Journal of Intelligent & Fuzzy Systems - Volume 40, issue 3

Authors: Malarvizhi, K. | Amshakala, K.

Article Type: Research Article

Abstract: In this paper, a novel Feature-Reduction Fuzzy C-means (FRFCM) with Feature Linkage Weight (FRFCM-FLW) algorithm is introduced. By the combination of FRFCM and feature linkage weight, a new feature selection model is developed, called a Feature Linkage Weight Based FRFCM using fuzzy clustering. The larger amounts of features are superior to the complication of the problem, and the larger the time that is exhausted in creating the outcome of the classifier or the model. Feature selection has been established as a high-quality method for preferring features that best describes the data under certain criteria or measure. The proposed method …presents three stages namely, 1) Data Formation: The process of data collection and data cleaning; 2) FRFCM-FLW. The proposed method can decrease feature elements routinely, and also construct excellent clustering results. The proposed method calculates a novel weight for every feature by combining modified Mahalanobis distance with feature δm variance in FRFCM algorithm; 3) Fuzzy C-means (FCM) cluster. The proposed FRFCM-FLW method proves high Accuracy Rate (AR), Rand Index (RI) and Jaccard Index (JI) ratio when compared to other feature reduction algorithms like WFCM, EWKM, WKM, FCM and FRFCM algorithms. Show more

Keywords: Data mining, fuzzy logic, feature selection, FCM

DOI: 10.3233/JIFS-201395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4563-4572, 2021

Authors: Dhaiban, Ali Khaleel | Jabbar, Baydaa Khalaf

Article Type: Research Article

Abstract: Many studies have attempted to understand the true nature of COVID-19 and the factors influencing the spread of the virus. This paper investigates the possible effect the COVID-19 pandemic spreading in Iraq considering certain factors, that include isolation and weather. A mathematical model of cases representing inpatients, recovery, and mortality was used in formulating the control variable in this study to describe the spread of COVID-19 through changing weather conditions between 17th March and 15th May, 2020. Two models having deterministic and an uncertain number of daily cases were used in which the solution for the model using the Pontryagin …maximum principle (PMP) was derived. Additionally, an optimal control model for isolation and each factor of the weather factors was also achieved. The results simulated the reality of such an event in that the cases increased by 118%, with an increase in the number of people staying outside of their house by 25%. Further, the wind speed and temperature had an inverse effect on the spread of COVID-19 by 1.28% and 0.23%, respectively. The possible effect of the weather factors with the uncertain number of cases was higher than the deterministic number of cases. Accordingly, the model developed in this study could be applied in other countries using the same factors or by introducing other factors. Show more

Keywords: COVID-19 pandemic, optimal control, pontryagin maximum principle, chance-constrained, isolation, weather factors

DOI: 10.3233/JIFS-201419

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4573-4587, 2021

Authors: Lu, Ziqiang | Zhu, Yuanguo | Shen, Jiayu

Article Type: Research Article

Abstract: Uncertain fractional differential equation driven by Liu process plays an important role in describing uncertain dynamic systems. This paper investigates the continuous dependence of solution on the parameters and initial values, respectively, for uncertain fractional differential equations involving the Caputo fractional derivative in measure sense. Several continuous dependence theorems are obtained based on uncertainty theory by employing the generalized Gronwall inequality, in which the coefficients of uncertain fractional differential equation are required to satisfy the Lipschitz conditions. Several illustrative examples are provided to verify the validity of the obtained results.

Keywords: Uncertainty theory, fractional differential equation, Caputo derivative, continuous dependence

DOI: 10.3233/JIFS-201428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4589-4598, 2021

Authors: Poongodi, K. | Kumar, Dhananjay

Article Type: Research Article

Abstract: The Frequent Episode Mining (FEM) is a challenging framework to identify frequent episodes from a sequence database. In a sequence, an ordered collection of events defines an episode, and frequent episodes are only considered by the earlier studies. Also, it doesn’t support for the serial based episode rule mining. In this work, the episode rules are mined with precise and serial based rule mining considering the temporal factor, so that, the occurrence time of the consequent is specified in contrast to the traditional episode rule mining. The proposed work has a larger number of candidates and specific time constraints to …generate the fixed-gap episodes, and mining such episodes from whole sequence where the time span between any two events is a constant which is utilized to improve the proposed framework’s performance. In order to improve the efficiency, an Optimal Fixed-gap Episode Occurrence (OFEO) is performed using the Natural Exponent Inertia Weight based Swallow Swarm Optimization (NEIWSSO) algorithm. The temporal constraints significantly evaluate the effectiveness of episode mining, and a noticeable advantage of the present work is to generate optimal fixed-gap episodes for better prediction. The effective use of memory consumption and performance enhancement is achieved by developing new trie-based data structure for Mining Serial Positioning Episode Rules (MSPER) using a pruning method. The position of frequent events is updated in the precise-positioning episode rule trie instead of frequent events to reduce the memory space. The benchmark datasets Retail, Kosarak, and MSNBC is used to evaluate the proposed algorithm’s efficiency. Eventually, it is found that it outperforms the existing techniques with respect to memory consumption and execution time. On an average, the proposed algorithm achieves 28 times lesser execution time and consumes 45.5% less memory space for the highest minimum support value on the Retail dataset compared to existing methods. Show more

Keywords: Frequent episode mining, fixed-gap episode occurrence, natural exponent inertia weight, support of fixed-gap episode

DOI: 10.3233/JIFS-201438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4599-4615, 2021

Authors: Kudłacik, Przemysław | Łęski, Jacek M.

Article Type: Research Article

Abstract: The article presents a thorough analysis of fuzzy inference introduced by Baldwin and compares this approach to Zaheh’s compositional rule of inference. The comparison is performed in order to analyze the equivalence of the two methods and describe practical aspects of this fact for simple and compound premises, indicating advantages and disadvantages of both approaches. The main aim of the analysis is focus on the computational complexity of the methods. The most important feature of Baldwin’s inference is transfer of the inference process into a truth space, unified for all input variables. Such environment allows to obtain one fuzzy truth …value describing a compound premise in a sequence of low dimensional computations. The article proves equality of such approach with the compositional rule of inference. Therefore, this solution is much more computationally efficient in case of compound cases, for which compositional rule of inference is multidimensional. Show more

Keywords: Fuzzy inference, fuzzy truth value, fuzzy sets

DOI: 10.3233/JIFS-201443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4617-4636, 2021

Authors: Deng, Xue | Chen, Chuangjie

Article Type: Research Article

Abstract: Considering that most studies have taken the investors’ preference for risk into account but ignored the investors’ preference for assets, in this paper, we combine the prospect theory and possibility theory to provide investors with a portfolio strategy that meets investors’ preference for assets. Firstly, a novel reference point is proposed to give investors a comprehensive impression of assets. Secondly, the prospect return rate of assets is quantified as trapezoidal fuzzy number, and its possibilistic mean value and variance are regarded as prospect return and risk and then used to define the fuzzy prospect value. This new definition is presented …to denote the score of an asset in investors’ subjective cognition. And then, a prospect asset filtering frame is proposed to help investors select assets according to their preference. When assets are selected, another new definition called prospect consistency coefficient is proposed to measure the deviation of a portfolio strategy from investors’ preference. Some properties of the definition are presented by rigorous mathematical proof. Based on the definition and its properties, a possibilistic model is constructed, which can not only provide investors optimal strategies to make profit and reduce risk as much as possible, but also ensure that the deviation between the strategies and investors’ preference is tolerable. Finally, a numerical example is given to validate the proposed method, and the sensitivity analysis of parameters in prospect value function and prospect consistency constraint is conducted to help investors choose appropriate values according to their preferences. The results show that compared with the general M-V model, our model can not only better satisfy investors’ preference for assets, but also disperse risk effectively. Show more

