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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: Azimirad, Ehsan
Article Type: Research Article
Abstract: Edge Detection is the first stage in the image division into separate parts. Image division is the partitioning of a digital image to the different zones or the set of pixels. Edge detection is one of the techniques applied in digital image processing often. The purpose of detecting pixels is to match the edges in the image. Filtering, Enhancement, and Detection are three steps in edge detection. Images are usually destroyed by casual changes in intensity intervals called noise or confusion. some noise variations include salt and pepper, pulse, and Gaussian. However, there is a relation between edge detection power …and noise reduction. Using filters to the noise reduction causes the loss of edge detection power. For facilitating the edges detection, it is essential for the determination of pixels’ intensity constraints in their neighborhood. Many points in an image have a nontransparent slope, and all of them are not the edges of the joint space. Therefore, some of the linear and nonlinear methods such as Sobel, Prewitt, and Robert have to be used to determine the edge points. The fuzzy logic and the system based on it, is one of the most effective methods for edge detection. This paper presents an optimized rule-based fuzzy inference system and designs the efficiency mask matric. The simulation results for edge detection are presented using the traditional edge detection techniques, including Binary Filter, Sobel Filter, Prewitt Filter, and Robert Filter. Also, it is presented using the fuzzy approach. The simulation results show that the designed fuzzy system has been able to detect the edges of the image more accurately and help to increase the sharpness and quality of the edges. Therefore, the proposed method has more accurate and more reliable results and reduces false edge detection comparison to the traditional methods. Show more
Keywords: Edge detection, fuzzy sets, optimized fuzzy system, membership functions
DOI: 10.3233/JIFS-213008
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2363-2373, 2022
Authors: Mystica, A. | Senthil kumar, V.S. | Sakthi abirami, B.
Article Type: Research Article
Abstract: AA2014 is an Al-Cu alloy friction stir welded under different combinations of rotational speed (800, 1000 and 1200 rpm) and transverse speed (44, 60, 72 mm/min) under minimum quantity lubrication condition with graphene nanofluid as coolant. Design of experiments is performed using Taguchi L9 orthogonal array. Analysis of variance technique is adapted to find the most influencing input parameter (rotational speed, transverse speed) of each output response (ultimate tensile strength, % elongation, microhardness and grain size). Regression and fuzzy logic based models are developed to predict the output responses. The reliability of the predicted results is tested by calculating the correlation coefficient. …The predicted results from regression and fuzzy logic are then compared with the experimental results. The results of trend analysis exhibit the substantial influence of both the input parameters on the output responses. The results from ANOVA reveals that the rotational speed highly influences ultimate tensile strength and grain size while transverse speed majorly influences microhardness. The error in prediction using fuzzy model is observed to be significantly limited with correlation coefficients in the range of 0.70–0.96. The developed models are observed to be highly efficient and therefore can be used for prediction in any uncertain engineering applications. Show more
Keywords: Friction stir welding, minimum quantity lubrication, graphene nanofluid, regression modelling, fuzzy logic
DOI: 10.3233/JIFS-213032
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2375-2390, 2022
Authors: Bokir, Abdullah | Narasimha, V.B
Article Type: Research Article
Abstract: High utility mining is gaining prominence, and with the increasing set of business intelligence models, the scope of such significant practices is high. Rather than focusing only on profitability as one key utility metric, today’s organizations believe in having more robust levels of the multi-objective filtering process. In this manuscript, a contemporary model of the high utility mining process is proposed, wherein the multiple averages are used for grading the recommendation of the itemsets for merchandise. The model’s key advantage is its dynamic approach. The goods-related period of the average time interval can be flexible, alongside the fusion of multiple …utility thresholds of diversified features chosen for itemsets recommendation. The performance analysis has been carried out by using a multi-fold cross-validation strategy. The results obtained for cross-validation show that the proposed model is outperforming the contemporary models with significant precision, specificity, sensitivity, and accuracy having values 97%, 95%, 98%, and 97% in respective order. Whereas, the contemporary models HUPM-MUO and MOEA-FHUI have obtained 93% and 90%, 88% and 82%, 89%, and 84%, and 89% and 83% in respective order of the corresponding metrics. The experimental study of the model denotes the effectiveness and ease with which the solution can generate results and produce significant output in the real-time environment for more dynamic and periodic decisions by different organizations. Show more
Keywords: High Utility Mining, multiple-utility factors, economic order quantity, exponential moving average, inventory storage cost
