuristic approach in order to determine the optimal parameter of the kernel function. The proposed algorithm is entitled DR-KPLS+SVM. The algorithm is tested on the OASIS MRI database. The proposed kernel-based classifier is found to be better performant than the existing methods. Show more

Keywords: Dimensionality reduction, CAD system, Optimization, KLPS, classification

DOI: 10.3233/JIFS-210595

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Alkan, Nurşah | Kahraman, Cengiz

Article Type: Research Article

Abstract: A circular intuitionistic fuzzy set (CIFS) recently introduced by Atanassov as a new extension of intuitionistic fuzzy sets is represented by a circle whose radius is r and whose center is composed of membership and non-membership degrees. The idea is similar to type-2 fuzzy sets, which are based on the fuzziness of membership functions with a third dimension. CIFSs help us define membership functions more flexibly, taking into account the vagueness in membership and non-membership degrees. In this study, TOPSIS, which is a multi-criteria decision-making (MCDM) method, is developed under circular intuitionistic fuzzy environment. The proposed CIF-TOPSIS method is …applied to determine the most appropriate pandemic hospital location selection problem. Then, a sensitivity analysis based on criteria weights and the weight of the decision maker’s optimistic and pessimistic attitudes are conducted to check the robustness of the decisions given by the proposed approach. A comparative analysis with the single-valued intuitionistic fuzzy TOPSIS, Pythagorean fuzzy TOPSIS, picture fuzzy TOPSIS methods is also performed to verify the developed approach and to demonstrate its effectiveness. Show more

Keywords: Circular intuitionistic fuzzy sets, intuitionistic fuzzy sets, MCDM, TOPSIS, pandemic, hospital location selection

DOI: 10.3233/JIFS-219193

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2021

Authors: Yan, Wei | Ding, Yuhan

Article Type: Research Article

Abstract: With the rapid development of Semantic Web, the retrieval of RDF data has become a research hotspot. As the main method of data retrieval, keyword search has attracted much attention because of its simple operation. The existing RDF keyword search methods mainly search directly on RDF graph, which is no longer applicable to RDF knowledge graph. Firstly, we propose to transform RDF knowledge graph data into type graph to prune the search space. Then based on type graph, we extract frequent search patterns and establish a list from frequent search patterns to pattern instances. Finally, we propose a method of …the Bloom coding, which can be used to quickly judge whether the information our need is in frequent search patterns. The experiments show that our approach outperforms the state-of-the-art methods on both accuracy and response time. Show more

Keywords: RDF knowledge graph, keyword, type graph, frequent search pattern, Bloom coding

DOI: 10.3233/JIFS-210950

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Hu, Junying | Qian, Xiaofei | Cheng, Hao | Tan, Changchun | Liu, Xinbao

Article Type: Research Article

Abstract: Based on phase space reconstruction (PSR) and hybrid VNS-SVR model, a remaining useful life (RUL) prediction method for aircraft engines is proposed. The proposed hybrid model combines support vector regression (SVR), which has been successfully adopted for regression problems, with the variable neighborhood search (VNS). First, the phase space reconstruction is used to transform the selected one-dimensional performance sequences of aircraft engines into matrix forms, which increases the data information and improve the learning efficiency of the model effectively. Then, SVR is used to construct the prediction model. Meanwhile, a VNS algorithm is proposed to optimize the kernel parameters. Finally, …the hybrid model is used to RUL prediction of the aircraft engines. The experimental results show that the method has a good prediction performance. Show more

Keywords: Remaining useful life, support vector regression, phase space reconstruction, variable neighborhood search, aircraft engine

DOI: 10.3233/JIFS-210740

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Liu, Liming | Li, Ping | Chu, Maoxiang | Gao, Chuang

Article Type: Research Article

Abstract: Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Hence, it is necessary to study BOF steelmaking modeling. In this paper, a novel regression algorithm is proposed by using nonparallel support vector regression with weight information (WNPSVR) for the end-point prediction of BOF steelmaking. The weight information is excavated by K -nearest neighbors (KNNs) algorithm. Since the whale optimization algorithm (WOA) has the characteristics of fast convergence speed and a few adjustment parameters, WOA is applied to optimize the parameters in the objective function of WNPSVR. Compared with traditional prediction models, WNPSVR-WOA is not easy to fall …into local minimum values and is insensitive to noise. Thus, the prediction and control of molten steel end-point information are more accurate. Experimental results verify the effectiveness and feasibility of the proposed model. Within different error bounds (0.005 wt.% for carbon content model and 10°C for temperature model), the hit rates of carbon content and temperature are 89% and 95%, respectively. Meanwhile, a double hit rate of 85% is achieved. The above results conclude that our WNPSVR-WOA has important reference value for actual BOF application and can improve the steel product quality. Moreover, WNPSVR-WOA can also be used to other fields. Show more

Keywords: Basic oxygen furnace, end-point information prediction, nonparallel support vector regression, weight information, whale optimization algorithm

DOI: 10.3233/JIFS-210007

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Sun, Kexin | Xin, Yuelan | Ma, Yide | Lou, Meng | Qi, Yunliang | Zhu, Jie

Article Type: Research Article

Abstract: U-Net is a commonly used deep learning model for mammogram segmentation. Despite outstanding overall performance in segmenting, U-Net still faces from two aspects of challenges: (1) the skip-connections in U-Net have limitations, which may not be able to effectively extract multi-scale features for breast masses with diverse shapes and sizes. (2) U-Net only merges low-level spatial information and high-level semantic information through concatenating, which neglects interdependencies between channels. To address these two problems, we propose the U-shape adaptive scale network (ASU-Net), which contains two modules: adaptive scale module (ASM) and feature refinement module (FRM). In each level of skip-connections, ASM …is used to adaptively adjust the receptive fields according to the different scales of the mass, which makes the network adaptively capture multi-scale features. Besides, FRM is employed to allows the decoder to capture channel-wise dependencies, which make the network can selectively emphasize the feature representation of useful channels. Two commonly used mammogram databases including the DDSM-BCRP database and the INbreast database are used to evaluate the segmentation performance of ASU-Net. Finally, ASU-Net obtains the Dice Index (DI) of 91.41% and 93.55% in the DDSM-BCRP database and the INbreast database, respectively. Show more

Keywords: Mammograms, mass segmentation, convolutional neural network, adaptive scale module, feature refinement module

