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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: Kuppulakshmi, V. | Sugapriya, C. | Nagarajan, D.
Article Type: Research Article
Abstract: Inventory plays an important role in the production process. One of the primary reasons why inventory management modeling is essential for the industry is because it will suffer immensely if there are insufficient food products to stock during the shutdown period. By determining the combined optimal cost of the retailers and wholesalers, this research significantly improves the service of the supply chain from wholesaler to retailer. The stochastic number for the imperfect perishable items is provided in this inventory study. By altering the parameter values, the uniform distribution is used to calculate these damaged items. This approach identifies the backordering …quantity for both regular and uncertain fish band circumstances. The cost of maintaining the inventory will rise significantly of increased wastage due to a rise in deteriorating, which will result in the loss of perishable food items. The primary goal of this research paper is to transport them without being destroyed until they reach their desired consumers. By determining the back ordering quantity during a shutdown, one can decrease the overall expenses incurred by the retailers. These computational complexity measures are proven in a fuzzy uncertain environment. The main goal of this paper is to analyze the variation of demand during the unanticipated period and find the optimum total cost of the perishable products. The growth of production in a particular area at a particular time, interconnect with another large number of products in the same area and is calculated by Verhulst’s demand with time depended on proficiency rate. Concerning the existing Verhulst’s demand pattern for the production process, this paper introduced that for perishable items in a fuzzy unanticipated situation. A bountiful system analysis is performed to find the cost function under fuzzy environment and the sensitivity analysis is carried out to perform the key representation constant. Show more
Keywords: Perishable items, unanticipated period, verhulst’s demand, hexagonal fuzzy number, backorders, geographical market, fish ban period
DOI: 10.3233/JIFS-220832
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 801-814, 2023
Authors: Yang, Yulin | Sheng, Yuhong
Article Type: Research Article
Abstract: The contradiction between logistics and distribution capacity and people’s increasing demand has attracted more and more attention from all walks of life. At present, there are many problems and deficiencies in logistics in China. This paper establishes the location model of the national supply chain logistics center, taking the vegetable logistics distribution as an example. Based on the principle of maximum satisfaction and satisfying demand, a vegetable material flow location model with the ability to predict the annual yield of vegetables is proposed by using the advantages of Holt’s linear trend method and immune algorithm in solving multi-objective optimization problems. …Finally, the algorithm is applied to select the optimal logistics center in 31 provinces in China to maximize customer satisfaction. Thus, Chengdu, Guangzhou, Nanchang, Nanjing, Shijiazhuang, and Changchun re used as vegetable logistics centers. Show more
Keywords: Uncertainty theory, location model, immune algorithm, uncertain programming, expected value model
DOI: 10.3233/JIFS-220885
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 815-825, 2023
Authors: Wu, Mengmeng
Article Type: Research Article
Abstract: Using Ultra-High Performance Concrete (UHPC) as the highly resistant material is widely advised in constructing sensitive structures to enhance safety. The utilization of eco-friendly contents such as fly-ash and silica-fume replacing cement can decrease the pollution rate in the production process of concrete and improve the compressive strength (CS) factor. There are many ways to appraise the CS of concretes as empirically and mathematically Artificial Neural Networks (ANN) as the high-accurate model is used in the present study. In this regard, Radial Basis Function (RBF) coupling with Biogeography-Based Optimization (BBO) and Flow Direction Algorithm (FDA) created the two high-accurate frameworks: …BBO-RBF and FDA-RBF. Enhancing the accuracy of RBF to predict the CS and decreasing the ANN net complexity leads to having better results evaluated by various metrics. Therefore, using the proposed frameworks, the correlation index of R2 to model the CS in the training phase for FDA-RBF was calculated at 0.9, although BBO-RBF could get 0.85, with a 0.5% difference. However, the RMSE of FDA-RBF was 9 MPa, and for BBO-RBF, this index was calculated at 10 MPa the former model has about three percent more accuracy than the latter. Show more
Keywords: Ultra-high-performance concrete, radial basis function, flow direction algorithm, biogeography-based optimization, compressive strength
DOI: 10.3233/JIFS-221092
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 827-837, 2023
Authors: Xiong, Qiang | Lian, Shuai | Zeng, Zhangying | He, Runxin | Zhu, Binxin | Yang, Xinqi
Article Type: Research Article
Abstract: The vulnerability patch R&D has become an important part of information security governance. An effective collaboration with software vendors in patch R&D is of great significance to reduce the existence time of information security risks. This works aims to explore the relationship between vulnerability information disclosure and patch R&D of software vendors. The data regarding the vulnerability and software vendors is gathered from third-party vulnerability sharing platforms, including (China’s national information security vulnerability database, CNNVD) and Tianyacha.com. Based on the theory of organizational information processing, linear regression model and Cox proportional risk regression model are built for appropriately addressing the …research questions. The results show that the vulnerability disclosure of the third-party sharing platform can improve the patch R&D probability of software vendors. The information processing requirements, such as vulnerability information attention, vulnerability score and whether vulnerabilities are disclosed in advance accelerate the vulnerability patch R&D. The enterprise information processing capability indicators, including the industry dependence of software product customers and the staff size of software vendors accelerate the patch R&D. The number of products affected by the vulnerabilities and the number of software copyrights of software vendors have no significant impact on patch R&D. Show more
Keywords: Patch R&D, vulnerability information disclosure, information processing theory, third-party vulnerability sharing platforms
DOI: 10.3233/JIFS-221316
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 839-853, 2023
Authors: Ibrahim, Hariwan Z.
Article Type: Research Article
Abstract: An n,m-rung orthopair fuzzy set is the one of the most useful expansions of fuzzy sets for coping with information uncertainties. In such circumstances, in this article, we define an n,m-rung orthopair fuzzy topology and investigate the basic aspects of this topology. We introduce their relationship with Fermatean fuzzy topology, Pythagorean fuzzy topology and intuitionistic fuzzy topology, and provide some examples. In addition, we introduce separated n,m-rung orthopair fuzzy sets and then we present the concept of diconnected n,m-rung orthopair fuzzy sets. Moreover, we study and characterize n,m-rung orthopair fuzzy continuous maps in great depth. Furthermore, we establish T 0 …and T 1 in n,m-rung orthopair fuzzy topology and discover the links between them. Finally, we create a new relation extension on n,m-rung orthopair fuzzy sets and provide a method for classifying children with learning disabilities. Show more
Keywords: n,m-ROFSs, n,m-ROFT, separated n,m-ROFSs, diconnected n,m-ROFS, n,m-ROFS continuous maps, T0 , T1 and relation on n,m-ROFSs
DOI: 10.3233/JIFS-221528
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 855-869, 2023
Authors: Hu, Shan | Han, Jialin | Rong, Lingda | Zong, Qiwei | Dai, Mingxiao
Article Type: Research Article
Abstract: After COVID-19, some initiatives such as Healthy China, and Smart Living have been widely mentioned. This study explored the factors influencing user satisfaction in sports and healthcare integration services to help system builders and interaction designers better seek opportunities and directions for systems construction. Based on grounded theory method, conducted semi-structured interviews with people who have home exercise needs, and then summarised the influencing factors after coding the raw information level by level. It applied the user experience honeycomb to classify potential variables, used principal component analysis (PCA) to extract representative evaluation indicators as observed variables, and followed the construction …of a theoretical model of the satisfaction factors. The structural equation model (SEM) was validated and analyzed to prove its scientific validity and reasonableness. Research showed that the core factors affecting the user experience of sports and healthcare integration system include usefulness, interactivity, usability, credibility, and findability, all of which have a positive and significant impact on user satisfaction. According to the results of empirical analysis, A multidimensional design strategy for sports and healthcare integration system is proposed to provide a reference for improving user satisfaction. Show more
Keywords: Post-Pandemic Era, user satisfaction, grounded theory, Principal Component Analysis (PCA), Structural Equation Model (SEM)
DOI: 10.3233/JIFS-221533
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 871-887, 2023
Authors: Wang, Jiangrui | Zhu, Jiwei | Zhao, Xin | Li, Liang | Wang, Bing
Article Type: Research Article
Abstract: Expert group decision-making in the process of engineering consulting is an important part of the smooth development of engineering projects. Whether the conceptual design scheme of the project is reasonable or not will directly affect the construction quality of the project. After the preliminary selection of a river ecological governance project, four conceptual design schemes were obtained. The owner invited 20 experts in relevant fields to make decisions on the four schemes collected in the early stage. The experts gave preference information for each scheme after reading the relevant materials of the project and clarifying the actual needs of the …project. Based on this background, this paper uses a combination of quantitative and qualitative methods to construct a model for group decision-making and conflict resolution in the engineering consulting process. We use the preference relationship to reflect the degree of experts’ preference for the scheme, cluster them through similarity calculation, calculate the conflict degree of group preference and personal preference respectively, and comprehensively use the sequence difference method and personal preference correction method to resolve the conflict, so that their opinions can be quickly agreed within the specified time. The results calculated by model are consistent with the actual situation of the project, which verifies the effectiveness of the model proposed in this paper and can provide a reference for similar project decision-making and conflict resolution process. Show more
Keywords: Group decisions and negotiations, engineering consulting, conflict resolution, preference correction
DOI: 10.3233/JIFS-222099
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 889-904, 2023
Authors: Sureshkumar, T. | Sivaraj, R. | Vijayakumar, M.