DOI: 10.3233/JIFS-213037
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2391-2405, 2022
Authors: Jiang, Huimin | Guo, Gaicong | Sabetzadeh, Farzad | Chan, Kit Yan
Article Type: Research Article
Abstract: Previous studies developed consumer preference models mainly through customer surveys, ignoring the variability of consumer preferences over time. In addition, it is difficult to obtain time series data based on the customer surveys. In recent years, some previous studies tried to analyse consumer preferences based on online reviews. However, they have not solved the problems of modelling variational consumer preference based on time series data with the consideration of the ambiguity of emotions expressed by customers in online reviews. To solve the above problems, this article proposes the particle swarm optimization (PSO) based dynamic evolving neural-fuzzy inference system (DENFIS) method …to model variational consumer preferences based on online customer reviews. Using the time series data mined by the sentiment analysis method and the product attribute settings of the review products, the PSO-based DENFIS method is offered to dynamically model consumer preferences, in which PSO is used to adjust DENFIS parameters adaptively. Show more
Keywords: Consumer preference, opinion mining, DENFIS, particle swarm optimization, new product development
DOI: 10.3233/JIFS-213057
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2407-2418, 2022
Authors: Chotikunnan, Phichitphon | Panomruttanarug, Benjamas
Article Type: Research Article
Abstract: Iterative Learning Control (ILC) is an intelligent control algorithm that can effectively handle a tracking error in any system that operates in a repetitive manner. In practice, it is hardly possible to implement a single gain learning control law to improve the tracking performance due to the existence of large transient growth. To prevent the growth, this paper proposes a time-varying learning control design using the unique concept of fuzzy logic control to track the desired trajectory as well as the desired control input signal. The proposed control design is developed on both serial and parallel ILC configurations. The two …configurations are initially constructed and implemented on a robotic manipulator with the use of a single gain learning control law. To avoid bad transients, the gain adjustment mechanism based on fuzzy logic control is introduced to vary the learning gain in each time step for enhancing the robustness of the system. According to the simulation and experiment on a robotic manipulator, both ILC structures with the proposed mechanism achieve the desired learning performance without bad transients. Show more
Keywords: Iterative learning control, serial ILC, parallel ILC, fuzzy logic control, robotic manipulator
DOI: 10.3233/JIFS-213082
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2419-2434, 2022
Authors: Han, Meng | Shan, Zhihui | Han, Qiang
Article Type: Research Article
Abstract: High utility quantitative itemsets (HUQI) mining is a new research topic in the field of data mining. It not only provides high utility itemset (HUI), but also provides quantitative information of individual item in the itemset. HUQI can provide decision makers with information about items and their purchase quantities. However, the currently proposed HUQI mining algorithms assume that the datasets are static. In order to solve this problem, an incremental quantitative utility list (IQUL) data structure is proposed to store item information, including item name, item number, transaction weight utility of item, each entry in the list stores the transaction …identifier, the utility of the original data, the remaining utility, the utility of the incremental data, the remaining utility, and the sum of the utility and the remaining utility. When data is inserted, the item information will be updated. Based on IQUL, an incrementally updating HUQI (IHUQI) mining algorithm is proposed to mine HUQI on incremental update data. A large number of experiments on real datasets show that the IHUQI algorithm can effectively mine HUQI Experimental results show better performance on sparse datasets. Show more
Keywords: Incremental mining, high utility quantitative itemsets, high utility itemsets, utility list, itemsets mining
DOI: 10.3233/JIFS-213136
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2435-2448, 2022
Authors: Ge, Liang | Lin, Yongquan | Li, Senwen | Zeng, Bo
Article Type: Research Article
Abstract: Urban traffic flow prediction is a critical problem in the intelligent transportation system, and it’s very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the complex road network and dynamic traffic conditions. However, existing methods either rely too much on prior knowledge or the data itself when modeling spatial-temporal dependency and few researchers consider them in combination. In this paper, a new spatial-temporal network for traffic flow prediction, which can comprehensively capture the complex spatial and temporal dependency based on prior knowledge and data-driven, is proposed. In particular, in the perspective of local and global …spatial dependency in road networks, we construct a dynamic weighted graph by finding the spatial and semantic neighborhoods of road nodes based on road networks and the similarities between traffic data on different roads. Besides, the temporal trend module and implicit temporal dependency module are combined to capture the temporal transitivity of traffic flow and implicit dependencies between time point pairs. The experiment results of our proposed model outperform the state-of-the-art baselines. Show more
