Journal of Intelligent & Fuzzy Systems - Volume 41, issue 1

Authors: Wu, Qiongling | Lin, Jian | Zhang, Shaohan | Tian, Zhiyong

Article Type: Research Article

Abstract: This paper constructs the continuous-Young optimal weighted arithmetic averaging (C-YOWA) operator and the continuous-Young optimal weighted geometric (C-YOWG) operator based on definite integral and Young inequality. A series of special cases and main properties of the proposed aggregation operators are also investigated. In order to integrate heterogeneous interval data and obtain more accurate prediction results, the heterogeneous interval combination prediction (HICP) model based on C-YOWA operator, C-YOWG operator and Theil coefficient is proposed. The HICP model consider not only the existence of both additive and multiplicative interval information, but also the preference information of experts. Finally, the model is applied …to the empirical analysis of wind energy prediction. The comparison of results shows that the established model can effectively improve the accuracy of prediction. Show more

Keywords: Combination prediction, continuous aggregation operator, interval number, young inequality, Theil coefficient

DOI: 10.3233/JIFS-210004

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1031-1048, 2021

Authors: Dong, Yuanxiang | Deng, Xinglu | Hu, Xinyu | Chen, Weijie

Article Type: Research Article

Abstract: Suppliers can be regarded as unavoidable sources of external risks in modern supply chains, which may cause disruption of supply chains. A resilient supplier usually has a high adaptive ability to reduce the vulnerability against disruptions and recover from disruption to keep continuity in operations. This paper develops an effective multi-attribute group decision-making (MAGDM) framework for resilient supplier selection. Because of the many uncertainties in resilient supplier selection, the dual hesitant fuzzy soft sets (DHFSSs), a very flexible tool to express uncertain and complex information of decision-makers, is utilized to cope with it. In order to obtain the resilient supplier’s …partial order relationship and consider the psychological behavior of decision-makers, this paper proposes the MAGDM framework with DHFSSs based on the TOPSIS method and prospect theory for resilient supplier selection. Furthermore, we consider the consensus level among experts of different backgrounds and experiences and propose a consensus measure method based dual hesitant fuzzy soft numbers (DHFSNs) before selecting a resilient supplier. The expert weights are calculated by the group consensus level between the expert and the group opinions. Meanwhile, we define the entropy of DHFSSs to determine the attribute weights objectively in the decision-making process. Based on this, the proposed method is applied to a practical manufacturing enterprise with an international supply chain for a resilient supplier selection problem. Finally, by performing a sensitivity analysis and a comparative analysis, the results demonstrate the robustness and validity of the proposed method. Show more

Keywords: Resilient supplier selection, group decision making, dual hesitant fuzz soft sets, consensus measure, entropy

DOI: 10.3233/JIFS-210025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1049-1067, 2021

Authors: Liao, Wei | Wei, Xiaohui | Lai, Jizhou

Article Type: Research Article

Abstract: A novel actor-critic algorithm is introduced and applied to zero-sum differential game. The proposed novel structure consists of two actors and a critic. Different actors represent the control policies of different players, and the critic is used to approximate the state-action utility function. Instead of neural network, the fuzzy inference system is applied as approximators for the actors and critic so that the specific practical meaning can be represented by the linguistic fuzzy rules. Since the goals of the players in the game are completely opposite, the actors for different players are simultaneously updated in opposite directions during the training. …One actor is updated updated toward the direction that can minimize the Q value while the other updated toward the direction that can maximize the Q value. A pursuit-evasion problem with two pursuers and one evader is taken as an example to illustrate the validity of our method. In this problem, the two pursuers the same actor and the symmetry in the problem is used to improve the replay buffer. At the end of this paper, some confrontations between the policies with different training episodes are conducted. Show more

Keywords: Fuzzy inference system, differential game, reinforcement learning, pursuit-evasion problem, deterministic policy gradient

DOI: 10.3233/JIFS-210032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1069-1082, 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. 41, no. 1, pp. 1083-1095, 2021

Authors: Rehman, Hafiz Asadul | Zafar, Kashif | Khan, Ayesha | Imtiaz, Abdullah

Article Type: Research Article

Abstract: Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order …to compare performance of EBSAA with Genetic Algorithm (GA) and Particle Swarm Align Algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases. Show more

Keywords: Multiple sequence alignment, Particle swarm optimization, Bioinformatics, Genetic algorithm, swarm intelligence, bird swarm algorithm

DOI: 10.3233/JIFS-210055

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1097-1114, 2021

Authors: Zhou, Ze-Nan | Zhou, Zhiheng | Huang, Junchu

Article Type: Research Article

Abstract: Patch-based deep convolutional neural network (DCNN) has been proved to have advanced performance in no-reference image quality assessment (NR-IQA). However, these methods generally take global quality score as the quality score of each patch mainly since local quality score is not provided. Unfortunately, the perceived quality of image patch is difficult to maintain a high degree of consistency. Thus, the use of the same global quality score in different patches of the same image may hinder training of DCNNs. In this paper, we propose a universal and nearly cost-free model called Gaussian Random Jitter (GRJ). According to the uncertainty of …the perceived quality, GRJ divided the training images into high-confidence distorted images and low-confidence distorted images, and reasonably assigned different local quality scores to each patch through specific gaussian functions with the global quality score as the mean value and the undetermined hyperparameter as the standard deviation. We took one of the most advanced patch-based DCNNs models as backbone and tested the improved performance over three widely used image quality databases. We show that our model can further improve the performance of patch-based models and even help them comparable with those of state-of-the-art NR-IQA algorithms. Show more

Keywords: Patch, gaussian distribution, convolutional neural networks, no-reference image quality assessment

DOI: 10.3233/JIFS-210063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1115-1124, 2021

Authors: Imran, Muhammad | Ali, Yasir | Malik, Mehar Ali | Hasnat, Kiran

Article Type: Research Article

Abstract: Chromatic spectrum of a colored graph G is a multiset of eigenvalues of colored adjacency matrix of G . The nullity of a disconnected graph is equal to sum of nullities of its components but we show that this result does not hold for colored graphs. In this paper, we investigate the chromatic spectrum of three different classes of 2-regular bipartite colored graphs. In these classes of graphs, it is proved that the nullity of G is not sum of nullities of components of G . We also highlight some important properties and conjectures to extend this problem …to general graphs. Show more