Article Type: Research Article
Abstract: The Internet of Things (IoT) has altered the world in the last few years due to its capacity to impact almost every part of life. However, IoT raises concerns about data security and privacy because it collects data from devices via the cloud, increasing its vulnerability to hacking. IoT security is a serious issue that has delayed its widespread adoption. Several security and privacy solutions have been proposed for IoT contexts that meet prevalent security criteria such as authentication, integrity, and secrecy. However, due to resource restrictions and heterogeneous IoT devices, present solutions are unable to address the security requirements …of the approaching large-scale IoT paradigm. Blockchain, well known for bitcoin and Ethereum, provides an intriguing approach for IoT security. The IoT and blockchain technologies may be combined and significant improvements in distributed systems have been made as a result of the widespread use of IoT technology. A novel framework with a unique design was proposed to improve security in bitcoin transaction by combining blockchain and SHA-256 hash algorithm. Additionally, the performance of proposed framework is compared with the state-of-the-art algorithms like MD5 and SHA1 in term of encryption time, power consumption, latency, speed and security. It is observed that the proposed framework takes 12 ms lesser latency than MD5 and consumes 2.7Wh lesser power consumption than SHA1 and provides better security than both the techniques. Show more
Keywords: Blockchain, IoT, security, bitcoin, privacy
DOI: 10.3233/JIFS-220366
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 905-918, 2023
Authors: Hu, Chunjiao | Huang, Hengjie
Article Type: Research Article
Abstract: Feature selection is one basic technology for data mining. This paper investigates feature selection for interval-valued data via fuzzy rough iterative computation model (FRIC -model). To depict the similarity between samples in an interval-valued decision information system (IVDIS), the fuzzy symmetry relation in an IVDIS is first introduced from the perspective of “The similarity between information values is fed back to the feature set”. After that, several attribute evaluation functions, such as fuzzy positive regions, dependency functions and attribute importance functions are defined. Subsequently, FRIC -model for interval-valued data is established by using the iterations of these functions. Next, An …feature selection algorithm in an IVDIS based on this model is presented. Lastly, numerical experiments and statistics tests are carried out to estimate the performance of the presented algorithm. The experimental results illustrate that the presented algorithm maintains high classification accuracy, and does not occupy too much memory. These findings will provide new perspective for feature selection in an IVDIS. Show more
Keywords: IVDIS, Feature selection, FRS, Attribute evaluation function, FRIC-model
DOI: 10.3233/JIFS-221621
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 919-938, 2023
Authors: Rajesh, D. | Rajanna, G.S.
Article Type: Research Article
Abstract: Smart Dust environment face additional challenges as a result of the use of movable Smart Dust basestation(BS), despite its benefits. The main point of contention is the BS positioning updates to the smart dust nodes. Each smart object ought to be aware of the BS location so that it can send its data to the BS. According to the prevailing Flooding approach, the moveable BS must continuously distribute its location throughout the network in order to inform smart dust nodes about the BS location. In every case, visit positioning upgrades from the BS can result in maximal power usage as …well as enhanced network breakdowns. Different sorts of routing architectures can be used to reduce BS position updating. A routing strategy based on the movable BS is successful if it preserves the network network’s power consumption and latencies to a minimum. The study’s main goal is to develop an energy-efficient routing mechanism focused on adaptive movable BS modification. In the Smart Dust Head (SDH) establishing the inferred surroundings, the most latest movable BS location will be preserved. As a result, rather than soliciting SDH in the environment, the location of the BS is propagated to the smart dust nodes located at the sectors in integrated networking. By transmitting request information to the nearest sector, the remaining SDH can find the most current BS location. The message’s recipient is determined based on the information gathered. The best fuzzy related clustering algorithm will be used to accomplish this. The Enhanced Oppositional grey wolf optimization (EOGWO) methodology can be used to perform the improvement. Optimum network throughput, low latency, and other metrics are used to assess performance. To enhance productivity, the findings will be analyzed and compared to previous routing methodologies. Show more
Keywords: Data collection, smart dust, lifetime, energy utilization, and movable BS
DOI: 10.3233/JIFS-221719
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 939-949, 2023
Authors: Sridhar, M. | Pankajavalli, P.B.
Article Type: Research Article
Abstract: In the resource-constrained wireless sensor network (WSN) geographic routing has been considered as an attractive method where it exploits the location data instead of global topology to transmit the data. The geographic routing protocol faces the routing issues when it is used by a heterogeneous device and utilizes high energy during the propagation of data. The lifespan of the sensor network depends on the efficiency of energy and capacity of the battery. Hence, successful data transmission, enrichment of battery capacity and energy utilization is necessary for WSN. To attain this requirements an effective change is made in the data transmission …environment and network topology. In this paper proposed a dynamic cluster based duty cycle scheduling is initiated for the data transmission. The cluster-based scheduling and routing in geographic routing protocol (CSRGR) utilize the clustering mechanism which in turn reduces the consumption of energy and maximizes the throughput. The objective function of the proposed approach provides a scheduling and routing strategy. The demonstration of simulation results shows the effective cluster size balancing with data transmission range dynamically. The proposed algorithm is compared with the existing approach and from the results, the energy consumption is minimum for diverse scenarios. Show more
Keywords: Duty cycle schedule, throughput, energy efficiency, routing, scheduling cluster, and geographic routing
DOI: 10.3233/JIFS-220966
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 951-961, 2023
Authors: Saravana Kumar, K. | Ramasubramanian, S.
