Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Rafikiran, Shaik. | Devadasu, G. | Rajendhar, P. | Likhitha, R. | Basha, CH Hussaian

Article Type: Research Article

Abstract: The fuel cell-dependent electric vehicle systems are giving an important role in the present automotive systems because their features are less air pollution, high flexibility, reduced oil dependency, and more reliability. However, the fuel stack delivers nonlinear output V-I characteristics. So, the extraction of peak power from the fuel source is very difficult. In this work, a Variable Step Size Radial Basis Functional Network-based Adaptive Fuzzy Logic Controller (VSSDE-AFLC) is proposed for tracking the peak power point of the fuel cell system. The merits of the proposed Maximum Power Point Tracking (MPPT) controller are high tracing speed of functioning point …of the fuel cell, more flexibility, high abundant, acceptable oscillations across MPP, and less dependency on modeling of the fuel stack. Also, the single switch converter is utilized for increasing the voltage supply of the fuel cell. The features of the proposed converter are wide input operation, less voltage stress, high supply voltage conversion ratio, and good dynamic response. The proposed fuel cell-dependent boost converter is implemented by utilizing the MATLAB/Simulink software, and the converter is tested successfully by using the desired programmable DC supply. Show more

Keywords: Boost converter, conversion ratio, duty cycle, fast tracing speed, high voltage gain, fewer voltage ripples, and fast dynamic response

DOI: 10.3233/JIFS-224007

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6303-6321, 2023

Authors: Shakunthala, M. | HelenPrabha, K.

Article Type: Research Article

Abstract: Stroke is a type of cerebrovascular disorder that has a significant impact on people’s lives and well-being. Quantitative investigation of MRI imaging of the brain plays a critical role in analyzing and identifying therapy for stroke. A block primarily provokes stroke in the brain’s blood supply. Deep learning algorithms can be used to identify strokes in patients in a short period. Proposed deep learning methods are used to classify strokes using magnetic resonance imaging (MRI) images. Early detection enhances treatment opportunities and saves lives, which is the primary motivation of the proposed work. Deep learning methods have emerged as significant …research trends in recent years, particularly for classifying different types of stroke such as ischemic and hemorrhagic stroke. A dataset of 13,850 MRI images of stroke patients was collected from various reliable sources, including Madras scans and labs, Radiopaedia, Kaggle datasets, and online databases. Among these images, 7,810 were identified as cases of ischemic stroke, while 6,040 represented hemorrhagic strokes. For training purposes, a total of 9,700 images were used, with 4,150 images employed for testing. A comparative analysis of ANN, SVM, NB, ELM, KNN and Enhanced CNN technique is carried out, and 98.4% of classification accuracy is obtained by using Enhanced CNN. Statistical analysis of parameters such as accuracy, precision, F1-score, and recall was conducted, demonstrating that the Enhanced CNN method outperformed SVM, NB,ELM, KNN and ANN classifiers. The Enhanced CNN method achieved an accuracy of 0.984, precision of 0.949, recall of 0.972, and an F1-score of 0.960 on the training dataset, which is significantly higher than the other classifiers. Furthermore, the Enhanced CNN algorithm’s ability to automatically learn features and efficiently process large datasets enhances its potential as a powerful tool for accurately classifying stroke lesions. Show more

Keywords: Magnetic Resonance Imaging (MRI), Enhanced-CNN, hemorrhagic stroke, ischemic stroke, deep learning

DOI: 10.3233/JIFS-230024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6323-6338, 2023

Authors: Al-shami, Tareq M. | Hosny, Rodyna A. | Abu-Gdairi, Radwan | Arar, Murad

Article Type: Research Article

Abstract: Our target in the present work, is presenting the idea of weakly soft preopen (ws -preopen) subsets and studying some of its characterizations. With the assistance of some elucidative examples, the interrelationships between ws -preopen sets and some extensions of soft open sets are studied. Under some conditions such as extended and hyperconnected soft topologies, several motivating results and relationships are acquired. The interior and closure operators that built through ws -preopen and ws -preclosed subsets are introduced. Their main features that construe the relations among them are established. Soft continuity with respect to theses classes of soft subsets are …studied and their substantial characteristics are investigated. Generally, the systematic relations and outcomes that are lost through the scope of this study are discussed. The proposed line in the current study will present new ways to discover novel concepts in the field of soft topology. Show more

Keywords: ws-preopen set, extended soft topology, ws-preinterior, ws-preclosure, ws-precontinuous function

DOI: 10.3233/JIFS-230191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6339-6350, 2023

Authors: Al-shami, Tareq M. | Arar, Murad | Abu-Gdairi, Radwan | Ameen, Zanyar A.