Keywords: Possibility theory, prospect theory, portfolio selection, asset altering framework, prospect consistency coefficient

DOI: 10.3233/JIFS-201457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4637-4660, 2021

Authors: Hu, Chengxiang | Zhang, Li | Liu, Shixi

Article Type: Research Article

Abstract: Multigranulation rough set (MGRS) theory provides an effective manner for the problem solving by making use of multiple equivalence relations. As the information systems always dynamically change over time due to the addition or deletion of multiple objects, how to efficiently update the approximations in multigranulation spaces by making fully utilize the previous results becomes a crucial challenge. Incremental learning provides an efficient manner because of the incorporation of both the current information and previously obtained knowledge. In spite of the success of incremental learning, well-studied findings performed to update approximations in multigranulation spaces have relatively been scarce. To address …this issue, in this paper, we propose matrix-based incremental approaches for updating approximations from the perspective of multigranulation when multiple objects vary over time. Based on the matrix characterization of multigranulation approximations, the incremental mechanisms for relevant matrices are systematically investigated while adding or deleting multiple objects. Subsequently, in accordance with the incremental mechanisms, the corresponding incremental algorithms for maintaining multigranulation approximations are developed to reduce the redundant computations. Finally, extensive experiments on eight datasets available from the University of California at Irvine (UCI) are conducted to verify the effectiveness and efficiency of the proposed incremental algorithms in comparison with the existing non-incremental algorithm. Show more

Keywords: Dynamic data, approximations, multigranulation, matrix, knowledge discovery

DOI: 10.3233/JIFS-201472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4661-4682, 2021

Authors: Sedova, Nelly | Sedov, Viktor | Bazhenov, Ruslan | Bogatenkov, Sergey

Article Type: Research Article

Abstract: The authors continued their research on the development of an intelligent automatic ships pilot containing a controller based on fuzzy logic. Its features are determined by the optimizer based on a genetic algorithm. It also contains a modular unit of neural network models of ship navigation paths, as well as a neural network classifier. This paper is devoted to the description of a neural network classifier designed to classify the movement patterns of marine vessels to identify the peculiarities of the ship depending on its type and sailing conditions. The introduction of such classifier to an autopilot allows for more …precise consideration of multivariate and difficult to formalize factors affecting the vessel while operating, such as varying weather conditions, irregular waves, hydrodynamic characteristics of the vessel, draft, water under the keel, rate of the vessel sailing, etc. The article outlines the technique concerning the development of a neural network classifier and the results of its computer modelling on the example of a refrigerated transport vessel type. The authors used such methods for obtaining and processing findings as spectral estimation, machine learning methods, in particular, neural network technology and computer or simulation modelling. Show more

Keywords: Neural network classifier, automatic course-keeping, fuzzy logic, autopilot

DOI: 10.3233/JIFS-201495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4683-4694, 2021

Authors: Jasrotia, Swati | Singh, Uday Pratap | Raj, Kuldip

Article Type: Research Article

Abstract: In this article, we introduce and study some difference sequence spaces of fuzzy numbers by making use of λ -statistical convergence of order (η , δ  + γ ) . With the aid of MATLAB software, it appears that the statistical convergence of order (η , δ  + γ ) is well defined every time when (δ  + γ ) > η and this convergence fails when (δ  + γ ) < η . Moreover, we try to set up relations between (Δv , λ )-statistical convergence of order (η , δ  + γ ) and strongly (Δv , p , λ )-Cesàro summability of order (η …, δ  + γ ) and give some compelling instances to show that the converse of these relations is not valid. In addition to the above results, we also graphically exhibits that if a sequence of fuzzy numbers is bounded and statistically convergent of order (η , δ  + γ ) in (Δv , λ ), then it need not be strongly (Δv , p , λ )-Cesàro summable of order (η , δ  + γ ). Show more

Keywords: Cesàro summability, difference operator, fuzzy numbers, λ-statistical convergence

DOI: 10.3233/JIFS-201539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4695-4703, 2021

Authors: Zhong, Leiguang | Luo, Yiyue | Zhang, Xin | Zhang, Hongyu | Wang, Jianqiang

Article Type: Research Article

Abstract: User rating information on multiple predefined aspects gathered by hotel recommendation systems generally shows a deviation between the overall rating and detailed criteria ratings. In this study, to address this deviation, we proposed a novel hotel recommendation method that clusters users with different preferences into different groups using the K-means algorithm. Moreover, we allocated weights to different criteria and obtained a comprehensive score. A case study on actual data from Tripadvisor.com showed that compared with three other models, our proposed model demonstrated a more impressive performance. This research can offer advantages to hotel service providers and customers in terms of …decision making. Show more

Keywords: Recommender system, hotel recommendation, multi criteria rating, K-means, Tripadvisor.com

DOI: 10.3233/JIFS-201577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4705-4720, 2021

Authors: Xu, Junxiang | Guo, Jingni | Zhang, Jin | Liu, Weihua | Ma, Hui

Article Type: Research Article

Abstract: In order to study the influence of travelers’ self-adaptive adjustment behavior on transportation network under the assumption of bounded rationality, using cellular automaton to discretize the selection model under the analytic paradigm in the existing research, abstract each cell into a traveler, and describe the traveler characteristics with finite rationality characteristics through the travel risk attitude and travel generalized cost budget. Cellular automata and cumulative prospect theory is used to establish the travel route choice model, giving the dynamic evolution process of different reference points for travelers and taking the actual regional transportation network of Sichuan Tibet region in China …as the study object, analyzes the impact of bounded rational travel behavior on route choice. The model and algorithm proposed in our study can not only guide the transportation organization of Sichuan Tibet region, but also provide theoretical support for the implementation of regional transportation planning and traffic control scheme in the future. Show more

Keywords: Bounded rationality, cellular automaton, cumulative prospect theory, dynamic reference points, travel route choice

DOI: 10.3233/JIFS-201578

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4721-4735, 2021

Authors: Bakhthemmat, Ali | Izadi, Mohammad

Article Type: Research Article

Abstract: Many scientists apply fully dynamic bin packing problem solving for resource allocation of virtual machines in cloud environments. The goal of problem-solving is to reduce the number of allocated hosts (bins) and virtual machines (items) migration rates for reducing energy consumption. This study demonstrates a greedy futuristic algorithm (proposed algorithm) for fully dynamic bin packaging with an average asymptotic approximation ratio of 1.231, better than other existing algorithms. The proposed algorithm identifies inappropriate local selections using special futuristic conditions to prevent them as much as possible. Eventually, suitable choices determine and discard the improper ones. The proposed algorithm illustrates an …asymptotic approximation ratio of (t/ (t-1)) OPT, where the value of t depends on the distribution of the arrived and departed items. Also, OPT is the number of bins by an optimal solution. Finally, in experiments of datasets using a maximum utilization of 80% of each bin, the average migration rate is 0.338. Using the proposed method for allocating resources in the cloud environment can allocate hosts to a virtual machine using almost optimal use. This allocation can reduce the cost of maintaining and purchasing hosts. Also, this method can reduce the migration rate of virtual machines. As a result, decreasing migration improves the energy consumption cost in the cloud environment. Show more