Keywords: Spectrum of graph, nullity of graph, graph coloring

DOI: 10.3233/JIFS-210066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1125-1133, 2021

Authors: Liu, Haitao | Zhang, Qiang

Article Type: Research Article

Abstract: This paper studies cooperative games in which players have multiple attributes. Such games are applicable to situations in which each player has a finite number of independent additive attributes in cooperative games and the payoffs of coalitions are endogenous functions of these attributes. The additive attributes cooperative game, which is a special case of the multiattribute cooperative game, is studied with respect to the core, the conditions for existence and boundedness and methods of transformation regarding a general cooperative game. A coalitional polynomial form is also proposed to discuss the structure of coalition. Moreover, a Shapley-like solution called the efficient …resource (ER) solution for additive attributes cooperative games is studied via the axiomatical method, and the ER solution of two additive attribute games with equivalent total resources coincides with the Shapley value. Finally, some examples of additive attribute games are given. Show more

Keywords: Multiple attributes, cooperative games, Shapley value, core, solution

DOI: 10.3233/JIFS-210088

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1135-1150, 2021

Authors: Zulqarnain, Rana Muhammad | Xin, Xiao Long | Garg, Harish | Ali, Rifaqat

Article Type: Research Article

Abstract: In this article, we investigate the multi-criteria decision-making complications under Pythagorean fuzzy soft information. The Pythagorean fuzzy soft set (PFSS) is a proper extension of the Pythagorean fuzzy set (PFS) which discusses the parametrization of the attributes of alternatives. It is also a generalization of the intuitionistic fuzzy soft set (IFSS). The PFSS is used to precisely evaluate the deficiencies, anxiety, and hesitation in decision-making (DM). The most essential determination of the current study is to advance some operational laws along with aggregation operators (AOs) within the Pythagorean fuzzy soft environs such as Pythagorean fuzzy soft interaction weighted average (PFSIWA) …and Pythagorean fuzzy soft interaction weighted geometric (PFSIWG) operators with their desirable features. Furthermore, a DM technique has been established based on the developed operators to solve multi-criteria decision-making (MCDM) problems. Moreover, an application of the projected method is presented for the selection of an effective hand sanitizer during the COVID-19 pandemic. A comparative analysis with the merits, effectivity, tractability, along with some available research deduces the effectiveness of this approach. Show more

Keywords: Pythagorean fuzzy sets, Pythagorean fuzzy soft sets, PFSIWA operator, PFSIWG operator, hand sanitizer

DOI: 10.3233/JIFS-210098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1151-1171, 2021

Authors: Lina, Wang | Zeshui, Xu

Article Type: Research Article

Abstract: Risk management is a significant part of the success of a public-private partnership (PPP) project. There are four phrases for the process of risk management: Constructing a risk management environment, identifying risk factors, evaluating risk factors, and allocating risk factors. After identifying risk factors, it is imperative to analyze and evaluate critical risk factors, which can help participants formulate strategies to allocate risk factors, and thus alleviate the possible adverse results. The objectives of analyzing and evaluating risk factors focus on two aspects: The possibilities of risk occurrence and the degrees of risk loss. On behalf of determining the critical …risk factors effectively, we take the probability degree and linguistic expressions into consideration to manifest experts’ perspectives. We consider critical risk factors in terms of the probabilistic linguistic terms with weakened hedges from the evidential reasoning approach view. The linguistic terms with weakened hedges are applied to express the degree of risk risk loss, and the possibilities of risk occurrence collect from the probabilities of linguistic terms with weakened hedges. First, the commonality function and plausibility function are applied to correct the possibilities of risk occurrence for linguistic terms with weakened hedges. Next, we build a risk evaluation model from experts’ risk propensity and risk perceptions. Moreover, a case study of the risk analyzing and evaluating process of a PPP project is applied to illustrate the availability and effectiveness of the proposed model. We contrast the introduced model with other approaches. Finally, the advantages of this model intend to improve the linguistic terms with weakened hedges for the probabilistic linguistic terms with weakened hedges and evaluate risk factors considering the evidence reasoning approach. Show more

Keywords: Risk evaluation, Probabilistic linguistic terms with weakened hedges, Evidential reasoning theory, Public-private partnership project

DOI: 10.3233/JIFS-210101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1173-1191, 2021

Authors: Zhao, Yifan | Tian, Shuicheng

Article Type: Research Article

Abstract: Aiming at the problem of large error and long time of early warning response in the traditional system, this paper designs a hazard identification early warning system based on random forest algorithm in underground coal mine. By random classification decision forest created dangerous content in different areas of the downhole information input into the decision tree as a test sample, according to the result of the output of the leaf node determine the risk level of decision trees, and USES the high precision of decision forest classification ability the threat level assessment test sample, radically reducing hazards identification error. Then, …based on the evaluation results, combined with the threshold value of warning criteria to identify the gas exceeding limit area, and determine the fire source warning level, so as to realize the hazard source identification and warning. The simulation results show that the average hazard location identification error of the system is only 4.1%, and the warning response time can be controlled within 9 s. Show more

Keywords: Underground hazard sources, identify early warning, random forest algorithm, decision forest, risk assessment, alarm criteria threshold

DOI: 10.3233/JIFS-210105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1193-1202, 2021

Authors: Kavitha, N | Ruba Soundar, K | Sathis Kumar, T

Article Type: Research Article

Abstract: In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and Improved Hough Transform (IHT) with the Improved DragonFly Algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition …problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images. Show more

Keywords: Face recognition, kernel ensemble learning machine, adaptive kuwahara filter, improved dragonfly algorithm

DOI: 10.3233/JIFS-210130

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1203-1216, 2021

Authors: Li, Lulu

Article Type: Research Article

Abstract: Set-valued data is a significant kind of data, such as data obtained from different search engines, market data, patients’ symptoms and behaviours. An information system (IS) based on incomplete set-valued data is called an incomplete set-valued information system (ISVIS), which generalized model of a single-valued incomplete information system. This paper gives feature selection for an ISVIS by means of uncertainty measurement. Firstly, the similarity degree between two information values on a given feature of an ISVIS is proposed. Then, the tolerance relation on the object set with respect to a given feature subset in an ISVIS is obtained. Next, λ …-reduction in an ISVIS is presented. What’s more, connections between the proposed feature selection and uncertainty measurement are exhibited. Lastly, feature selection algorithms based on λ -discernibility matrix, λ -information granulation, λ -information entropy and λ -significance in an ISVIS are provided. In order to better prove the practical significance of the provided algorithms, a numerical experiment is carried out, and experiment results show the number of features and average size of features by each feature selection algorithm. Show more

