Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Purchase individual online access for 1 year to this journal.
Price: EUR 315.00Impact Factor 2024: 1.7
The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Arya, R. | Vimina, E.R.
Article Type: Research Article
Abstract: Local feature descriptors are efficient encoders for capturing repeated local patterns in many of the computer vision applications. Majority of such descriptors consider only limited local neighborhood pixels to encode a pattern. One of the major issues while considering more number of neighborhood pixels is that it increases the dimensionality of the feature descriptor. The proposed descriptor addresses these issues by describing an effective encoding pattern with optimal feature vector length. In this paper, we have proposed Local Neighborhood Gradient Pattern (LNGP) for Content-Based Image Retrieval (CBIR) in which the relationship between a set of neighbours and the centre pixel …is considered to obtain a compact 8-bit pattern in the respective pixel position. The relationship of the gradient information of immediate, next-immediate, and diagonal neighbours with the centre pixel is considered for pattern formation, and thus the local information based on pixels in three directions are captured. The experiments are conducted on benchmarked image retrieval datasets such as Wang’s 1K, Corel 5K, Corel 10K, Salzburg (Stex), MIT-Vistex, AT & T, and FEI datasets and it is observed that the proposed descriptor yields average precision of 71.88%, 54.57%, 40.66%, 71.85%, 86.12%, 82.54%, and 68.54% respectively in the mentioned datasets. The comparative analysis of the recent descriptors indicates that the proposed descriptor performs efficiently in CBIR applications. Show more
Keywords: Local binary patterns, intensity gradient, feature extraction, image retrieval, image descriptor
DOI: 10.3233/JIFS-212604
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4477-4499, 2022
Authors: Saravanakumar, S. | Saravanan, T.
Article Type: Research Article
Abstract: In today’s world, Alzheimer’s Disease (AD) is one of the prevalent neurological diseases where early disease prediction can significantly enhance the compatibility of patient treatment. Nevertheless, accurate diagnosis and optimal feature selection play a vital challenge in AD detection. Most of the existing diagnosis systems failed to attain superior prediction accuracy and precision rate. In order to mitigate these constraints, a new efficient Convolutional Neural Network-based Stacked Long Short-Term Memory (CNN-SLSTM) methodology has been proposed in this paper. The key objective of the proposed model is to examine the brain’s condition and evaluate the changes that occur throughout the interracial …period. The proposed model includes multi-feature learning and categorization in which the raw Electroencephalography (EEG) data will be passed via the feature extractor to decrease the computing complexity and execution time. Afterward, the SLSTM network is constructed with completely linked layer and activation layers to record the temporal relationship between features and the next stage of AD. The proposed CNN-SLSTM model can be trained using real-time EEG sensor data. The performance results clearly apparent that the proposed model can efficiently predict the AD with superior accuracy of 98.67% and precision of 98.86% when compared with existing state-of-the-art techniques. Show more
Keywords: Alzheimer’s disease prediction, convolutional neural network, diagnosis, EEG data, multi-feature extraction
DOI: 10.3233/JIFS-212797
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4501-4516, 2022
Authors: Xiao, Yaning | Sun, Xue | Guo, Yanling | Cui, Hao | Wang, Yangwei | Li, Jian | Li, Sanping
Article Type: Research Article
Abstract: Honey badger algorithm (HBA) is a recently developed meta-heuristic algorithm, which mainly simulates the dynamic search behavior of honey badger in wild nature. Similar to other basic algorithms, HBA may suffer from the weakness of poor convergence accuracy, inadequate balance between exploration and exploitation, and ease of getting trapped into the local optima. In order to address these drawbacks, this paper proposes an enhanced honey badger algorithm (EHBA) to improve the search quality of the basic method from three aspects. First, we introduce the highly disruptive polynomial mutation to initialize the population. This is considered from increasing the population diversity. …Second, Lévy flight is integrated into the position update formula to boost search efficiency and balance exploration and exploitation capabilities of the algorithm. Furthermore, the refraction opposition-based learning is applied to the current global optimum of the swarm to help the population jump out of the local optima. To validate the function optimization performance, the proposed EHBA is comprehensively analyzed on 18 standard benchmark functions and IEEE CEC2017 test suite. Compared with the basic HBA and seven state-of-the-art algorithms, the experimental results demonstrate that EHBA can outperform other competitors on most of the test functions with superior solution accuracy, local optima avoidance, and stability. Additionally, the applicability of the proposed method is further highlighted by solving four engineering design problems. The results indicate that EHBA also has competitive performance and promising prospects for real-world optimization tasks. Show more
Keywords: Honey badger algorithm, highly disruptive polynomial mutation, Lévy flight, refraction opposition-based learning, engineering design problems
DOI: 10.3233/JIFS-213206
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4517-4540, 2022
Authors: Abiyev, Rahib H. | Sadikoglu, Gunay | Alsalihi, Adnan | Abizada, Rufat
Article Type: Research Article