Keywords: Fully dynamic bin packing, special futuristic conditions, futuristic greedy, migration reducing

DOI: 10.3233/JIFS-201581

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4737-4760, 2021

Authors: Ahmadini, Abdullah Ali H. | Ahmad, Firoz

Article Type: Research Article

Abstract: This paper investigates novel intuitionistic fuzzy preferences relations to determine the imprecise linguistic terms with fuzzy goals. The proposed intuitionistic fuzzy goal programming (IFGP) considers the degree of vagueness and hesitations simultaneously. Different sorts of membership functions such as linear, exponential, parabolic, and hyperbolic have been introduced to depict the linguistic importance term. The overall satisfaction level is achieved by maximizing the convex combination of each fuzzy goals and the preference relations simultaneously. To verify and validate the proposed IFGP model, a numerical example is presented with the comparative study. Further, it is also applied to a banking financial statement …management system problem. The proposed IFGP approach outperforms over others. At last, the conclusion and future research direction are suggested based on the performed study. Show more

Keywords: Intuitionistic fuzzy set, membership and non-membership function, score functions, imprecise goal hierarchy

DOI: 10.3233/JIFS-201588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4761-4777, 2021

Authors: Akram, Muhammad | Shahzadi, Gulfam | Shahzadi, Sundas

Article Type: Research Article

Abstract: An q -rung orthopair fuzzy set is a generalized structure that covers the modern extensions of fuzzy set, including intuitionistic fuzzy set and Pythagorean fuzzy set, with an adjustable parameter q that makes it flexible and adaptable to describe the inexact information in decision making. The condition of q -rung orthopair fuzzy set, i.e., sum of q th power of membership degree and nonmembership degree is bounded by one, makes it highly competent and adequate to get over the limitations of existing models. The basic purpose of this study is to establish some aggregation operators under the …q -rung orthopair fuzzy environment with Einstein norm operations. Motivated by innovative features of Einstein operators and dominant behavior of q -rung orthopair fuzzy set, some new aggregation operators, namely, q -rung orthopair fuzzy Einstein weighted averaging, q -rung orthopair fuzzy Einstein ordered weighted averaging, generalized q -rung orthopair fuzzy Einstein weighted averaging and generalized q -rung orthopair fuzzy Einstein ordered weighted averaging operators are defined. Furthermore, some properties related to proposed operators are presented. Moreover, multi-attribute decision making problems related to career selection, agriculture land selection and residential place selection are presented under these operators to show the capability and proficiency of this new idea. The comparison analysis with existing theories shows the superiorities of proposed model. Show more

Keywords: Einstein operators, q-rung orthopair fuzzy numbers, averaging operators, generalized weighted averaging operators

DOI: 10.3233/JIFS-201611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4779-4798, 2021

Authors: Singh, Harjeet | Sharma, R.K. | Malarvel, Muthukumaran

Article Type: Research Article

Abstract: Formation of Gurmukhi character/akshara from the recognized strokes in online handwriting recognition systems is a challenging task. In this paper, the task of character and akshara formation in an unconstrained environment have been addressed. After the recognition of online handwritten strokes the Gurmukhi akshara is formed using a hybrid approach. Two classifiers, namely, Support Vector Machine (SVM) and Recurrent Neural Network (RNN) have been experimented in this study. The classifier, yielded the maximum cross-validation accuracy has been utilized for stroke recognition. A total of 52,500 word samples have been collected from 175 writers in order to train the classifiers. Three …post processing algorithms have been proposed in this article for improving the character and akshara recognition accuracy. The proposed methodology when tested on a dataset of 21,500 aksharas, written by 50 new writers, achieved average the accuracy rate of 97.1% and 87.1% for base character and akshara recognition, respectively. Show more

Keywords: Online handwritten Gurmukhi script recognition, post processing, SVM, RNN, association of strokes

DOI: 10.3233/JIFS-201613

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4799-4809, 2021

Authors: Liu, Zhibing | Zhou, Chi | Wang, Guoli

Article Type: Research Article

Abstract: We consider an order problem for two channels consisting of one common retailer and two competing suppliers that are subject to supply uncertainty. A new concept of supply risk level (SRL) of a channel is proposed to quantitatively characterize the supply risk of the channel due to supply uncertainty. Under different SRLs, we study the order strategies for the two channels in integrated and decentralized supply chains. Regardless of whether the game is integrated or decentralized, we find that the different SRLs give rise to a difference between the belief-degree costs of the two channels that directly influences the optimal …order strategy, market supply and profit of each channel. This implies that the decision maker of the supply chain can take different risk attitudes toward the supply uncertainty of the two channels to adjust the potential market supply and profit of each channel because the decision maker generally replaces the SRL with his or her risk preference. Under given SRLs, we find that integration is a better strategy than decentralization. However, the channel profit with a higher belief-degree cost under decentralization is greater than that under integration in some cases. Finally, proper risk preferences for both channels are suggested to strike a balance between supply reliability and supply risk. Show more

Keywords: Supply uncertainty, supply risk level, supply chain competition, belief-degree cost

DOI: 10.3233/JIFS-201663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4811-4833, 2021

Authors: Bouteraa, Yassine | Abdallah, Ismail Ben | Ibrahim, Atef | Ahanger, Tariq Ahamed

Article Type: Research Article

Abstract: In this paper, a robotic system dedicated to remote wrist rehabilitation is proposed as an Internet of Things (IoT) application. The system offers patients home rehabilitation. Since the physiotherapist and the patient are on different sites, the system guarantees that the physiotherapist controls and supervises the rehabilitation process and that the patient repeats the same gestures made by the physiotherapist. A human-machine interface (HMI) has been developed to allow the physiotherapist to remotely control the robot and supervise the rehabilitation process. Based on a computer vision system, physiotherapist gestures are sent to the robot in the form of control instructions. …Wrist range of motion (RoM), EMG signal, sensor current measurement, and streaming from the patient’s environment are returned to the control station. The various acquired data are displayed in the HMI and recorded in its database, which allows later monitoring of the patient’s progress. During the rehabilitation process, the developed system makes it possible to follow the muscle contraction thanks to an extraction of the Electromyography (EMG) signal as well as the patient’s resistance thanks to a feedback from a current sensor. Feature extraction algorithms are implemented to transform the EMG raw signal into a relevant data reflecting the muscle contraction. The solution incorporates a cascade fuzzy-based decision system to indicate the patient’s pain. As measurement safety, when the pain exceeds a certain threshold, the robot should stop the action even if the desired angle is not yet reached. Information on the patient, the evolution of his state of health and the activities followed, are all recorded, which makes it possible to provide an electronic health record. Experiments on 3 different subjects showed the effectiveness of the developed robotic solution. Show more

Keywords: Gesture control, human robot interaction, internet of things, rehabilitation robotics

DOI: 10.3233/JIFS-201671

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4835-4850, 2021

Authors: Tao, Yuwen | Jiang, Yizhang | Xia, Kaijian | Xue, Jing | Zhou, Leyuan | Qian, Pengjiang