Keywords: Rough set theory, ISVIS, feature selection, similarity degree, λ-reduction, λ-discernibility matrix, λ-information granulation, λ-information entropy, λ-significance, algorithm

DOI: 10.3233/JIFS-210135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1217-1235, 2021

Authors: Wang, Lu

Article Type: Research Article

Abstract: With the prosperity of national economy and the development of highway construction, highway freight transportation plays an increasingly important role in the market economy. Due to its great flexible characteristic, highway freight transportation has been the main mode of transportation in China. On the macro level, there are many factors affecting the development of highway freight transportation especially under the background of the new era. Based on the historical data of the development of highway freight transportation, grey entropy analysis method is applied to analyze the significance of influencing factors for the development of highway freight transportation whose key indicator …is highway freight turnover. Then GM (1, N) model is established to predict the development trend of highway freight turnover and its significant influencing factors. Finally, main problems existing in highway freight transportation and development prospect were discussed and analyzed. The research results show that the three most significant factors affecting the development of road freight turnover in China are the total state revenue, GDP and average distance of highway freight. The established GM (1, N) model can conduct high precision prediction for the development of highway freight transportation. Opportunities and challenges of highway freight transportation industry coexist and its development prospect is promising. It is expected to provide beneficial references for the development strategy and decision-making of highway freight transportation in China. Show more

Keywords: Highway freight transportation, significance analysis, grey entropy analysis method, GM (1, N) prediction model

DOI: 10.3233/JIFS-210141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1237-1246, 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. 41, no. 1, pp. 1247-1259, 2021

Authors: Bahrami, Vahid | Kalhor, Ahmad | Masouleh, Mehdi Tale

Article Type: Research Article

Abstract: This study intends to investigate the dynamic model estimation and the design of an adaptive neural network based controller for a passive planar robot, performing 2-DoF motion pattern which is in interaction with an actuated cable-driven robot. In fact, the main goal of applying this structure is to use a number of light cables to drive serial robot links and track the desired reference model by the robot’s end-effector. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. In this way, upon applying sliding mode error dynamics, it is necessary …to determine a vector that contains the matrices related to the robot dynamics. However, finding these matrices requires the use of computational approaches such as Newton-Euler or Lagrange. In addition, since the purpose of this paper is to express comprehensive methods, so with increasing the number of links and degrees of freedom of the robot, finding the dynamics of the robot becomes more difficult. Therefore, the Adaptive Neural Network (ANN) with specific inputs has been used for estimation unknown matrices of the system and the controller design has been performed based on it. So, the main idea in using an adaptive controller is the fact there is no pre-knowledge for the dynamic modeling of the system since the human arm could have different dynamic properties. Hence, the controller is formed by an ANN and robust term. In this way, the adaptation laws of the parameters are extracted by Lyapunov approach, and as a result, as aforementioned, the asymptotic stability of the whole of the system is guaranteed. Simulation results certify the efficiency of the proposed method. Finally, using the Roots Mean Square Error (RMSE) criteria, it has been revealed that, in the presence of bounded disturbance with different amplitude, adding the robust term to the controller leads to improve the tracking error about 34% and 62%, respectively. Show more

Keywords: Dynamic model estimation, adaptive neural network controller, lyapunov approach, passive planar robot, actuated cable-driven robot and rehabilitation setup

DOI: 10.3233/JIFS-210180

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1261-1280, 2021

Authors: Wenbo Huang, First A. | Changyuan Wang, Second B. | Hongbo Jia, Third C.

Article Type: Research Article

Abstract: Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is …applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition. Show more

Keywords: EM, EEG, tactile feedback, wireless sensor network, flying driving, brain electrical signals, data fusion

DOI: 10.3233/JIFS-210191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1281-1296, 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. 41, no. 1, pp. 1297-1307, 2021

Authors: Guo, Wang | Liu, Xingmou | Ma, You | Zhang, Rongjie

Article Type: Research Article

Abstract: The correct identification of gene recombination cold/hot spots is of great significance for studying meiotic recombination and genetic evolution. However, most of the existing recombination spots recognition methods ignore the global sequence information hidden in the DNA sequence, resulting in their low recognition accuracy. A computational predictor called iRSpot-DCC was proposed in this paper to improve the accuracy of cold/hot spots identification. In this approach, we propose a feature extraction method based on dinucleotide correlation coefficients that focus more on extracting potential DNA global sequence information. Then, 234 representative features vectors are filtered by SVM weight calculation. Finally, a convolutional …neural network with better performance than SVM is selected as a classifier. The experimental results of 5-fold cross-validation test on two standard benchmark datasets showed that the prediction accuracy of our recognition method reached 95.11%, and the Mathew correlation coefficient (MCC) reaches 90.04%, outperforming most other methods. Therefore, iRspot-DCC is a high-precision cold/hot spots identification method for gene recombination, which effectively extracts potential global sequence information from DNA sequences. Show more

Keywords: Recombination spots, correlation coefficient, DNA property matrix, support vector machines, convolutional neural network

DOI: 10.3233/JIFS-210213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1309-1317, 2021

Authors: Shahzadi, Sundas | Rasool, Areen | Sarwar, Musavarah | Akram, Muhammad

Article Type: Research Article

Abstract: Bipolarity plays a key role in different domains such as technology, social networking and biological sciences for illustrating real-world phenomenon using bipolar fuzzy models. In this article, novel concepts of bipolar fuzzy competition hypergraphs are introduced and discuss the application of the proposed model. The main contribution is to illustrate different methods for the construction of bipolar fuzzy competition hypergraphs and their variants. Authors study various new concepts including bipolar fuzzy row hypergraphs, bipolar fuzzy column hypergraphs, bipolar fuzzy k -competition hypergraphs, bipolar fuzzy neighborhood hypergraphs and strong hyperedges. Besides, we develop some relations between bipolar fuzzy k …-competition hypergraphs and bipolar fuzzy neighborhood hypergraphs. Moreover, authors design an algorithm to compute the strength of competition among companies in business market. A comparative analysis of the proposed model is discuss with the existing models such bipolar fuzzy competition graphs and fuzzy competition hypergraphs. Show more