Article Type: Research Article
Abstract: Cardiovascular disease (CVD) is a severe public health concern globally. Early and accurate CVD diagnosis is a difficult task but a necessary endeavour required to prevent further damage and protect patients’ lives. Machine Learning (ML)-based Clinical Decision Support Systems (CDSS) have the potential to assist healthcare providers in making accurate CVD diagnoses and treatments. Clinical data usually contains missing values (MVs); hence, the incorporated imputation techniques for ML have become a critical consideration when working with real-world medical datasets. Furthermore, removing instances with MVs will lead to essential data loss and produce incorrect results. To overcome these issues, this paper …proposes an efficient and reliable CDSS with Ensemble Two-Fold Classification (ETC) framework for classifying heart diseases. The effectiveness of the proposed ETC framework using different supervised ML algorithms is evaluated with four distinct imputation methods for handling MVs over the standard benchmark dataset, viz., the University of California, Irwin (UCI). Experimental results show that our proposed ETC framework with the k-Nearest Neighbors(k-NN) imputation method achieves better classification accuracy of 0.9999 and a lesser error rate of 0.0989 compared to other imputation methods and classifiers with similar execution times. Show more
Keywords: Clinical dataset, classification, data pre-processing, decision support system, heart disease prediction, imputation, machine learning algorithms, missing values
DOI: 10.3233/JIFS-221165
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 963-980, 2023
Authors: Sahoo, Santosh Kumar
Article Type: Research Article
Abstract: Social distance is considered one of the most effective prevention techniques to prevent the spread of Covid19 disease. To date, there is no proper system available to monitor whether social distancing protocol is being followed by individuals or not in public places. This research has proposed a hybrid deep learning-based model for predicting whether individuals maintain social distancing in public places through video object detection. This research has implemented a customized deep learning model using Detectron2 and IOU for monitoring the process. The base model adapted is RCNN and the optimization algorithm used is Stochastic Gradient Descent algorithm. The model …has been tested on real time images of people gathered in textile shops to demonstrate the real time application of the developed model. The performance evaluation of the proposed model reveals that the precision is 97.9% and the mAP value is 84.46, which makes it clear that the model developed is good in monitoring the adherence of social distancing by individuals. Show more
Keywords: Covid19, social-distancing, deep learning, Detectron 2, Intersection over Union, video object detection
DOI: 10.3233/JIFS-221174
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 981-999, 2023
Authors: Song, Hui-Hui | Wang, Ying-Ming | Jia, Xiang | Meng, Meng-Jun
Article Type: Research Article
Abstract: In order to avoid the hesitation of choosing between aggressive and benevolent strategies, we propose two cross-efficiency models to get interval cross-efficiency (ICE) from the relatively neutral angle in fuzzy environment, and then propose a novel aggregation method for ICE to solve the full ranking of Decision-Making Units (DMUs). Firstly, regard the expected value of fuzzy data as the input and output of Data Envelopment Analysis (DEA) method based on fuzzy set theory. Secondly, construct the cross-efficiency models based on the fuzzy expected values from the relatively neutral angle, and generate the lower and upper bounds of ICE for all …DMUs, which determines the interval cross-efficiency matrix (ICEM). Thirdly, project all ICE onto the plane as points, then seek the optimal rally point for each DMU based on ICEM as the comprehensive ICE. Fourthly, rank the comprehensive ICE to obtain the complete ranking of DMUs by using the optimal number sorting method. Finally, the proposed model is applied to the evaluation of manufacturing enterprises, and the results are compared with different models to prove its effectiveness. Show more
Keywords: Interval cross-efficiency DEA, fuzzy sets, fuzzy numbers, the optimal rally point, aggregation method
DOI: 10.3233/JIFS-221482
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1001-1015, 2023
Authors: Alqahtani, Yahya | Jamil, Muhammad Kamran | Alshehri, Hamdan | Ahmad, Ali | Azeem, Muhammad
Article Type: Research Article
Abstract: In November of 2019 year, there was the first case of COVID-19 (Coronavirus) recorded, and up to 3rd of April of 2020, 1,116,643 confirmed positive cases, and around 59,158 dying were recorded. Novel antiviral structures of the 2019 pandemic disease Coronavirus are discussed in terms of the metric basis of their molecular graph. These structures are named arbidol, chloroquine, hydroxy-chloroquine, thalidomide, and theaflavin. Metric dimension or metric basis is a concept in which the whole vertex set of a structure is uniquely identified with a chosen subset named as resolving set. Moreover, the fault-tolerant concept of those structures …is also included in this study. By this concept of vertex-metric resolvability of COVID antiviral drug structures are uniquely identified and help to study the structural properties of the structure. Show more
Keywords: COVID antiviral drug structures, vertex metric dimension, vertex fault-tolerant metric dimension, locating number, locating set
DOI: 10.3233/JIFS-220964
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1017-1028, 2023
Authors: Shobana Nageswari, C. | Vimal Kumar, M.N. | Vini Antony Grace, N. | Thiyagarajan, J.
Article Type: Research Article
Abstract: Ultrasound image quality management and assessment are an important stage in clinical diagnosis. This operation is often carried out manually, which has several issues, including reliance on the operator’s experience, lengthy labor, and considerable intra-observer variance. As a result, automatic quality evaluation of Ultrasound images is particularly desirable in medical applications. This research work plans to perform the fetal heart chamber segmentation and classification using the novel intelligent technology named as hybrid optimization algorithm Tunicate Swarm-based Grey Wolf Algorithm (TS-GWA). Initially, the US fetal images data is collected and data undergoes the preprocessing using the total variation technique. From the …preprocessed images, the optimal features are extracted using the TF-IDF approach. Then, Segmentation is processed on optimally selected features using Spatially Regularized Discriminative Correlation Filters (SRDCF) method. In the final step, the classification of fetal images is done using the Modified Long Short-Term Memory (MLSTM) Network. The fitness function behind the optimal feature selection as well as the hidden neuron optimization of MLSTM is the maximization of PSNR and minimization of MSE. The PSNR value is improved from 3.1 to 9.8 in the proposed method and accuracy of the proposed classification algorithm is improved from 1.9 to 12.13 compared to other existing techniques. The generalization ability and the adaptability of proposed TS-GWA method are described by conducting the various performance analysis. Extensive performance result shows that proposed intelligent techniques performs better than the existing segmentation methods. Show more
Keywords: Fetal heart chamber segmentation, optimal feature selection, modified long short term memory tunicate swarm-based grey wolf algorithm, fetal heart chamber classification
DOI: 10.3233/JIFS-221654
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1029-1041, 2023
Authors: Zhao, Guiping | Wang, Hongmei | Li, Zhanfa
Article Type: Research Article
Abstract: The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA ). Although meticulous experimental studies are needed to determine a rock’s CWA , predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR ) models were created: a traditional SVR , two …ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR ). Results show that all models have acceptable performance in predicting the CWA with R 2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods. Show more
Keywords: Capillary water absorption, building stones, prediction, support vector regression, ensembled SVR, hybrid SVR
DOI: 10.3233/JIFS-221207
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1043-1055, 2023
Authors: Nithinsha, S. | Anusuya, S.
Article Type: Research Article
Abstract: The objective of the research work is to propose an intrusion detection system in a cloud environment using K-Means clustering-based outlier detection. In the open access and dispersed cloud architecture, the main problem is security and confidentiality because these are easily susceptible to intruders. Intrusion Detection System (IDS) is a commonly used method to identify the various attacks on the cloud which is easy to access from a remote area. The existing process can’t provide the data to transmit securely. This work describes and notifies the modernly established IDS and alarm management methods by giving probable responses to notice and …inhibit the intrusions in the cloud computing environment and to overcome the security and privacy issue. Proposed K-means Clustering based Outlier Detection (KmCOD) is used to detect the intruders and efficiently secure the data from malicious activity, where it is formulated respectively to increase the trustworthiness of the system by using applying intrusion detection techniques to virtual machines thus keeping the system safe and free from intrusion also provides system reliability. The parametric measures such as the detection rate, trace preprocessing, and correctly identified and incorrectly identified malicious activity are chosen. The performance analysis shows the accuracy of outlier detection as 81%, detection rate achieves 76%, packet arrival rate reaches 79%, pre-processing trace achieves 74%, and malicious activity rate of 21%. Show more
Keywords: Cloud, intrusion detection, data security, clustering algorithm, outlier detection, data privacy, anomaly
DOI: 10.3233/JIFS-220574
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1057-1068, 2023
Authors: Senthilkumar, D. | Reshmy, A.K. | Paulraj, S.
