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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: Zeng, Shaohua | Wang, Qi | Wang, Shuai | Liu, Ping
Article Type: Research Article
Abstract: Shadow detection is a significant preprocessing work that soil type is classified with machine vision. Thus, Density peak clustering based on histogram fitting(DPCHF) is proposed to segment soil image shadows. First, its clustering centers are adaptively obtained by constructing a new parameterless density formula and decision value measure. Then the Fourier series are drawn into it to approximate the gray histogram and a part of gray-levels are allocated by valley points of the histogram fitting curve. Finally, an optimization model is established to optimize the threshold of detecting the shadow in the soil image, and the remaining gray-levels are clustered …by the threshold. The simulation results show that DPCHF is better than the contrast algorithm. The average brightness standard deviations of the shadow and non-shadow are respectively 20.9348 and 20.3081 with DPCHF. It can realize the adaptive shadow detection of soil images and there is not the “domino” error propagation in it. Show more
Keywords: Shadow detection, density peak clustering, soil image, machine vision
DOI: 10.3233/JIFS-211633
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2963-2971, 2022
Authors: Chen, Dengfeng | Wang, Shuaiju | Zhang, Wen | Yang, Yalong | Chen, Pengwen
Article Type: Research Article
Abstract: With the development of infrastructure construction in China, the number of bridges is increasing rapidly, putting a strain on operation management and structural maintenance. Bridge Information Management System (BIMS) uses information and digital technology to ensure the maintenance of the bridge in long-term operation. However, large-scale bridge models are large in size, complex in structure, rich in information and occupy more computer resources, which harms performance of the BIMS. This paper aims to design and develop a bridge information visualization management system based on Building Information Model (BIM). The geometry and material information for the model is extracted from the …IFC by the secondary development of Revit. The lightweight method of the bridge model is studied and implemented, and the model volume is reduced to less than 20% by using the lightweight algorithm. Also, a Web-BIM-oriented model visualization scheme is proposed to render the lightweight model into the browser. This system integrates BIM with the Internet of Things (IoT) and contains information visualization, human-computer interaction and user collaboration. Such a BIMS can effectively relieve the pressure of bridge operation and maintenance, while also providing managers with a safe and reliable platform. Show more
Keywords: Bridge information management system, BIM, lightweight, Draco, WebGL
DOI: 10.3233/JIFS-211988
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2973-2984, 2022
Authors: Li, Congdong | Wang, Dan | Yang, Weiming
Article Type: Research Article
Abstract: Reusing design knowledge of products is a useful way to solve the efficiency issue of complex product design. The design knowledge is tacit, empirical, and unstructured and there exists insufficient case matching and inefficient design reuse in complex products design process. Aiming at these problems, this paper presents an improved case-based reasoning methodology combining ontology with two-stage retrieval. Firstly, a knowledge domain ontology model of complex product design is constructed, and the technology of ontology-based data access is introduced to automatically generate a case knowledge base with semantic information. Then, a new two-stage case retrieval method integrated semantic query with …similarity calculation is proposed. The case subset is selected by query statements. It has the characteristic of isomorphism with design problem. The retrieval mechanism is applied to compress the traversal space, reduce the redundancy of semantic similarity calculation, improve the retrieval efficiency, and fulfill the target of case reuse. Finally, a variant design of the chiller unit as an example is executed to illustrate the use of the proposed method, and experiments are organized to evaluate its performance. The result shows that the proposed approach has an average precision of 92% and high stability, outperforming existing methods. Show more
Keywords: Knowledge representation, domain ontology, case retrieval, product design, case-based reasoning
DOI: 10.3233/JIFS-212927
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2985-3002, 2022
Authors: Nimmala, Satyanarayana | Vikranth, B. | Muqthadar Ali, Syed | Usha Rani, Rella | Rambabu, Bandi
Article Type: Research Article
Abstract: High Blood Pressure (HBP) is one of the major significant medical concerns of many people around the globe today. HBP is so common symptom many people across the globe are experiencing, irrespective of age, gender, region, and religion. HBP prediction ahead of time can help the person to avoid the consequences such as heart stroke, kidney failure, eye damage, sexual dysfunction, and even death. HBP prediction in advance is a challenging issue as it is associated with many biopsychosocial factors. Heuristic and meta-heuristic-based Machine Learning Models (MLM) exclusively supervised machine learning techniques are becoming part and parcel of medical data …diagnosis. However, the reliability of outcome, usability, and understandability of such stand-alone models in processing medical data in real-time are not up to the mark. To overcome such limitations, in this paper we proposed an intelligent majority voting and heuristic-based user-friendly hybrid classifier to predict HBP (An Intelli MOC). The model considers AA-AOC (Anger level, Anxiety level-Age, Obesity level, and Cholesterol level) of a person to predict the HBP of a person. The proposed model is said to be majority vote-based and hybrid as it considers the output of three classifiers and assigns the count for each decision class. The model is said to be heuristic-based as it uses a mathematical and Fuzzy approach in obtaining the fuzzified values of each attribute in AA-AOC. The experiments are conducted on real-time data set collected from a medical diagnostic center Doctor C, Hyderabad, India. The model is executed on 1200 data records, 65% of data is used to train the model and 35% of data is used to test the model. The output of the model proved that the proposed model outperformed in terms of accuracy, precision, recall, and F-measure compared with all available state-of-the-art, existing MLM. Show more
Keywords: Hypertension, age, obesity, anger, anxiety, classification, obesity, machine learning models, and cholesterol
DOI: 10.3233/JIFS-212649
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3003-3020, 2022
Authors: Sreeja, S | Muhammad Noorul Mubarak, D.