Article Type: Research Article

Abstract: This work introduces weakly soft β-open subsets, a new family of soft-open sets. By this family, we expand a soft topology to a soft structure which is neither supra-soft topology nor infra-soft topology. The connections between this class of soft sets and other celebrated classes via soft topology are examined with some elucidative examples. Also, it is established some relationships under conditions of extended and hyperconnected soft topologies. Furthermore, the interior and closure operators are structured along with weakly soft β-open and weakly soft β-closed sets. Finally, the class of weakly soft β-continuous functions is introduced and its main characterizations …are studied. It is investigated the systematic relationships and findings that are lost for this kind of soft continuity as well as it is shown the conditions required to maintain some of these relationships such as full, extended and hyperconnected soft topologies. Show more

Keywords: Extended soft topology, weakly soft β-open set, β-closure, weakly soft β-interior, and weakly soft β-continuous

DOI: 10.3233/JIFS-230858

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6351-6363, 2023

Authors: Tang, Zhong

Article Type: Research Article

Abstract: Architectural aesthetics improve the appearance and value of a building/construction structure based on shape, color, rigidity, etc., appealingly. It includes the maximum safety requirements, durability, structural ability, etc. Therefore the aesthetic implementation requires high-level data accumulation and analysis to satisfy the earlier constraints. This article develops a Selective Aesthetic Application Paradigm (SAAP) for meeting the user criteria in structural design for region-specific adaptability. The proposed paradigm gathers information on the region, people’s expectations, visibility, and structural performance for the aesthetic design application. The proportion considerations in the application are subject to vary according to the region’s adaptability and performance. The …proportion of the accumulated data influence in the application is determined using deep learning. In the learning paradigm, two-layered configurations for region-adaptability and performance measures are trained to provide aesthetic design application recommendations. Based on the suggestion and recommendation, the deep learning module is trained to rectify design errors. The training is independent of the previous two error and adaptability verification layers. It is performed using the qualified (selected) aesthetic design with a previous history of user satisfaction. Show more

Keywords: Architectural aesthetics, data analysis, deep learning, error detection

DOI: 10.3233/JIFS-231076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6365-6379, 2023

Authors: Mohamed, Mohamed S. | Elzayady, Hossam | Badran, Khaled M. | Salama, Gouda I.

Article Type: Research Article

Abstract: The use of hateful language in public debates and forums is becoming more common. However, this might result in antagonism and conflicts among individuals, which is undesirable in an online environment. Countries, businesses, and educational institutions are exerting their greatest efforts to develop effective solutions to manage this issue. In addition, recognizing such content is difficult, particularly in Arabic, due to a variety of challenges and constraints. Long-tailed data distribution is often one of the most significant issues in actual Arabic hate speech datasets. Pre-trained models, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT), have …become more popular in numerous natural language processing (NLP) applications in recent years. We conduct extensive experiments to address data imbalance issues by utilizing oversampling methods and a focal loss function in addition to traditional loss functions. Quasi-recurrent neural networks (QRNN) are employed to fine-tune the cutting-edge transformer-based models, MARBERTv2, MARBERTv1, and ARBERT. In this context, we suggest a new approach using ensemble learning that incorporates best-performing models for both original and oversampled datasets. Experiments proved that our proposed approach achieves superior performance compared to the most advanced methods described in the literature. Show more

Keywords: Text classification, Arabic hate speech, oversampling method, transformers, ensemble learning

DOI: 10.3233/JIFS-231151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6381-6390, 2023

Authors: Bergamini, Mariane Gavioli | Oliveira, Gustavo H.C. | Ribeiro, Eduardo P. | Leandro, Gideon Villar

Article Type: Research Article

Abstract: Accurate modeling of electric power generating unit and its hydraulic turbine regulation systems provides support for the speed controller synthesis and stability analysis. It is however a difficult task due to the presence of many non-linear factors in this system. an approach to estimate the parameters of hydraulic turbine regulatory system models is to derive the physical representation of each component and, through simulation, to compare to compare their models, outputs with real data obtained from a hydroelectric plant located in Brazil. The objective of this paper is to find the best values that will represent the system under study …as a whole. This problem can be seen as an optimization problem. To find its feasible and optimal solution, this work proposes a new metaheuristics multi-objective based on the Lion Algorithm (LA), called the Multi-Objective Lion Algorithm (MOLA), and its application in the estimation of parameters of the system under study. In addition, the new metaheuristic proposed is validated by using a set of benchmark cases. The results have demonstrated that MOLA outperforms or at least performs similarly to Multi-objective Grey Wolf Optimizer (MOGWO), Multiple Objective Particle Swarm Optimization (MOPSO), Multi-objective Salp Swarm Algorithm (MSSA), Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Non-dominated Sorting Genetic Algorithm III (NSGA-III) in the optimization of multi-objective benchmark functions. These results, suggest that the proposed MOLA algorithm works efficiently. Show more

Keywords: Parameter estimation, hydraulic turbine regulator system, multi-objective optimization, metaheuristics, multi-objective lion optimization algorithm

DOI: 10.3233/JIFS-232155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6391-6412, 2023