DOI: 10.3233/JIFS-210393

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Li, Shugang | Wang, Ru | Zhang, Yuqi | Lu, Hanyu | Cai, Nannan | Yu, Zhaoxu

Article Type: Research Article

Abstract: Identifying potential social media influencers (SMIs) accurately can achieve a long-time and effective concept marketing at a lower cost, and then promote the development of the corporate brand in online communities. However, potential SMIs discrimination often faces the problem of insufficient available information of the long-term evolution of the network, and the existing discriminant methods based on link analysis fail to obtain more accurate results. To fill this gap, a consensus smart discriminant algorithm (CSDA) is proposed to identify the potential SMIs with the aid of attention concentration (AC) between users in a closed triadic structure. CSDA enriches and expands …the users’ AC information by fusing multiple attention concentration indexes (ACIs) as well as filters the noise information caused by multi-index fusion through consensus among the indexes. Specifically, to begin with, to enrich the available long-term network evolution information, the unidirectional attention concentration indexes (UACIs) and the bidirectional attention concentration indexes (BACIs) are defined; next, the consensus attention concentration index (CACI) is selected according to the principle of minimum upper and lower bounds of link prediction bias to filter noise information; the potential SMI is determined by adaptively calculating CACI among the user to be identified, unconnected user group and their common neighbor. The validity and reliability of the proposed method are verified by the actual data of Twitter. Show more

Keywords: Concept marketing, social media influencers, attention concentration index, consensus smart discriminant algorithm

DOI: 10.3233/JIFS-201809

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Zhang, Lingyun | Zhang, Pingjian

Article Type: Research Article

Abstract: Computational aesthetics, which uses computers to learn human aesthetic habits and ultimately replace humans in scoring images, has become a hot topic in recent years due to its wide application. Most of the initial research is to manually extract features and use classifiers such as support vector machines to score images. With the development of deep learning, traditional manual feature extraction methods are gradually replaced by convolutional neural networks to extract more comprehensive features. However, it is a huge challenge to artificially design an aesthetic neural network. Recently, Neural Architecture Search has upsurged to find suitable neural networks for many …tasks in deep learning. In this paper, we first attempt to combine Neural Architecture Search with computational aesthetics. We design and apply a customized progressive differentiable architecture search strategy to obtain a light-weighted and efficient aesthetic baseline model. In addition, we simulate the multi-person rating mechanism by outputting the distribution of the aesthetic value of the image, replacing the previous classification scheme of judging the beauty and unbeauty of the image by the threshold value, and propose a self-weighted Earth Mover’s Distance loss to better fit human subjective scoring. Based on the baseline model, we further introduce several strategies including an attention mechanism, the dilated convolution, and adaptive pooling, to enhance the performance. Finally, we design several groups of comparative experiments to demonstrate the effectiveness of our baseline aesthetic model and the introduced improvement strategies. Show more

Keywords: Artificial intelligence, deep learning, convolutional neural networks, computational aesthetics, neural architecture search

DOI: 10.3233/JIFS-210026

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Wang, Jing | Yu, Liying

Article Type: Research Article

Abstract: In Dempster-Shafer theory, belief structure plays a key role, which provides a useful framework for information representation of uncertain variables. Basic Probability Assignment (BPA) is the most important component, which is difficult to be determined due to the uncertainty of information. Generally, there are two ways to get BPA of evidential theory: One is a subjective judgment of the expert’s experience, Interval Belief Structure (IBS) can solve the fuzziness and uncertainty of expert’s judgment. The other is objective calculation by sampling existing data, in which BPA is viewed as the point estimate. Therefore, one of the contributions of this paper …is that the definitions and theories of Confidential Interval Belief Structure (CIBS) is developed to describe BPA in Dempster-Shafer theory, which can give a range of population parameter values and contain more information to deal with the uncertainty and fuzziness of existing data. And then, based on evidential reasoning rule for counter-intuitive behavior, another contribution of this paper is that the extended evidential reasoning approach with CIBS is proposed to obtain the combined belief degree. The proposed method can be flexibly adjusted by appropriate errors and confidence levels, which is the main advantage. Finally, a case of sustainable operation of Shanghai rail transit system to verify the feasibility of proposed method and great performance of the extended method is shown. Show more

Keywords: Evidential reasoning, confidence interval, belief structures

DOI: 10.3233/JIFS-210286

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Zhang, Qiang | Zhang, Luyu | Sun, Bingzhen

Article Type: Research Article

Abstract: In 2020, the spread of the COVID-19 epidemic has attracted global attention. As a large-scale group that is receiving higher education, college students also show greater mood swings. How to reduce the psychological harm of anxiety to college students is a problem that needs to be solved urgently. Based on this, this paper proposes an evaluation model for the anxiety level of college students in different regions under the influence of COVID-19. First of all, the general influence index of college student’s anxiety level is obtained by correlation analysis. Secondly, the logical OR of the double quantization variable precision fuzzy …set model and the degree fuzzy rough set model is used to establish the evaluation model of the anxiety level of college students under the influence of COVID-19. Finally, used big data, the idea of fuzzy upper and lower approximation, combined with the principle of maximum membership in fuzzy set theory, achieved the quantitative ranking of the anxiety levels of college students in different areas. The research shows that when the accuracy of decision-making is 45%, the anxiety level of the township college students group and the provincial capital or municipality college students group is higher. When the accuracy of decision-making is 65%, the anxiety level of the provincial capital or municipality college students group is higher than others. Show more

Keywords: Fuzzy rough set, two universes, COVID-19, college students anxiety level

DOI: 10.3233/JIFS-202760

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Zeng, Detian | Shi, Jingjia | Zhan, Jun | Liu, Shu

Article Type: Research Article

Abstract: To use the electromagnetic chuck to precisely absorb industrial parts in manufacturing, this paper presents a hybrid algorithm for grasping pose optimization, especially for the part with a large surface area and irregular shape. The hybrid algorithm is based on the Gaussian distribution sampling and the hybrid particle swarm optimization (PSO). The Gaussian distribution sampling based on the geometric center point is used to initialize the population, and the dynamic Alpha-stable mutation enhances the global optimization capability of the hybrid algorithm. Compared with other algorithms, the experimental results show that ours achieves the best results on the dataset presented in …this work. Moreover, the time cost of the hybrid algorithm is near a fifth of the conventional PSO in the discovery of optimal grasping pose. In summary, the proposed algorithm satisfies the real-time requirements in industrial production and still has the highest success rate, which has been deployed on the actual production line of SANY Group. Show more