Article Type: Research Article
Abstract: MRI-Only Radiation (RT) now avoids some of the issues associated with employing Computed Tomography(CT) in RT chains, such as MRI registration to a separate CT, excess dosage administration, and the cost of recurrent imaging. The fact that MRI signal intensities are unrelated to the biological tissue’s attenuation coefficient poses a problem. This raises workloads, creates uncertainty as a result of the required inter-modality image registrations, and exposes patients to needless radiation. While using only MRI would be preferable, a method for estimating a pseudo-CT (pCT)or synthetic-CT(sCT) for producing electron density maps and patient positioning reference images is required. As Deep …Learning(DL) is revolutionized in so many fields these days, an effective and accurate model is required for generating pCT from MRI. So, this paper depicts an efficient DL model in which the following are the stages: a) Data Acquisition where CT and MRI images are collected b) preprocessing these to avoid the anomalies and noises using techniques like outlier elimination, data smoothening and data normalizing c) feature extraction and selection using Principal Component Analysis (PCA) & regression method d) generating pCT from MRI using Deep Convolutional Neural Network and UNET (DCNN-UNET). We here compare both feature extraction (PCA) and classification model (DCNN-UNET) with other methods such as Discrete Wavelet Tranform(DWT), Independent Component Analysis(ICA), Fourier Transform and VGG16, ResNet, AlexNet, DenseNet, CNN (Convolutional Neural Network)respectively. The performance measures used to evaluate these models are Dice Coefficient(DC), Structured Similarity Index Measure(SSIM), Mean Absolute Error(MAE), Mean Squared Error(MSE), Accuracy, Computation Time in which our proposed system outperforms better with 0.94±0.02 over other state-of-art models. Show more
Keywords: Computed tomography, deep convolutional neural network, magnetic resonance imaging, principal component analysis, pseudocomputed tomography
DOI: 10.3233/JIFS-213367
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3021-3037, 2022
Authors: Chen, Xi | Jin, Wenquan | Wu, Qirui | Zhang, Wenbo | Liang, Haiming
Article Type: Research Article
Abstract: Automatic risk classification of diseases is one of the most significant health problems in medical and healthcare domain. However, the related studies are relative scarce. In this paper, we design an intelligent diagnosis model based on optimal machine learning algorithms with rich clinical data. First, the disease risk classification problem based on machine learning is defined. Then, the K-means clustering algorithm is used to validate the class label of given data, thereby removing misclassified instances from the original dataset. Furthermore, naive Bayesian algorithm is applied to build the final classifier by using 10-fold cross-validation method. In addition, a novel class-specific …attribute weighted approach is adopted to alleviate the conditional independence assumption of naive Bayes, which means we assign each disease attribute a specific weight for each class. Last but not least, a hybrid cost-sensitive disease risk classification model is formulated, and a practical example from the University of California Irvine (UCI) machine learning database is used to illustrate the potential of the proposed method. Experimental results demonstrate that the approach is competitive with the state-of-the-art classifiers. Show more
Keywords: Disease diagnosis, hybrid cost-sensitive machine learning (HCML), K-means clustering, naive Bayes (NB), conditional independence assumption
DOI: 10.3233/JIFS-213486
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3039-3050, 2022
Authors: Abughazalah, Nabilah | Khan, Majid | Batool, Syeda Iram
Article Type: Research Article
Abstract: Designing of nonlinear confusion component of block cipher is one of the most important and inevitable research problem. Nowadays mostly heuristic search schemes were utilized for the construction of these confusion component. To construct, a cryptographically secure confusion component several algebraic structures were utilized. The thirst for new algebraic structure for the construction of these nonlinear confusion component has always been a point of interest. In this communication, we have utilized a maximal cyclic subgroup of unit of Galois ring. The offered algorithm is more general as compared to Galois field. The class of Boolean functions over Galois ring fall …in mixed category which are not completely balanced. Boolean functions having higher nonlinearity and others cryptographic aspects added an inevitable significance in the construction of modern block ciphers. The primary idea of this article is to structure non-balanced Boolean functions on n variables, where n is an even integer, sustaining strict avalanche criterion (SAC) and bit independent criterion (BIC). By comparing SAC with available cryptographic Boolean functions, the constructed multivalued Boolean function acquire highest nonlinearity which does not follow the existing nonlinearity bound of Boolean functions. These newly proposed S-boxes consists of n basic Boolean functions which satisfy the balancedness and non-balancedness criterion. Therefore, these S-box structure lies within a less balanced and more bent Boolean function categories. Show more
Keywords: S-box, Galois Ring, maximal cyclic subgroup
DOI: 10.3233/JIFS-213591
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3051-3065, 2022
Authors: Sonia, | Tiwari, Pratiksha | Gupta, Priti
Article Type: Research Article
Abstract: Soft and fuzzy sets are generic tools to deal with uncertainty. Both contemporary sets are not suitable to deal with all type of uncertain parameters. In this paper the hybridization of soft with extended fuzzy set information measures are derived. Interval-valued intuitionistic fuzzy soft set theory is a powerful tool for dealing with uncertainty of knowledge in information systems. In this paper, firstly some distance and similarity measures for interval-valued intuitionistic fuzzy soft sets were proposed. Further, some new entropy measures are also introduced by using the similarity measures. The validity of these measures is also proved. Applications of the …distance measures is also used in the field of multi attribute decision making and medical diagnosis. The proposed measures are also compared with an existing measure to prove its significance. Show more
Keywords: Interval valued intuitionistic fuzzy soft set (IVIFSS), interval number, hesitant factor, Fuzzy soft set (FSS)
DOI: 10.3233/JIFS-212647
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3067-3086, 2022
Authors: Mohan, V. | Senthilkumar, S.
Article Type: Research Article
Abstract: Due to the shortage of fossil fuel usage, the solar Photovoltaic (PV) energy has increased grownup over the last decade. Most conventional applications of renewable energy are being phased out in order to reduce costs and save the environment. PV plants undergo numerous failures in faults detection and ultimate power developments. These consequences demonstrate in the environmental field and internal components. Even when internal standards are followed, the faults are unavoidable and undetectable. Due to this, the performance of manufacturing plants are not predictable. As a result, a proper fault detection mechanism is required for a PV system to detect …faults and avoid energy losses. To address these issues, this research work proposed Internet of Things (IoT) sensor-based fault identification in a solar PV system. The PV panel status is monitored using pressure, light intensity, voltage, and current sensors. These sensor data’s are stored in the cloud for further analysis using a web-based control server. To classify the sensor data, models of Support Vector Machine (SVM), and Extreme Learning Machine (ELM) are utilized. The experimental results indicate that ELM achieves a classification accuracy of 96.32%. Which is higher than SVM and other optimization control techniques. The proposed model uses the IoT cloud to provide real-time monitoring and fault detection in plant environmental and electrical parameters. Show more
Keywords: Internet of things, solar energy, fault detection, extreme machine learning, support vector machine
DOI: 10.3233/JIFS-220012
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3087-3100, 2022
Authors: Pandiyan, S. | Krishnamoorthy, D.