Keywords: Bipolar fuzzy competition hypergraphs, bipolar fuzzy k-competition hypergraphs, bipolar fuzzy neighborhood hypergraphs

DOI: 10.3233/JIFS-210216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1319-1339, 2021

Authors: Nandal, Amita | Blagojevic, Marija | Milosevic, Danijela | Dhaka, Arvind | Mishra, Lakshmi Narayan

Article Type: Research Article

Abstract: This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert …a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios; 93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios; 95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods. Show more

Keywords: Covid-19, deep learning, eucledian distance, fuzzy logic, negative likelihood, hashing and machine learning

DOI: 10.3233/JIFS-210222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1341-1351, 2021

Authors: Yang, Lehua | Li, Dongmei | Tan, Ruipu

Article Type: Research Article

Abstract: Solving the shortest path problem is very difficult in situations such as emergency rescue after a typhoon: road-damage caused by a typhoon causes the weight of the rescue path to be uncertain and impossible to represent using single, precise numbers. In such uncertain environments, neutrosophic numbers can express the edge distance more effectively: membership in a neutrosophic set has different degrees of truth, indeterminacy, and falsity. This paper proposes a shortest path solution method for interval-valued neutrosophic graphs using the particle swarm optimization algorithm. Furthermore, by comparing the proposed algorithm with the Dijkstra, Bellman, and ant colony algorithms, potential shortcomings …and advantages of the proposed method are deeply explored, and its effectiveness is verified. Sensitivity analysis performed using a 2020 typhoon as a case study is presented, as well as an investigation on the efficiency of the algorithm under different parameter settings to determine the most reasonable settings. Particle swarm optimization is a promising method for dealing with neutrosophic graphs and thus with uncertain real-world situations. Show more

Keywords: Interval-valued neutrosophic numbers, neutrosophic graph, particle swarm optimization algorithm, shortest path problem

DOI: 10.3233/JIFS-210233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1353-1373, 2021

Authors: Yu, Xiaobing | Liu, Zhenjie | Wu, XueJing | Wang, Xuming

Article Type: Research Article

Abstract: Differential evolution (DE) is one of the most effective ways to solve global optimization problems. However, considering the traditional DE has lower search efficiency and easily traps into local optimum, a novel DE variant named hybrid DE and simulated annealing (SA) algorithm for global optimization (HDESA) is proposed in this paper. This algorithm introduces the concept of “ranking” into the mutation operation of DE and adds the idea of SA to the selection operation. The former is to improve the exploitation ability and increase the search efficiency, and the latter is to enhance the exploration ability and prevent the algorithm …from trapping into the local optimal state. Therefore, a better balance can be achieved. The experimental results and analysis have shown its better or at least equivalent performance on the exploitation and exploration capability for a set of 24 benchmark functions. It is simple but efficient. Show more

Keywords: Differential evolution, simulated annealing, ranking, mutation operator, selection operator

DOI: 10.3233/JIFS-210239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1375-1391, 2021

Authors: Al-shami, Tareq M. | Alshammari, Ibtesam | El-Shafei, Mohammed E.

Article Type: Research Article

Abstract: In 1982, Pawlak proposed the concept of rough sets as a novel mathematical tool to address the issues of vagueness and uncertain knowledge. Topological concepts and results are close to the concepts and results in rough set theory; therefore, some researchers have investigated topological aspects and their applications in rough set theory. In this discussion, we study further properties of N j -neighborhoods; especially, those are related to a topological space. Then, we define new kinds of approximation spaces and establish main properties. Finally, we make some comparisons of the approximations and accuracy measures introduced herein and their counterparts …induced from interior and closure topological operators and E -neighborhoods. Show more

Keywords: Nj-neighborhoods, Ej-neighborhoods, j-neighborhood space, lower and upper approximations, accuracy measure, topological space

DOI: 10.3233/JIFS-210272

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1393-1406, 2021

Authors: Nguyen, Huyen Trang | Chu, Ta-Chung

Article Type: Research Article

Abstract: Understanding employees’ perceptions in team collaboration may help managers select and develop effective teamwork and efficient job completion. Numerous criteria, including qualitative and quantitative, and their importance weights must be considered in evaluating individual diversity perception; therefore, evaluating individual diversity perception is a fuzzy multiple criteria decision-making (MCDM) problem. The purpose of this paper is to use a fuzzy MCDM method to evaluate the personal perception of working in a diverse workgroup. A ranking method using the mean of relative values is proposed to rank the final fuzzy values to complete the model. Formulas of the ranking procedure are derived …to help execute the decision-making procedure and a numerical comparison is conducted to demonstrate the advantage of the proposed ranking method. In addition, a survey about personal diversity perception and willingness to work verifies the feasibility and validity of the proposed mean of relative values based fuzzy MCDM method. The results indicate that decision-makers prefer to work in a different countries-same working field group. More experienced decision-makers, unlike students, prefer to work in the same working sector group. Show more

Keywords: Individual diversity perception, fuzzy MCDM, ranking method, mean of relative values

DOI: 10.3233/JIFS-210291

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1407-1428, 2021

Authors: He, Yan | Wei, Guiwu | Chen, Xudong | Wei, Yu

Article Type: Research Article

Abstract: The financial products selection in the financial services sector is a traditional multi-attribute group decision making (MAGDM) problem. Probabilistic uncertain linguistic sets (PULTSs) could be used to evaluate the financial products with uncertain linguistic terms and corresponding weights (probabilistic). The bidirectional projection (BP) method could take the bidirectional projection values into account. In this paper, we develop an integration model of information entropy and BP method under PULTSs. First of all, utilizing information entropy derives the priority weights of attributes. Next, utilizing the BP method of the PULTSs to obtain the final ranking of the alternatives. To depict the BP …method, the formative vectors of two alternatives are defined, and a weighted vector model and inner product are improved under the PULTSs. In addition, through giving the case of financial products selection and some existing MAGDM methods for comparative analysis, it is proved that the method is practical and effective. The proposed approach also contributes to the effective selection of appropriate options in other decision-making matters. Show more

Keywords: Multi-attribute group decision making (MAGDM), probabilistic uncertain linguistic sets (PULTSs), bidirectional projection (BP) method, information entropy, financial products selection