Article Type: Research Article

Abstract: The use of machine learning technology to recognize electrical signals of the brain is becoming increasingly popular. Compared with doctors’ manual judgment, machine learning methods are faster. However, only when its recognition accuracy reaches a high level can it be used in practice. Due to the difference in the data distributions of the training dataset and the test dataset and the lack of training samples, the classification accuracies of general machine learning algorithms are not satisfactory. In fact, among the many machine learning methods used to process epilepsy electroencephalogram (EEG) signals, most are black box methods; however, in medicine, methods …with explanatory power are needed. In response to these three challenges, this paper proposes a novel technique based on domain adaptation learning, semi-supervised learning and a fuzzy system. In detail, we use domain adaptation learning to reduce deviation from the data distribution, semi-supervised learning to compensate for the lack of training samples, and the Takagi-Sugen-Kang (TSK) fuzzy system model to improve interpretability. Our experimental results show that the performance of the new method is better than those of most advanced epilepsy classification methods. Show more

Keywords: EEG signal recognition, epilepsy classification, integrated learning mechanism, domain adaptation learning, semi-supervised learning, TSK fuzzy system

DOI: 10.3233/JIFS-201673

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4851-4866, 2021

Authors: Yang, Yanli | Li, Chenxia

Article Type: Research Article

Abstract: Generalization ability is known as an important performance index of artificial neural networks (ANNs). The generalization ability of an ANN usually refers to its ability to recognize untrained samples, but it lacks quantitative analysis. A method is designed by using frequency-domain signals to observe the generalization ability of deep feedforward neural networks (DFFNNs) which are popular ANN models. This method allows us to observe that the generalization ability of DFFNNs is limited to a small neighborhood around the trained samples. Then, the relationship between sample similarity and the DFFNN’s generalization performance is further analyzed. The analysis results show that the …correlation coefficient between samples has a certain positive correlation with the DFFNN’s generalization performance. Based on this new understanding, an algorithm in which shadows of the trained samples are added into the training set is proposed to improve the generalization ability of DFFNNs. The proposed algorithm is tested with some simulated signals and some real-world data. The tests show that the proposed method can indeed improve the DFFNN’s generalization ability by only changing the training sample set. Show more

Keywords: Deep feedforward neural network, deep learning, artificial neural network, generalization ability, correlation coefficient

DOI: 10.3233/JIFS-201679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4867-4876, 2021

Authors: Shavaki, Fahimeh Hosseinnia | Jolai, Fariborz

Article Type: Research Article

Abstract: Today with the outbreak of the COVID-19 many people prefer to stay home and buy their required products from online sellers and receive them in their home or office at their desired times. This change has increased the workload of online retailers. In an online retailing system, lots of orders containing different products arrive dynamically and must be delivered in the due dates requested by customers, so there is a limited time to retrieve products from their storage locations, pack them, load them on trucks, and deliver to their destinations. In this study, we deal with the integrated order batching …and delivery planning of an online retailer that stores a variety of products in a warehouse and sells them online. A mixed-integer nonlinear programming model is proposed that decides on order batching, scheduling of batches, assigning orders to trucks, and scheduling and routing of trucks simultaneously in an offline setting. This model clarifies the domain of the problem and its complexity. Two rule-based heuristic algorithms are developed to solve the problem in the online setting. The first algorithm deals with two sub-problems of order batching and delivery planning separately and sequentially, while the second algorithm considers the relationship between two sub-problems. An extensive numerical experiment is carried out to evaluate the performance of algorithms in different problem sizes, demonstrating that the second algorithm by integrating two sub-problems leads to a minimum of 14% reduction in cost per delivered order, as the main finding of this study. Finally, the effect of several parameters on the performance of algorithms is analyzed through a sensitivity analysis, and some managerial insights are provided to help the retail managers with their decision-making that are the other findings of this paper. Show more

Keywords: Delivery planning, online retailing, order batching, rule-based heuristic, specific due dates

DOI: 10.3233/JIFS-201690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4877-4903, 2021

Authors: Chandrasekaran, Gokul | Karthikeyan, P.R. | Kumar, Neelam Sanjeev | Kumarasamy, Vanchinathan

Article Type: Research Article

Abstract: Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony …Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms. Show more

Keywords: System-on-chip, test scheduling, Dragonfly algorithm, Ant Lion optimization

DOI: 10.3233/JIFS-201691

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4905-4917, 2021

Authors: Kumar, Deepika | Batra, Usha

Article Type: Research Article

Abstract: Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. However, machine learning algorithms have given rise to progress across different domains. There are various diagnostic methods available for cancer detection. However, cancer detection through histopathological images is considered to be more accurate. In this research, we have proposed the Stacked Generalized Ensemble (SGE) approach for breast cancer classification into Invasive Ductal Carcinoma+ and Invasive Ductal Carcinoma-. SGE is inspired by the stacking model which utilizes output predictions. …Here, SGE uses six deep learning models as level-0 learner models or sub-models and Logistic regression is used as Level – 1 learner or meta – learner model. Invasive Ductal Carcinoma dataset for histopathology images is used for experimentation. The results of the proposed methodology have been compared and analyzed with existing machine learning and deep learning methods. The results demonstrate that the proposed methodology performed exponentially good in image classification in terms of accuracy, precision, recall, and F1 measure. Show more

Keywords: Breast cancer, histopathology images, SGE, classification, machine learning, deep learning

DOI: 10.3233/JIFS-201702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4919-4934, 2021

Authors: Nazari, Mohammad Hassan | Bagheri Sanjareh, Mehrdad | Moradi, Mohammad Bagher | Hosseinian, Seyed Hossein

Article Type: Research Article

Abstract: This paper presents an economical approach for reliability improvement, harmonic mitigation and loss reduction in microgrids and active distribution networks that include of the distributed generations (DGs) considering technical constraints. The proposed method is a stochastic approach based on the calculation of the locational marginal price (LMP) in each DG bus. The problem is as a game-theoretic that each DG is taken as a single player considering its contributions on the aforementioned objectives. In this regard, each player gets a financial incentive as incremental price, based on a fair method using cooperative game-theoretic sharing strategy. In other words, each DG …that aligns its generation with the aforementioned objectives will increase the price of selling energy. This increase in prices will lead to higher profits. Therefore, DGs are interested in volunteering to accomplish network goals. As a tool for system management, the proposed method can control the impact of the pricing in the form of incentives to satisfy each objective depending on its decision in the incentive allocation procedure. To obtain a more realistic framework, demands are considered as the uncertainty parameters. To validate the proposed method, it is evaluated on the real Taiwan Power Company (TPC) network. The promising results indicate that the total loss is decreased by 54.5%, harmonics are mitigated by 12.3% and the reliability is improved by 12.6%. Show more

Keywords: Reliability, loss, pricing, harmonic, microgrid, active distribution network, game theory

DOI: 10.3233/JIFS-201703

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4935-4955, 2021

Authors: Meng, Lv | Shaohong, Feng

Article Type: Research Article

Abstract: To cope with the smooth implementation of apron control transfer at Chinese airports, two new departments were established, namely apron tower and airport operation command center. Therefore, based on interview texts of controllers and commanders of these two departments, this paper uses text mining and Decision-making Trial and Evaluation Laboratory-Interpretative Structural Modeling Method methods to determine key influence factors and factor hierarchy that affect communication and collaboration in their daily work. The results show that for controllers, key influence factors are mainly personnel development and professional abilities. These factors are located at the bottom of the factor hierarchy and are …the basis for ensuring smooth communication and collaboration. For commanders, key influence factors are mainly personnel professional abilities and flight status. These factors are at the top of the factor hierarchy and are focus points that affect communication and collaboration. Hence, the case analysis results show the application potential of these three methods in the field of civil aviation. The combined use of these three methods can enable airport managers to clearly understand the degree of influence between factors. Show more