Article Type: Research Article
Abstract: End-to-end authentication in 5G communication networks is a prominent requirement due to the growing application demands and autonomously shared user data. The lack of data-related attributes and the communicating platform serves as a challenging issue in securing the shared content. Besides, administering security for the generated data is less feasible due to un-traceability and handoff experienced in the network. A Non-Redundant Traffic Authentication Scheme (NRTAS) is presented and the main objective of this proposed work provide a reliable authentication based on classifying the traffic. A traffic classification model is developed to categorize the traffic generated by the user equipment. A …tree-based process is employed for linear and discrete authentication to enhance network performances. To succeed high connectivity secure 5G communication and information sharing in a heterogeneous platform is presented. The effectiveness of proposed NRTAS-5G communication network approach is executed in Opportunistic network environment (ONE) simulator version: 1.2. The NRTAS achieves 8.09% less access time, reduces the traffic load by 13.69%, and improves the success ratio by 5.36% for 150 UEs (User Equipment’s). Show more
Keywords: 5 G communication, data security, key generation, traffic classification, sequential authentication
DOI: 10.3233/JIFS-212750
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3101-3114, 2022
Authors: Dong, Yuanyuan | Li, Jinghua | Liu, Tiansen | Fan, Minmin | Yu, Shuao | Zhu, Yu
Article Type: Research Article
Abstract: Waste recycling companies, as a climate-friendly institution, have broadly influenced the sustainability of the economic, ecological, and social spheres, while some waste products covering personal privacy actually make their suppliers hesitant to sell them to recycling companies. To inspire suppliers in this pro-environmental behavior and recycling companies’ proactive privacy protection behaviors, this study establishes a dynamic evolutionary game model underpinned by the Prospect Theory targeting the relationship between the government and waste mobile phone recycling companies. By developing a revenue perception matrix, this study analyzes recycling companies’ privacy protection behaviors under different government decisions, particularly to reveal an interaction mechanism …that interprets bilateral behavior choice. This study presents the following findings. (1) The degree of government supervision on recycling companies’ behavior choice and the actual cost and benefits these companies’ recycling strategies influence evolutionary game results. (2) Recycling companies’ privacy protection capability improves the effectiveness of government supervision strategies, while an increase in government’s perception and supervision costs could restrict companies’ privacy protection behaviors and government’s follow-up supervision strategies. (3) Moderate government sanctions (e.g. the fines) help normalize recycling companies’ privacy protection behaviors, but enhancing companies’ sensitivity to privacy value negatively influences privacy protection. (4) Lastly, an increase in loss aversion coefficient has a negative impact on recycling companies’ privacy protection while improves the outcomes of government supervision. Overall, this study contributes to develop a two-party evolutionary strategy under different policy decisions and recycling companies’ behavior choice. Therefore, we suggest that waste mobile phone recycling companies and the government synergistically focus on suppliers’ privacy protection. Show more
Keywords: Prospect theory, dynamic evolutionary game, protecting suppliers’ privacy, waste recycling
DOI: 10.3233/JIFS-212962
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3115-3132, 2022
Authors: Xiaolian, Liao | Guohua, Chen
Article Type: Research Article
Abstract: A portfolio selection model with return as triangular intuitionistic fuzzy number is developed in this study to assess and select portfolios on China Stock Exchange. Although the portfolio selection has been widely investigated, and most studies have regarded return and risk as the main decisive criteria, there are many uncertainties in the financial market, such as social, political and human psychological factors, which makes it difficult for us to describe the risks in line with empirical evidence. To fill this gap, first, a literature review was conducted to clarify the current situation and shortcomings of portfolio selection. Second, the triangular …intuitionistic fuzzy number was used to fuzzify the coefficients of the objective function and the constraints in the portfolio model. Third, the triangular intuitionistic fuzzy number model was transformed into a linear programming model by using the selected exact ranking function, and the model was solved by MATLAB. Finally, the historical monthly returns of 10 stocks in China’s Stock Exchange from December 2017 to November 2020, lasting 36 months, were selected to demonstrate the model. The results indicate that the portfolio selection model with triangular intuitionistic fuzzy number can better meet the uncertainty of the real securities market, and more reasonable investment decision guidance can be provided for investors. Besides, the limitations of this study are pointed out, and implications and directions for future research are discussed. Show more
Keywords: Portfolio selection model, rate of return, triangular intuitionistic fuzzy number
DOI: 10.3233/JIFS-213123
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3133-3139, 2022
Authors: Anu Disney, D. | Merline, A.
Article Type: Research Article
Abstract: Recently, Heterogeneous Networks (HetNet) are concentrated more in order to improve transmission coverage and spectrum efficiency. When compared to HetNet, Non-Orthogonal Multiple Access (NOMA) is used to allow multiple users to share the same frequency band resource. Moreover, cross-tier links are improved in realistic HetNets due to channel delay and random perturbation. In this research, a fuzzy logic-based user association and pairing was proposed to improve the system robustness and energy utilization. Here, fuzzy logic overcomes the limitations of traditional channel-based and gain-based pairing schemes. The proposed fuzzy logic-based system evaluates distance, channel gain, and reference signal power for mode …selection and enhances pairing features. Nature-inspired cuckoo search optimization is also used to improve coverage probability, feasible rate, and energy efficiency by optimizing the path loss, transmit power, and distance between user and base station. The proposed model analysis is carried out through intense simulation to demonstrate improved performance when compared to conventional state-of-the-art techniques. Show more
Keywords: Non-orthogonal multiple access, heterogeneous networks, energy utilization, fuzzy logic, cuckoo search optimization
DOI: 10.3233/JIFS-213444
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3141-3154, 2022
Authors: Singh, Ashutosh | Singh, Yogenrda Narain
Article Type: Research Article
Abstract: In recent years, biometrics is most extensively used for people authentication over a range of applications. The growing use of biometrics have raised the issues of security and privacy of the templates stored in the database. Various biometric template protection methods have been presented in the past, but the majority of them require a trade-off between matching efficiency and template security. This paper suggests a hybrid technique of template protection for multibiometric system with improved efficiency and robustness against fraudulent attacks. It works over the fusion of different biometrics, in particular the proposed technique is tested on a multimodal system …using face and ECG biometrics. Both biometrics and multimodal templates are processed using domain-specific pre-trained models. The template features are projected in a random subspace using a matrix with standard normally distributed values. It prepares a cancelable template that protects the features of original template. To further enhance the security of the system, the cancelable template is quantized using multi-level random fuzziness technique. Thus, adding a second level of defence against fraudulent attacks. The proposed method reports an optimum accuracy of 99.94% with an equal error rate (EER) off 6 x 10-2 . Show more
Keywords: Biometrics, template protection, random projection, random fuzziness
DOI: 10.3233/JIFS-212814