Article Type: Research Article
Abstract: Multi-Target Regression (MTR) is used to study the relationship between the same set of input variables and multiple continuous target variables simultaneously. A dataset with many input and output variables is the prime issue to address in the MTR, which is computationally complex to build a prediction model. Also, dimensionality reduction from multiple target variables is a challenging and essential task that aims to reduce the size of the dataset to optimize the time complexity of analysis and remove the redundant and irrelevant variables. This paper proposes an efficient feature selection strategy, Multi-Target Feature Subset Selection (MTFSS), for MTR that …constructs a unique subset of features by considering multiple targets. On the other hand, two feature evaluators, correlation and ReliefF, support the MTR dataset without discretization. Furthermore, two new score functions, weighted mean aggregation strategy and threshold function, are introduced to identify the significant features. To evaluate the effectiveness of the proposed MTFSS, experiments were carried out on a benchmark dataset. The experimental results demonstrate that the proposed MTFSS can select fewer features and perform better than the original dataset results. Also, the correlation-based feature evaluator performs better than ReliefF with better performance. Show more
Keywords: Multi-target regression, feature selection, correlation, ReliefF
DOI: 10.3233/JIFS-220412
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1069-1083, 2023
Authors: George, Remya | Jose, Reshma | Meenakshy, K. | Jarin, T. | Senthil Kumar, S.
Article Type: Research Article
Abstract: Law enforcement teams across the globe experience the highest occupational stress and stress-related diseases. Physical exercise and an active lifestyle are recommended as part of their profession to equip them to fight stress and related health adversities. The research is carried out using objective measures of Heart Rate Variability (HRV), Electro Dermal Activity (EDA), Heart Rate Recovery (HRR), and subjective questionnaires. HRV was generated with an electrocardiogram (ECG) signal acquired using NI myRIO 1900 interfaced with the Vernier EKG sensor. HRR was acquired with the help of a Polar chest strap exercise heart rate monitor and EDA acquisition was carried …out with Mindfield E-Sense electrodes. Then statistical features are extracted from the collected data, and feed to the AQCNN (Aquila convolution neural network) classifier to predict the stress. Signal analyses were done in Kubios 4.0, Ledalab V3.x in a MATLAB environment. The results pointed out that exercise training is effective in increasing the vagal tone of the Autonomic Nervous System (ANS) and hence improves the recovery potential of the cardiovascular system from stress. The proposed AQCNN method improves the accuracy by 95.12% which is better than 93.13%, 85.36% and 80.13% from Statistical technique, CNN and ML-SVM respectively. The findings have the potential to influence decision-making in the selection and training of recruits in high-stress positions, hence optimizing the cost and time of training by identifying maladaptive recruits early. Show more
Keywords: Exercise training, ANS adaptation, machine learning, stress-recovery, heart rate variability, heart rate recovery, electrodermal activity
DOI: 10.3233/JIFS-221588
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1085-1097, 2023
Authors: Saeed, Maha Mohammed | Al-Ghour, Samer | Mehmood, Arif | Al-Shomrani, Mohammed M. | Park, Choonkil | Lee, Jung Rye
Article Type: Research Article
Abstract: This work investigates the new notion of operators, including the interior operator, exterior operator and closure operator in bipolar vague soft topological spaces. On the basis of these notions few results are addressed in bipolar vague soft topological spaces. Lastly, the intriguing concept is that of a sequence’s limit and on the basis of this concept few more results are addressed in bipolar vague soft topological spaces.
Keywords: Bipolar vague soft set, bipolar vague soft operations, bipolar vague soft topological space, bipolar vague soft α-open sets, bipolar vague soft α-close sets, bipolar vague soft operators, bipolar vague soft equence
DOI: 10.3233/JIFS-220498
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1099-1116, 2023
Authors: Miao, Yong | Liu, Zedong | Zhuang, Zijing | Yan, Xiaofeng
Article Type: Research Article
Abstract: The most significant parameter in groundwater movement in stones is capillary water absorption. Specifying the capillary water absorption (CWP ) of rocks needs hard and laborious experimental work, while prediction models can reduce the cost and required time. To this aim, different rock specimens were gathered from various rocks. For the prediction processes, the hybrid adaptive neuro-fuzzy inference system (ANFIS) models also were proposed to determine the optimal value of two constituent parameters of the ANFIS, which the particle swarm optimization (PSO) and whale optimization algorithm (WOA) algorithm applied to the ANFIS for this aim. Results present that ANFIS processes …have passable accomplishment in forecasting the CWA with R 2 larger than 0.832 and 0.917 for the training and testing data, respectively, a good connection among actual and anticipated values. Considering developed models, the ANFIS model optimized with WOA performs better than another model in training and testing datasets. In the training dataset, the value of R2 and RRSE is 0.917 and 29.29% for the WOA-ANFIS model, while the PSO-ANFIS model is 0.911 and 30.50%, respectively. Overall, it is clear that WOA-ANFIS can be recognized as the proposed model, which shows its capability to find the optimal value of two constituent parameters of the ANFIS. Show more
Keywords: Capillary water absorption, building stones, prediction, adaptive neuro-fuzzy inference system, hybrid ANFIS
DOI: 10.3233/JIFS-220640
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1117-1127, 2023
Authors: Mohammed, Awsan | Ghaithan, Ahmed | Al-Yami, Fahad
Article Type: Research Article
Abstract: The oil and gas industry is one of the harshest environments on reinforced concrete structures. Enhancing the reliability of these industries has been identified as a critical goal to meet anticipated production targets and maintain competitiveness. The purpose of this paper is to rank and prioritize risk factors on reinforced concrete structural systems in the oil and gas industry to reduce failures and improve system reliability. The risk factors influencing reinforced concrete systems are identified based on the experts interviewed who specialized in risk analysis. In this paper, a risk assessment approach based on a hybrid fuzzy failure mode and …effect analysis is developed in order to rank the factors and improve the process of reinforced concrete maintenance prioritization. The developed approach is also compared with the other two methods; namely, conventional failure mode and effect analysis (FMEA) and grey rational analysis (GRA) integrated with FMEA. The three developed approaches are designed to acquire the highest risk priority number (RPN) values; conventional RPN, GRA-FMEA RPN, and Fuzzy-FMEA RPN. These values will be utilized as the focus of improvements to reduce the possibility of some kind of failure occurring a second time and improve the deteriorated reinforced concrete structure to minimize the likelihood of failures. The results revealed that high-risk systems include the compression train, steam turbine, and combustion gas turbine generator, while the majority require maintenance of the supporting concrete foundation as soon as second-degree deterioration occurs. Furthermore, the results indicated that the Fuzzy FMEA approach was appropriate for assessing deteriorated reinforced concrete structures.. This work represents a step forward in the development of a tool that can be used to assess the risk of degraded concrete structures and improve their integrity through proper monitoring and maintenance. Show more
Keywords: Risk assessment, concrete structures, oil and gas industry, fuzzy FMEA, grey rational analysis
DOI: 10.3233/JIFS-221328
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1129-1151, 2023
Authors: Shabbir, Wasif | Aijun, Li | Taimoor, Muhammad | Yuwei, Cui
Article Type: Research Article