Keywords: Decision-making Trial and Evaluation Laboratory, interpretative structural modeling method, text mining, communication and collaboration

DOI: 10.3233/JIFS-201704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4957-4966, 2021

Authors: Zhou, Ke | Ma, Gang | Wang, Yafei | Zheng, Junjun | Wang, Shilei | Tang, Yunying

Article Type: Research Article

Abstract: With the development of “Internet+”, online auction platforms of used cars have emerged a lot. As a typical representative of the continuous purchase environment, online sequential auction of used cars faces many uncertainties, including uncertain revenue and risk. To describe them, adopting fuzzy theory to create mean-variance model to estimate the revenue and risk is showed in this paper. Moreover, three types of sellers, aggressive, conservative and rational sellers are analyzed respectively, and strategy models are built, where the multi-criteria optimal function for the latter one is adapted Cobb-Douglas production function. Then, a genetic algorithm based on fuzzy simulation is …proposed through integrating the fuzzy simulation and 0-1 genetic algorithm, which can solve the models validly. Lastly, the practical example from Guazi website shows the optimal strategies derived by models can meet sellers’ demands, especially goals of both higher revenue and lower risk for rational sellers, which proves practicability of the model and validity of algorithm. Show more

Keywords: Online sequential auction, fuzzy theory, optimal strategy, genetic algorithm

DOI: 10.3233/JIFS-201719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4967-4977, 2021

Authors: Yu, Dejian | Chen, Yitong

Article Type: Research Article

Abstract: Green supply chain (GSC) practice can help enterprises expand the market share, enhance competitive advantage, achieve the sustainable development and maintain the balance among economic, social and environmental benefits. Based on these advantages, the amount of literatures in this field is gradually expanding especially in recent years. This paper combines the bibliometric and main path analysis (MPA) method to introduce the current status and development trend, and explore the dynamic evolution of knowledge and main research topics of this domain. The main results are as follows: (1) Sarkis J is the most prolific author and Hong Kong Polytechnic University is …the most productive institution of this field. (2) Articles on main path mainly focus on the application of GSC in various industries and can be divided into two categories based on the research content, including the evaluation and selection of green practices and green supplier, as well as the identification and evaluation of obstacles and drivers in green supply chain management (GSCM) practices. Moreover, the topics of theoretical innovation of evaluation method, evaluation of entire supply chain performance and circular economy (CE) based on the triple bottom line maybe the possible research direction for scholars. In general, this article not only provides a comprehensive and systematic longitudinal bibliometric overview but also presents the trajectory of knowledge diffusion of GSC domain. Show more

Keywords: Green and supply chain (GSC), bibliometrics, main path analysis

DOI: 10.3233/JIFS-201720

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4979-4991, 2021

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

Article Type: Research Article

Abstract: The two-dimensional belief function (TDBF  = (m A , m B )) uses a pair of ordered basic probability distribution functions to describe and process uncertain information. Among them, m B includes support degree, non-support degree and reliability unmeasured degree of m A . So it is more abundant and reasonable than the traditional discount coefficient and expresses the evaluation value of experts. However, only considering that the expert’s assessment is single and one-sided, we also need to consider the influence between the belief function itself. The difference in belief function can measure the difference between two belief functions, based …on which the supporting degree, non-supporting degree and unmeasured degree of reliability of the evidence are calculated. Based on the divergence measure of belief function, this paper proposes an extended two-dimensional belief function, which can solve some evidence conflict problems and is more objective and better solve a class of problems that TDBF cannot handle. Finally, numerical examples illustrate its effectiveness and rationality. Show more

Keywords: Two-dimensional belief function; divergence, Dempster-Shafer evidence theory, evidence conflict

DOI: 10.3233/JIFS-201727

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4993-5000, 2021

Authors: Zhu, Nan | Yin, Yuting

Article Type: Research Article

Abstract: With the great development of image display technologies and the widespread use of various image acquisition device, recapturing high-quality images from high-fidelity LCD (liquid crystal display) screens becomes relatively convenient. These recaptured images pose serious threats on image forensic technologies and bio-authentication systems. In order to prevent the security loophole of image recapture attack, we propose a recaptured image detection method based on multi-resolution residual-based correlation coefficients. Specifically, we first classify the divided image blocks into three categories according to their content complexity. Then, for each classified block, sharpness degree is used as metric to select the local representative block. …Finally, pixel-wise correlation coefficients in the residual of the local representative blocks are adopted as features for training and testing. Single database experiments demonstrate that our proposed method not only performs very close to the state-of-the-art methods on relative low-quality NTU-ROSE and BJTU-IIS databases, but also improves the performance on the most difficult-to-detect ICL-COMMSP database obviously, which verifies the effectiveness of the proposed multi-resolution strategy and the used residual-based correlation coefficients. Besides, mixed database experiments verify the superiority of the generalization ability of our proposed method. Moreover, it is robust to JPEG compression. Show more

Keywords: Image forensics, recaptured image detection, image credibility, bio-authentication, correlation coefficients

DOI: 10.3233/JIFS-201746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5001-5013, 2021

Authors: Yu, Dawei | Yang, Jie | Zhang, Yun | Yu, Shujuan

Article Type: Research Article

Abstract: The Densely Connected Network (DenseNet) has been widely recognized as a highly competitive architecture in Deep Neural Networks. And its most outstanding property is called Dense Connections, which represent each layer’s input by concatenating all the preceding layers’ outputs and thus improve the performance by encouraging feature reuse to the extreme. However, it is Dense Connections that cause the challenge of dimension-enlarging, making DenseNet very resource-intensive and low efficiency. In the light of this, inspired by the Residual Network (ResNet), we propose an improved DenseNet named Additive DenseNet, which features replacing concatenation operations (used in Dense Connections) with addition operations …(used in ResNet), and in terms of feature reuse, it upgrades addition operations to accumulating operations (namely ∑ (·)), thus enables each layer’s input to be the summation of all the preceding layers’ outputs. Consequently, Additive DenseNet can not only preserve the dimension of input from enlarging, but also retain the effect of Dense Connections. In this paper, Additive DenseNet is applied to text classification task. The experimental results reveal that compared to DenseNet, our Additive DenseNet can reduce the model complexity by a large margin, such as GPU memory usage and quantity of parameters. And despite its high resource economy, Additive DenseNet can still outperform DenseNet on 6 text classification datasets in terms of accuracy and show competitive performance for model training. Show more

Keywords: DenseNet, ResNet, deep learning, text classification

DOI: 10.3233/JIFS-201758

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5015-5025, 2021

Authors: Pirozmand, Poria | Ebrahimnejad, Ali | Alrezaamiri, Hamidreza | Motameni, Homayun

Article Type: Research Article

Abstract: In software incremental development methodology, the product develops in several releases. In each release, one set of the requirements is suggested for development. The development team must select a subset of the proposed requirements for development in the next release such that by consideration the limitation of the problem provides the highest satisfaction to the customers and the lowest cost to the company. This problem is known as the next release problem. In complex projects where the number of requirements is high, development teams cannot choose an optimized subset of the requirements by traditional methods, so an intelligent algorithm is …required to help in the decision-making process. The main contributions of this study are fivefold: (1) The customer satisfaction and the cost of every requirement are determined by use of fuzzy numbers because of the possible changing of the customers’ priorities during the product development period; (2) An improved approximate approach is suggested for summing fuzzy numbers of different kinds, (3) A new metaheuristic algorithm namely the Binary Artificial Algae Algorithm is used for choosing an optimized subset of requirements, (4) Experiments performed on two fuzzy datasets confirm that the resulted subsets from the suggested algorithm are free of human mistake and can be a great guidance to development teams in making decisions. Show more