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3155-3171, 2022
Authors: Han, Nana | Qiao, Junsheng
Article Type: Research Article
Abstract: Rough sets, as a powerful tool to deal with uncertainties and inaccuracies in data analysis, have been continuously concerned and studied by many scholars since it was put forward, especially the research on various rough set models. On the other hand, overlap and grouping functions, as two newly aggregation operators and mathematical model to handle the problems involving in information fusion, have been successfully applied in many real-life problems. In this paper, based on overlap and grouping functions, we propose a new fuzzy rough set model named (G O , O )-fuzzy rough sets and consider its characterizations along …with topological properties. Properly speaking, firstly, we utilize QL -operators (and also QL -implications) constructed from overlap and grouping functions and fuzzy negations to define the lower approximation operator in (G O , O )-fuzzy rough set model named G O -lower fuzzy rough approximation operator and the upper approximation operator in (G O , O )-fuzzy rough set model is considered as the O -upper fuzzy rough approximation operator in (I O , O )-fuzzy rough set model proposed by Qiao recently. Secondly, we discuss lots of basic properties of (G O , O )-fuzzy rough sets, especially for the properties of G O -lower fuzzy rough approximation operator. Thirdly, we focus on the relationship between (G O , O )-fuzzy rough sets and concrete fuzzy relations. Finally, we give the topological properties of the upper and lower approximation operators in (G O , O )-fuzzy rough set model. Show more
Keywords: (GO, O)-fuzzy rough set, overlap function, grouping function, fuzzy relation, fuzzy topology
DOI: 10.3233/JIFS-213261
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3173-3187, 2022
Authors: Zhao, Li | Sun, Meng | Yang, Binbin | Xie, Junpeng | Feng, Jiqiang
Article Type: Research Article
Abstract: With the digital transformation of enterprises, the traditional security defense technology has been unable to meet the security requirements of enterprises, and the data security and privacy protection have brought great challenges to the Internet. Therefore, taking zero trust as the security concept and taking the network boundary as the best practice landing technology architecture, this paper studies the zero trust access authorization and control of network boundary based on cloud big data fuzzy clustering of. Through the network stealth technology, it constructs a virtual boundary for the enterprise, uses the cloud big data fuzzy clustering algorithm to mine the …user behavior related data, and designs the trust evaluation mechanism to obtain the user trust level. The dynamic access authorization control mechanism is designed to judge the access requests in and out of the permission boundary. Combined with the user’s trust level, the legal requests and illegal requests are distinguished to complete the zero trust access authorization and control of network boundary. Experimental results show that: the method can accurately control the access authorization of the network boundary, improve the success rate of access authorization and control interaction; the interception rate of illegal access is high, and it has high securit. Show more
Keywords: Cloud sea, big data, fuzzy clustering, network boundary, zero trust, access authorization and control
DOI: 10.3233/JIFS-220128
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3189-3201, 2022
Authors: Li, Mingwei | Chen, Juan
Article Type: Research Article
Abstract: Post-evaluating the effect of HSR on the tourism economy is necessary for regions (especially those relying on tourism industry development) to decide on their future HSR solutions or for other regions’ benchmarking for decision-making. However, the effect of HSR on the tourism economy is hysteretic (lagged and unstable), making identifying an appropriate time point crucial for accurately post-evaluating the effect. This current study addresses the seldom-touched issue of optimizing the post-evaluating time point of the effect of HSR projects on the tourism economy, offering a feasible solution based on the Vector Auto-Regression model. The current study uses this method on …the empirical example of Henan Province in central China, identifying three years after HSR introduction as the appropriate time point. This study identifies the time-factor concerns of HSR’s externalities and contributes methodologically and practically to investigating HSR’s effects on regional tourism development. Show more
Keywords: High-Speed Rail (HSR), regional tourism economy, post-evaluation, time point, hysteresis effect
DOI: 10.3233/JIFS-212640
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3203-3218, 2022
Authors: Lv, Jian | Mao, Qinghua | Li, Qingwen | Chen, Shudong
Article Type: Research Article
Abstract: Emergency events are happening with increasing frequency, inflicting serious damage on the economic development and human life. A reliable and effective emergency decision making method is great for reducing various potential losses. Hence, group emergency decision making (GEDM) has drawn great attention in past few years because of its advantages dealing with the emergencies. Due to the timeliness and complexity of GEDM, vagueness and regret aversion are common among decision makers (DMs), and decision information usually needs to be expressed by various mathematical forms. To this end, this paper proposes a novel GEDM method based on heterogeneous probabilistic hesitant information …sets (PHISs) and regret theory (RT). Firstly, the PHISs with real numbers, interval numbers and linguistic terms are developed to depict the situation that decision group sways precariously between several projects and best retain the original assessment. In addition, the score functions, the divergence functions and some operations of the three types of PHISs are defined. Secondly, the normalization model of PHISs is presented to remove the influence of different dimensions on information aggregation. Thirdly, group satisfaction degree (GSD) based on the score functions and the divergence functions is combined with RT for completely portraying the regret perception of decision group. Then, we introduce Dempster-Shafer (DS) theory to determine the probabilities of future possible states for emergency events. Finally, an example of coronavirus disease 2019 (COVID-19) situation is given as an application for the proposed GEDM method, whose superiority, stability and validity are demonstrated by employing the comparative analysis and sensitivity analysis. Show more
Keywords: Group emergency decision making, heterogeneous decision information, probabilistic hesitant information sets, regret theory, group satisfaction degree
DOI: 10.3233/JIFS-213336
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3219-3237, 2022
Authors: Yang, Shuaishuai | Cong, Qiumei | Yu, Wen | Yang, Jian | Song, Jian
Article Type: Research Article
Abstract: Aiming at the problem that fuzzy neural network (FNN) is difficult to be adjusted automatically its structure when there is no the threshold of loss function, as well as the problem that the neuron number of the regularization layer of FNN is adjusted by self-organizing algorithm when the structure of FNN is not stable yet, a structural design strategy of self-organizing recursive FNN based on the Boston matrix (SORFNN-BOSTON) is proposed. Compared with other self-organizing algorithms, the method used in this paper does not need to set the threshold of loss function. In addition to the indicators representing the importance …of neurons in most self-organizing algorithms, the change rate is used to represent the change of the parameters of the neural network. The change rate is used to determine when the relevant parameters are stable, which further improves the reliability of the neuron adjustment process. Through the simulation of predicting Mackey-Glass time sequence, the final number of neurons in the hidden layer and the testing error are 6 and 0.110 respectively. Comparisons with other self-organizing algorithms show that the testing error decreased by 76.6% at most and 13.3% at least, which proves the practicability of the method. Show more
Keywords: Boston matrix, self-organizing algorithm, fuzzy neural network, nonlinear system simulation
DOI: 10.3233/JIFS-213461
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3239-3249, 2022
Authors: Sreedevi, S. | Angeline Ezhilarasi, G.