Abstract: Flight performance of unmanned aerial vehicles (UAVs) strongly depends on implemented attitude tracking control. For designing better controllers, nonlinear control design techniques are often opted instead of control design based on linearized models. Uncertainty in nonlinear dynamics estimation may arise due to inaccuracies in aerodynamic derivatives and simplifications/assumptions made during the derivation of nonlinear models. This paper considers attitude tracking control of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs. An integral chain differentiator (ICD) is used to provide the analytical redundancy to the gyros used to measure the angular rates. Two control design schemes are proposed, a …neuro-fuzzy adaptive sliding mode control (NFASMC) and an ICD approximation-based fuzzy adaptive sliding mode control (ICD-FASMC). In NFASMC, the uncertain part of the dynamics is estimated using an adaptive radial basis function neural network. Gyro sensor output errors are estimated in real-time, using ICD based error estimation scheme and used in the control law along with the sensor’s corrupted outputs. In ICD-FASMC, the uncertain dynamics and angular rates of UAV are estimated using the ICD such that the requirement of the gyro sensor outputs for control design is bypassed. The switching gain of the designed controllers is made adaptive using fuzzy logic to mitigate the chattering effect. The stability of the proposed controllers is proved using the Lyapunov approach. The proposed schemes are implemented using a nonlinear simulation of a fixed-wing UAV. Simulation results are presented to show the effectiveness of the proposed techniques. Show more
Keywords: Neural network, tracking control, sliding mode control, fuzzy logic, UAV, sensors
DOI: 10.3233/JIFS-222630
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1153-1168, 2023
Authors: Subramanian, Kannimuthu | Kandhasamy, Premalatha
Article Type: Research Article
Abstract: Mining high utility itemsets (HUIs) from transaction databases is one of the current research areas in the data mining field. HUI mining finds itemsets whose utility meets a predefined threshold. It enables users to quantify the usefulness or preferences of products by utilizing different values. Since utility mining approaches do not satisfy the downward closure property, the cost of candidate generation for HUI mining in terms of time and memory space is excessive. This paper presents Genetic Algorithm based Particle Swarm Optimization (GA-PSO), which can efficiently prune down the number of candidates and optimally acquire the complete set of high …utility itemsets. The proposed algorithm’s performance is assessed using the synthetic dataset T20.I6.D100K and the real-time supermarket dataset, which comprises 38765 transactions and 167 unique products. It performs very effectively in terms of time and memory on large databases constituted of small transactions, which are challenging for existing high utility itemsets mining algorithms to manage. Experiments on real-world applications show the importance of high utility itemsets in business decisions, as well as the distinction between frequent and high utility itemsets. Show more
Keywords: Data mining, high utility itemset, genetic algorithm, particle swarm optimization, stagnation
DOI: 10.3233/JIFS-220871
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1169-1189, 2023
Authors: Riaz, Muhammad | Jamil, Nimra
Article Type: Research Article
Abstract: The idea of a cubic bipolar fuzzy set (CBFS ) is a new hybrid extension of the cubic set (CS) and the bipolar fuzzy set (BFS). A CBFS is a strong model to deal with bipolarity and fuzziness in terms of positive membership grades (PMGs) and negative membership grades (NMGs). A positive interval and a positive numbers represent a PMG to express the degree of belongingness of a specific property, and a negative interval and a negative number represent a NMG which defines the degree of non-belongingness of the specific property (or satisfaction level of its counter property). The …aim of this paper is to define the cubic bipolar fuzzy topology under P-order (CBFS P topology) as well as the cubic bipolar fuzzy topology under R-order (CBFS R topology). We investigate certain properties and results of CBFS P topology and CBFS R topology. Topological structures on CBFSs are helping in the development of new artificial intelligence (AI) techniques for healthcare domain strategies and investigating various critical diseases. Such techniques allow for the early detection and investigation of diseases, assisting clinicians in minimizing the possible risk factors. An extended linear assignment model (LAM) and superiority and inferiority ranking method (SIR method) are proposed for healthcare diagnosis based on newly developed structures. The proposed LAM and SIR method are successfully applied for investigation of critical diseases. Moreover, we discuss a comparison analysis of investigations made by suggested techniques with some existing approaches. Show more
Keywords: Cubic bipolar fuzzy set, cubic bipolar fuzzy topology, computational intelligence, linear assignment model, SIR method, healthcare
DOI: 10.3233/JIFS-222224
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1191-1212, 2023
Authors: Carmalatta, J. | Diwakaran, S. | Uma Maheswari, P. | Raja, S. | Robinson, Y. Harold | Julie, E. Golden | Kumar, Raghvendra | Son, Le Hoang | Le, Chung | Tung, Nguyen Thanh | Long, Hoang Viet
Article Type: Research Article
Abstract: In Passive Clustered Wireless Sensor Networks (WSNs), energy is lost in a sensor node during the data transmission. In order to avoid the energy loss due to data transmission, a data prediction technique is implemented. In this paper, we present a new multi-point data prediction technique, in which the prediction algorithm is initially implemented at both member nodes and cluster heads. The algorithm is updated to cluster head by member nodes by tracking temporal correlation of data. Neuro-Fuzzy model is used as a predictor in both member nodes and cluster heads. The simulation is performed using MATLAB and the overall …energy in nodes seems to increase. The mean square error (MSE) value is reduced to greater extend. Show more
Keywords: Neuro-fuzzy, wireless sensor networks, clustering, cluster head, mean square error value, energy consumption.
DOI: 10.3233/JIFS-212214
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1213-1228, 2023
Authors: Esmaeili, Mahin
Article Type: Research Article
Abstract: This paper presents a new combined algorithm for the fuzzy Travelling Salesman Problem (FTSP) based on a composition of the Intelligent Water Drops (IWD) and the Electromagnetism-like (EM) algorithms. In a FTSP, the time consumed distance between cities i and j can be described by vague knowledge, such as fuzzy quantity. The main goal of FTSP is to achieve the minimum distance of Hamilton circuit of G graph, where the Hamilton circuit is a closed route of cities (i.e., nodes) of G that have been visited only once. The proposed algorithm transfers the generated responses by …the IWD to the EM, where the best answer is selected. Importantly, the computed results from both algotithm are compared and the best is accumulated. In other words, in each iteration, the best result is collected by comparison between the current and previous hierarchies until the halt condition is fulfilled. Finally, the results of the genetic algorithm (GA), IWD and EM algorithms are compared, so that the efficiency of the proposed combined IWD-EM algorithm is determined. Show more
Keywords: Fuzzy travelling salesman problem (TSP), intelligent water drops (IWD) algorithm, electromagnetism-like (EM) algorithm, genetic algorithm (GA)
DOI: 10.3233/JIFS-213121
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1229-1240, 2023
Authors: Wu, Meiqin | Chen, Ruixin | Fan, Jianping
Article Type: Research Article
Abstract: Multi-criteria decision-making methods often include attributes with uncertain nature in practical applications, single-valued neutrosophic set is an important approach to solve above problem. QUALIFLEX method is a traditional decision method that makes decision by comparing different permutations of alternatives. In this paper, QUALIFLEX method is developed to solve the MCDM problem with the element of decision matrix is the single-valued neutrosophic number. Besides, since the defects of the original QUALIFLEX method about fusing information of different attributes, this paper uses Dempster-Shafer theory of evidence to integrate the information about weight and alternatives. Finally, by comparing the result with other MCDM …methods, we find that the new method can not only obtain reasonable results, but also explain the decision results by probability theory. This paper not only develops the traditional MCDM method, but also a meaningful attempt to apply AI algorithm in MCDM method. Show more