Authors: Zheng, Yunchao

Article Type: Research Article

Abstract: Traditional Chinese art is vast and profound, with various colors having rich meanings. The combination of colors can vividly and intuitively represent various characteristics of things. Fully reflecting the characteristics of traditional Chinese folk art in graphic design can achieve extremely strong expressive effects. In current graphic design, the artistic colors of traditional Chinese folk art have not yet been fully displayed, and there is a lack of understanding of the profound connotation of traditional Chinese art. The graphic design industry has a very broad development space. The comprehensive evaluation of graphic design effects based on color psychology is a …classical multiple attribute group decision making (MAGDM) problems. In this work, we shall present some novel Dice similarity measures (DSM) of T-spherical fuzzy sets(T-SFSs) and the generalized Dice similarity measures (GDSM) of and indicates that the DSM and asymmetric measures (projection measures) are the special cases of the GDSM in some parameter values. Then, we propose the GDSM-based MAGDM models with T-SFSs. Then, we apply the GDSMs between T-SFSs to MAGDM. Finally, an illustrative example for comprehensive evaluation of graphic design effects based on color psychology is given to demonstrate the efficiency of the GDSMs. The main contributions of this paper are summarized: (1) some novel Dice similarity measures (DSM) and the generalized Dice similarity measures (GDSMs) of T-spherical fuzzy sets(T-SFSs) are proposed; (2) The weighted Dice similarity measures (WDSM) and the weighted generalized Dice similarity measures (WGDSMs) of T-spherical fuzzy sets(T-SFSs) are proposed to solve the MAGDM; (3) an illustrative example for comprehensive evaluation of graphic design effects based on color psychology is given to demonstrate the efficiency of the WGDSM; (4) Some comparative analysis are used to show the effectiveness of the proposed Dice similarity measures. Show more

Keywords: Multiple attribute group decision making, Dice similarity measures (DSMs), generalized Dice similarity measures (GDSMs), T-spherical fuzzy sets, graphic design effects

DOI: 10.3233/JIFS-232296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6413-6427, 2023

Authors: Ramachandran, L. | Mohan, V. | Senthilkumar, S. | Ganesh, J.

Article Type: Research Article

Abstract: White Spot Syndrome Virus (WSSV) is a major virus found in shrimp that causes huge economic loss in shrimp farms. A selective diagnostic approach for WSSV is required for the early diagnosis and protection of farms. This work proposes a novel recognition method based on improved Convolutional Neural Network (CNN) namely Dense Inception Convolutional Neural Network (DICNN) for diagnoses of WSSV disease. Initially, the process of data acquisition and data augmentation is carried out. The Inception structure is then used to improve the performance of multi-dimensional feature extraction. As a result, the proposed work has the highest accuracy of 97.22% …when compared to other traditional models. The proposed work is targeted to Litopenaeus Vannamei (LV), and Penaeus Monodon (PM) diversities for major threats detection of White Spot Syndrome (WSS). Performance metrics related to accuracy have been compared with other traditional models, which demonstrate that our model will efficiently recognize shrimp WSSV disease. Show more

Keywords: Convolutional neural networks, disease identification, image augmentation, white spot syndrome virus

DOI: 10.3233/JIFS-232687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6429-6440, 2023

Authors: Krishna Veni, K.S. | Senthil Kumar, N. | Srinivas, R.

Article Type: Research Article

Abstract: In the electrical energy transmission and distribution sector, power transformers play an important role. Early fault diagnosis and prognosis are essential to ensure continuous operation and also to prepare a proper maintenance schedule based on the requirements. The occurrence of a fault in the transformer will lead to the formation of various gases inside the transformer tank. For fault diagnosis in the transformer, Dissolved Gas Analysis (DGA) is an excellent method. An Artificial Intelligence (AI) based fault diagnosis and prognosis system using dissolved gases in transformer oil is helpful to predict the health state of the transformer well in advance. …Hence, based on the fault severity level, the remaining useful life of the transformer, fault type and current state of the transformer can be estimated effectively by imparting AI to the existing system. A Two-Tier Fuzzy Logic Controller (TTFLC) is proposed in this article to find the type of fault and health index (HI) of the transformer. For further fault prognosis, an effective Gated Recurrent Network (GRN) based deep learning enabled future learning estimator is used for predicting the Criticality Index (CI) of the Transformer. The performance of the proposed method is evaluated for both data from the IEEE data set and expert data collected from the southern Tamil Nadu region. The proposed system shows better results even in multivariate, complex process systems. The diagnosis accuracy of the proposed system is obtained as 95.28% and it compared with conventional methods such as Rogers Ratio Method (RRM), Duval Triangle Method (DTM) and Duval Pentagon Method (DPM) and other AI based methods such as Radial Basis Neural Network (RBNN), k-nearest neighbors (KNN). The diagnosis accuracy of other conventional and AI based methods are less than 90% for the collected dataset. Show more

Keywords: Transformer, dissolved gas analysis, two tier fuzzy logic controller, fault diagnosis, fault prognosis, gated recurrent network, health index, criticality index

DOI: 10.3233/JIFS-223592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6441-6452, 2023