Keywords: Next release problem, software requirements, fuzzy numbers, binary artificial algae algorithm

DOI: 10.3233/JIFS-201759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5027-5041, 2021

Authors: Gulfam, Muhammad | Mahmood, Muhammad Khalid | Smarandache, Florentin | Ali, Shahbaz

Article Type: Research Article

Abstract: In this paper, we investigate two new Dombi aggregation operators on bipolar neutrosophic set namely bipolar neutrosophic Dombi prioritized weighted geometric aggregation (BNDPWGA) and bipolar neutrosophic Dombi prioritized ordered weighted geometric aggregation (BNDPOWGA) by means of Dombi t-norm (TN) and Dombi t-conorm (TCN). We discuss their properties along with proofs and multi-attribute decision making (MADM) methods in detail. New algorithms based on proposed models are presented to solve multi-attribute decision-making (MADM) problems. In contrast, with existing techniques a comparison analysis of proposed methods are also demonstrated to test their validity, accuracy and significance.

Keywords: Bipolar neutrosophic set, bipolar neutrosophic Dombi prioritized aggregation operators, decision-making environment

DOI: 10.3233/JIFS-201762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5043-5060, 2021

Authors: Basher, M.

Article Type: Research Article

Abstract: A k -Zumkeller labeling for the graph G  = (V , E ) is an assignment f of a label to each vertices of G such that each edge uv  ∈ E is assigned the label f  (u ) f  (v ), the resulting edge labels are k distinct Zumkeller numbers. In this paper, we prove that the graph P m  × P n is k -Zumkeller graph for m , n  ≥ 3 while P m  × C n and C m  × C n are k -Zumkeller graphs for n  ≡ 4 (mod2). …Also we show that the graphs P m  ⊗ P n and P m  ⊗ C n for m , n  ≥ 3 admit k -Zumkeller labeling. Further, the graph C m  ⊗ C n where m or n is even admit a k -Zumkeller labeling. Show more

Keywords: Zumkeller number, k-Zumkeller labeling, Cartesian and tensor product of graphs, 05C78

DOI: 10.3233/JIFS-201765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5061-5070, 2021

Authors: Zhai, Jia | Zheng, Haitao | Bai, Manying | Jiang, Yunyun

Article Type: Research Article

Abstract: This paper explores a multiperiod portfolio optimization problem under uncertain measure involving background risk, liquidity constraints and V-shaped transaction costs. Unlike traditional studies, we establish multiperiod mean-variance portfolio optimization models with multiple criteria in which security returns, background asset returns and turnover rates are assumed to be uncertain variables that can be estimated by experienced experts. When the returns of the securities and background assets follow normal uncertainty distributions, we use the deterministic forms of the multiperiod portfolio optimization model. The uncertain multiperiod portfolio selection models are practical but complicated. Therefore, the models are solved by employing a genetic algorithm. …The uncertain multiperiod model with multiple criteria is compared with an uncertain multiperiod model without background risk and an uncertain multiperiod model without liquidity constraint respectively, we discuss how background risk and liquidity affect optimal terminal wealth. Finally, we give two numerical examples to demonstrate the effectiveness of the proposed approach and models. Show more

Keywords: Uncertainty theory, multiple criteria, uncertain multiperiod mean-variance model, background risk, liquidity constraint

DOI: 10.3233/JIFS-201769

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5071-5086, 2021

Authors: Maghawry, Eman | Ismail, Rasha | Gharib, Tarek F.

Article Type: Research Article

Abstract: Paroxysmal Atrial Fibrillation (PAF) is a special class of Atrial Fibrillation. Predicting PAF events from electrocardiogram (ECG) signal streams plays a vital role in generating real-time alerts for cardiac disorders. These alerts are extremely important to cardiologists in taking precautions to prevent their patients from having a stroke. In this study, an effective predictive approach to PAF events using the Extreme Learning Machine classification technique is proposed. Besides, we propose a feature extraction method that integrates new ECG signal features to its time-domain ones. The new features are based on the construction of sparse vectors for peaks in ECG signals …that provide high overlap between similar ECGs. The proposed prediction approach with the new ECG features representation were evaluated on a real PAF dataset using the five-fold cross-validation method. Experiments show promising results for predicting PAF in terms of accuracy and execution time compared to other existing studies. The proposed approach achieved classification accuracy of 97% for non-streaming ECG signals mode and 94.4% for streaming mode. Show more

Keywords: Paroxysmal atrial fibrillation, feature extraction, extreme learning machine, electrocardiogram (ECG) signals classification, streaming ECG Signals

DOI: 10.3233/JIFS-201832

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5087-5099, 2021

Authors: Yao, Shuaiyu | Yang, Jian-Bo | Xu, Dong-Ling

Article Type: Research Article

Abstract: In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three …aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana , Haberman ’s survival , and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes. Show more

Keywords: Probabilistic modeling, interpretable inference and classification, maximum likelihood evidential reasoning (MAKER) framework, belief rule-base, machine learning

DOI: 10.3233/JIFS-201833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5101-5117, 2021

Authors: Zhang, Dongli | Yang, Yanbo | Wang, Weican | You, Xinshang

Article Type: Research Article

Abstract: During the development of regional economy, introducing collaborative innovation is an important policy. Constructing a scientific and effective measurement for evaluating the collaborative innovation degree is essential to determine an optimum collaborative innovation plan. As this problem is complex and has a long-lasting impact, this paper will propose a novel large scale group decision making (LSGDM) method both considering decision makers’ social network and their evaluation quality. Firstly, the decision makers will be detected based on their social connections and aggregated into different subgroups by an optimization algorithm. Secondly, decision makers are weighted according to their important degree and decision …information, where the information is carried by interval valued intuitionistic fuzzy number (IVIFN). During the information processing, IVIFN is put in rectangular coordinate system considering its geometric meaning. And some related novel concept are given based on the barycenter of rectangle region determined by IVIFN. Meanwhile, the criteria’s weights are calculated by the accurate degree and deviation degree. A classical example is used to illustrate the effect of weighting methods. In summary, a large scale group decision making method based on the geometry characteristics of IVIFN (GIVIFN-LSGDM) is proposed. The scientific and practicability of GIVIFN-LSGDM method is illustrated through evaluating four different projects based on the constructed criteria system. Comparisons with the other methods are discussed, followed by conclusions and further research. Show more

Keywords: Keywords: Large scale group decision making, intuitionistic fuzzy number, social network analysis, interval valued intuitionistic fuzzy number, Barycenter

DOI: 10.3233/JIFS-201848

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5119-5138, 2021

Authors: Jenefa, A. | BalaSingh Moses, M.