DOI: 10.3233/JIFS-210313

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1429-1443, 2021

Authors: Botsa, Devaki Rani | Peddi, Phani Bushan Rao | Boddu, Vikas

Article Type: Research Article

Abstract: This paper proposes a new method to rank the parametric form of fuzzy numbers based on defuzzification. The defuzzification process use centroids, value, ambiguity and decision levels on fuzzy number developed from the parametric form of a generalized fuzzy number. The proposed method avoids reducing function to remove lower alpha levels and can overcome the shortcomings in some of the existing fuzzy ranking methods. The proposed method can effectively rank symmetric fuzzy numbers with the same core and different heights, fuzzy numbers with the same support and different cores, crisp numbers, crisp numbers having the same support and different heights, …and fuzzy numbers having compensation of areas. A demonstration of the proposed method through examples and a comparative study with other methods in the literature shows that the proposed method gives effective results. Show more

Keywords: Fuzzy numbers, ranking, value, ambiguity, centroids, decision level

DOI: 10.3233/JIFS-210327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1445-1459, 2021

Authors: Wang, Lei | Peng, Xindong

Article Type: Research Article

Abstract: It is prominent important for managers to assess the personal risk of mental patients. The evaluation process refers to numerous indexes, and the evaluation values are general portrayed by uncertainty information. In order to conveniently model the complicated uncertainty information in realistic decision making, interval-valued complex Pythagorean fuzzy set is proposed. Firstly, with the aid of Einstein t-norm and t-conorm, four fundamental operations for interval-valued complex Pythagorean fuzzy number (IVCPFN) are constructed along with some operational properties. Subsequently, according to these proposed operations, the weighted average and weighted geometric forms of aggregation operators are initiated for fusing IVCPFNs, and their …anticipated properties are also examined. In addition, a multiple attribute decision making issue is examined under the framework of IVCPFNs when employing the novel suggested operators. Ultimately, an example regarding the assessment on personal risk of mental patients is provided to reveal the practicability of the designed approach, and the attractiveness of our results is further found through comparing with other extant approaches.The main novelty of the coined approach is that it not only can preserve the original assessment information adequately by utilizing the IVCPFNs, but also can aggregate IVCPFNs effectively. Show more

Keywords: Multiple attribute decision making, Einstein operation, interval-valued complex Pythagorean fuzzy number, aggregation operators, personal risk

DOI: 10.3233/JIFS-210352

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1461-1486, 2021

Authors: Tang, Bin | Guo, Shiwei | Yeboah, Mathias | Wang, Zhenhua | Cheng, Song

Article Type: Research Article

Abstract: After sudden outbreak of COVID-19 pandemic, the university campuses were closed and millions of university teachers and students had to shift teaching and learning activities from the classrooms to online courses in China. The COVID-19 pandemic undoubtedly brought significant negative effects to university education activities. How does COVID-19 influenced teaching quality and the degree of influences have been studied by many researches. However, the online course quality which is influences by COVID-19 pandemic was commonly evaluated qualitatively rather than quantitatively. In order to obtain quantitative evaluation results of online course quality during the pandemic period, the integrated FCE-AHP evaluation was …applied. Based on real case of online courses, the influence factors of online course quality were divided into four first-level indicators and further subdivided into 14 second level indicators. The weight vectors of evaluation indicators were determined based on experts’ comments from the Teaching Affairs Committee and the fuzzy evaluation memberships were calculated based on questionnaire results of 2021 students. The evaluation results revealed that the integral performance of online courses is acceptable and the performances of students and hardware are relative weaker. Finally, some improvement measures were conducted to deal with difficulties encountered in online courses during COVID-19 pandemic period. Show more

Keywords: Fuzzy comprehensive evaluation, analytic hierarchy process, COVID-19, online courses, quantitative evaluation

DOI: 10.3233/JIFS-210362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1487-1498, 2021

Authors: Wang, Qian

Article Type: Research Article

Abstract: With the rapid development of China’s economic globalization in the new era, the demand for English majors is obviously on the rise, which puts forward new and higher requirements for application-oriented undergraduate colleges to train compound English majors. However, from the perspective of teaching quality evaluation of English majors in application-oriented undergraduate colleges, the results are not optimistic. Therefore, it is an important task for higher education research in China to explore the problems existing in the process of teaching quality evaluation for English majors in application-oriented undergraduate colleges and how to better train qualified and versatile talents for English …majors to adapt to the economic and social development in the new era. The teaching quality evaluation of college English is frequently viewed as a multi-attribute group decision-making (MAGDM). Thus, a novel MAGDM method is used to tackle it. Depending on the conventional CODAS method and interval-valued intuitionistic fuzzy sets (IVIFSs), this paper designs a novel distance based IVIF-CODAS method to assess the teaching quality evaluation of college English. First of all, a related literature review is conducted. What’s more, some necessary theories related to IVIFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria is decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IVIFSs, the conventional CODAS method is extended to the IVIFSs to calculate assessment score of every alternative. Therefore, all alternatives can be ranked and the one with the best teaching quality. Eventually, an application about teaching quality evaluation of college English and some comparative methods have been employed to show the superiority of the developed method. The results illustrate that the defined framework is very useful for assessing the teaching quality of college English. Show more

Keywords: MAGDM issues, interval-valued intuitionistic fuzzy sets (IVIFSs), CODAS method, CRITIC method, teaching quality, college English

DOI: 10.3233/JIFS-210366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1499-1508, 2021

Authors: Yue, Xiaofeng | Ma, Guoyuan | Liu, Fuqiuxuan | Gao, Xueliang

Article Type: Research Article

Abstract: Due to the complexity and variety of textures on Strip steel, it is very difficult to detect defects on rigid surfaces. This paper proposes a metal surface defect classification method based on an improved bat algorithm to optimize BP neural network. First, this paper uses the Local Binary Pattern(LBP) algorithm to extract features from six types of defect images including inclusion, patches, crazing, pitted, rolled-in, and scratches, and build a feature sample library with the extracted feature values. Then, the WG-BA-BP network is used to classify the defect images with different characteristics. The weighted experience factor added by the network …can control the flight speed of the bat according to the number of iterations and the change of the fitness function. And the gamma distribution is added in the process of calculating loudness, which enhances the local searchability. The BP network optimized by this method has higher accuracy. Finally, to verify the effectiveness of the method, this article introduces the five evaluation indicators of accuracy, precision, sensitivity, specificity, and F1 value under the multi-class model. To prove that this algorithm is more feasible and effective compared with other swarm intelligence algorithms. The best prediction performance of WG-BA-BP is 0.010905, and the accuracy rate can reach 0.9737. Show more

