Journal of Intelligent & Fuzzy Systems - Volume 43, issue 4

Authors: Xiao, Hui-Min | Wu, Shou-Wen | Wang, Liu

Article Type: Research Article

Abstract: In the process of large-scale group decision making (LSGDM), probabilistic linguistic term set (PLTS) is an useful tool to represent the preferences of expert. There is a common case that experts tend to provide incomplete preferences due to various reasons. However, previous methods which cope with the missing values never took experts′ level of cognition over alternatives and attributes into account. In reality, because of limited knowledge reservation and the complexity of decision problem, experts have diverse familiarity with each scheme and attribute. For handling the defect, we propose a novel method to fill missing preference values, based on …the combination of knowledge-match degree and trust degree of experts providing reference information. We obtain the knowledge-match degree through the accuracy and reliability of preference as well as the trust degree through social network analysis technology (SNA), and use the probabilistic linguistic weighted average operator (PLWA) to integrate the referential values into preferences of the missing expert. Moreover, to solve the consensus problem at minimal cost, a consensus model based minimum adjust is developed in which the consensus degree of identified elements are all lowest at three aspects including decision matrix, internal experts and intra-group. On the basis of the trust relationship, revising the preference with low consensus guarantees regulated experts′ real aspiration. In addition, a new approach to measure the weight of sub-group is proposed in the light of trust in-degree which considers the reliability of experts in the same subgroup.The feasibility and validity of the LSGDM method are tested by using a numerical example and comparing with other methods. Show more

Keywords: Incomplete preference, knowledge-match degree, trust degree, social network analysis, probabilistic linguistic term set

DOI: 10.3233/JIFS-212569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4037-4060, 2022

Authors: Younus, Awais | Ghaffar, Iram

Article Type: Research Article

Abstract:  Optimal control is a very important field of study, not only in theory but in applications, and fractional optimal control is also a significant branch of research in theory and applications. Based on the concept of fuzzy process, a fuzzy fractional optimal control problem is presented. In this article, we derived the necessary and sufficient optimality conditions for a class of fuzzy-fractional optimal control problems (FFOCPs) with gH-Atangana-Baleanu fuzzy-fractional derivative expressed in Caputo sense. The main aim is to find the best possible control that minimizes the fuzzy performance index and satisfies the related ABC fuzzy-fractional dynamical systems. We also …presented some examples for more illustration of the subject. Show more

Keywords: Fuzzy-fractional derivative, Atangana-Baleanu fuzzy-fractional derivative, generalized Hakahara differentiability, 90C46, 34K36, 93C42

DOI: 10.3233/JIFS-213028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4061-4070, 2022

Authors: Gnanavel, V. K. | Baskaran, J.

Article Type: Research Article

Abstract: Power quality disturbance (PQD) defines the presence of inconsistencies that occur in the usual wave shapes of voltage and current signals. Power quality is considered the main challenge for power industry with the increase in dynamic load and highly subtle electronic devices. Besides, the islanding events, particularly unintended islanding, grasp significant challenges and it needs to be identified at the early stage. Islanding is an anomalousstate in the power system, where the distributed generators (DGs) are placed on supplying electrical energy to the local load even after the shortage of the major grid. Therefore, it is essential to identify and …differentiate the PQ events and islanding events in ensuring pollution-free power, equipment, and labor safety. With this motivation, this paper presents an automated optimal deep learning based islanding detection (AODL-ID) technique. The proposed AODL-ID technique involves three major stages namely decomposition, classification, and hyperparameter tuning. Firstly, an empirical mode decomposition (EMD) approach is utilized to decompose the basic signals from the polluted signals. In addition, bidirectional gated recurrent neural network (BiGRNN) technique is employed for the classification of islanding and non-islanding PQ events in the wind energy penetrated DG systems by means of features (Voltage and current (RMS, half-cycle, peak and fundamental) Frequency. Power Factor / Cos Phi. Power and energy (active, reactive, harmonic, apparent)). Since the hyperparameters play a significant role in overall classification performance, the hyperparameter tuning of the BiGRNN model takes place using chaotic crow search algorithm (CCSA). To examine the enhanced classification outcome of the AODL-ID technique, a set of experimental analyses is carried out and the outcomes are investigated interms of various evaluation metrics. The simulation outcomes highlighted the supremacy of the AODL-ID technique over the compared techniques. Show more

Keywords: Distributed generation systems, Islanding detection, power quality, deep learning, parametertuning, electrical energy

DOI: 10.3233/JIFS-213129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4071-4081, 2022

Authors: Zhang, Zhe | Zhang, Yiyang | Li, Xiang | Qian, Yurong | Zhang, Tao

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4083-4093, 2022

Authors: Musa, Sagvan Younis | Asaad, Baravan Abdulmuhsen

Article Type: Research Article

Abstract: The most significant and fundamental topological property is connectedness (resp. disconnectedness). This property highlights the most important characteristics of topological spaces and helps to distinguish one topology from another. Taking this into consideration, we investigate bipolar hypersoft connectedness (resp. bipolar hypersoft disconnectedness) for bipolar hypersoft topological spaces. With the help of an example, we show that if there exist a non-null, non-whole bipolar hypersoft sets which is both bipolar hypersoft open and bipolar hypersoft closed over 𝒰, then the bipolar hypersoft space need not be a bipolar hypersoft disconnected. Furthermore, we present the concepts of separated bipolar hypersoft sets …and bipolar hypersoft hereditary property. Show more