Keywords: Dempster-Shafer theory of evidence, QUALIFLEX, Single-valued neutrosophic set, multiple criteria decision making (MCDM), evidential reasoning
DOI: 10.3233/JIFS-220194
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1241-1256, 2023
Authors: Liu, Yitong | Mu, Xuewen
Article Type: Research Article
Abstract: A new neural network is proposed to solve the second-order cone constrained variational inequality (SOCCVI) problems. Instead of the smoothed Fishcer-Burmeister function, a smooth regularized Chen-Harker-Kanzow-Smale (CHKS) function is used to handle relevant complementarity conditions. By using a neural network approach based on the CHKS function, the KKT conditions corresponding to the SOCCVI are solved. Some stability properties of the neural network can be verified by the Lyapunov method. When the parameters of the neural network are different, the achieved convergence speed will also vary. Further by controlling the corresponding parameters, the neural network can achieve a faster convergence speed …than a classical model. Numerical simulations are applied to examine the computing capability of the neural network as well as the influence of parameters on it. Show more
Keywords: Neural network, Second-order cone, Variational inequality, CHKS function, Lyapunov method
DOI: 10.3233/JIFS-220972
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1257-1268, 2023
Authors: Xu, Yunjian | Guo, Aiyin
Article Type: Research Article
Abstract: The orthophotos of Pinus tabulaeformis and seabuckthorn were collected by UAV, these images were used as test images, and the performance of six image segmentation algorithms were qualitatively analyzed and quantitatively compared such as fuzzy pixel clustering and watershed algorithms. The error rate, relative final measurement accuracy, and running time are used as evaluation criteria. The experimental results show that the segmentation algorithms’ performance of the affected forest image is closely related to the image-capturing height and noise. Finally, the guiding suggestions for the application of the orthophoto segmentation algorithm are given from unmanned aerial vehicles in the affected forest …area. Show more
Keywords: Forest diseases and insect pests, Unmanned aerial vehicle (UAV) orthographic image, fuzzy pixel clustering, watershed algorithm
DOI: 10.3233/JIFS-221403
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1269-1281, 2023
Authors: Pandurangan, Raji | Jayaseelan, Samuel Manoharan | Rajalingam, Suresh | Angelo, Kandavalli Michael
Article Type: Research Article
Abstract: The traffic signal recognition model plays a significant role in the intelligent transportation model, as traffic signals aid the drivers to driving the more professional with awareness. The primary goal of this paper is to proposea model that works for the recognition and detection of traffic signals. This work proposes the pre-processing and segmentation approach applying machine learning techniques are occurred recent trends of study. Initially, the median filter & histogram equalization technique is utilized for pre-processing the traffic signal images, and also information of the figures being increased. The contrast of the figures upgraded, and information about the color …shape of traffic signals are applied by the model. To localize the traffic signal in the obtained image, then this region of interest in traffic signal figures are extracted. The traffic signal recognition and classification experiments are managed depending on the German Traffic Signal Recognition Benchmark-(GTSRB). Various machine learning techniques such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Convolutional neural network (CNN)- General Regression Neural Network (GRNN) is used for the classification process. Finally, the obtained results will be compare in terms of the performance metrics like accuracy, F1 score, kappa score, jaccard score, sensitivity, specificity, recall, and precision. The result shows that CNN-GRNN with ML techniques by attaining 99.41% accuracy compare to other intelligent methods. In this proposed technique is used for detecting and classifying various categories of traffic signals to improve the accuracy and effectiveness of the system. Show more
Keywords: Traffic signal images, traffic signs, median filter, gabor filter, forecasting
DOI: 10.3233/JIFS-221720
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1283-1303, 2023
Authors: Jin, Hui | Li, Jun-qing
Article Type: Research Article
Abstract: With the emphasis of the exhaust gas emission of the transportation vehicles, the mode of picking up and delivering products simultaneously has become a challenging issue in the vehicle routing problem (VRP). To remedy this issue, we investigate a special VRP with realistic constraints including product classification, pickup-delivery, and time window (PC-VRPSPDTW). Then, a hybrid algorithm combining tabu search and artificial immune algorithm (TS-AIA) is proposed. In the proposed algorithm, the earliest time and residual capacity (ETRC) heuristic is designed to generate the initial population. Then, two metaheuristics including variable neighborhood search and large neighborhood search are cooperated to balance …the exploration and exploitation abilities. Besides, a new crossover operator is designed to increase the population diversity. Finally, a series of comparative experiments on the extension version of the Solomon’s benchmarks are performed to verify the effectiveness of the proposed algorithm. Show more
Keywords: Product classification, pickup and delivery, hybrid complementary metaheuristic, tabu search, artificial immune algorithm
DOI: 10.3233/JIFS-222118
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1305-1322, 2023
Authors: Chen, Tuantuan | Xin, Delin | Zhang, Zhongwen | Chen, Hu | Jiang, Qiangqiang
Article Type: Research Article
Abstract: Mineshaft development mode design and decision-making is a complex system theory problem with significant implications for the mine’s investment cost, operational safety, and production efficiency. Because of the many factors that influence mine shaft development mode decision-making, various decision-makers have different worries and inclinations, resulting in greater subjectivity and uncertainty in the decision-making process. The concept of a specialty chain was born out of a belief in group decision-making. By merging and assessing the decision-making information of different groups in the same specialty chain, a systematic decision-making index database of the mine shaft development model was created. To elucidate the …correlation model and hierarchical link between the decision-making indexes, the Interpretative Structure Model (ISM) was applied. The multilevel decision-making index system of mine shaft development mode was established. The decision-making group structure was optimized. The relative importance of the Analytical Hierarchy Process (AHP) was modified to determine the scale. A collaborative weight determination method of multiple decision-making groups was established to reduce the influence of individual subjective consciousness on decision-making results. The ISM-GAHP-FCA decision-making model of mine shaft development mode was built in conjunction with Fuzzy Comprehensive Analysis (FCA) to increase fuzzy decision-making information’s integration and analysis ability. The decision-making outcomes from the analysis of 10 typical mine shaft types in China are adaptable to the actual situation. The model can effectively express the hierarchy, significance, and fuzziness of mine shaft development mode decision-making indexes and limit the interference of decision-maker subjectivity on decision-making results. Show more
Keywords: Mine development mode, mineshaft, interpretive structural model, group decision making, analytic hierarchy process, fuzzy comprehensive evaluation
DOI: 10.3233/JIFS-212119
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1323-1336, 2023
Authors: Li, Jingmin | Xu, Shuzhen
Article Type: Research Article
Abstract: As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system …is constructed.Secondly, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises. Show more
Keywords: Iron and steel enterprises, safety assessment, neural network, optimization algorithm, deep learning
DOI: 10.3233/JIFS-220246
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1337-1348, 2023
Authors: Soumya, T.V. | Sabu, M.K.