Keywords: Image classification, BP neural network, Bat Algorithm, weighted experience factor, Gamma distribution

DOI: 10.3233/JIFS-210374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1509-1521, 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. 41, no. 1, pp. 1523-1529, 2021

Authors: Li, Baolin | Yang, Lihua

Article Type: Research Article

Abstract: Picture fuzzy set (PFS) and linguistic term set (LTS) are two significant notions in multi-criteria decision-making (MCDM). In practice, decision-makers sometimes need utilize the multiple probable membership degrees for an uncertain linguistic term to express evaluation information. Motivated by these, to better convey the vagueness and uncertainty of cognitive information, multi-valued picture fuzzy uncertain linguistic set combining picture hesitant fuzzy set with uncertain linguistic term set is proposed. We firstly define the concepts of multi-valued picture fuzzy uncertain linguistic set and multi-valued picture fuzzy uncertain linguistic number. Hamacher operations are more general and flexible in information fusion, thus, Hamacher operations …and comparison method are developed at the same time. Improved generalized Heronian Mean operator can simultaneously reflect correlations between values and prevent the redundant calculation. Then, two operators of improved generalized weighted Heronian mean and improved generalized geometric weighted Heronian mean in view of Hamacher operations are proposed. Meanwhile, some distinguished properties and instances of two operators are explored as well. Moreover, a novel MCDM approach applying the developed operators is constructed. Ultimately, an illustrative example on vendor selection is performed, and sensitivity analysis and comparison analysis are provided to verify the powerfulness of the proposed method. Show more

Keywords: Hamacher, improved generalized heronian operator, multi-criteria decision-making, multi-valued picture fuzzy uncertain linguistic set

DOI: 10.3233/JIFS-210404

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1531-1552, 2021

Authors: Akram, Muhammad | Siddique, Saba | Ahmad, Uzma

Article Type: Research Article

Abstract: The main objective of this research article is to classify different types of m -polar fuzzy edges in an m -polar fuzzy graph by using the strength of connectedness between pairs of vertices. The identification of types of m -polar fuzzy edges, including α -strong m -polar fuzzy edges, β -strong m -polar fuzzy edges and δ -weak m -polar fuzzy edges proved to be very useful to completely determine the basic structure of m -polar fuzzy graph. We analyze types of m -polar fuzzy edges in strongest m -polar fuzzy path and m -polar fuzzy cycle. Further, we define …various terms, including m -polar fuzzy cut-vertex, m -polar fuzzy bridge, strength reducing set of vertices and strength reducing set of edges. We highlight the difference between edge disjoint m -polar fuzzy path and internally disjoint m -polar fuzzy path from one vertex to another vertex in an m -polar fuzzy graph. We define strong size of an m -polar fuzzy graph. We then present the most celebrated result due to Karl Menger for m -polar fuzzy graphs and illustrate the vertex version of Menger’s theorem to find out the strongest m -polar fuzzy paths between affected and non-affected cities of a country due to an earthquake. Moreover, we discuss an application of types of m -polar fuzzy edges to determine traffic-accidental zones in a road network. Finally, a comparative analysis of our research work with existing techniques is presented to prove its applicability and effectiveness. Show more

Keywords: α-strong m-polar fuzzy edges, β-strong m-polar fuzzy edges, Menger’s theorem for m-polar fuzzy graphs, Traffic-accidental zones in a road network, Flowchart

DOI: 10.3233/JIFS-210411

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1553-1574, 2021

Authors: Vaiyapuri, Thavavel | Alaskar, Haya | Sbai, Zohra | Devi, Shri

Article Type: Research Article

Abstract: Medical images that are acquired with reduced radiation exposure or following the administration of imaging agents with a low dose, are often known to experience problems by the noise stemming from acquisition hardware as well as psychological sources. This noise can adversely affect the quality of diagnosis, but also prevent practitioners from computing quantitative functional information. With a view to overcoming these challenges, the current paper puts forward optimization of multi-objective for denoising medical images within the wavelet domain. This proposed technique entails the use of genetic algorithm (GA) to get the threshold optimized within the denoising framework of wavelets. …Two purposes are associated with this technique: First, its ability to adapt with different noise types of noise in the image without requiring prior information about the imaging process per se. In addition, it balances relevant diagnostic details’ preservation against the reduction of noise by considering the optimization of the error factor of Liu and SNR as the foundation of objective function. According to the implementation of this method on magnetic resonance (MR) and ultrasound (US) images of the brain, a better performance has been observed as compared to the existing wavelet-based denoising methods with regard to quantitative and qualitative metrics. Show more

Keywords: Medical image denoising, rician noise, speckle noise, wavelet thresholding, threshold optimization, optimization techniques, multi-objective optimization

DOI: 10.3233/JIFS-210429

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1575-1588, 2021

Authors: Yin, Fangchen | Ji, Qinzhi | Jin, Chengwei | Wang, Jing

Article Type: Research Article

Abstract: Milling force prediction is one of the most important ways to improve the quality of products and stability in robot stone machining. In this paper, support vector machines (SVMs) are introduced to model the milling force of white marble, and the model parameters in the SVMs are optimized by the improved quantum-behaved particle swarm optimization (IQPSO) algorithm. A set of online inspection data from stone-machining robotic manipulators is adopted to train and test the model. The overall performance of the model is evaluated based on the decision coefficient (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE), …and the results obtained by IQPSO-SVM are superior to those of the PSO-SVM model. On this basis, the relationship between the milling force of white marble and various machining parameters is explored to obtain optimal machining parameters. The proposed model provides a tool for the adjustment of machining parameters to ensure stable machining quality. This approach is a new method and concept for milling force control and optimization research in the robotic stone milling process. Show more

Keywords: Robot stone machining, quantum-behaved particle swarm algorithm, regression of support vector machines, milling force of white marble, machining parameters

DOI: 10.3233/JIFS-210430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1589-1609, 2021