Article Type: Research Article

Abstract: Application Traffic Identification is an imperative device for sorting out the system as it is the most popular approach to distinguish and characterize the network traffic created from different applications. The classification using conventional Port-based and Payload-based techniques has become counterproductive due to inconsistencies. However, in recent times, approaches with machine learning and statistical techniques have guaranteed higher accuracy. However, learning techniques are inadequate for solving problems with Time and Memory intricacies in vast datasets. Hence, the proposed paper presents a novel scheme of Statistical based traffic classification named Multi-Phased Statistical Based Classification methodology that renders Semi-supervised machines with advanced …K-medoid clustering and C5.0 Classification algorithm. The proposed system displays a classic competence in observing the known and unknown application flows by statistical features utilization scheme that enhances the classification preciseness. Further, the trial results show that the proposed work outperforms previous approaches by achieving a higher granularity of 98–99% and reducing complexities. Ultimately, the new proposed work is evaluated on our campus traffic traces (AU-IDS). It is proven that the proposed approach accomplishes a higher exactness rate and thus encourages its implementation in real-time. Show more

Keywords: Communication networks, machine learning, clustering methods, semi supervised learning, statistical learning

DOI: 10.3233/JIFS-201895

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5139-5157, 2021

Authors: Bai, Haoyue | Zhang, Haofeng | Wang, Qiong

Article Type: Research Article

Abstract: Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both …preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method. Show more

Keywords: Zero shot learning, domain shift, dual auto-encoder, discriminative projection

DOI: 10.3233/JIFS-201920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5159-5170, 2021

Authors: Ramalingam, S. | Baskaran, K.

Article Type: Research Article

Abstract: Wireless Sensor Networks (WSNs) are consistently gathering environmental weather data from sensor nodes on a random basis. The wireless sensor node sends the data via the base station to the cloud server, which frequently consumes immoderate power consumption during transmission. In distribution mode, WSN typically produces imprecise measurable or missing data and redundant data that influence the whole network of WSN. To overcome this complexity, an effective data prediction model was developed for decentralized photovoltaic plants using hybrid Harris Hawk Optimization with Random Forest algorithm (HHO-RF) primarily based on the ensemble learning approach. This work is proposed to predict the …precise data and minimization of error in WSN Node. An efficient model for data reduction is proposed based on the Principal Component Analysis (PCA) for processing data from the sensor network. The datasets were gathered from the Tamil Nadu photovoltaic power plant, India. A low cost portable wireless sensor node was developed for collecting PV plant weather data using Internet of Things (IoT). The experimental outcomes of the proposed hybrid HHO-RF approach were compared with the other four algorithms, namely: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Long Short Term Memory (LSTM) algorithm. Results show that the determination coefficient (R2 ), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of the HHO-RF model are 0.9987, 0.0693, 0.2336 and 0.15881, respectively. For the prediction of air temperature, the RMSE of the proposed model is 3.82 %, 3.84% and 6.92% model in the lowest, average and highest weather days. The experimental outcomes of the proposed hybrid HHO-RF model have better performance compared to the existing algorithms. Show more

Keywords: Wireless sensor network, data prediction, internet of things, machine learning, harris hawk optimization, random forest, photovoltaic plant, error minimization

DOI: 10.3233/JIFS-201921

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5171-5195, 2021

Authors: Jin, Ting | Ding, Hui | Li, Bo | Xia, Hongxuan | Xue, Chenxi

Article Type: Research Article

Abstract: As an economic lever in financial market, interest rate option is not only the function of facilitating the bank to adjust the market fund supply and demand relation indirectly, but also provides the guarantee for investors to choose whether to exercise the right at the maturity date, thereby locking in the interest rate risk. This paper mainly studies the price of the interest rate ceiling as well as floor under the uncertain environment. Firstly, from the perspective of expert reliability, rather than relying on a large amount of historical financial data, to consider interest rate trends, and further assume that …the dynamic change of the interest rate conforms to the uncertain process. Secondly, since uncertain fractional-order differential equations (UFDEs) have non-locality features to reflect memory and hereditary characteristics for the asset price changes, thus is more suitable to model the real financial market. We construct the mean-reverting interest rate model based on the UFDE in Caputo type. Then, the pricing formula of the interest rate ceiling and floor are provided separately. Finally, corresponding numerical examples and algorithms are given by using the predictor-corrector method, which support the validity of the proposed model. Show more

Keywords: Fractional differential equation, uncertain theory, interest rate, mean-reverting, predictor-corrector method

DOI: 10.3233/JIFS-201930

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5197-5206, 2021

Authors: Abu-Saleem, M.

Article Type: Research Article

Abstract: The main aim of this article is to present neutrosophic folding and neutrosophic retractions on a single-valued neutrosophic graph Ğ from the viewpoint of geometry and topology. For this reason, we use a sequence of neutrosophic transformations on Ğ to obtain a new single-valued neutrosophic graph G ˇ 1 which contains different parameters under new conditions. We deduce the isometric neutrosophic folding on neutrosophic spheres and neutrosophic torii. Also, we determine the relationship between the limit neutrosophic folding and the limit of neutrosophic retraction on Ğ. Theorems regulating these relations are attained.

Keywords: Single valued neutrosophic graph, neutrosophic folding, neutrosophic retraction, 51H20, 57N10, 57M05, 14F35, 20F34

DOI: 10.3233/JIFS-201957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5207-5213, 2021

Authors: Feng, Rui | Huang, Cheng-Chen | Luo, Kun | Zheng, Hui-Jun

Article Type: Research Article

Abstract: The West Lake of Hangzhou, a world famous landscape and cultural symbol of China, suffered from severe air quality degradation in January 2015. In this work, Random Forest (RF) and Recurrent Neural Networks (RNN) are used to analyze and predict air pollutants on the central island of the West Lake. We quantitatively demonstrate that the PM2.5 and PM10 were chiefly associated by the ups and downs of the gaseous air pollutants (SO2 , NO2 and CO). Compared with the gaseous air pollutants, meteorological circumstances and regional transport played trivial roles in shaping PM. The predominant meteorological factor …for SO2 , NO2 and surface O3 was dew-point deficit. The proportion of sulfate in PM10 was higher than that in PM2.5 . CO was strongly positively linked with PM. We discover that machine learning can accurately predict daily average wintertime SO2 , NO2 , PM2.5 and PM10 , casting new light on the forecast and early warning of the high episodes of air pollutants in the future. Show more

Keywords: Random forest, recurrent neural network, air pollutants prediction

DOI: 10.3233/JIFS-201964

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5215-5223, 2021

Authors: Elmuogy, Samir | Hikal, Noha A. | Hassan, Esraa

Article Type: Research Article

Abstract: Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID-19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s …life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training, 524 validation, 524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 in terms of accuracy, precision, recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool. Show more

Keywords: Deep learning, CNN, COVID-19 dataset, automatic classification, CT scan

DOI: 10.3233/JIFS-201985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5225-5238, 2021

Authors: Lin, Rongde | Li, Jinjin | Chen, Dongxiao | Huang, Jianxin | Chen, Yingsheng

Article Type: Research Article

Abstract: Fuzzy covering rough set model is a popular and important theoretical tool for computation of uncertainty, and provides an effective approach for attribute reduction. However, attribute reductions derived directly from fuzzy lower or upper approximations actually still occupy large of redundant information, which leads to a lower ratio of attribute-reduced. This paper introduces a kind of parametric observation sets on the approximations, and further proposes so called parametric observational-consistency, which is applied to attribute reduction in fuzzy multi-covering decision systems. Then the related discernibility matrix is developed to provide a way of attribute reduction. In addition, for multiple observational parameters, …this article also introduces a recursive method to gradually construct the multiple discernibility matrix by composing the refined discernibility matrix and incremental discernibility matrix based on previous ones. In such case, an attribute reduction algorithm is proposed. Finally, experiments are used to demonstrate the feasibility and effectiveness of our proposed method. Show more