Keywords: Particle swarm optimization, Gaussian distribution, alpha-stable distribution, grasping pose

DOI: 10.3233/JIFS-210520

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Yang, Zhan | Li, Chengliang | Zhao, Zhongying | Li, Chao

Article Type: Research Article

Abstract: Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment …classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM. Show more

Keywords: Aspect-based sentiment analysis, sentiment classification, dependency-relationship, attention mechanism

DOI: 10.3233/JIFS-202747

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Hamal, Serhan | Senvar, Ozlem

Article Type: Research Article

Abstract: Many studies have used different financial ratios for financial accounting fraud detection. This study focuses on multi criteria decision-making (MCDM) for ranking financial ratios in detecting financial accounting fraud using interval-valued spherical fuzzy sets (IVSFS) and single-valued spherical fuzzy sets (SVSFS) to overcome uncertainties in decision-making process of financial analysts. This study proposes an integrated Analytic Hierarchy Process (AHP) and Multi-Objective Optimization by a Ratio Analysis plus the Full Multiplicative Form (MULTIMOORA) approach using IVSFS and SVSFS. Comparative results are obtained and discussed in prioritization of financial ratios for both IVSFS and SVSFS.

Keywords: Financial ratios, financial accounting fraud detection, single-valued spherical fuzzy sets (SVSFS), interval-valued spherical fuzzy sets (IVSFS), analytic hierarchy process (AHP), multi-objective optimization by a ratio analysis plus the full multiplicative form (MULTIMOORA)

DOI: 10.3233/JIFS-219195

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-28, 2021

Authors: Wang, Zuli | Chiong, Raymond | Fan, Zongwen

Article Type: Research Article

Abstract: Malicious web domains represent a serious threat to online users’ privacy and security, causing monetary loss, theft of private information, and malware attacks, among others. In recent years, machine learning methods have been widely used as prediction models to identify malicious web domains. In this study, we propose a Fuzzy-Weighted Least Squares Support Vector Machine (FW-LS-SVM) model for malicious web domain identification. In our proposed model, a fuzzy-weighted operation is applied to each data sample considering the fact that different samples may have different importance. This fuzzy-weighted operation is also able to alleviate the influence of noise data and improve …the model’s robustness by assigning weights to error constraints. For comparison purposes, three commonly used single machine learning classifiers and three widely used ensemble models are included in our experiments, in order to assess the performance of our proposed FW-LS-SVM and its ensemble version. Hyperlink indicators and uniform resource locator-based features are used to train the prediction models. Experimental results show that our proposed approach is highly effective in identifying malicious web domains, outperforming the well-established single and ensemble models being compared. Show more

Keywords: Malicious web domain identification, support vector machines, fuzzy weights, Online attacks

DOI: 10.3233/JIFS-200943

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Sekar, Aravindkumar | Perumal, Varalakshmi

Article Type: Research Article

Abstract: Automatic road crack detection is a prominent challenging task, in view of that, a novel approach is proposed using multi-tasking Faster-RCNN to detect and classify road cracks. In this present study, we have collected the road images (a dataset of 19300 images) from the Outer Ring Road of Chennai, Tamil Nadu, India. The collected road images were pre-processed using various conventional image processing techniques to identify the ground-truth label of the bounding boxes for the cracks. We present a novel multi-tasking Faster-RCNN based approach using the Global Average Pooling(GAP) and Region of Interest (RoI) Align techniques to detect the road …cracks. The RoI Align is used to avoid quantizing the stride. So that the information loss can be minimized and the bi-linear interpolation can be used to map the proposal to the input image. The resulting features from RoI Align are given as input to the GAP layer which drastically reduces the multi-dimension features into a single feature map. The output of the GAP layer is given to the fully connected layer for classification (softmax) and also to a regression model for predicting the crack location using a bounding box. F1-measure, precision, and recall were used to evaluate the results of classification and detection. The proposed model achieves the accuracy-97.97%, precision-99.12%, and recall-97.25% for classification using the MIT-CHN-ORR dataset. The experimental results show, that the proposed approach outperforms the other state-of-the-art methods. Show more

Keywords: Multi-tasking faster-RCNN, RoI align, road crack detection, road crack classification

DOI: 10.3233/JIFS-210475

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Xiao, Yanjun | Liang, Shitong | Wang, Xiaolei | Jiang, Yunfeng | Liu, Weiling | Sun, Lingyu

Article Type: Research Article

Abstract: The abnormal vibration of the loom spindle will seriously affect the quality of the textile. Based on the inherent embedded control system of the rapier loom, this paper develops an embedded system that monitors and analyzes the vibration signal of the spindle to determine the cause of the spindle failure. The system improves the traditional vibration sensor signal acquisition method, design the sensor peripheral auxiliary circuit and vibration signal conditioning circuit, and design the data storage and communication module so that the system has the characteristics of low cost, strong flexibility and scalability. The embedded algorithm program of Fast Fourier …transform is developed, optimized, and is applied to embedded platform, therefore the system can analyze the characteristics of vibration signal in frequency domain. Finally, back propagation neural network (BPNN) is introduced to investigate and match the relationship between the vibration spectrum characteristics and fault types of the loom spindle. The extracted back propagation (BP) learning result is a mathematical mapping formula, which enables the embedded system to analyze and determine the cause of vibration fault by using this formula. System design is conducive to improving the level of production intelligence and reducing personnel costs in the production process. Show more

Keywords: Loom, vibration detection, embedded system, back propagation neural network, fast fourier transform

DOI: 10.3233/JIFS-211269

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Liu, S.P. | Sun, Q. | Li, S.H.