Authors: Dang, Xingyue | Liao, Shan | Cheng, Pengsen | Liu, Jiayong

Article Type: Research Article

Abstract: Recently, deep learning methods have been applied to deal with the opinion target extraction (OTE) task with fruitful achievements. On the other hand, since the features captured by the embedding layer can make a multiple-perspective analysis from a sentence, an embedding layer that can grasp the high-level semantics of the sentences is of essence for processing the OTE task and can improve the performance of model into a more efficient manner. However, most of the existing studies focused on the network structure rather than the significant embedded layer, which may be the fundamental reason for the problem of relatively poor …performance in this field, not mention the Chinese extraction model. To compensate these shortcomings, this paper proposes a model using multiple effective features and Bidirectional Encoder Representations from Transformers (BERT) on the architecture of Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Field (CRF) for Chinese opinion target extraction task, namely MF-COTE, which can construct features from different perspectives to capture the context and local features of the sentences. Besides, to handle the difficult case of multiple nouns in one sentence, we innovatively propose noting words feature to regulate the model emphasize on the noun near the transition or contrast word, thus leading a better opinion target location. Moreover, to demonstrate the superiorities of the proposed model, extensive comparison experiments are systematically conducted compared with other existing state-of-the-art methods, with the F1-score of 90.76%, 92.10%, 89.63% on the Baidu, the Dianping, and the Mafengwo dataset, respectively. Show more

Keywords: Chinese opinion target extraction, multiple features, noting words, BERT, Long short-term memory

DOI: 10.3233/JIFS-210440

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1611-1626, 2021

Authors: Garg, Harish | Ali, Zeeshan | Yang, Zaoli | Mahmood, Tahir | Aljahdali, Sultan

Article Type: Research Article

Abstract: The paper aims to present a concept of a Complex interval-valued q-rung orthopair uncertain linguistic set (CIVQROULS) and investigated their properties. In the presented set, the membership grades are considered in terms of the interval numbers under the complex domain while the linguistic features are added to address the uncertainties in the data. To further discuss more, we have presented the operation laws and score function for CIVQROULS. In addition to them, we present some averaging and geometric operators to aggregate the different pairs of the CIVQROULS. Some fundamental properties of the proposed operators are stated. Afterward, an algorithm for …solving the decision-making problems is addressed based on the proposed operator using the CIVQROULS features. The applicability of the algorithm is demonstrated through a case study related to brain tumors and their effectiveness is compared with the existing studies. Show more

Keywords: Aggregation operators, classifications of brain tumors, complex interval valued; q-rung orthopair uncertain linguistic sets

DOI: 10.3233/JIFS-210442

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1627-1656, 2021

Authors: Rodriguez, Luis | Castillo, Oscar | Garcia, Mario | Soria, Jose

Article Type: Research Article

Abstract: The main goal of this paper is to outline a new optimization algorithm based on String Theory, which is a relative new area of physics. The String Theory Algorithm (STA) is a nature-inspired meta-heuristic, which is based on studies about a theory stating that all the elemental particles that exist in the universe are strings, and the vibrations of these strings create all particles existing today. The newly proposed algorithm uses equations based on the laws of physics that are stated in String Theory. The main contribution in this proposed method is the new techniques that are devised in order …to generate potential solutions in optimization problems, and we are presenting a detailed explanation and the equations involved in the new algorithm in order to solve optimization problems. In this case, we evaluate this new proposed meta-heuristic with three cases. The first case is of 13 traditional benchmark mathematical functions and a comparison with three different meta-heuristics is presented. The three algorithms are: Flower Pollination Algorithm (FPA), Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO). The second case is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting a statistical comparison of these results with respect to FA and GWO. In addition, we are presenting a third case, which is the optimization of a fuzzy inference system (FIS), specifically finding the optimal design of a fuzzy controller, where the main goal is to optimize the membership functions of the FIS. It is important to mention that we used these study cases in order to analyze the proposed meta-heuristic with: basic problems, complex problems and control problems. Finally, we present the performance, results and conclusions of the new proposed meta-heuristic. Show more

Keywords: New algorithm, stochastic process, performance, string theory, metaheuristics, control problem

DOI: 10.3233/JIFS-210459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1657-1675, 2021

Authors: Saeed, Muhammad | Ahsan, Muhammad | Ur Rahman, Atiqe | Saeed, Muhammad Haris | Mehmood, Asad

Article Type: Research Article

Abstract: Brain tumors are one of the leading causes of death around the globe. More than 10 million people fall prey to it every year. This paper aims to characterize the discussions related to the diagnosis of tumors with their related problems. After examining the side effects of tumors, it encases similar indications, and it is hard to distinguish the precise type of tumors with their seriousness. Since in practical assessment, the indeterminacy and falsity parts are frequently dismissed, and because of this issue, it is hard to notice the precision in the patient’s progress history and cannot foresee the period …of treatment. The Neutrosophic Hypersoft set (NHS) and the NHS mapping with its inverse mapping has been design to overcome this issue since it can deal with the parametric values of such disease in more detail considering the sub-parametric values; and their order and arrangement. These ideas are capable and essential to analyze the issue properly by interfacing it with scientific modeling. This investigation builds up a connection between symptoms and medicines, which diminishes the difficulty of the narrative. A table depending on a fuzzy interval between [0, 1] for the sorts of tumors is constructed. The calculation depends on NHS mapping to adequately recognize the disease and choose the best medication for each patient’s relating sickness. Finally, the generalized NHS mapping is presented, which will encourage a specialist to extricate the patient’s progress history and to foresee the time of treatment till the infection is relieved. Show more

Keywords: Tumor, neutrosophic hypersoft, mapping, inverse mapping

DOI: 10.3233/JIFS-210482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1677-1699, 2021

Authors: Gou, Zhinan | Li, Yan

Article Type: Research Article

Abstract: With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, …topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods. Show more

Keywords: Query expansion, user profile, topic model, Word2Vec

DOI: 10.3233/JIFS-210508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1701-1711, 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. 41, no. 1, pp. 1713-1726, 2021

Authors: Li, Yingxin | Li, Shihua | Peng, Shuangyun | Zhao, Shoulu | Yang, Wenxian | Qiu, Lidan

