Journal of Intelligent & Fuzzy Systems - Volume 43, issue 5

Authors: Prabhu, S. | Deepa, S. | Arulperumjothi, M. | Susilowati, Liliek | Liu, Jia-Bao

Article Type: Research Article

Abstract: Power utilities must track their power networks to respond to changing demand and availability conditions to ensure effective and efficient operation. As a result, several power companies continuously employ phase measuring units (PMUs) to continuously check their power networks. Supervising an electric power system with the fewest possible measurement equipment is precisely the vertex covering graph-theoretic problems otherwise a variation of the dominating set problem, in which a set D is defined as a power dominating set (PDS) of a graph if it supervises every vertex and edge in the system with a couple of rules. If the distance …vector eccentrically characterizes each node in G with respect to the nodes in R , then the subset R of V  (G ) is a resolving set of G . The problem of finding power dominating set and resolving set problems are proven to be NP-complete in general. The finite subset R of V  (G ) is said to be resolving-power dominating set (RPDS) if it is both resolving and power dominating set, which is another NP-complete problem. The η p  (G ) is the minimal cardinality of an RPDS of a graph G . A neural network is a collection of algorithms that tries to figure out the underlying correlations in a set of data by employing a method that replicates how the human brain functions. Various neural networks have seen rapid progress in multiple fields of study during the last few decades, including neurochemistry, artificial intelligence, automatic control, and informational sciences. Probabilistic neural networks (PNNs) offer a scalable alternative to traditional back-propagation neural networks in classification and pattern recognition applications. They do not necessitate the massive forward and backward calculations that ordinary neural networks entail. This paper investigates the resolving-power domination number of probabilistic neural networks. Show more

Keywords: Metric dimension, basis, phasor measurement unit, power domination, probabilistic neural network, resolving set

DOI: 10.3233/JIFS-220218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6253-6263, 2022

Authors: Kumar, B. Praveen | Hariharan, K. | Shanmugam, R. | Shriram, S. | Sridhar, J.

Article Type: Research Article

Abstract: Integration of the latest technological advancements such as Internet of Things (IoT) and Computational Intelligence (CI) techniques is an active research area for various industrial applications. The rapid urbanization and exponential growth of vehicles has led to crowded traffic in cities. The deployment of IoT infrastructures for building smart and intelligent traffic management system greatly improves the quality and comfort of city dwellers. This work aims at building a cost effective IoT enabled traffic forecasting system using deep learning techniques. The case study experimentation is done in a real time traffic environment. The main contributions of this work include: (i) …deploying road side sensor station built with ultrasonic sensor and Arduino Uno controller for obtaining traffic flow data (ii) building an IoT cloud system based on open source Thingspeak cloud platform for monitoring real time traffic (iii) performing short term traffic forecast using Recurrent Neural Network (RNN) models such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The performance of the prediction model is compared with the traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) and Convolutional Neural Network (CNN). The results show good performance metrics with RMSE of 5.8, 7.9, 10.2 for LSTM model and 6.7, 8.6, 10.9 for GRU model for three different scenarios such as whole day, morning congested hour and evening congested hour datasets. Show more

Keywords: IoT, cloud, vehicle detector, traffic flow forecast, time series prediction, RNN, LSTM, GRU

DOI: 10.3233/JIFS-220230

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6265-6276, 2022

Authors: Vinothkumar, V. | Kanimozhi, R.

Article Type: Research Article

Abstract: To increase the life and efficiency of power electronics equipment in a utility distribution system, the power quality improvement is essential part. In this work, to improve power quality by using Robust Resilient Back Propagation Neural Network (RBPNN) scheme for a Photovoltaic (PV)-Integrated Unified Power Quality Conditioner (UPQC) with cascaded multi-level inverter configurations are described. Among the proposed methods, there is no need to use a transformer and filter when multilevel UPQC is applied, and it is one of the great advantages. The proposed UQPC offers a PV array composition with a power converter connected to a DC-link capacitor that …can compensate for voltage sag, swell, voltage interruption, harmonics and reactive power. The Robust Resilient Back Propagation Neural Network controller is generate gating pulses to the UPQC. The reference currents and voltages for the controller are estimated using Synchronous Reference Frame (SRF) theory. The proposed cascaded multi-level inverter-based UPQC is designed using Matlab/Simulink Software. The simulation results confirm that the proposed method gives good results compared with existing Adaptive neural Fuzzy Inference System (ANFIS) and fuzzy logic methods. A real-time hardware system is also established to validate the simulation results. The effectiveness of the proposed system RBPN-UPQC approach is compared for both simulation and experimental results gives better low THD level 1.22%. Show more

