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

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