Article Type: Research Article

Abstract: In recent years, supply chain risk management has been followed with interest due to the short life cycle of products. How to identify risk indicators can help evaluate risks on supply chains. Commonly adopted methods such as Fuzzy to determine the level of risks have limitations. In this paper, a framework of supply chain risk evaluation is first proposed and risk indicators are identified by theoretical surveys from 35 keywords and empirical analysis from 448 questionnaires. Moreover, both linguistic risk assessment model and Cloud model are used to evaluate risks of supply chain. The Cloud model evaluation results are between …general risk and high risk but closer to high risk. In addition, Cloud expected value of risk is 6.54 which is within the high-risk range, and evaluation results are also high risk. It is shown that when the weights are the same, the cloud model can determine the priority of risk indicators, and reflect volatility and randomness comparing with other evaluation methods. Show more

Keywords: Cloud model, supply chain risk management, word frequency, risk identification, risk evaluation

DOI: 10.3233/JIFS-210883

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Traneva, Velichka | Tranev, Stoyan

Article Type: Research Article

Abstract: Analysis of variance (ANOVA) is an important method in data analysis, which was developed by Fisher. There are situations when there is impreciseness in data In order to analyze such data, the aim of this paper is to introduce for the first time an intuitionistic fuzzy two-factor ANOVA (2-D IFANOVA) without replication as an extension of the classical ANOVA and the one-way IFANOVA for a case where the data are intuitionistic fuzzy rather than real numbers. The proposed approach employs the apparatus of intuitionistic fuzzy sets (IFSs) and index matrices (IMs). The paper also analyzes a unique set of data …on daily ticket sales for a year in a multiplex of Cinema City Bulgaria, part of Cineworld PLC Group, applying the two-factor ANOVA and the proposed 2-D IFANOVA to study the influence of “ season ” and “ ticket price ” factors. A comparative analysis of the results, obtained after the application of ANOVA and 2-D IFANOVA over the real data set, is also presented. Show more

Keywords: Decision making, index matrix, intuitionistic fuzzy sets, variation analysis

DOI: 10.3233/JIFS-219212

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Tao, Ning | Xiaodong, Duan | Lu, An | Tao, Gou

Article Type: Research Article

Abstract: A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and …company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified. Show more

Keywords: Flexible job-shop scheduling, deteriorating effect, emergency order insertion, disruption management, multi-phase quantum particle swarm optimization

DOI: 10.3233/JIFS-210166

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Gao, Yin | Jia, Lifen

Article Type: Research Article

Abstract: Uncertain delay differential equations (UDDEs) charactered by Liu process can be employed to model an uncertain control system with a delay time. The stability of its solution always be a significant matter. At present, the stability in measure for UDDEs has been proposed and investigated based on the strong Lipschitz condition. In reality, the strong Lipschitz condition is so strictly and hardly applied to judge the stability in measure for UDDEs. For the sake of solving the above issue, the stability in measure based on new Lipschitz condition as a larger scale of applications is verified in this paper. In …other words, if it satisfies the strong Lipschitz condition, it must satisfy the new Lipschitz conditions. Conversely, it may not be established. An example is provided to show that it is stable in measure based on the new Lipschitz conditions, but it becomes invalid based on the strong Lipschitz condition. Moreover, a special class of UDDEs is verified to be stable in measure without any limited condition. Besides, some examples are investigated in this paper. Show more

Keywords: Stability in measure, Liu process, uncertain process, uncertain delay differential equations

DOI: 10.3233/JIFS-210089

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Karbasaki, M. Miri | Balooch Shahriari, M. R. | Sedaghatfar, O.

Article Type: Research Article

Abstract: This article identifies and presents the generalized difference (g-difference) of fuzzy numbers, Fréchet and Gâteaux generalized differentiability (g-differentiability) for fuzzy multi-dimensional mapping which consists of a new concept, fuzzy g-(continuous linear) function; Moreover, the relationship between Fréchet and Gâteaux g-differentiability is studied and shown. The concepts of directional and partial g-differentiability are further framed and the relationship of which will the aforementioned concepts are also explored. Furthermore, characterization is pointed out for Fréchet and Gâteaux g-differentiability; based on level-set and through differentiability of endpoints real-valued functions a characterization is also offered and explored for directional and partial g-differentiability. The sufficient …condition for Fréchet and Gâteaux g-differentiability, directional and partial g-differentiability based on level-set and through employing level-wise gH-differentiability (LgH-differentiability) is expressed. Finally, to illustrate the ability and reliability of the aforementioned concepts we have solved some application examples. Show more

Keywords: Fuzzy multi-dimensional mappings, g-(linear continuous) function, g-differentiability, Fréchet g-derivative, Gâteaux g-derivative, Directional g-derivative, Partial g-derivative

DOI: 10.3233/JIFS-210530

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Shahmirani, Mohammad Reza Amiri | Nikghalb Rashti, Abbas Akbarpour | Adib Ramezani, Mohammad Reza | Golafshani, Emadaldin Mohammadi

Article Type: Research Article

Abstract: Prediction of structural damage prior to earthquake occurrence provides an early warning for stakeholders of building such as owners and urban managers and can lead to necessary decisions for retrofitting of structures before a disaster occurs, legislating urban provisions of execution of building particularly in earthquake prone areas and also management of critical situations and managing of relief and rescue. For proper prediction, an effective model should be produced according to field data that can predict damage degree of local buildings. In this paper in accordance with field data and Fuzzy logic, damage degree of building is evaluated. Effective …parameters of this model as an input data of model consist of height and age of the building, shear wave velocity of soil, plan equivalent moment of inertia, fault distance, earthquake acceleration, the number of residents, the width of the street for 527 buildings in the city. The output parameter of the model, which was the damage degree of the buildings, was also classified as five groups of no damage, slight damage, moderate damage, extensive damage, and complete damage. The ranges of input and output classification were obtained based on the supervised center classification (SCC-FCM) method in accordance with field data. Show more

Keywords: Earthquake, damage degree, fuzzy model, membership function, rules of fuzzy model

DOI: 10.3233/JIFS-202424

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Sindhu, Muhammad Sarwar | Rashid, Tabasam | Kashif, Agha

Article Type: Research Article

Abstract: Aggregation operators are widely applied to accumulate the vague and uncertain information in these days. Hamy mean (HM) operators play a vital role to accumulate the information. HM operators give us a more general and stretchy approach to develop the connections between the arguments. Spherical fuzzy sets (SpFSs ), the further extension of picture fuzzy sets (P c FSs ) that handle the data in which square sum of membership degree (MD), non-membership degree (NMD) and neutral degree (ND) always lie between closed interval [0, 1]. In the present article, we modify the HM operators like spherical fuzzy HM …(S p FHM ) operator and weighted spherical fuzzy HM (WS p FHM ) operator to accumulate the spherical fuzzy (S p F ) information. Moreover, various properties and some particular cases of S p FHM and the WS p FHM operators are discussed in details. Also, to compare the results obtained from the HM operators a score function is developed. Based on WS p FHM operator and score function, a model for multiple criteria decision-making (MCDM) is established to resolve the MCDM problem. To check the significance and robustness of the result, a comparative analysis and sensitivity analysis is also performed. Show more