Abstract: Sensory experiences that include vision, hearing, touching, smelling and tasting are important parameters that enable people to trade effectively in retail stores. In this study, based on multisensory attributes the evaluation of customer satisfaction is considered using fuzzy set theory and conjoint analysis. Fuzzy set theory is one of the best methodologies for describing the meaning of linguistic values that express customer preferences. However, there may be different customer and expert opinions in the evaluation of preferences by expressing linguistic values. In the paper, a type-2 fuzzy set is used to handle these uncertainties. This paper proposes the combination of …type-2 fuzzy sets and conjoint analysis in order to evaluate customer satisfaction using customer opinions about sensory variables such as sight, sound, taste, touch and smell when purchasing goods in retail stores. For this purpose, using statistical survey results and type-2 fuzzy sets the customer satisfaction degrees were determined. The methodology used for the determination of customer satisfaction is based on conjoint analysis that uses the similarity measure to determine the closest opinions of the customers and experts for the evaluation of customer satisfaction degrees. The obtained experimental results indicate the efficiency of the presented approach in the determination of customer satisfaction in retail markets. Show more
Keywords: Sensory attributes, customer satisfaction, fuzzy sensory evaluation, conjoint analysis
DOI: 10.3233/JIFS-213218
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4541-4554, 2022
Authors: Basumatary, Bhimraj | Wary, Nijwm | Khaklary, Jeevan Krishna | Garg, Harish
Article Type: Research Article
Abstract: These days, the appraisal of the COVID-19 vulnerability has become a difficult errand for the whole world. The COVID-19 administration dynamic issue frequently includes numerous elective arrangements clashing standards. In this paper, we present a multi-criteria decision-making (MCDM) procedure based on the fuzzy VIKOR method to survey the COVID-19 vulnerability in the state of Assam, India. The trapezoidal fuzzy number is utilized to evaluate the rating of the loads for the set-up models. We have observed environment, social, and Medical factors after observing the spread of COVID-19. To study and to have comments, a committee of five experts has been …formed from a different region of Assam to observe and comment to identify Coronavirus’s weakest factors. For a better survey, we have divided the state into four areas namely Rural Area, Urban Area, Market Area in Rural Area, and Market Area in Urban Area. The current research looked at how the fuzzy VIKOR selects provinces for urgent adaptation needs differently than a traditional MCDM technique. Show more
Keywords: Assam, COVID-19, trapezoidal fuzzy number, fuzzy VIKOR, vulnerability region
DOI: 10.3233/JIFS-213279
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4555-4564, 2022
Authors: Prabhakaran, Priyanka | Subbaiyan, Anandakumar | Bhaskaran, Priyanka | Velusamy, Sampathkumar
Article Type: Research Article
Abstract: In India, Rail mode of transport serves as frequently preferrable transit systems operating with the optimal cost. Typically, the Indian Railways transport thousands of people on a day-to-day basis in addition to transporting large consignment of goods. Therefore, it is important for the trains to ensure that they run on quality tracks. At times, these tracks are challenged by the friction generated by continuous passage of trains in addition to over corrosions that occur due to their environmental imbalance. Preventive Track Maintenances (PTMs) have been recently introduced by railways for enhancing the quality of railway tracks, but on the contrary, …it was failed to focus on the actual needs or emergencies of railway tracks. Moreover, none of the existing methods have been tested with real time datasets. Specifically, holding only two class labels are being considered resulting in the reduction of classification performances. But the major challenging task is that the real-time datasets fall under the category of multi-variant data. Hence, this study aims to provide a Decision Support System (DSS) that predicts the Railway Track Quality (RTQ) from the real time datasets available on the track inspection data of the Indian metro rail system. The proposed research uses clustering and classification processes for achieving Predictive Track maintenance (PTM). Furthermore, the proposed method of RPTMs includes five steps namely data collection, data transformations, clustering of data, preventive maintenances, and evaluations. The undertaken datasets are transformed into numeric formats for the creation of clusters using Kernel Mean Weight Fuzzy Local Information C Means (KMWFLICMs). The resultant clusters from the data have five major types of clusters such as Normal, Low risks, Medium, High, and Emergency Risks based on the parameters of gauge, cross level attributes, turnouts and versine of mainline. From the inferred cluster results, the dataset was further classified to choose maintenance status from four major classes namely No Actions, Fixed Maintenances, Investigate Maintenances, and Emergency Maintenance pertinent to the outcomes of FWCNNs (Fuzzy Weight Convolution Neural Networks). The proposed system was experimented on MATLAB and evaluated against various machine learning approaches. Therefore, the obtained statistical results confirmed that the proposed FWCNN model had afforded higher accuracy in predicting the maintenance interventions based on relevant risk category. Show more
Keywords: Track quality, railway track maintenance, railroad tracks, preventive maintenance, kernel mean weight fuzzy local information C means (KMWFLICM), clustering algorithm, fuzzy weight convolution neural network (FWCNN), metro rail
DOI: 10.3233/JIFS-213439
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4565-4586, 2022
Authors: ShirMohammadi, Mohammad Mehdi | Esmaeilpour, Mansour
Article Type: Research Article