Article Type: Research Article
Abstract: The optimal location and rating of distributed generators in the distribution network is important role in the incremental consumer load. Proposed research determines a novel approach for the optimal placement rating of distributed generators. To achieve the multi-objectives of expanded work is to minimize the power loss, reduce operating cost, and development of magnitude output voltage. At present, the objectives are achieved by using spider monkey optimization algorithm (SMOA). Design and prescribed optimization is considered various constraints such as voltage limit, dg (Distributed Generator), real power generation limit, current limit, and power equality constraint estimated. The losses sensitivity factor involves …the best selection of the optimal locations to fix dg units. In this context proposes to determine the optimal rating of dg units based on the searching behavior of spider monkeys. IEEE (Institute of Electrical and Electronics Engineering) 33-bus, and IEEE 69-bus distribution systems with various load levels are tested the validation of presented method. The entire work is done by MATLAB/Simulink program, and the SMOA is also compared with familiar algorithms to show the efficiency for an optimal rating of the power system. Show more
Keywords: Spider monkey optimization algorithm, power system, distribution generator, power quality, simulation
DOI: 10.3233/JIFS-220210
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3251-3269, 2022
Authors: Wang, Gaihua | Dai, Yingying | Zhang, Tianlun | Lin, Jinheng | Chen, Lei
Article Type: Research Article
Abstract: Remote sensing image change detection is to analyze the change information of two images from the same area at different times. It has wide applications in urban expansion, forest detection, and natural disaster. In this paper, Feature Fusion Network is proposed to solve the problems of slow change detection speed and low accuracy. The MobileNetV3 block is adopted to efficiently extract features and a self-attention module is applied to investigate the relationship between heterogeneous feature maps (image features and concatenated features). The method is tested in data sets SZTAKI and LEVIR-CD. With 98.43 percentage correct classification, it is better than …other comparative networks, and its space complexity is reduced by about 50%. The experimental results show that it has better performance and can improve the accuracy or speed of change detection. Show more
Keywords: Attention mechanisms, change detection, depth separable convolution, siamese network
DOI: 10.3233/JIFS-211432
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3271-3282, 2022
Authors: Malik, Meenakshi | Nandal, Rainu | Dalal, Surjeet | Maan, Ujjawal | Le, Dac-Nhuong
Article Type: Research Article
Abstract: In recent years, driver behavior analysis plays a vital role to enhance passenger coverage and management resources in the smart transportation system. The real-world environment possesses the driver principles contains a lot of information like driving activities, acceleration, speed, and fuel consumption. In big data analysis, the driver pattern analyses are complex because mining information is not utilized to feature evaluations and classification. In this paper, a new efficient Fuzzy Logical-based driver behavioral pattern analysis has been proposed to offer effective recommendations to the drivers. Primarily, the feature selection can be carried out with the assist of fuzzy logical subset …selection. The selected features are then evaluated using frequent pattern information and these measures will be optimized with a multilayer perception model to create behavioral weight. Afterward, the information weights are trained with a test through an optimized spectral neural network. Finally, the neurons are activated by a recurrent neural network to classify the behavioral approach for the superior recommendation. The proposed method will learn the characteristics of driving behaviors and model temporal features automatically without the need for specialized expertise in feature modelling or machine learning techniques. The simulation results manifest that the proposed framework attains better performance with 98.4% of prediction accuracy and 86.8% of precision rate as compared with existing state-of-the-art methods. Show more
Keywords: Fuzzy logic, feature selection and classification, neural network, behavioral analysis
DOI: 10.3233/JIFS-212007
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3283-3292, 2022
Authors: Fan, Jianping | Fang, Wenting | Wu, Meiqin
Article Type: Research Article
Abstract: In order to cope with increasingly severe environmental problems, the development of new energy vehicles has been strongly supported. The rapid development of new energy vehicles has led to the development of power batteries. It is vital to choose the appropriate new energy vehicle battery which is the power source of the new energy vehicles. This paper proposes a new model based on D numbers, which combines the Best-worst method (BWM) and Evaluation based on Distance from Average Solution (EDAS) method. First, in order to express the uncertainty of expert decision-making, this paper uses D number to describe the evaluation …information. Then the D-BWM model is applied to determine the weight of the given criteria. Next, the D-EDAS model is constructed for the selection of new energy vehicle battery suppliers. The results show that this newly proposed model is reasonable. Finally, the validity and robustness of the model in this paper are demonstrated through sensitivity analysis. Show more
Keywords: D numbers, BWM, EDAS, new energy vehicles, battery suppliers
DOI: 10.3233/JIFS-220001
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3293-3309, 2022
Authors: Wang, Pei | Zhao, Zhengwei | Wang, Zhihong | Li, Zhaowen
Article Type: Research Article
Abstract: A fuzzy set-valued information system (FSVIS) is a special information system (IS) where the value of an object under each attribute or each attribute value is a fuzzy set. Homomorphism is a powerful mathematical tool to deal with FSVISs, which can be used to study relationships among them. Based on data compression, we obtain some characterizations about FSVISs and their homomorphisms. First, some homomorphisms between FSVISs are introduced. After that, attribute reduction based on tolerance relation in a FSVIS is studied. Eventually, we get invariant characterizations of FSVISs based on some special homomorphisms under data compression.
Keywords: FSVIS, θ-reduction, θ-core, homomorphism, tolerance relation, data compression, characterization
DOI: 10.3233/JIFS-213186
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3311-3321, 2022
Authors: Li, Ping | Yu, Jiong | Li, Min | Chen, JiaYin | Yang, DeXian | He, ZhenZhen
Article Type: Research Article
Abstract: In this paper, we propose a unified framework for an abstractive summarization method which uses the prompt language model and a pointer mechanism. The abstractive summarization problem usually includes a text encoder and a text decoder. Current methods usually employ an encoder-decoder architecture to condense and paraphrase a document. To better paraphrase a document, we propose a unified framework for an abstractive summarization model that only uses a topic-sensitive decoder. Our model has a prompt input module, a text decoder and a pointer mechanism. We apply our model to Xsum, Gigaword, and CNN/DailyMail summarization datasets, and experimental results demonstrate that …our model has achieved state-of-the-art results on the Xsum dataset and comparable results on the other two datasets. Show more
Keywords: Abstractive summarization, masked language model, pointer mechanism, text decoder
DOI: 10.3233/JIFS-213500
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3323-3335, 2022
Authors: Wang, Bing | Huang, Xianglin | Cao, Gang | Yang, Lifang | Wei, Xiaolong | Tao, Zhulin
Article Type: Research Article
Abstract: Many micro-video related applications, such as personalized location recommendation and micro-video verification, can be benefited greatly from the venue information. Most existing works focus on integrating the information from multi-modal for exact venue category recognition. It is important to make full use of the information from different modalities. However, the performance may be limited by the lacked acoustic modality or textual descriptions in uploaded micro-videos. Therefore, in this paper visual modality is explored as the only modality according to its rich and indispensable semantic information. To this end, a hybrid-attention and frame difference enhanced network (HAFDN) is proposed to generate …the comprehensive venue representation. Such network mainly contains two parallel branches: content and motion branches. Specifically, in the content branch, a domain-adaptive CNN model combined with temporal shift module (TSM) is employed to extract discriminative visual features. Then, a novel hybrid attention module (HAM) is introduced to enhance extracted features via three attention mechanisms. In HAM, channel attention, local and global spatial attention mechanisms are used to capture salient visual information from different views. In addition, convolutional Long Short-Term Memory (convLSTM) is enforced after HAM to better encode the long spatial-temporal dependency. A difference-enhanced module parallel with HAM is devised to learn the content variations among adjacent frames, which is usually ignored in prior works. Moreover, in the motion branch, 3D-CNNs and LSTM are used to capture movement variation as a supplement of content branch in a different form. Finally, the features from two branches are fused to generate robust video-level representations for predicting venue categories. Extensive experimental results on public datasets verify the effectiveness of the proposed micro-video venue recognition scheme. The source code is available at https://github.com/hs8945/HAFDN. Show more
Keywords: Micro-video venue recognition, robust visual features, hybrid attention module, difference enhanced module
DOI: 10.3233/JIFS-213191
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3337-3353, 2022
Authors: Santhana Marichamy, V. | Natarajan, V.