Article Type: Research Article
Abstract: The cogent area, Probabilistic rough sets, offers methods that are used to trisect the data into positive, negative and boundary regions for optimum (α, β) pairs. These basic methods generate three regions based on a single quality, including cost, entropy, impurity, correlation and variance, thereby the best (α, β) pair is generated. The optimization of multiple qualities has significance in real-life applications; however, experiments rarely discussed the optimization of different criteria together in probabilistic rough sets. This probe conducts multi-objective optimization of uncertainty, impurity and correlation, to determine a trisection at optimal (α, β) pairs. For that, this work proposes …a hybrid method that involves Weighted Sum and Artificial Bee Colony Algorithm to optimize the thresholds. The results are compared with the Information-theoretic rough sets and Game-theoretic rough sets. The proposed method outperforms regarding optimal qualities, multiple optimum thresholds, minimal size of boundary regions, and better evaluation results. By attesting the study on experimental data sets, optimal (α, β) pairs are obtained at which the uncertainty and impurity are minima. Moreover, the correlation at this threshold is reasonable. From the application viewpoint, it reduces the cost of further analysis by generating the minimum delayed decision and maximizes the benefit with optimal decisions by considering multiple optimized qualities simultaneously. Show more
Keywords: Multi-objective optimization, probabilistic rough sets, artificial bee colony algorithm, entropy, gini index
DOI: 10.3233/JIFS-221359
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1349-1367, 2023
Authors: Xu, Wei | Mao, Jun-Jun | Zhu, Meng-Meng
Article Type: Research Article
Abstract: The group decision-making problem usually involves decision makers (DMs) from different professional backgrounds, which leads to a considerable point, that it is the fact that there will be a certain difference in the professional cognition, risk preference and other hidden inherent factors of these DMs to the objective things that need to be evaluated. To improve the reasonability of decision-making, these hidden inherent preference (HIP) of DMs should be determined and eliminated prior to decision making. As a special form of fuzzy set, q-rung orthopair fuzzy numbers (q-ROFNs) is a useful tool to process uncertain information in decision making problems. …Hence, under the environment of q-ROFNs, the determination of HIP based on distance from average score is proposed and a risk model is established to eliminate the HIP by analyzing the possible impact. Meanwhile, a dominant function is proposed, which extends the comparison method between q-ROFNs and an integrated decision-making method is provided. Finally, considering the application background of double carbon economy, an example by selecting the best design of electric vehicles charging station (EVCS) is conducted to illustrate the proposed method, and the feasibility and efficiency are verified. Show more
Keywords: group decision-making, q-ROFNs, hidden inherent preference, risk model, double carbon economy
DOI: 10.3233/JIFS-221702
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1369-1384, 2023
Authors: Al Ghour, Samer
Article Type: Research Article
Abstract: In this paper, we introduce soft somewhat ω-continuous soft mappings and soft somewhat ω-open soft mappings as two new classes of soft mappings. We characterize these two concepts. Also, we prove that the class of soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mappings contains the class of soft somewhat continuous (resp. soft somewhat open) soft mappings. Moreover, we obtain some sufficient conditions for the composition of two soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mappings to be a soft somewhat ω-continuous (resp. a soft somewhat ω-open) soft mapping. Furthermore, we introduce some sufficient conditions for restricting a soft …somewhat ω-continuous (resp. soft somewhat ω-open) soft mapping to being a soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mapping. In addition to these, we introduce extension theorems regarding soft somewhat ω-continuity and soft somewhat ω-openness. Finally, we investigate the correspondences between the novel notions in soft topology and their general topological analogs. Show more
Keywords: Soft somewhat continuous soft mapping, soft ω-continuous soft mappings, soft somewhat open soft mapping, soft generated soft topological space
DOI: 10.3233/JIFS-222098
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1385-1396, 2023
Authors: Li, Fang | Lu, Weihua | Yang, Xiyang | Guo, Chong
Article Type: Research Article
Abstract: In the existing short-term forecasting methods of time series, two challenges are faced: capture the associations of data and avoid cumulative errors. For tackling these challenges, the fuzzy information granule based model catches our attention. The rule used in this model is fuzzy association rule (FAR), in which the FAR is constructed from a premise granule to a consequent granule at consecutive time periods, and then it describes the short-association in data. However, in real time series, another association, the association between a premise granule and a consequent granule at non-consecutive time periods, frequently exists, especially in periodical and seasonal …time series. While the existing FAR can’t express such association. To describe it, the fuzzy long-association rule (FLAR) is proposed in this study. This kind of rule reflects the influence of an antecedent trend on a consequent trend, where these trends are described by fuzzy information granules at non-consecutive time periods. Thus, the FLAR can describe the long-association in data. Correspondingly, the existing FAR is called as fuzzy short-association rule (FSAR). Combining the existing FSAR with FLAR, a novel short-term forecasting model is presented. This model makes forecasting at granular level, and then it reduces the cumulative errors in short-term prediction. Note that the prediction results of this model are calculated from the available FARs selected by the k-medoids clustering based rule selection algorithm, therefore they are logical and accurate. The better forecasting performance of this model has been verified by comparing it with existing models in experiments. Show more
Keywords: Trend fuzzy information granule, fuzzy long-association rule, long-association, k-medoids clustering based rule selection algorithm, short-term forecasting
DOI: 10.3233/JIFS-222721
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1397-1411, 2023
Authors: Amini, Mohammad | Targhi, Alireza Tavakoli | Hosseinzadeh, Mehdi | Farivar, Faezeh | Bidaki, Reza
Article Type: Research Article
Abstract: Handwriting problems, also known as dysgraphia, are defined as a disorder or difficulty in producing written language associated with writing mechanics. The occurrence of handwriting problems among elementary students varies from 10 to 34%. With negative impacts on educational performance, handwriting problems cause low self-confidence and disappointment in the students. In this research, a pen-tablet was employed to sample children’s handwriting, which revealed online features of handwriting such as kinematic and temporal features as well as wrist and hand angles and pen pressure on the surface. This digitizer could also extract the online handwriting features when the pen was not …in contact with the surface. Such features are called in-air features. The purpose of this study was to propose a method for diagnosing dysgraphia along with an evaluation of the impact of in-air features on the diagnosis of this disorder. A rich dataset (OHF-1) of online handwriting features of dysgraphic and non-dysgraphic students was prepared. After the extraction of a huge set of features and choosing a feature selection method, three machine learning methods, i.e. SVM, Random Forest and AdaBoost were compared and with the SVM method, an accuracy of 85.7% in diagnosing dysgraphia was achieved, when both in-air and on-surface features were included. However, while using purely in-air data or merely on-surface features, accuracies of 80.9% and 71.4% were achieved, respectively. Our findings showed that in-air features had a significant amount of information related to the diagnosis of dysgraphia. Consequently, they might serve as a significant part of the dysgraphia diagnosis. Show more
Keywords: Handwriting Analysis, Identification of Dysgraphia, In-Air Analysis, Machine Learning, Online Handwriting Features
DOI: 10.3233/JIFS-221708
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1413-1424, 2023
Authors: Jebin Bose, S. | Kalaiselvi, R.
Article Type: Research Article
Abstract: In today’s world, Android has become the most significant and standard operating system for smartphones. The acceptance of the rapidly growing android system has outcome in a significant enhancement in the number of malware on comparing earlier days. There were several antimalware programs that are designed efficiently for protecting the sensitive data of the user in a mobile system from the occurrence of such attacks. Detection of malware system based on deep learning model along with the use of optimization technique is presented in this work. Initially, android malware dataset input is acquired and the normalization process is done. The …feature selection is carried along with the optimization technique Recurrent Tuna Swarm Optimization. By this, an optimal selection of features can be attained. Show more
Keywords: Android system, malware detection, deep learning model, recurrent tuna swarm optimization, dynamic attention-based LSTM
DOI: 10.3233/JIFS-220828
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1425-1438, 2023
Authors: Manju, S.C. | Geetha, J. | Somasundaram, K.
Article Type: Research Article
Abstract: Topological indices play a significant role in molecular chemistry, spectral graph theory, network theory, etc. We aim to contribute some new results on PI and weighted PI indices. The vertex PI index of a graph G is given by, PI (G ) = ∑e ∈E (G ) (|V (G ) | - N G (e )). The weighted PI index of a graph G is given by, PI w (G ) = ∑e =(u ,v )∈E (G ) (d G (u ) + d G (v ))(|V (G ) | - N G (e )). We obtained the PI and weighted PI indices for powers …of paths, cycles, and their complements in this study. Also, for a regular graph, a relationship between PI and weighted PI indices is established, and using this relation the weighted PI index is calculated for k th power of a cycle. Show more
Keywords: PI index, Weighted PI index, Power of a graph, Complement of a graph., AMS subject classification: 05C07, 05C09, 05C12.