Abstract: Traffic control prediction is one of the important issues of smart cities in that, by studying traffic parameters, there can be provided more peace and comfort in appropriate traffic routes. Combination of new and different technologies and scientific technical models for this complex prediction has always been paid attention to by researchers. In this paper, by presenting and improving one of the new methods of data collection with traffic congestion index, the appropriate models for predicting traffic control have been compared. Rapid and inexpensive collection of information and, the dynamics and momentary changes of traffic flows showed that the use …of wavelet neural network was more accurate than other models of traffic control prediction. The application of combined Wavelet Neural Network with Complete Ensemble Empirical Mode Decompositionin traffic control prediction in this paper as CEEMD & WNN showed that the prediction accuracy increased compared to ARIMA, WNN, HYBRID ARIMA & WNN, TN methods and this new method has reasonable performance against the evaluation criteria to predict traffic control. Show more
Keywords: Wavelet neural network, artificial neural network, prediction, traffic control, complete ensemble empirical mode decomposition
DOI: 10.3233/JIFS-213557
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4587-4599, 2022
Authors: Okuwobi, Idowu Paul | Ding, Zhixiang | Wan, Jifeng | Ding, Shuxue
Article Type: Research Article
Abstract: Artificial intelligent (AI) systems for clinical-decision support are an important tool in clinical routine. It has become a crucial diagnostic tool with adequate reliability and interpretability in disease diagnosis and monitoring. Undoubtedly, these models are faced with insufficient data challenges for training, which often directly determines the model’s performance. In order word, insufficient data for model training leads to inefficiency in the model built. To overcome this problem, we propose an AI-driven model by transfer learning in accurate diagnosis for medical decision support. Our approach leverages the shortage of data with a pretrained model by training the neural network with …a fraction of the new dataset. For this purpose, we utilized the VGG19 network as the backbone network to support our model in integrating known features with the newly learned features for accurate diagnosis and decision making. Integrating this trained model speeds up the training phase and improve the performance of the proposed model. Experimental results show that the proposed model is effective and efficient in diagnosing different medical diseases. As such, we anticipated that this diagnosis tool will ultimately aid in facilitating early treatment of these treatable diseases, which will improve clinical out-comes. Show more
Keywords: Optical coherence tomography (OCT), choroidal neovascularization (CNV), diabetic macular edema (DME), age-related macular degeneration (AMD), convolutional neural networks (CNN), artificial intelligence (AI)
DOI: 10.3233/JIFS-220066
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4601-4612, 2022
Authors: Kotteswari, K. | Bharathi, A.
Article Type: Research Article
Abstract: Cloud computing is an on-demand model that computes shared and dynamic resource availability in a remote or independent location. Cloud computing provides many services online to clients in a pay-as-you-go manner. Nowadays, many organizations use cloud computing techniques with the prime motive that cost can be reduced, and resources are dynamically allocated. Performance evaluation and measurement approaches for cloud computing help the cloud services consumer to evaluate their cloud system based on performance attributes. Although the researchers have proposed many techniques and approaches in this direction in past decades, none of them has attained widespread industrial benefit. This paper proposes …a novel quality evaluation methodology named Stochastic Neural Net (SNN) to evaluate the cloud quality of Infrastructure as a Service (IaaS). This model deeply measures the performance by considering every activity of the IaaS system. Based on their characteristics, these works suggest key QoS factors for individual parts and activities. The individual QoS metric makes the SNN methodology acquire accurate results regarding performance measurement. The performance evaluation result can be used to improve the cloud computing system. The proposed model is compared with other standard models. The experimental comparison shows that the proposed model is more efficient than other standard models. Show more
Keywords: IaaS, stochastic model, performance measure, neural network, availability
DOI: 10.3233/JIFS-220501
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4613-4628, 2022
Authors: IssanRaj, R. | Visalakshi, S.
Article Type: Research Article
Abstract: The behaviour and effective performance of solar cell is represented by Triple Diode Solar Cell Module (TDSCM) circuit with five parameters for different environmental conditions. The equations described the solar modules behaviour are usually implicit in nature and the parameter extraction was very complexity. From the Photovoltaic (PV) module data sheet, one can identify the four equations applying to single, double, and triple diode parameters. For getting fifth equation researchers have gone with several approximations, which concludes the computation complexity, convergence problem, and low accuracy issues. In the proposed work the fifth equation are framed under the area characteristics curve …(V-I & P-V) concept using Simpson’s approximation. To find which PV module is less accuracy and non-linearity consideration for the performance level. Therefore, to overcome these issues the multi-objective Genetic Algorithm (GA) optimization method are prescribed to frame the fifth equation of the Simpson’s rules. This works improved non-linearity performance and gives the high accuracy modelling compare to other single, double diode methods. Show more
Keywords: Photovoltaic cell model, solar cell modeling, genetic algorithm, triple diode model, Simpson’s rule
DOI: 10.3233/JIFS-220561
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4629-4643, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl