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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: Yang, Eunsuk
Article Type: Research Article
Abstract: This paper deals with semilinear extensions of implicational tonoid and partial Galois logics. To this end, first the class of implicational tonoid prelinear logics is defined and it is verified that these logics are semilinear in an algebraic context, namely an implicational tonoid logic is semilinear if it is complete over linearly ordered matrices. Next, a relational semantics is introduced for finitary implicational tonoid prelinear logics and it is proved that these logics are complete on the semantics. Thirdly the term “semilinear” is generalized to a notion to be applied in a set-theoretic context and it is verified …that finitary implicational tonoid prelinear logics are semilinear in this context. Finally some extensions satisfying abstract Galois, dual Galois properties are introduced together with similar relational semantics for them and it is shown that these logics are semilinear in both contexts. Show more
Keywords: Implicational tonoid logic, weakly implicative logic, tonoid, Routley–Meyer–style semantics, semilinear logic
DOI: 10.3233/JIFS-212549
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1541-1552, 2022
Authors: Uz, Mehmet E.
Article Type: Research Article
Abstract: Two 10-storey benchmark buildings exposed to different earthquakes are considered in the study in order to analyse the performance and capability of the design of the Tuned Mass Damper (TMD) with the optimal properties. Two optimisation algorithms, i.e. the Modified Genetic Algorithm (MGA) and the Grey Wolves Optimization (GWO) method, are used in the investigation. Firstly, the effectiveness of MGA and GWO, under optimally designed TMD system is verified by comparing the results with the ones obtained by other methods. In a second part, the optimum design of TMD system is determined by including mass of TMD as a design …variable so as to assess the feasibility of MGA and GWO. The MGA and GWO methods hold the better responses based on the reduction in the displacement, drift and acceleration of all stories subjected to different seismic excitations. The smaller properties of the TMD are attained using the methods of MGA and GWO as compared to the ones obtained by the Den Hartog and Warburton methods based on the objective function. Therefore, the MGA and GWO approaches lead to more practical and efficient solutions, which allows us to design economically the TMD systems rather than that of the other methods based on the reduction of structural responses. The results show that the efficiency of the parameters and modifications can be enhanced by selecting the proper access in the regulation output with requirements to be diminished. Show more
Keywords: Seismic response, TMD, optimization, genetic algorithm, grey wolfs optimization
DOI: 10.3233/JIFS-212553
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1553-1567, 2022
Authors: Saxena, Arti | Dubey, Y.M. | Kumar, Manish
Article Type: Research Article
Abstract: On the everlasting demand for better accuracy, high speed, and the inevitable approach for the high-quality surface finish as the basic requirements in the process industry, there felt the requirement to develop models which are reliable for predicting surface roughness (SR) as it is having a crucial role in the process industries. In this paper, SBCNC-60 of HMT make used to study the purpose of machining, while cutting speed (CS), feed rate (FR), and the depth of cut (DoC) were considered as parameters for machining of P8 material. Turning experiments data is studied by keeping two parameters constant at the …mid-level out of three parameters. An artificial intelligence technique named fuzzy was engaged in working out for surface roughness and material removal rate (MRR) to design the models of reliable nature for the predictions. The accurate prediction performance of the fuzzy logic model was then better analyzed by calculating MAPE, RMSE, MAD, and correlation coefficient between experimental values and fuzzy logic predictions. MAPE, RMSE, MAD, and correlation coefficient calculated 2.66%, 8.20, 6.44, and 0.98 for MRR and 4.19%,1.16, 0.86 and 0.90 for SR, respectively. Hence, the proposed fuzzy logic rules efficiently predict the SR and MRR on P8 material with higher accuracy and computational cost. Show more
Keywords: Correlation coefficient (R), Fuzzy Logic (FL), Mean Absolute difference (MAD), Mean Absolute Percentage Error (MAPE), MRR, Root mean square error (RMSE), Surface roughness (SR)
DOI: 10.3233/JIFS-212566
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1569-1582, 2022
Authors: Muthunagai, S.U. | Anitha, R.
Article Type: Research Article
Abstract: As a result of the advancements in Industry 4.0, the amount of data collected within industries are continuously expanding to achieve an innovative environment within the industry by maximizing asset usage. Meanwhile, the redundancy rate is increasing in cloud storage, which has an impact on data storage and analysis. To lower the rate of redundancy, the proposed system comprises a Time series-based deduplication technique. In the Time series-based deduplication technique, the Adaptive Multi-Pattern Boyer Moore Horspool (AM-BMH) algorithm, and Merkle tree were used to produce time-series data. Another significant challenge is that the geographically distributed cloud system has encountered that …the data placement methodology with high-priced transportation costs for data transmission. To overcome this issue, an optimal data placement strategy using Modified Distribution is proposed. Thus the proposed Time Series-based Deduplication and Optimal Data Placement Strategy (TDOPS) is found to be effective when compared with the existing system. The various parameters like space reduction, efficient retrieval, data transportation costs, and data transmission time are taken into the account in the cloud environment for an evaluation. The proposed scheme saves 98 percent of storage space, 55 percent computation overhead, and improves 60% of cloud storage efficacy. Show more
Keywords: Data placement, deduplication, modified distribution, Merkle tree, time series analysis
DOI: 10.3233/JIFS-212568
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1583-1597, 2022
Authors: Lin, Rongde | Li, Jinjin | Chen, Dongxiao | Chen, Yingsheng | Huang, Jianxin
Article Type: Research Article
Abstract: Attribute reduction is an important issue in data mining, machine learning and other applications of big data processing. Covering-based rough set and intuitionistic fuzzy (IF) set models are both the effective theoretical tools of uncertainty or imprecise computation, and thus IF covering rough set model has been acknowledged as a positive approach to attribute reduction. Based on IF covering rough set model, this study explores a kind of parameterized IF observational consistency in IF multi-covering decision system, and proposes an attribute reduction method. This article firstly defines the concepts of regular IF β -covering, parameterized IF observational sets on the …regular IF β -covering approximation space. Secondly, the parameterized IF observational consistency is defined to be the principal of attribute reduction in the IF multi-covering decision system, and the related IF discernibility matrix is developed to provide a way of attribute reduction. For multi-observational consistency corresponding to an observational parameters set, an unified multi-observational discernibility matrix is constructed, which avoids the disadvantage of needing to construct multiple corresponding discernibility matrices separately. Furthermore, an attribute reduction algorithm based on iterative dissolving of unified multi-observational discernibility matrix is proposed, and the experiment to demonstrate effectiveness of algorithm is presented. Experiments with UCI datasets shows that, the proposed method is a good way for improving both the rates of attribute-reduced and the classification accuracy of reduced datasets. Show more
Keywords: Attribute reduction, intuitionistic fuzzy covering-based rough set, parameterized observational consistency, intuitionistic fuzzy discernibility matrix
DOI: 10.3233/JIFS-212585
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1599-1619, 2022
Authors: Khatatneh, Khalaf | Filist, Sergey | Al-Kasasbeh, Riad Taha | Aikeyeva, Altyn Amanzholovna | Namazov, Manafaddin | Shatalova, Olga | Shaqadan, Ashraf | Miroshnikov, Andrey
Article Type: Research Article
Abstract: Modern medical risk classification systems focus on traditional risk factors and modeling methods. The available modeling tools do not allow reliable prediction of the of disease severity. In this study we develop prediction model of recurrent myocardial infarction in the rehabilitation period using several health variables generated in virtual flows. Hybrid decision modules with health data flows were used to build prognostic model for the prediction of disease. The vector of input information features consists of two subvectors: the first reflects real flows, the second reflects virtual flows. Complex interrelations among input data are modelled using Neural Network structure. …The model classification quality of the intellectual cardiovascular catastrophe prediction system was tested on a sample composed of 230 patients who had acute myocardial infarction. For prediction, three categories of risk factors were identified: traditional factors, factors associated with stressful overloads, and risk factors derived from bio-impedance studies. During the rehabilitation period, the level of molecular products of lipid peroxidation and the antioxidant potential of blood serum were also studied. Experimental studies of various modifications of the proposed classifier model were conducted, consisting of sequential disconnection from the aggregator of solutions of “weak” classifiers at various hierarchical levels. The mathematical model show predictions accuracy of correct prognosis for the risk of myocardial infarction exceeding 0.86. Prediction quality indicators are higher than the known ASCORE cardiovascular catastrophe prediction system, on average, by 14%. Show more
Keywords: Hybrid decision module, latent variable, GMDH model, neural network, aggregators of fuzzy decision rules, recurrent myocardial infarction
DOI: 10.3233/JIFS-212617
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1621-1632, 2022
Authors: Tran, Van Quan | Nguyen, Linh Quy
Article Type: Research Article
Abstract: Taking advantage of dredged sediments as lightweight materials is a useful solution to protect the environment and save natural materials in the field of construction. In which unconfined compression strength is an important criterion to determine the application in the construction project. It is difficult to find the optimal mixing ratio based on design standards or construction conditions because the unconfined compression strength (UCS) is affected by the mixing ratio of the materials and additives. In this study, the Machine Learning (ML) models consisting of Extreme Gradient Boosting (XGB) model and Linear regression models are investigated to design components for …reinforced lightweight soil based on the influence of unconfined compression strength of the test sample which is water content, cement content, air foam content, waste fishing net. To evaluate the effectiveness of the proposed ML models, several evaluation criteria including Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R2 ) are proposed. The results show that the predictions of the XGB model have high accuracy with R2 = 0.9695, RMSE = 5.5849 kPa and MAE = 4.1583 kPa for the testing dataset. Sensitivity analysis evaluates the influence of input variables on UCS and the interaction between input variables to help design RLS components optimally. Show more
Keywords: Unconfined compressive strength, reinforced lightweight soil, extreme gradient boosting, machine learning
DOI: 10.3233/JIFS-212621
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1633-1650, 2022
Authors: Vivek Joe Bharath, Amaladoss | Thirumarimurugan, Marimuthu
Article Type: Research Article
Abstract: Early detection and diagnosis of faulty events in industrial processes can represent economic, social and environmental profits. When the process has a great quantity of sensors or actuators, the Fault Detection and Isolation (FDI) task is very difficult. Advanced statistical based FDI methods are extensively used for fault detection and isolation purposes. In this work, three multivariate statistical techniques such as neural network based Principal Component Analysis (PCA), neural network-based Fischer Discriminant Analysis (FDA) and Correspondence Analysis (CA) was applied to the multivariate data extracted from laboratory scale shell and tube heat exchanger. Performance metric such as detection delay, estimated …time of occurrence of fault, misclassification rate was computed for those three techniques for the detection and isolation of biases in sensors and actuators. Correspondence Analysis was proven to perform better when compared to PCA and FDA. CA was observed to perform FDI with minimal detection delay (which is less than or equal to 7 seconds) and lower misclassification rate (which is less than or equal to 6%) in case of both sensor & actuator faults. PCA and FDA showed significant detection delay and missed alarm rate for single and multiple fault identification. Show more
Keywords: Orthogonal projection, χ2 distance, neural network, misclassification rate, detection delay
DOI: 10.3233/JIFS-212631
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1651-1668, 2022
Authors: Ravindran, Vijay | Vennila, C.
Article Type: Research Article
Abstract: Internet of Things (IoT) proposed a new digital computing paradigm enabling interaction between devices and machines. It deliberately creates connectivity between the internet, electronics, and other forms of hardware. A novel modern cluster supervisor-based cluster head selection algorithm (MCSBCH) is proposed for the Wireless Sensor Network (WSN). The proposed cluster supervisor mechanism is responsible for controlling and monitoring the network effectively. In this approach, the cluster supervisor is the heart of the network, and the whole mechanism work under its supervision. The Cluster supervisor (CS) monitors the node’s energy level and allocates CH (cluster head) node. Each node’s energy level …is considered for electing the CH. Obviously when the cluster head energy level gets drained, then it allocates the next higher energy node as cluster head. The assigned CH is the next node with the highest energy level known as the backup node. This cluster supervisor (CS) is supported by the cluster head (CH) and other backup nodes in the network. The proposed MCSBCH is boosted with an enhanced clustering routing protocol. An experimental result is based on the aspect of a lifetime, energy consumption, and throughput, to test the proposed mechanism performance. Show more
Keywords: WSN, IoT, energy consumption, cluster communication and management
DOI: 10.3233/JIFS-212632
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1669-1679, 2022
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