Keywords: Bipolar hypersoft connected (resp. bipolar hypersoft disconnected), bipolar hypersoft topology, bipolar hypersoft sets, separated bipolar hypersoft sets, bipolar hypersoft hereditary property

DOI: 10.3233/JIFS-213009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4095-4105, 2022

Authors: Khurana, Khushboo | Deshpande, Umesh

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4107-4132, 2022

Authors: Wu, Zhao | Jiang, Feng | Cao, Rui

Article Type: Research Article

Abstract: The rapid and effective identification of leaf diseases of woody fruit plants can help fruit farmers prevent and cure diseases in time to improve fruit quality and minimize economic losses, which is of great significance to fruit planting. In recent years, deep learning has shown its unique advantages in image recognition. This paper proposes a new type of network based on deep learning image recognition method to recognize leaf diseases of woody fruit plants. The network merges the output of the convolutional layer of ResNet101 and VGG19 to improve the feature extraction ability of the entire model. It uses the …transfer learning method to partially load the trained network weights, reducing model training parameters and training time. In addition, an attention mechanism is added to improve the efficiency of network information acquisition. Meanwhile, dropout, L2 regularization, and LN are used to prevent over-fitting, accelerate convergence, and improve the network’s generalization ability. The experimental results show that the overall accuracy of woody fruit plant leaf diseases identification based on the model proposed in this paper is 86.41%. Compared with the classic ResNet101, the accuracy is improved by 1.71%, and the model parameters are reduced by 96.63%. Moreover, compared with the classic VGG19 network, the accuracy is improved by 2.08%, and the model parameters are reduced by 96.42%. After data set balancing, the overall identification accuracy of woody fruit plant leaf diseases based on the model proposed in this paper can reach 86.73%. Show more

Keywords: Model fusion, transfer learning, neural network, image recognition

DOI: 10.3233/JIFS-213388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4133-4144, 2022

Authors: Li, Qi | Hong, Liang

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-220201

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4145-4159, 2022

Authors: Mohammed, Parves | Jabeen Begum, S.

Article Type: Research Article

Abstract: In present scenario, Heart Disease has become the vital cause of mortality and diagnosis of heart diseases is a great confrontation in the field of medical data analysis. Data Mining is an efficient technique for processing and analyzing larger databases for deriving hidden knowledge appropriately. Hence, it is incorporated in medical data analysis for assisting in effective decision making and disease predictions. With that concern, this paper concentrates on framing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using the advanced data mining conceits. The model considers the significant features of patient data that are available in benchmark datasets. Here, …the main objective of the proposed model is to enhance the classification accuracy of patient data on classes under NORMAL and ABNORMAL. For enhancing the classification accuracy, the proposed integrated model utilizes the algorithms such as Decision Tree Algorithm, Naive Baye’s Classification and Ensemble Classifiers called Random Forest and Bagging. Further, performance evaluation is performed for analyzing the proposed work. For that, images from UCI repository are utilized and the comparative analysis shows that the proposed work produces better results than the existing models compared. Show more

Keywords: Heart disease diagnosis, data mining, classification accuracy, decision tree algorithm, Naive Baye’s classification, random forest and bagging

DOI: 10.3233/JIFS-220306

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4161-4171, 2022

Authors: Gopinath, S. | Sakthivel, K. | Lalitha, S.

Article Type: Research Article

Abstract: The recent advancement of big data technology causes the data from agriculture domain to enter into the big data. They are not conventional techniques in existence to process such a large volume of data. The processing of large datasets involves parallel computation and analysis model. Hence, it is necessary to use big data analytics framework to process a large image datasets. In this paper, an automated big data framework is presented to classify the plant disease condition. This framework consists of a series operations that leads into a final step. When the classification is carried out using novel image classifier. …The image classifier is designed using a Convolutional Recurrent Neural Network Classifier (CRNN) algorithm. The classifier is designed in such a way that it provides classification between a normal leaf and an abnormal leaf. The classification of plant images over large datasets that includes banana plant, pepper, potato, and tomato plant. Which is compared with other existing big data plant classification techniques like convolutional neural network, recurrent neural network, and deep neural network, artificial neural network with forward and backward propagation. The result shows that the proposed method obtains improved detection and classification of diseased plants compared to other the convolutional neural network (94.14%), recurrent neural network (94.07%), deep neural network (94%), artificial neural network with forward (93.96%), and backward propagation method (93.66%). Show more

Keywords: Big data technology, convolutional recurrent neural network, weighted naïve bayesian network classifier algorithm, plant disease classification

DOI: 10.3233/JIFS-220747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4173-4186, 2022