Article Type: Research Article
Abstract: In this manuscript proposes an efficient big data security analysis on HDFS based on the combination of Improved Deep Fuzzy K-means Clustering (IDFKM) Algorithm and Modified 3D rotation data perturbation algorithm using health care database. To compile a similar group of data, an Improved Deep Fuzzy K-means Clustering (IDFKM) Algorithm is used as partitioning the medical data. After clustering, Modified 3D rotation data perturbation technique is used to satisfy the privacy requirement of the client. Modified 3D rotation Data Perturbation technique perturbs each and every sensitive data of the cluster and all the key parameters values used for clustering have …warehoused in the database file sector. The proposed approach is executed by Java program, its efficiency is assessed by Health care database. The metrics under the study of memory usage attains higher accuracy 34.765%, 23.44%, 52.74%, 18.74%, lower execution time 35.23%, 23.76%, 27.86%, 27.76%, higher Efficiency 26.85%, 38.97%, 28.97%, 35.65%. then the proposed method is compared with the existing methods such asSecurity Analysis of SDN Applications for Big Data with spoofing identity, Tampering with data, Repudiation threats, Information disclosure, Denial of service and Elevation of privileges (STRIDE), Big Data Analysis-based Secure Cluster Management for using Ant Colony Optimization (ACA) Optimized Control Plane in Software-Defined Networks, System Architecture for Secure Authentication and Data Sharing in Cloud Enabled Big Data Environment using LemperlZivMarkow Algorithm (LZMA) and Density-based Clustering of Applications with Noise (DBSCAN), Big Data Based Security Analytics using data based security analytics (BDSA) approach for Protecting Virtualized Infrastructures in Cloud Computing respectively. Show more
Keywords: Hadoop distributed file system (HDFS), cloud storage, hadoop, data security, Improved Deep Fuzzy K-means Clustering (IDFKM) Algorithm, modified 3D rotation data perturbation algorithm
DOI: 10.3233/JIFS-213024
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3355-3372, 2022
Authors: Rajanandhini, V.M. | Elangovan, G.
Article Type: Research Article
Abstract: As the population increases day by day, there has been a tremendous increment of ownership over two-wheeler and four-wheeler systems. Thereby usage of public transports system decreases than private vehicles system. In this scenario, this study focused on the significant factors in the mode choice behaviour of commuters from the extensive household survey conducted by Thiruvarur City to obtain detailed information on the modal split pattern analysis. The questionnaire of 4857 respondents was used in the Multinomial Logit Model using the Statistical Packages for Social Science (SPSS) tool to predict the mode choice behaviour of commuters. A total of seven …explanatory variables were extracted from the responses; the Multinomial logit model was calibrated to fit the data, and the chi-square test was used to measure the goodness of fit. Apart from the MLM model, commuter behaviour prediction analysis is also made using soft computing technologies, which involves multi-linear regression and neuro-fuzzy model, keeping uncertainties involved in commuter behaviour to avoid collecting household data. It was found that two-wheeler commuters are more in numbers for their travel choice as cost minimizing than four-wheelers, public transport system commuters. Consequently, the developed model predicts the behaviour of commuters in a precise manner that closely matches the actual conduct. Show more
Keywords: Adaptive Neuro-Fuzzy System, mode choice analysis, Multinomial Logit Modelling, regression, soft computing, transportation
DOI: 10.3233/JIFS-213198
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3373-3391, 2022
Authors: Gao, Ying | Li, Shan | Ba, Tao | Ren, Tong
Article Type: Research Article
Abstract: The stability of unmanned vehicle is related to the safety of the vehicle itself. In the process of unmanned vehicle control, there will be collision phenomenon in the process of meeting the vehicle. To solve the above problem, the design of unmanned interaction system based on visual cognition is proposed. The hardware structure of the system is designed based on 80C51 single chip microcomputer, including ARM processor, GPS receiving module, driving record signal collecting module, etc. The PID controller design based on neural network is optimized, and the design of unmanned interactive system based on visual cognition is completed. Experimental …results show that the designed system can identify the surrounding environment in real time, make corresponding decisions, let the vehicle avoid the wrong vehicle operation, and save Oil consumption. Show more
Keywords: Visual cognition, unmanned driving, interactive system, communication serial port, display module
DOI: 10.3233/JIFS-211657
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3393-3401, 2022
Authors: Li, Qiqi | Qin, Zhongfeng | Liu, Zhe
Article Type: Research Article
Abstract: Traditional support vector regression dedicates to obtaining a regression function through a tube, which contains as many as precise observations. However, the data sometimes cannot be imprecisely observed, which implies that traditional support vector regression is not applicable. Motivated by this, in this paper, we employ uncertain variables to describe imprecise observations and build an optimization model, i.e., the uncertain support vector regression model. We further derive the crisp equivalent form of the model when inverse uncertainty distributions are known. Finally, we illustrate the application of the model by numerical examples.
Keywords: Imprecise observations, uncertain variables, support vector regression, uncertainty theory
DOI: 10.3233/JIFS-212156
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3403-3409, 2022
Authors: Rahman, K. | Khan, H. | Abdullah, S.
Article Type: Research Article
Abstract: The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused millions of infected cases globally and still poses an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 649824. Recently, some mathematical models have been constructed for better understanding the coronavirus infection. Mostly, these models are based on classical integer-order derivative using real numbers …which cannot capture the fading memory. So at the current position it is a challenge for the world to understand and control the spreading of COVID-19. Therefore, the aim of our paper is to develop some novel techniques, namely complex Pythagorean fuzzy weighted averaging (abbreviated as CPFWA) operator, complex Pythagorean fuzzy ordered weighted averaging (abbreviated as CPFOWA) operator, complex Pythagorean fuzzy hybrid averaging (abbreviated as CPFHA) operator, induced complex Pythagorean fuzzy ordered weighted averaging (abbreviated as I-CPFOWA) operator and induced complex Pythagorean fuzzy hybrid averaging (abbreviated as I-CPFHA) operator to analysis the spreading of COVID-19. At the end of the paper, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested approach along with a sensitivity analysis, showing the feasibility and reliability of its results. Show more
Keywords: Complex Pythagorean set, CPFWA operator, CPFOWA operator, CPFHA operator, I-CPFOWA operator, I-CPFHA operator
DOI: 10.3233/JIFS-212160
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3411-3427, 2022
Authors: Liu, Xiaole | Huang, Cheng | Fang, Yong
Article Type: Research Article
Abstract: A drive-by download is a method of hackers planting the Web Trojan, which exploits browser vulnerabilities to execute malicious software. Because people usually access web pages with various browsers daily, drive-by downloads have become one of the most common threats in recent years. Most previous studies utilize the abstract syntax tree(AST) with deep learning methods to detect such attacks, which achieved high accuracy but are time-consuming and challenging to explain. Also, some methods use dynamic analysis, which needs a specific environment and is time-consuming with the complex operation. In order to solve these problems, the paper proposes DDIML , an …explainable machine learning model based on novel features with static analysis. These features are extracted from five aspects: code obfuscation, URL redirection, special behaviors, encoding characters, and CSS attributes. The most popular machine learning algorithm, Random forest, is applied for building the classifier detection model. In addition, we use both local and global explanations to improve the model and prove that the proposed model could be trusted. The Experimental results show that our proposed model can efficiently detect drive-by downloads with a detection precision of 0.983 and a recall of 0.980. The average detection time for each sample is only 16.07ms in total. Show more