DOI: 10.3233/JIFS-221436
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1439-1452, 2023
Authors: Al-Sharqi, Faisal | Ahmad, Abd Ghafur | Al-Quran, Ashraf
Article Type: Research Article
Abstract: Interval complex neutrosophic soft set (ICNSS) is the generalization of complex neutrosophic soft set (CNSS) as it provides an interval-based membership structure to handle the complex neutrosophic soft data. However, in the definition of the ICNSS, parameters set is a classical set, and the parameters have the same degree of importance which is considered as 1. This poses a limitation in modeling of some problems. Therefore, we introduce the concept of fuzzy parameterized interval complex neutrosophic soft set (FP-ICNSS) based on idea that each of elements of parameters set has got an importance degree. The basic theoretical operations and properties …are defined and verified on FP-ICNSS. For FP-ICNSS, we conceptualize the relevant mapping and study the properties of the FP-ICNSS images and inverse images. Then, we propose a new algorithm that is applicable in the field of medical diagnosis and decision-making problems for selection right product. Moreover, an illustrative example is presented which depicts its validity for successful application to the problems involving vagueness and uncertainties. Eventually, a comparison between the proposed model and the existing methods is conducted to clarify the importance of this model. Show more
Keywords: Complex neutrosophic set, fuzzy parameterized-interval neutrosophic soft set, interval neutrosophic set, interval-complex neutrosophic soft set, soft set
DOI: 10.3233/JIFS-221579
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1453-1477, 2023
Authors: Ahmad, Uzma | Nawaz, Iqra
Article Type: Research Article
Abstract: In this paper, we introduced Wiener index ( WI ) and average Wiener index ( AWI ) of directed rough fuzzy graph (DRFG). WI is the most extensively used index in graph theory. This index is based on the geodesic distance between two vertices. If there is no directed path from vertex x to vertex y in DRFG, we assume that the weight of geodesic from vertex x to vertex y is zero. In this paper, we investigate the connection between …WI and connectivity index ( CI ), which is one of the most prominent index, by presenting several examples and results. We introduced the concept of complete directed rough fuzzy graph (CDRFG) along with some useful results like CDRFG have no weak edges. We also compute the WI for CDRFG. Moreover, we discussed three types of vertices: Wiener enhancing vertex (WEV), Wiener reducing vertex (WRV), and Wiener neutral vertex (WNV). The proposed study of DRFG is suitable for modeling uncertainties and unclear data information in the real life circumstances. In the end, we proposed an application of the WI in the human trafficking network. We also presented a detailed comparative analysis and comparison table by comparing our result for both CI and WI for the same human trafficking network. Show more
Keywords: Directed rough fuzzy graph, connectivity index, wiener index, human trafficking, AMS (MSC): 03E72, 68R10, 68R05
DOI: 10.3233/JIFS-221627
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1479-1495, 2023
Authors: Venkataramanan, K. | Kannan, P. | Sivakumar, M.
Article Type: Research Article
Abstract: This manuscript proposes a hybrid method for optimum sizing and energy management (EM) of hybrid energy storage systems (HESSs) in Electric vehicle (EV). The proposed hybrid method is combined performance of Honey Badger Algorithm (HBA) and recalling-enhanced recurrent neural network (RERNN), commonly called HBA-RERNN method. The major objective of proposed system is reducing the vehicle life time cost. The HESSs are incorporated with battery and super capacitor (SC). The proposed method is utilized to solve combined energy management and optimization size. Based on the variables, such as size of battery pack and super capacitor pack, HESS size is reflected. Depend …on various sensitivity factors, optimum hybrid energy storage systems size and financial costs are analyzed. At last, the performance of proposed system is implemented on MATLAB site and compared with several existing systems. From this simulation outcome, it concludes that the proposed system diminishes the overall cost and battery degradation cost as 66625 USD than the existing systems. The efficiency of the proposed system achieves 94.8763%. Show more
Keywords: Electric vehicle, hybrid energy storage system, energy management, cost Reduction, sizing, vehicle life time, sensitivity analysis, battery pack
DOI: 10.3233/JIFS-222503
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1497-1515, 2023
Authors: Swathi, P. | Kumar, G. | Jeyabalan, R. | Nishanthini, R.
Article Type: Research Article
Abstract: An one-one correspondence function λ from V (G ) ∪ E (G ) to the set {1, 2, …, |V (G ) | + |E (G ) |} is a total labeling of a finite undirected graph G without loops and multiple edges, where |V (G ) |and |E (G ) | are the cardinality of vertex and edge set of G respectively. A perfectly antimagic total labeling is a totally antimagic total labeling whose vertex and edge-weights that are also pairwise distint. Perfectly antimagic total (PAT) graph is a graph having such labeling. The topic of discovering perfectly antimagic total labeling of some families of …graphs is discussed in this paper. We also came up with certain conclusions about dual of a perfectly antimagic total graphs. Finally, we provided that the necessary and sufficient condition for a dual of a regular and irregular PAT graph to be a PAT graph. Show more
Keywords: Totally antimagic total labeling, antimagic total labeling, total labeling
DOI: 10.3233/JIFS-221279
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1517-1523, 2023
Authors: Javid, Irfan | Ghazali, Rozaida | Zulqarnain, Muhammad | Hassan, Norlida
Article Type: Research Article
Abstract: The important task in the medical field is the early detection of disease. Heart disease is one of the greatest challenging diseases in all other diseases subsequently 17.3 million people died once a year due to heart disease. A minute error in heart disease diagnosis is a risk for an individual lifespan. Precise heart disease diagnosis is consequently critical. Different approaches including data mining have been used for the prediction of heart disease. However, there are some solemn concerns related to the data quality for example inconsistencies, missing values, noise, high dimensionality, and imbalanced statistics. In order to improve the …accuracy of Data Mining based prediction systems, techniques for data preparation were applied to increase the quality of the data. The foremost objective of this paper is to highlight and summarize the research work about (i) data preparation techniques mostly used, (ii) the impact of pre-processing procedures on the accuracy of a heart disease prediction system, (iii) classifier enactment with data pre-processing techniques, (4) comparison in terms of accuracy of the different pre-processing model. A systematic literature review on the use of data pre-processing in heart disease diagnosis is carried out from January 2001 to July 2021 by studying the published material. Almost 30 studies were designated and examined related to the above-mentioned benchmarks. The literature review concludes that data reduction and data cleaning pre-processing techniques are mostly used in heart disease prediction systems. Overall this study concludes that data pre-processing has improved the accuracy of models used for heart disease prediction. Some hybrid models including (ANN+CHI), (ANN+PCA), (DNN+CHI) and (SVM+PCA) have shown improved accuracy level. However, due to the lack of clarification, there is a number of limitations and challenges in order to implementing these models in the real world. Show more
Keywords: Heart disease, data pre-processing, cardiomyopathy, data mining, literature review
DOI: 10.3233/JIFS-220061
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1525-1545, 2023
Authors: Ramshankar, N. | Joe Prathap, P.M.
Article Type: Research Article
Abstract: Nowadays, people always use online promotions to know about best shops to buy the best products. This shopping experience and shopper’s opinion about the shop can be observed by the customer-experience shared on social media. A new customer when searching a shop needs information about manufacturing date (MRD) and manufacturing price (MRP), offers, quality, and suggestions which are only provided by the previous customer experience. Several approaches were used previously for predicting the product details, but no one approach provides accurate information. To overcome these issues, Reviewer Reliability and XGboost whale Optimized Sentiment Analysis for Online Product Recommendation is proposed …in this manuscript.Initially, Amazon Product recommendation datathe data are preprocessed and given to XGboost Classifier that classifies the product recommendation result as, good, bad and average. Generally the XGboost Classifier does not reveal any adoption of optimization techniques for computing the optimal parameters for assuring accurate classification of product recommendation. Therefore in this work, proposed Whale optimization algorithm utilized to optimize the weight parameters of the XGboost. Then the proposed model is implemented in MATLAB. The proposed method attains 18.31%, 12.81%, 45.75%, 26.97% and 25.55% lower Mean Absolute error, 18.31%, 12.81%, 27.97%, 25.97%, and 25.55% higher Mean absolute percentage error and 15.31%, 10.33%, 25.86%, 22.86% and 15.22% lower Mean Square Error than the existing methods. Show more
Keywords: Whale optimization algorithm (WOA), XGBoost classifier, sentiment analysis, online product shopping reviews, recommendation system
DOI: 10.3233/JIFS-221633
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1547-1562, 2023
Authors: Peng, Su-Mian
Article Type: Retraction
DOI: 10.3233/JIFS-219324
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1563-1563, 2023
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