Keywords: Spherical fuzzy sets, MCDM, linear programming model, Hamy mean operator

DOI: 10.3233/JIFS-201708

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2021

Authors: Bayturk, Engin | Esnaf, Sakir | Kucukdeniz, Tarik

Article Type: Research Article

Abstract: Facility location selection is a vital decision for companies that affects both cost and delivery time over the years. However, determination of the facility location is a NP-hard problem. A hybrid algorithm that combines revised weighted fuzzy c-means with Nelder Mead (RWFCM-NM) performs well when compared with well-known algorithms for the facility location problem (FLP) with deterministic customer demands and positions. The motivation of the study is both analyzing performance of the RWFCM-NM algorithm with probabilistic customer demands and positions and proposing a new approach for this problem. This paper develops two new algorithms for FLP when customer demands and …positions are probabilistic. The proposed algorithms are a probabilistic fuzzy c-means algorithm and Nelder-Mead (Probabilistic FCM-NM), a probabilistic revised weighted fuzzy c-means algorithm and Nelder Mead (Probabilistic RWFCM-NM) for the un-capacitated planar multi-facility location problem when customer positions and customer demands are probabilistic with predetermined service level. Proposed algorithms performances were tested with 13 data sets and comparisons were made with four well known algorithms. According to the experimental results, probabilistic RWFCM-NM algorithm demonstrates superiority on compared algorithms in terms of total transportation costs. Show more

Keywords: Multi-facility location problem, Nelder-Mead, probabilistic fuzzy c-means, probabilistic demand and position

DOI: 10.3233/JIFS-219204

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Cinar, Ulas | Cebi, Selcuk

Article Type: Research Article

Abstract: Risk management is the key factor to obtain safety in the working environment and its effectiveness increases with accuracy assessment and robust analysis. However, it is hard to succeed because of uncertainties in the working environment. Therefore, there are a lot of risk assessment methods in the literature to assess occupational health and safety risks. The traditional risk assessment methods handle each activity in the working environment separately and they do not consider the interactions among them. Furthermore, in these methods, potential outcomes of the risk parameters are considered based on the most possible outcome although there may be more …than one potential outcome. Differ from the traditional methods, The House of Safety method has been proposed to consider all potential outcomes and handle the interactions among the activities. In this study, an extension of The House of Safety is proposed to consider interactions among potential risks and to determine the most effective prevention method based on the potential risks. Hence, this extension provides an evaluation of the whole system. The proposed model has been developed by integrating Fuzzy Inference System (FIS), Fuzzy Analytical Hierarchy Process (FAHP), and DEMATEL into Quality Function Deployment (QFD). In this direction, FIS is used to determine activity-related probabilities, “FAHP” is utilized to identify all possible damage potentials of risks, and the DEMATEL is used to clarify interactions among risks. Finally, all information produced by these methods were aggregated to obtain total risk scores by using QFD. In addition, a second home has been created to link prevention and risks. Therefore, an effective prevention plan has been made to eliminate priority risks with all effective parameters. This stage provides the opportunity for optimum prevention plan against risk or risk groups dominating the system at the same time. In this study, unlike traditional methods including a partial risk assessment perspective, an integrated method that takes into account the risks on their own and the interactions between them is proposed in the literature, and the proposed approach has been applied to an open pit mine. Show more

Keywords: The house of safety, risk assessment method, occupational safety, fuzzy inference system, fuzzy AHP, DEMATEL, QFD

DOI: 10.3233/JIFS-219208

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Piltan, Farzin | Kim, Jong-Myon

Article Type: Research Article

Abstract: Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective technique for leak detection and localization is presented in this study. The proposed scheme has four main steps. First, the learning autoregressive technique is selected to approximate the flow signal under normal conditions and extract the mathematical state-space formulation with uncertainty estimations using a combination of robust autoregressive and …support vector regression techniques. In the next step, the intelligence-based learning observer is designed using a combination of the robust learning backstepping method and a fuzzy-based technique. The learning backstepping algorithm is the main part of the algorithm that determines the leak estimation. After estimating the signals, in the third step, their classification is performed by the support vector machine algorithm. Finally, to find the size and position of the leak, the multivariable backstepping algorithm is recommended. The effectiveness of the proposed learning control algorithm is analyzed using both experimental and simulation setups. Show more

Keywords: Pipeline, fuzzy logic, autoregressive algorithm, support vector regression, backstepping observer, support vector machine, learning backstepping observer, multivariable backstepping algorithm, leak detection, leak size classification, leak localization

DOI: 10.3233/JIFS-219197

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Oztaysi, Başar | Onar, Sezi Cevik | Kahraman, Cengiz

Article Type: Research Article

Abstract: The REGIME method is an easy to understand techniques which is based on pairwise comparisons and which can use qualitative data for decision making problems. The steps of the technique are simple and can be easily adopted to complex decision problems. Classical REGIME techniques use crisp numbers to evaluate qualitative evaluations. In this paper we propose Pythagorean Fuzzy REGIME (PF-REGIME) techniques which integrates Pythagorean Fuzzy Sets with REGIME technique. The proposed PF-REGIME is applied to waste disposal site selection problem. The decision model is constructed for three alternatives and five criteria in order to demonstrate the performance of the proposed …PF-REGIME method. Show more

Keywords: Waste disposal, pythagorean fuzzy sets, REGIME Method

DOI: 10.3233/JIFS-219199

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Emin Baysal, M. | Sarucan, Ahmet | Büyüközkan, Kadir | Engin, Orhan

Article Type: Research Article

Abstract: The distributed permutation flow shop scheduling (DPFSS) is a permutation flow shop scheduling problem including the multi-factory environment. The processing times of the jobs in a real life scheduling problem cannot be precisely know because of the human factor. In this study, the process times and due dates of the jobs are considered triangular and trapezoidal fuzzy numbers for DPFSS environment. An artificial bee colony (ABC) algorithm is developed to solve the multi-objective distributed fuzzy permutation flow shop (DFPFS) problem. First, the proposed ABC algorithm is calibrated with the well-known DPFSS instances in the literature. Then, the DPFSS instances are …fuzzified and solved with the algorithm. According to the results, the proposed ABC algorithm performs well to solve the DFPFS problems. Show more