Keywords: Unified power quality conditioner, photovoltaic, resilient back propagation neural network, harmonics, power quality

DOI: 10.3233/JIFS-220231

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6277-6294, 2022

Authors: Kırmacı, Volkan | Guler, Evrim | Kaya, Hüseyin

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-220274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6295-6306, 2022

Authors: Jain, Vipin | Kashyap, Kanchan Lata

Article Type: Research Article

Abstract: This work presents the analysis of significant sentiments and attitudes of people towards the COVID-19 vaccination. The tweeter messages related to the COVID-19 vaccine is used for sentiment evaluation in this work. The proposed work consists of two steps: (i) natural processing language (NLP) and (ii) classification. The NLP is utilized for text pre-processing, tokenization, data labelling, and feature extraction. Further, a stack-based ensemble machine learning model is used to classify sentiments as positive, negative, or neutral. The stack ensemble machine learning model includes seven heterogeneous machine learning techniques namely, Naive Bayes, Logistic regression, Decision Tree, Random Forest, AdaBoost Classifier, …Gradient Boosting, and extreme Gradient Boosting (XGB). The highest classification accuracy of 97.2%, 88.34%, 88.22%, 85.23%, 86.30%, 87.54%, 86.63%, and 88.78% is achieved by ensemble machine learning model, Logistic regression, AdaBoost, Decision Tree, Naive Bayes, Random Forest, Gradient Boosting, and XGB Classifier, respectively. Show more

Keywords: COVID-19 vaccinations, sentiments, social-media, machine learning, ensemble machine learning

DOI: 10.3233/JIFS-220279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6307-6319, 2022

Authors: Saad, Muhammad | Rafiq, Ayesha

Article Type: Research Article

Abstract: T-spherical fuzzy sets, the direct extension of fuzzy sets, intuitionistic fuzzy sets and picture fuzzy sets are examined in this composition, and a mathematical examination among them is set up. A T-spherical fuzzy set can demonstrate phenomenon like choice utilizing four trademark capacities indicating the level of choice of inclusion, restraint, resistance, and exclusion, another example of such situation is that human opinion cannot be restricted to yes or no but it can be yes, abstain, no and refusal. T-spherical fuzzy set can deal the said situation with a boundless space. With the assistance of some mathematical outcomes, it is …talked about that current similarity measures have a few drawbacks and could not be implemented where the data is in T-spherical fuzzy mode. Thus, some new similarity measures in T-spherical fuzzy environment are proposed, with the assistance of certain outcomes, it is demonstrated that the suggested similarity measures are generalization of current ones. Further the proposed similarity measures are applied in pattern recognition with numerical supportive examples. The maximum spanning tree clustering algorithm has been extended into T-spherical fuzzy context and supports our theory with numerical examples. A parallel investigation of fresh and existing similarity measures have been made and some of the benefits of designated work have been discussed. Show more

Keywords: T-spherical fuzzy sets, T-spherical fuzzy similarity measures, pattern recognition, maximum spanning tree, clustering

DOI: 10.3233/JIFS-220289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6321-6331, 2022

Authors: Suphalakshmi, A. | Ahilan, A. | Jeyam, A. | Subramanian, Malliga

Article Type: Research Article

Abstract: Cervical cancer is the most common and deadly malignancy affecting women worldwide. The prediction and treatment of this malignancy are necessary in order to avoid serious complications. In recent days, deep learning has enhanced the accuracy of cervical cancer prediction in its early stages. In this study, a deep learning based EN-FELM approach is proposed to detect and classify the cervical cells. Initially, the pap smear images are pre-processed to eliminate the background distortions. The EfficientNet is a reversed bottleneck MBConv used for feature extraction. Consequently, fuzzy extreme learning machine (FELM) is used to classify the healthy, benign, low squamous …intraepithelial lesions (LSIL) and high squamous intraepithelial lesions (HSIL). The proposed model acquires the best classification accuracy on Herlev and SIPaKMeD datasets range of 99.6% and 98.5% respectively. As a result, the classification using FELM produces more efficient and accurate result which is significantly high compared to the traditional classifiers. The proposed EN-FELM improves the overall accuracy of 0.2%, 0.13% and 14.6% better than Autoencoder, LSTM and KNN with CNN respectively. Show more