Keywords: Attribute reduction, fuzzy discernibility matrix, fuzzy multi-covering systems, incremental discernibility matrix, observational consistency, refined discernibility matrix

DOI: 10.3233/JIFS-201998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5239-5253, 2021

Authors: Qi, Ping | Shu, Hong | Zhu, Qiang

Article Type: Research Article

Abstract: Computation offloading is a key computing paradigm used in mobile edge computing. The principle of computation offloading is to leverage powerful infrastructures to augment the computing capability of less powerful devices. However, the most existing computation offloading algorithms assume that the mobile device is not moving, and these algorithms do not take into account the reliability of task execution. In this paper, we firstly present the formalized description of the workflow, the wireless signal, the wisdom medical scenario and the moving path. Then, inspired by the Bayesian cognitive model, a trust evaluation model is presented to reduce the probability of …failure for task execution based on the reliable behaviors of multiply computation resources. According to the location and the velocity of the mobile device, the execution time and the energy consumption model based on the moving path are constructed, task deferred execution and task migration are introduced to guarantee the service continuity. On this basis, considering the whole scheduling process from a global viewpoint, the genetic algorithm is used to solve the energy consumption optimization problem with the constraint of response time. Experimental results show that the proposed algorithm optimizes the workflow under the mobile edge environment by increasing 20.4% of successful execution probability and decreasing 21.5% of energy consumption compared with traditional optimization algorithms. Show more

Keywords: Edge computing, computation offloading, trust evaluation model, energy consumption

DOI: 10.3233/JIFS-202025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5255-5273, 2021

Authors: Lun, Xiangmin | Yu, Zhenglin | Wang, Fang | Chen, Tao | Hou, Yimin

Article Type: Research Article

Abstract: In order to develop an efficient brain-computer interface system, the brain activity measured by electroencephalography needs to be accurately decoded. In this paper, a motor imagery classification approach is proposed, combining virtual electrodes on the cortex layer with a convolutional neural network; this can effectively improve the decoding performance of the brain-computer interface system. A three layer (cortex, skull, and scalp) head volume conduction model was established by using the symmetric boundary element method to map the scalp signal to the cortex area. Nine pairs of virtual electrodes were created on the cortex layer, and the features of the time …and frequency sequence from the virtual electrodes were extracted by performing time-frequency analysis. Finally, the convolutional neural network was used to classify motor imagery tasks. The results show that the proposed approach is convergent in both the training model and the test model. Based on the Physionet motor imagery database, the averaged accuracy can reach 98.32% for a single subject, while the averaged values of accuracy, Kappa, precision, recall, and F1-score on the group-wise are 96.23%, 94.83%, 96.21%, 96.13%, and 96.14%, respectively. Based on the High Gamma database, the averaged accuracy has achieved 96.37% and 91.21% at the subject and group levels, respectively. Moreover, this approach is superior to those of other studies on the same database, which suggests robustness and adaptability to individual variability. Show more

Keywords: Brain-computer interface, Electroencephalography, motor imagery, convolutional neural network

DOI: 10.3233/JIFS-202046

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5275-5288, 2021

Authors: Liu, Jin | Xie, Jinsheng | Ahmadzade, Hamed | Farahikia, Mehran

Article Type: Research Article

Abstract: Entropy is a measure for characterizing indeterminacy of a random variable or an uncertain variable with respect to probability theory and uncertainty theory, respectively. In order to characterize indeterminacy of uncertain variables, the concept of exponential entropy for uncertain variables is proposed. For computing the exponential entropy for uncertain variables, a formula is derived via inverse uncertainty distribution. As an application of exponential entropy, portfolio selection problems for uncertain returns are optimized via exponential entropy-mean models. For better understanding, several examples are provided.

Keywords: Uncertain variable, uncertainty theory, exponential entropy, inverse uncertainty distribution, portfolio selection

DOI: 10.3233/JIFS-202073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5289-5293, 2021

Authors: Li, Yufeng | Jiang, HaiTian | Lu, Jiyong | Li, Xiaozhong | Sun, Zhiwei | Li, Min

Article Type: Research Article

Abstract: Many classical clustering algorithms have been fitted into MapReduce, which provides a novel solution for clustering big data. However, several iterations are required to reach an acceptable result in most of the algorithms. For each iteration, a new MapReduce job must be executed to load the dataset into main memory, which results in high I/O overhead and poor efficiency. BIRCH algorithm stores only the statistical information of objects with CF entries and CF tree to cluster big data, but with the increase of the tree nodes, the main memory will be insufficient to contain more objects. Hence, BIRCH has to …reduce the tree, which will degrade the clustering quality and decelerate the whole execution efficiency. To deal with the problem, BIRCH was fitted into MapReduce called MR-BIRCH in this paper. In contrast to a great number of MapReduce-based algorithms, MR-BIRCH loads dataset only once, and the dataset is processed parallel in several machines. The complexity and scalability were analyzed to evaluate the quality of MR-BIRCH, and MR-BIRCH was compared with Python sklearn BIRCH and Apache Mahout k-means on real-world and synthetic datasets. Experimental results show, most of the time, MR-BIRCH was better or equal to sklearn BIRCH, and it was competitive to Mahout k-means. Show more

Keywords: Clustering, BIRCH, k-means, MapReduce, Hadoop

DOI: 10.3233/JIFS-202079

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5295-5305, 2021

Authors: Hu, Chaofang | Zhang, Yuting

Article Type: Research Article

Abstract:  An interactive α -satisfactory method via relaxed order of desirable α -satisfactory degrees is proposed for multi-objective optimization with fuzzy parameters and linguistic preference in this paper. Fuzzy parameters existing in objectives and constraints of multi-objective optimization are defined as fuzzy numbers and α -level set is used to build the feasible domain of parameters. On the basis, the original problem with fuzzy parameters is transformed into multi-objective optimization with fuzzy goals. Linguistic preference of decision-maker is modelled by the relaxed order of desirable α -satisfactory degrees of all the objectives. In order to achieve a compromise between optimization and …preference, the multi-objective optimization problem is divided into two single-objective sub-problems: the preliminary optimization and the linguistic preference optimization. A preferred solution can be found by parameter adjustment of inner-outer loop. The minimum stable relaxation algorithm of parameter is developed for calculating the relaxation bound of maximum desirable satisfaction difference. The M-α -Pareto optimality of solution is guaranteed by the test model. The effectiveness, flexibility and sensitivity of the proposed method are well demonstrated by numerical example and application example to heat conduction system. Show more

Keywords: Multi-objective optimization, linguistic preference, fuzzy parameter, satisfactory degree

DOI: 10.3233/JIFS-202114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5307-5322, 2021

Authors: Konstantakopoulos, Grigorios D. | Gayialis, Sotiris P. | Kechagias, Evripidis P. | Papadopoulos, Georgios A. | Tatsiopoulos, Ilias P.

Article Type: Research Article

Abstract: Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of …them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm’s efficiency. The results indicate that the algorithm’s solutions have small deviation from the best-published and some non-dominated solutions are also obtained. Show more

Keywords: Vehicle routing problem, time windows, simultaneous pickups and deliveries, multiobjective optimization, evolutionary algorithm, logistics

DOI: 10.3233/JIFS-202129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5323-5336, 2021