Article Type: Research Article

Abstract: Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (FCM) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise” of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional FCM algorithm, this paper proposes …an improved FCM algorithm. This algorithm replaces the Euclidean distance of the traditional FCM algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images of the Fuxian Lake and Xingyun Lake basins during normal, wet, and dry periods as the data source. Under the same conditions, the clustering accuracy was compared with the traditional FCM algorithm, improved FCM algorithm, K-means clustering method and iterative self-organizing data analysis (ISODATA) clustering method. The experimental results show that the improved FCM algorithm has a higher water extraction accuracy than the traditional FCM algorithm, K-means clustering method and ISODATA clustering method. The kappa coefficient and overall accuracy (OA) of the improved FCM algorithm can be increased by 5.56%–9.45% and 2.66%–5.32%, respectively, and the omission error and commission error can be reduced by 1.72%–4.55% and 12.14%–22.10%, respectively. When the improved FCM algorithm is used, the extraction accuracy is higher for plateau deep lakes than for plateau shallow lakes, and the extraction effect for lakes with poor water environments is more significant than that of other methods. The improved FCM algorithm better maintains the integrity of the water boundary and overcomes the influence of a certain number of mountain shadows and urban building pixels on the clustering results. Show more

Keywords: Remote sensing, fuzzy clustering, FCM algorithm, mahalanobis distance, spectral angle matching

DOI: 10.3233/JIFS-210526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1727-1740, 2021

Authors: Nan, TaiBen | Zhang, Haidong | He, Yanping

Article Type: Research Article

Abstract: The overwhelming majority of existing decision-making methods combined with the Pythagorean fuzzy set (PFS) are based on aggregation operators, and their logical foundation is imperfect. Therefore, we attempt to establish two decision-making methods based on the Pythagorean fuzzy multiple I method. This paper is devoted to the discussion of the full implication multiple I method based on the PFS. We first propose the concepts of Pythagorean t-norm, Pythagorean t-conorm, residual Pythagorean fuzzy implication operator (RPFIO), Pythagorean fuzzy biresiduum, and the degree of similarity between PFSs based on the Pythagorean fuzzy biresiduum. In addition, the full implication multiple I method for …Pythagorean fuzzy modus ponens (PFMP) is established, and the reversibility and continuity properties of the full implication multiple I method of PFMP are analyzed. Finally, a practical problem is discussed to demonstrate the effectiveness of the Pythagorean fuzzy full implication multiple I method in a decision-making problem. The advantages of the new method over existing methods are also explained. Overall, the proposed methods are based on logical reasoning, so they can more accurately and completely express decision information. Show more

Keywords: Full implication multiple I method, PFS, RPFIO, decision-making problem

DOI: 10.3233/JIFS-210527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1741-1755, 2021

Authors: Karbasaki, M. Miri | Balooch Shahryari, 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. 41, no. 1, pp. 1757-1775, 2021

Authors: Kalli, SivaNagiReddy | Suresh, T. | Prasanth, A. | Muthumanickam, T. | Mohanram, K.

Article Type: Research Article

Abstract: Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism …is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure. Show more

Keywords: Background modeling, fuzzy c-means, motion object detection, video surveillance system

DOI: 10.3233/JIFS-210563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1777-1789, 2021

Authors: Cheng, Fangmin | Yu, Suihuai | Qin, Shengfeng | Chu, Jianjie | Chen, Jian

Article Type: Research Article

Abstract: Evaluating the quality of the user experience (UX) of existing products is important for new product development. Conventional UX evaluation methods, such as questionnaire, have the disadvantages of the great subjective influence of investigators and limited number of participants. Meanwhile, online product reviews on e-commerce platforms express user evaluations of product UX. Because the reviews objectively reflect the user opinions and contain a large amount of data, they have potential as an information source for UX evaluation. In this context, this study explores how to evaluate product UX through using online product reviews. A pilot study is conducted to define …the key elements of a review. Then, a systematic method of product UX evaluation based on reviews is proposed. The method includes three parts: extraction of key elements, integration of key elements, and quantitative evaluation based on rough number. The effectiveness of the proposed method is demonstrated by a case study using reviews of a wireless vacuum cleaner. Based on the proposed method, designers can objectively evaluate the UX quality of existing products and obtain detailed suggestions for product improvement. Show more

Keywords: User experience (UX) evaluation, Online product reviews, Opinion mining, UX aspect, Product design

DOI: 10.3233/JIFS-210564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1791-1805, 2021

Authors: Gogo, Kevin Otieno | Nderu, Lawrence | Mutua, Makau

Article Type: Research Article

Abstract: Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training …data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases. Show more

Keywords: Interval type 2 gaussian fuzzy logic, linear regression predictive models, intelligent system models, knowledge-bases

DOI: 10.3233/JIFS-210568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1807-1820, 2021

Authors: Deng, Min-Hui | Zhou, Xiao-Yu | Wang, Jian-Qiang | Li, Jun-Bo | Cheng, Peng-Fei

Article Type: Research Article

Abstract: The development of new energy industry is a pressing issue due to the deterioration of the environment. The selection of new energy projects is a critical problem for decision makers. Incomplete and uncertain information appears in the process of new energy project selection. Compared with other linguistic expressions, probabilistic linguistic term set (PLTS) simultaneously reflects all possible linguistic terms and their corresponding weights, which conforms to the cognitive habits of people. Thus, a multi-criteria decision-making framework under PLTS environment is constructed for energy project selection. Firstly, a normalised projection model of PLTS, which considers the distance and the angle between …two objects, is proposed to overcome the limitations of distance measurement. Secondly, a comprehensive weight-determination method combining the maximum deviation and expert scoring methods is developed to calculate the weight vector of the criteria. Furthermore, a projection-based VIKOR (Višekriterijumska optimizacija i kompromisno rešenje) method is established to select new energy projects, which can reflect the preferences of decision makers for group utility and individual regret. Finally, a numerical study on new energy project selection is performed to determine the validity and applicability of this method. Sensitive and comparative analyses are also conducted to reflect the rationality and feasibility of the method. Show more

Keywords: Multi-criteria decision-making, probabilistic linguistic term set, projection measurement, VIKOR method, new energy project selection

DOI: 10.3233/JIFS-210573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1821-1836, 2021

Authors: Zhu, Siyu | He, Chongnan | Song, Mingjuan | Li, Linna

Article Type: Research Article

Abstract: In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has …only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm. Show more

Keywords: Brand rice, fuzzy probability, kernel principal component analysis, two-parameter k-nearest neighbor algorithm

DOI: 10.3233/JIFS-210584

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1837-1843, 2021