Keywords: Distributed fuzzy permutation flow-shop, artificial bee colony, multi-objective, fuzzy completion time, agreement index

DOI: 10.3233/JIFS-219202

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Demircan, Murat Levent | Acarbay, Algı

Article Type: Research Article

Abstract: Payment is an important part of people’s daily lives, and although both online and offline transactions made by credit cards increase every day with the growing interest in e-commerce, 85–90% of worldwide payment transactions are still made by cash. However, the banks, giant payment institutions like Visa and MasterCard, and Payment Service Provider companies aim to increase the number of card transactions and enhance their platforms by supporting a wide range of channels with digital solutions, enhancing the security and providing value-added services. Having the user-friendly and secure payment platforms, and easy and quick integration opportunities with the merchants are …quite critical for the banks; which shift them to outsource Virtual Point of Sale platform and focus on their core business. In this article, the vendor selection process of a bank is researched and Evaluation based on Distance to Average Solution (EDAS) method is used for vendor selection. Show more

Keywords: Multiple criteria decision making, neutrosophic fuzzy EDAS method, fuzzy Systems, software development

DOI: 10.3233/JIFS-219198

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Yaşli, Fatma | Bolat, Bersam

Article Type: Research Article

Abstract: Occupational safety problems are no longer acceptable for any industrial environment. Lack of comprehensive and reliable evaluations for occupational safety causes many undesired events and harm to employees during the industrial process. In this study, it is aimed to develop an applicable risk analysis methodology for evaluating the undesired occupational events that occurred in the multi-process system where no historical accident records. The difficulty in obtaining and analyzing the data required for the determination of the occupational safety risks especially in the manually executed processes has been overcome with the Bayesian Network and interval type-2 fuzzy sets by using the …expert judgments. While BN enables to development of a comprehensive reasoning approach about the occurrence of the events, interval type-2 fuzzy sets better represent the ambiguity in the judgments by covering the uncertainty in a wider mathematical range with less computational effort according to other fuzzy sets. During multi-processes in industrial activity, various occupational undesired events may occur, including rare events with very serious consequences or frequent events with very low severity consequences. To able to consider all kinds of events occurring in an industrial environment from a holistic risk perspective, a novel fuzzy scale for specifying the probability and consequence of the events are proposed by the interval type-2 fuzzy numbers. Therefore, all undesired events regard-less the probability and consequence which may occur during the multi-processes in a system and the main root causes of these events can be observed within the proposed methodology. A case study is used to emphasize the effect of the proposed methodology for risk analysis of occupational safety in underground mining. The results have indicated that occupational safety education is the most contributing factor to occurring the undesired occupational events in underground mining. We believe that this study could help evaluate the safety risk of the multi-process systems comprehensively and holistically and proposing strategic planning for mitigating the occupational safety risks. Show more

Keywords: Bayesian network, interval type-2 fuzzy sets, occupational safety, risk analysis, underground mining

DOI: 10.3233/JIFS-219191

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Rashid, Tabasam | Sarwar Sindhu, M.

Article Type: Research Article

Abstract: Motivated by interval-valued hesitant fuzzy sets (IVHFSs) and picture fuzzy sets (P c FSs), a notion of interval-valued hesitant picture fuzzy sets (IVHP c FSs) is presented in this article. The concept of IVHP c FSs is put forward and some operational rules are developed to deal with it. The cosine similarity measures (SMs) are modified for IVHP c FSs to deal with interval-valued hesitant picture fuzzy (IVHP c F) data and the linear programming (LP) methodology is used to find out the criteria’s weights. A multiple criteria decision making (MCDM) approach is then developed …to tackle the vague and ambiguous information involved in MCDM problems under the framework of IVHP c FSs. For the validation and strengthen of the proposed MCDM approach a practical example is put forward to select the educational expert at the end. Show more

Keywords: Fuzzy sets, picture fuzzy sets, hesitant fuzzy sets, IVHFSs, LP technique

DOI: 10.3233/JIFS-219211

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Gerasimenko, Evgeniya | Kureichik, Vladimir V.

Article Type: Research Article

Abstract: Nowadays, various emergencies that are human-made or natural occur widely and threaten people’ lives and health. One of the most important evacuation tasks is the minimum cost lexicographic flow modelling task, which allows aggrieved to be transported to safe areas along the optimal transportation routes, taking into account the priority of shelters. It is inevitable that when modelling an evacuation scenario, an expert will encounter difficulties in setting network parameters due to the inherent uncertainty of a network, the rapid change in the nature of the movement, etc. Intuitionistic fuzzy sets allow simulating doubts and uncertainty of a decision-maker when …choosing a membership function. Due to the increasing complexity of the decision-making problems, experts cannot assess all the network parameters correctly because much specific knowledge is required, and each expert cannot be familiar with all attributes equally. Therefore, different decision-makers should have different values for various attributes. The algorithm proposed incorporates the search for the order of reliable shelters according to the modified TOPSIS method and the minimum cost paths in intuitionistic fuzzy conditions based on the non-standard subtraction operation. A case study is conducted to verify the proposed algorithm. Show more

Keywords: Lexicographic evacuation flow, intuitionistic network, fuzzy evacuation modeling

DOI: 10.3233/JIFS-219190

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Bezdan, Timea | Zivkovic, Miodrag | Bacanin, Nebojsa | Strumberger, Ivana | Tuba, Eva | Tuba, Milan

Article Type: Research Article

Abstract: Cloud computing represents relatively new paradigm of utilizing remote computing resources and is becoming increasingly important and popular technology, that supports on-demand (as needed) resource provisioning and releasing in almost real-time. Task scheduling has a crucial role in cloud computing and it represents one of the most challenging issues from this domain. Therefore, to establish more efficient resource employment, an effective and robust task allocation (scheduling) method is required. By using an efficient task scheduling algorithm, the overall performance and service quality, as well as end-users experience can be improved. As the number of tasks increases, the problem complexity rises …as well, which results in a huge search space. This kind of problem belongs to the class of NP-hard optimization challenges. The objective of this paper is to propose an approach that is able to find approximate (near-optimal) solution for multi-objective task scheduling problem in cloud environment, and at the same time to reduce the search time. In the proposed manuscript, we present a swarm-intelligence based approach, the hybridized bat algorithm, for multi-objective task scheduling. We conducted experiments on the CloudSim toolkit using standard parallel workloads and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. Simulation results prove great potential of our proposed approach in this domain. Show more