Keywords: Cervical cancer, fuzzy extreme learning machine (FELM), efficientnet, pap smear images, classification

DOI: 10.3233/JIFS-220296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6333-6342, 2022

Authors: Ulu, Cenk

Article Type: Research Article

Abstract: Almost all exact inversion methods provide inverse solutions for only one input variable of fuzzy systems. These methods have certain limitations on the fuzzy system structure such as monotonic rule bases, singleton rule consequents, and invertibility check. These requirements limit the modeling capabilities of the fuzzy systems and also may result in poor application performances. In this study, an exact analytical inversion method is presented for multi-input-single-output decomposable TS fuzzy systems with either singleton or linear consequents. In the proposed method, fuzzy system structures do not need to have monotonic rule bases, singleton rule consequents, or any invertibility conditions. Thus, …more flexible fuzzy systems can be used in inverse model based applications. The proposed method provides a simple and systematic way to obtain unique inverse solutions of all input variables simultaneously with respect to any desired system output value. For this purpose, an inversion trajectory approach that guarantees the existence and uniqueness of the inverse solutions is introduced. The inversion trajectory consists of a set of paths defined on the specific edges of universe of discourses of the decomposed fuzzy subsystems. Using this approach, the inverse definition of the overall fuzzy system can easily be derived only by inverting the related decomposed fuzzy subsystems on this inversion trajectory and then combining their inverse definitions. In this way, the inverse definition of the overall fuzzy system is obtained as consisting of analytical solutions of linear and quadratic equations for singleton and linear consequent cases, respectively. Simulation studies are given for the inversion of two and three-input-single-output fuzzy systems, and the exactness and effectiveness of the proposed method are demonstrated. Show more

Keywords: Fuzzy systems, decomposability, inversion, multivariable systems, Takagi-Sugeno fuzzy systems

DOI: 10.3233/JIFS-220329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6343-6356, 2022

Authors: Karabacak, Yusuf | Yaşar, Ali | Saritaş, İsmail

Article Type: Research Article

Abstract: In this paper, a simulation study enhanced to model that the speed control of brushless direct current (BLDC) motors used in electric vehicles with intelligent control methods. The simulation study was prepared in Matlab/Simulink environment. The first control method is Type-1 fuzzy logic control (T1FLC), and the second control method is the Intermittent Type-2 fuzzy logic control (IT2FLC) model. Membership functions for different membership numbers have been created for both types of FLC models. These are 3×3, 5×5, 7×7. Control methods are prepared in Matlab/M-file environment. The model is defined as the input variable of the error, which is the …difference between the reference speed and the motor speed, and the output variable of the Pulse Width Modulation (PWM) signal applied to the motor. The simulation study maintains the speed of the BLDC motor up to the reference speed with T1FLC and IT2FLC controllers, depending on the reference speed and applied load values. Depending on the number of different memberships, the effects of controller performances on the control of motor speed have been observed. The graphs and findings of the experiment are shown in the results and discussion section. Show more

Keywords: Speed control, fuzzy logic system, BLDC motor

DOI: 10.3233/JIFS-220344

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6357-6370, 2022

Authors: Al Ghour, Samer

Article Type: Research Article

Abstract: Let (Y , σ , B ) be a soft topological space. We introduce two new classes of soft subsets of (Y , σ , B ): soft connectedness relative to (Y , σ , B ) and soft θ -connectedness relative to (Y , σ , B ). We show that the class of soft connected subsets relative to (Y , σ , B ) includes the class of soft θ -connected subsets relative to (Y , σ , B ), but that these two classes do not always coincide. However, they coincide when (Y , σ , B …) is soft regular. We have provided several properties for each of these classes of soft sets. As two main results, we prove that for a given soft function f pu  : (Y , σ , B ) ⟶ (Y , σ , B ) and a soft subset H of (Y , σ , B ), the soft set f pu  (H ) is θ -connected relative to (Y , σ , B ) if (f pu is soft weakly continuous and H is connected relative to (Y , σ , B )) or (f pu is soft θ -continuous and H is θ -connected relative to (Y , σ , B )). Also, we investigate the correspondence between our new concepts in a soft topological space and their corresponding topological spaces properties. Moreover, we provide some examples to illustrate the obtained results and relationships. Show more

Keywords: θ-closure, soft separation, soft connected, soft θ-continuous, soft generated soft topological space, soft induced topological spaces

DOI: 10.3233/JIFS-220371

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6371-6381, 2022