Keywords: static analysis, drive-by downloads, features, random forest, explanation
DOI: 10.3233/JIFS-212496
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3429-3442, 2022
Authors: Shahzad, Muhammad | Tahir, M. Atif | Khan, M. Atta | Jiang, Richard | Malick, Rauf Ahmed Shams
Article Type: Research Article
Abstract: Drug sensitivity prediction to a panel of cancer cell lines using computational approaches has been a challenge for two decades. With the emergence of high-throughput screening technologies, thousands of compounds and cancer cell lines panels with drug sensitivity data are publicly available at various pharmacogenomics databases. Analyzing these data is crucial to improve cancer treatment and develop new anticancer drugs. In this work, we propose EBSRMF : Ensemble Based Similarity-Regularized Matrix Factorization, which is a bagging based framework to improve the drug sensitivity prediction on the Cancer Cell Line Encyclopedia (CCLE) data. Based on the fact that similar drugs and …cell lines exhibit similar drug response, we have investigated cell line and drug similarity matrices based on gene expression profiles and chemical structure respectively. The drug sensitivity value is used as outcome values which are the half maximal inhibitory concentrations (IC50). In order to improve the generalization ability of the proposed model, a homogeneous ensemble based bagging learning approach is also investigated where multiple SRMF models are used to train N subsets of the input data. The outcome of each training algorithm is aggregated using the averaging method to predict the outcome. Experiments are conducted on two benchmark datasets: CCLE and GDSC. The proposed model is compared with state-of-the-art models using multiple evaluation metrics including Root Means Square Error (RMSE) and Pearson Correlation Coefficient (PCC). The proposed model is quite promising and achieves better performance on CCLE dataset when compared with the existing approaches. Show more
Keywords: Drug sensitivity, matrix factorization, cancer, ensemble learning, keyword five
DOI: 10.3233/JIFS-212867
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3443-3452, 2022
Authors: Wei, Mingrun | Wang, Hongjuan | Cheng, Ru | Yu, Yue | Wang, Lukun
Article Type: Research Article
Abstract: Single image rain removal remains a crucial and challenging low-level image processing task while significantly for outdoor-based high-level computer vision tasks. Recently, deep convolutional neural networks (CNNs) have become the mainstream structure of removing rain streaks and obtained remarkable performance. However, most of the existing CNN-based methods mainly pay attention to completely removing rain streaks while neglecting the restoration of details after deraining, which suffer from poor visual performance. In this paper, we propose a deep residual attention and encoder-decoder network to overcome the above shortcoming. Specifically, we develop an excellent basic block that contains dual parallel paths which are …called rain removal network and detail restore network, respectively, to perform entirely and in-depth mapping relationships from rain to no-rain. The upper rain removal network is composed of dilated convolution and channel attention. This combination can explore the correlation between features from the dimensions of spatial and channel. Meanwhile, for the lower detail restore network, we construct a simple yet effective symmetrical encoder-decoder structure to prevent the loss of global structures information and encourage the details back. Furthermore, our network is end-to-end trainable, easy to implement and without giant parameter quantity. Extensive experiments on synthetic and real-world datasets have shown that our DRAEN achieves better accuracy and visual improvements against recent state-of-the-art methods. Show more
Keywords: Single image deraining, encoder-decoder network, image processing, feature fusion
DOI: 10.3233/JIFS-213134
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3453-3467, 2022
Authors: Yue, Shufang | Li, Ying | Zhao, Jianli
Article Type: Research Article
Abstract: In this paper, we provide a systematic characterization of finite BZMVdM -algebra by using semi-tensor product of matrices. The abstract operation law about logic of the finite algebra is transformed into the simple operation of concrete logical matrices. In addition, we study some properties of BZMVdM -algebra, such as homomorphism, isomorphism, and the product of the BZMVdM -algebra. Through logical matrix operation, the direct verifiable conditions for detecting the above properties are given.
Keywords: BZMVdM-algebra, semi-tensor product of matrices, isomorphism
DOI: 10.3233/JIFS-213173
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3469-3478, 2022
Authors: Wang, Hongxia | Ma, Wubin | Wang, Zhiru | Lu, Chenyang
Article Type: Research Article
Abstract: The prediction of residential building electricity consumption can help provide an early warning regarding abnormal energy use and optimize energy supply. In this study, a multiscale convolutional recurrent neural network (MCRNN) is proposed to predict residential building electricity consumption. The MCRNN model uses multiscale convolutional units to collect different information on environmental factors, such as temperature, air pressure, light, and uses a bidirectional recurrent neural network (Bi-RNN) to extract the long-term dependence information of these factors. In addition, a recurrent convolutional connection is used to filter the most useful multiscale and long-term information in the MCRNN model. The accuracy of …MCRNN is evaluated through an experiment using real data. The results show that MCRNN performs better than the other models. For instance, compared with the support vector regression (SVR) and random forest (RF) models, the MCRNN model has a 47.83% and 38.72% lower root mean square error (RMSE), respectively. The MCRNN model also shows a 37.81% and 70.38% higher accuracy, respectively, compared to the SVR and RF models. Show more
Keywords: Electricity consumption prediction, residential building, multiscale convolutional network, recurrent neural network
DOI: 10.3233/JIFS-213176
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3479-3491, 2022
Authors: Li, Helong | Liu, Shuli | Wang, Weizhong
Article Type: Research Article
Abstract: The Fine-Kinney model is a quantitative and effective method to identify and evaluate potential risks. The Fine-Kinney method has been widely used in practice, while the traditional Fine-Kinney method is difficult to access risk parameters precisely in practice. Besides, the current Fine-Kinney method fails to take into account the fact that decision makers are interrelated in practice. Further, the detailed relationships among the potential hazards cannot be reflected in the conventional Fine-Kinney method during the risk priority process, especially in the case of uncertain information. To compensate these deficiencies, this paper proposes an extended Fine-Kinney framework by integrating ORESTE (organísation, …rangement et Syn-thèse de données relarionnelles) (in French), Choquet integral, and Probabilistic Linguistic Term Sets (PLTSs). Firstly, the PLTSs are utilized to express the decision makers’ complex risk preference information. Then, the Choquet integral is used to integrate risk evaluation information, which can simulate the potential interaction relationships among individual risk evaluation of decision-makers. Next, an extended ORESTE based on the PLTSs method is used to obtain the priorities of potential hazards, in which distance measure of PLTSs is applied to replace Besson’s ranks. Moreover, the PIR (preference, indifference, and incomparability) structure is constructed to describe the detailed relationships between potential hazards. Finally, an illustrative example is described to illustrate the proposed risk evaluation method. After that, the rationality and efficiency of the proposed method are tested through the comparison with other similar methods. Show more
Keywords: Probabilistic linguistic term sets, Choquet integral, ORESTE method, Fine-Kinney method
DOI: 10.3233/JIFS-213326
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3493-3512, 2022
Authors: Bharathi, V. | Sakthivel, K.