Keywords: Traffic forecasting, spatial-temporal network, prior knowledge, data-driven
DOI: 10.3233/JIFS-213317
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2449-2462, 2022
Authors: Chai, Shaolong | Wang, Zeng
Article Type: Research Article
Abstract: In view of the shortcomings of the existing evaluation methods in fully considering the fuzziness and randomness of product design evaluation, a novel product design evaluation method based on FAHP and cloud model is proposed. First, a hierarchical structure model of product design evaluation is established. Second, fuzzy pairwise comparison of criteria is constructed through questionnaire survey, and the digital characteristics of weight cloud model are acquired by the proposed fuzzy weight model. Then, based on the factor scores, the digital characteristics of scoring cloud model are obtained by backward cloud. Finally, the digital characteristics of comprehensive evaluation cloud model …are obtained by using the proposed improved fuzzy composite operator, and the forward cloud is used to get the cloud picture for evaluation. Taking reading lamp as an example, the feasibility and effectiveness of the proposed method are verified. The results show that compared with the other two methods, the Kendall rank correlation coefficients of entropy of the method are increased by 0.17 and 0.33, respectively, which proves that the method achieves more accurate evaluation result under the complex criteria, and provides a more effective tool for decision makers and designers to evaluate and optimize design schemes. Show more
Keywords: FAHP, cloud model, design evaluation, reading lamp
DOI: 10.3233/JIFS-213331
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2463-2483, 2022
Authors: Cebi, Selcuk | Gündoğdu, Fatma Kutlu | Kahraman, Cengiz
Article Type: Research Article
Abstract: Risk assessment takes place depending on the expertise and subjective linguistic assessments of experts. Expert judgements are collected via a questionnaire or an interview including qualitative data. Pessimistic or optimistic status of experts can affect their perceptions on risk. Furthermore, expert judgments are affected by questions’ structure based on whether it is a positive type question (e.g., ‘What is the occurrence probability of the accident?) or a negative type question (e.g., ‘What is the non-occurrence probability of the accident?). All of these cases create uncertainties in the risk assessment process. For this reason, there are various studies using fuzzy risk …analysis models to address these uncertainties in risk assessment. However, there is not any risk assessment tool that considers the uncertainties caused by the factors mentioned above, simultaneously. Therefore, in this paper, we introduce the concept of decomposed fuzzy sets (DFS) to model human thoughts and perceptions in a more realistic and detailed way through optimistic and pessimistic membership functions. We present the basic operations on decomposed fuzzy sets and their properties. To demonstrate the utility of the proposed method, the method is applied to operational risk analysis in business processes. The data used in the application are collected from the managerial board of a construction company. The application results and advantages of the proposed method are presented together with a comparative analysis. Show more
Keywords: Intuitionistic fuzzy sets, fuzzy set theory, decomposed fuzzy sets, risk assessment, business management
DOI: 10.3233/JIFS-213385
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2485-2502, 2022
Authors: Thillai Rani, M. | Sai Pradeep, K.P. | Sivakumar, R. | Suresh Kumar, S.
Article Type: Research Article
Abstract: Electronics industry has attained huge development in last few decades due to the rapid increase in system design applications. With the growth of very large scale integration (VLSI) design, integrated circuits (ICs) are employed in many applications. VLSI design comprises many steps like system-level design, high-level synthesis (HLS), logic design, test generation, and physical design. HLS interprets behavior description and create digital hardware that executes the behavior. But, the power-process-voltage-temperature (PPVT) variation can causee many issues and reduce the performance of VLSI design circuits. In order to address these problems, Recurrent Deep Neural Learning Classification based High Level Synthesis (RDNLC-HLS) …Model is designed for better runtime adaptability with minimal time consumption. VLSI circuits are designed with the behavioral input and the output performance is measured at runtime. The behavioral description of the circuit is taken as input. Then, source code compilation process translates high level specification into Intermediate Representation (IR) and converts to control/data flow graph (CDFG). CDFG reveals data and control dependencies between operations. The proposed Recurrent Deep Neural Learning Classification based High Level Synthesis (RDNLC-HLS) Model is designed for providing better runtime adaptability with minimal time consumption. Finally, Register Transfer Level Generation is carried out to yield better runtime adaptability with minimal time. Simulation results on ISCAS’89 Benchmark Dataset, shows that the RDNLC-HLS model increases the FUSA with minimal error rate and CAT. Show more
Keywords: Deep learning, neural network, optimization techniques, VLSI circuits
DOI: 10.3233/JIFS-213406
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2503-2514, 2022
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