Keywords: Cloud computing, task scheduling, multi-objective optimization, bat algorithm, hybridization

DOI: 10.3233/JIFS-219200

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Temelcan, Gizem | Kocken, Hale Gonce | Albayrak, Inci

Article Type: Research Article

Abstract: Solving multiobjective linear programming (MOLP) problems and fully fuzzy multi-objective linear programming (FFMOLP) problems involves the trade-off process among several objectives. A new algorithm extended where FFMOLP problems are solved using a 2-player zero-sum game approach to deal with this case. Firstly, The FFMOLP problem is separated into a certain number of fully fuzzy linear programming (FFLP) problems and each is solved by applying any method. After forming a ratio matrix, a game theory approach is applied for finding the weights of objective functions and a weighted LP problem is constructed by these weights. Solving the weighted LP problem, a …fuzzy compromise solution of the FFMOLP problem is found. Constructing different ratio matrices, it is also possible to obtain more than one compromise solution to be offered to the decision-maker(s). Some examples are given to show the applicability of the algorithm. Show more

Keywords: Fully fuzzy multi-objective linear programming problem, compromise solution, ranking method, distance method and zero-sum game

DOI: 10.3233/JIFS-219192

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Namlı, Özge H. | Yanık, Seda | Nouri, Faranak | Serap Şengör, N. | Koyuncu, Yusuf Mertkan | Uçar, Ömer Berk

Article Type: Research Article

Abstract: In today’s competitive business environment, companies are striving to reduce costs and workload of call centers while improving customer satisfaction. In this study, a framework is presented that predicts and encourages taking proactive actions to solve customer problems before they lead to a call to the call center. Machine learning techniques are implemented and models are trained with a dataset which is collected from an internet service provider’s systems in order to detect internet connection problems of the customers proactively. Firstly, two classification techniques which are multi perceptron neural networks and radial basis neural networks are applied as supervised techniques …to classify whether the internet connection of customers is problematic or not. Then, by using unsupervised techniques, namely Kohonnen’s neural networks and Adaptive Resonance Theory neural networks, the same data set is clustered and the clusters are used for the customer problem prediction. The methods are then integrated with an ensemble technique bagging. Each method is implemented with bagging in order to obtain improvement on the estimation error and variation of the accuracy. Finally, the results of the methods applied for classification and clustering with and without bagging are evaluated with performance measures such as recall, accuracy and Davies-Bouldin index, respectively. Show more

Keywords: Call center problem prediction, classification, clustering, artificial neural networks, bagging

DOI: 10.3233/JIFS-219207

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Jia, Zhifu | Liu, Xinsheng

Article Type: Research Article

Abstract: In this paper, we propose complex uncertain differential equations (CUDEs) based on uncertainty theory. In order to describe the evolution of complex uncertain phenomenon related to belief degrees, we apply the complex Liu process to CUDEs. Firstly, we pose a concept of a linear CUDE and prove that homogeneous linear CUDE and general linear CUDE have solutions. Then, we prove existence and uniqueness theorem of a special CUDE. Further, we design a numerical algorithm to obtain inverse uncertainty distribution of the solution. Finally, as an application, we analyse the inverse uncertainty distributions of time integral of CUDEs and design numerical …algorithms to obtain inverse uncertainty distributions of time integral. Show more

Keywords: Complex uncertain differential equations, existence and uniqueness theorem, time integral

DOI: 10.3233/JIFS-211030

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Kuchta, Dorota | Zabor, Adam

Article Type: Research Article

Abstract: An analysis of the scientific literature on project cash flow control and fuzzy modelling shows that project cash flows are modelled using only basic approaches drawn from fuzzy theory, which may distort the credibility of the model. In this paper, we therefore propose to use the whole spectrum of fuzzy arithmetic, and to select operations that suit the nature of the cash flows in question, their dependencies and the preferences of the project manager. An analysis of the literature also shows that in practically all existing models of project costs and cash flow management, project costs and cash flows are …treated at a very high level of generality (without considering the various types of project, factors influencing their variability and signals warning of imminent cash-related problems), and estimations are not updated on an ongoing basis throughout the duration of the project. The results of a survey performed with the participation of 100 project managers show that this simplistic view of project cash flows may be distorting, and cannot guarantee the development of an efficient project cost and cash flow control system. We propose an approach that at least partially compensates for these drawbacks: it differentiates between types of project cash flows and the factors and triggers affecting changes in cash flows. Two case studies are used for a an initial verification of the approach. The paper concludes with suggestions for further research perspectives. Show more

Keywords: Fuzzy cash flow, project cash flow control, project cash flow types

DOI: 10.3233/JIFS-219183

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Aslam, Muhammad | Albassam, Mohammed

Article Type: Research Article

Abstract: In this paper, tests of skewness and kurtosis are introduced under neutrosophic statistics. The necessary measures and neutrosophic forms of these estimators are introduced. The application of the proposed tests is given using the data associated with heart diseases. From the real example analysis, the proposed tests are quite flexible and informative than the existing tests under classical statistics. In addition, it is concluded from the analysis that the proposed tests give information about the measure of indeterminacy in the presence of uncertainty.

Keywords: Skewness, kurtosis, normality, Neutrosophy, heart disease

DOI: 10.3233/JIFS-210375

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Narendiranath Babu, T. | Senthilnathan, N. | Pancholi, Shailesh | Nikhil Kumar, S.P. | Rama Prabha, D. | Mohammed, Noor | Wahab, Razia Sultana

Article Type: Research Article

Abstract: This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for …large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %. Show more

Keywords: Continuous variable transmission (CVT), Daubechies wavelet, fault diagnosis, fault classification, artificial neural network

DOI: 10.3233/JIFS-210199

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: AlAlaween, Wafa’ H. | AlAlawin, Abdallah H. | Al-Durgham, Lamees | Albashabsheh, Nibal T.

Article Type: Research Article

Abstract: A new integrated modelling architecture based on the concept of the fuzzy logic is presented to represent the turning process. Such an architecture consists of two stages. In the first stage, fuzzy logic systems (FLSs) having various topologies are employed to extract rule bases using perhaps limited amount of sparse data. In the second stage, the fuzzy rules extracted are assessed and integrated using the singular value decomposition-QR factorization (SVD-QR) paradigm in order to minimize the computational efforts. Such a step leads to reducing the number of fuzzy rules and results in a reduced FLS model. Such a reduced model …is then employed to represent the turning process and predict both the cutting force and the surface roughness. In addition, it provides a comprehensive understanding of the turning process presented linguistically in the form of If/Then rules. The proposed structure has been validated using a set of laboratory experiments. It has been noticed that it can predict both the cutting force and the surface roughness successfully. In addition, such an integrated architecture outperforms the artificial neural network, the well-known FLS, the radial basis functions and the multilinear regression model, where the overall improvement is of approximately 19%, 13%, 14% and 270%, respectively. Show more

Keywords: Fuzzy logic system, singular value decomposition-QR factorization (SVD-QR) algorithm, turning process

DOI: 10.3233/JIFS-202457

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Fan, Jianping | Wang, Shuting | Wu, Meiqin

Article Type: Research Article

Abstract: Failure modes and effects analysis (FMEA) is a useful reliability analysis technique to identify potential failure modes in a wide range of industries. However, the conventional FMEA method is deficient in dealing with the risk evaluation and prioritization method. To overcome the shortcomings, this paper presents a new risk priority model using Best-Worst Method based on D numbers (D-BWM) and the Measurement of Alternatives and Ranking according to COmpromise Solution based on D numbers (D-MARCOS). First, D numbers are used to deal with the uncertainty of FMEA team members’ subjective judgment. Second, the distance-based method is proposed to determine the …objective weight of each team member. Then, the D-BWM was used to determine the weight of risk factors. The combination rule of D number theory combined the evaluation information of multiple members into group opinions. Finally, D-MARCOS method is proposed to obtain the risk priority of the failure modes. An example and the results of comparative analysis show the method is effective. Show more

Keywords: Failure modes and effects analysis, D numbers, BWM, MARCOS

DOI: 10.3233/JIFS-202765

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Hikal, Noha A. | Shams, Mahmoud Y. | Salem, Hanaa | Eid, Marwa M.

Article Type: Research Article

Abstract: Mobile Ad hock Networks (MANETs) are currently used for developing the privacy and accuracy of modern networks. Furthermore, MANET applications are fit to be data-oriented systems, that introduce a secure and more robust data transmission protocol making it a topmost priority in the design. The lack of infrastructure in the existence of dynamic topology as well as limited resources of MANET is a major challenge facing those interested in the field. Further, the nonexistence of a formerly authorized trust relationship within the connected nodes produces instability of the detection process in MANETs. Basically, by adding adapted LEACH routing protocol to …MANET, enhancement of the preserved nodes vitality will be achieved, moreover, the load balancing with data loss reduction provides MANET ability to tracks along with shortest and limited paths. This paper proposes a newly developed detection scheme for both active and passive black-hole attacks in MANETs. Moreover, the scheme deals with assessing a group of selected features for each node-based AdaBoost-SVM algorithm. These features are collected from cluster members nodes based on Ad hoc On-demand Multipath Distance Vector (OMDV) with LEACH routing protocol clustering approaches. Although SVM is considered a more stable classifier, there are great influences of the AdaBoost weight adaption algorithm to enhance the classification process in terms of strengthening the weights of extracted features. This hybrid algorithm is essential for active black-hole attacks as well as for identifying passive black-hole attacks in MANET. The proposed scheme is tested against the effect of mobility variation to determine the accuracy of the detection process including the routing overhead protocol. The experimental results investigated that the accuracy of detecting both active and passive black-holes attacks in MANET reached 97% with a promising time complexity for different mobility conditions. Moreover, the proposed scheme provides an accurate decision about malicious vs benign node dropping behavior using an adjustable threshold value. Show more

Keywords: Ada-boost, support vector machine, AOMDV, black-hole attack, and MANET

DOI: 10.3233/JIFS-202471

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Şahin, Bünyamin | Şahin, Abdulgani

Article Type: Research Article

Abstract: In a graph G , a vertex v is dominated by an edge e , if e is incident with v or e is incident with a vertex which is a neighbor of v . An edge-vertex dominating set D is a subset of the edge set of G such that every vertex of G is edge-vertex dominated by an edge of D . The ev -domination number equals to the number of an edge-vertex dominating set of G which has minimum cardinality and it is denoted by γ ev  (G …). We here analyze double edge-vertex domination such that a double edge-vertex dominating set D is a subset of the edge set of G , provided that all vertices in G are ev -dominated by at least two edges of D . The double ev -domination number equals to the number of an double edge-vertex dominating set of G which has minimum cardinality and it is denoted by γ dev  (G ). We demonstrate that the enumeration of the double ev -domination number of chordal graphs is NP-complete. Moreover several results about total domination number and double ev -domination number are obtained for trees. Show more

Keywords: Trees, edge-vertex domination, double edge-vertex domination, total domination, domination

DOI: 10.3233/JIFS-219180

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Guo, Huijuan | Yao, Ruipu

Article Type: Research Article

Abstract: The symmetry between fuzzy evaluations and crisp numbers provides an effective solution to multiple attribute decision making (MADM) problems under fuzzy environments. Considering the effect of information distribution on decision making, a novel approach to MADM problems under the interval-valued q-rung orthopair fuzzy (Iq-ROF) environments is put forward. Firstly, the clustering method of interval-valued q-rung orthopair fuzzy numbers (Iq-ROFNs) is defined. Secondly, Iq-ROF density weighted arithmetic (Iq-ROFDWA) intermediate operator and Iq-ROF density weighted geometric average (Iq-ROFDWGA) intermediate operator are developed based on the density weighted intermediate operators for crisp numbers. Thirdly, combining the density weighted intermediate operators with the Iq-ROF …weighted aggregation operators, Iq-ROF density aggregation operators including Iq-ROF density weighted arithmetic (Iq-ROFDWAA) aggregation operator and Iq-ROF density weighted geometric (Iq-ROFDWGG) aggregation operator are proposed. Finally, effectiveness of the proposed method is verified through a numerical example. Show more

Keywords: Multiple attribute decision making (MADM), clustering, Iq-ROFDWAA aggregation operator, Iq-ROFDWGG aggregation operator

DOI: 10.3233/JIFS-210376

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021