Article Type: Research Article
Abstract: This paper focuses on a wheeled mobile robot that utilizes the Adaptive Nonlinear Sliding mode control technique for trajectory tracking and obstacle avoidance control. A parallel wheeled differential drive Mobile robot’s trajectory tracking control problem is investigated. For diverse initial conditions, the robot must follow a given course to reach it’s destination. To monitor and identify the obstacles in the path, an obstacle micro-controller is fixed to decide quick crash avoidance and follow the obstacle limit at a predetermined distance. It depends on the robot’s vector connections. An Adaptive Nonlinear Sliding Mode (ANSM) control concept is used for continuous trajectory …tracking or object monitoring in the path to avoid it. A Lapunov function control gives stability to each controller. The proposed simulation results demonstrate that the mobile robot can be applied to guarantee its protected development in an obscure obstacle environment. In detail, the proposed control gives another, more straightforward methodology with application esteems for tracking critical thinking in an obscure obstacle environment. Finally, based on these above characteristics, the proposed control strategy’s efficiency, simplicity, and accuracy prove. The steady-state error and mean squared error of the proposed ANSM are 6% and 0.07db, respectively. Show more
Keywords: Adaptive nonlinear sliding, trajectory tracking, collision avoidance, Lyapunov function
DOI: 10.3233/JIFS-213588
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3513-3525, 2022
Authors: Xuan, Cho Do | Huong, D.T. | Nguyen, Toan
Article Type: Research Article
Abstract: Detecting and warning Advanced Persistent Threat (APT) malware in Endpoint is essential because the current trend of APT attacker groups is to find ways to spread malware to users and then escalate privileges in the system. In this study, to improve the ability to detect APT malware on Endpoint machines, we propose a novel intelligent cognitive calculation method based on a model combining graph embeddings and Attention using processes generated by executable files. The proposed intelligent cognitive computation method performs 3 main tasks: i) extracting behaviors of processes; ii) aggregating the malware behaviors based on the processes; iii) detecting APT …malware based on behavior analysis. To carry out the task (i), we propose to use several data mining techniques: extracting processes from Event IDs in the operating system kernel; extracting abnormal behaviors of processes. For task (ii), a graph embedding (GE) model based on the Graph Convolutional Networks (GCN) network is proposed to be used. For task (iii), based on the results of task (ii), the paper proposes to use a combination of the Convolutional Neural Network (CNN) network and Attention network (called CNN-Attention). The novelty and originality of this study is an intelligent cognitive computation method based on the use, combination, and synchronization of many different data mining techniques to compute, extract, and represent relationships and correlations among APT malware behaviors from processes. Based on this new intelligent cognitive computation method, many meaningful anomalous features and behaviors of APT malware have been synthesized and extracted. The proposals related to data mining methods to extract malware’s features and the list of malware’s behaviors provided in this paper are new information that has not been published in previous studies. In the experimental section, to demonstrate the effectiveness of the proposed method in detecting APT malware, the study has compared and evaluated it with other approaches. Experimental results in the paper have shown the outstanding efficiency of the proposed method when ensuring all metrics from 96.6% or more (that are 2% to 6% higher than other approaches). Experimental results in the paper have proven that our proposed method not only has scientifically significant but also has practical meaning because the method has helped to improve the efficiency of analyzing and detecting APT malware on Endpoint devices. In addition, this research result also has opened up a new approach for the task of detecting other anomalies on the Endpoint such as malware, unauthorized intrusion, insider, etc. Show more
Keywords: APT, APT malware detection on Endpoint, event ID, behavior profile, deep learning, process profile, graph analysis, selecting and exacting features, abnormal behavior
DOI: 10.3233/JIFS-220233
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3527-3547, 2022
Authors: Salih, Mahmood M. | Al-Qaysi, Z.T. | Shuwandy, Moceheb Lazam | Ahmed, M.A. | Hasan, Kahlan F. | Muhsen, Yousif Raad
Article Type: Research Article
Abstract: To date, for the purpose of solving the complex problems in the area of expert system, Multi criteria decision making is the best technique to offer the suitable solution. In the academic literature, the MCDM methods suffered from many challenges. The most important challenges are uncertainty and vagueness. One of the latest MCDM method, called the fuzzy decision by opinion score method (FDOSM). However, there are still some vagueness issues around these methods (mention some of them). According to the advantage of the Fermatean fuzzy set in solving these issues, in this research extends FDOSM into Fermatean-FDOSM so as to …effectively benchmark the real-life problem. In this study, we present our methodology in two phases. The first phase presents the mathematical model of Fermatean-FDOSM which is composed of three stages of FDOSM. The second phase applied the new extension to benchmark the COVID-19 machine learning methods. The finding of Fermatean-FDOSM after comparing the result with the basic FDSOM and TOPSIS, is more logical and undergoing a systematic ranking. In the validation process, objective validation is applied to validate the final result of Fermatean-FDOSM. The result of Fermatean-FDOSM is valid, and more logical and in line with decision makers’ opinions. Show more
Keywords: Fuzzy decision by opinion score method (FDOSM), machine learning, Fermatean fuzzy, COVID-19, multi-criteria decision-making
DOI: 10.3233/JIFS-220707
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3549-3559, 2022
Authors: Liang, Meishe | Mi, Jusheng | Feng, Tao | Jin, Chenxia
Article Type: Research Article
Abstract: Knowledge acquisition in intuitionistic fuzzy information systems is of importance because those fuzzy information systems are often encountered in many real-life problems. Formal concept analysis is a simple and effective tool for knowledge acquisition. However, there is still little work on introducing knowledge acquisition methods based on formal concept analysis into intuitionistic fuzzy information systems. This paper mainly extends the formal concept theory into intuitionistic fuzzy information systems. Firstly, two pairs of adjoint mappings are defined in intuitionistic fuzzy formal contexts. It is verified that both pairs of adjoint mappings form Galois connections. Secondly, two types of intuitionistic fuzzy concept …lattices are constructed. After that, we also present the main theorems and propositions of the intuitionistic fuzzy concept lattices. Thirdly, we deeply discuss the attribute characteristics for type-1 generalized one-sided intuitionistic fuzzy concept lattice. Furthermore, a discernibility matrix-based algorithm is proposed for attribute reduction and the effectiveness of this algorithm is demonstrated by a practical example. The construction of intuitionistic fuzzy conceptS is meaningful for the complex and fuzzy information in real life. Show more
Keywords: Formal concept analysis, attribute reduction, galois connection, intuitionistic fuzzy formal context
DOI: 10.3233/JIFS-202719
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3561-3573, 2022
Authors: Zhang, Nan | Xue, Xiaoming | Jiang, Wei | Gu, Yuanhui | Shi, Liping | Chen, Xiaogang | Zhou, Jianzhong
Article Type: Research Article
Abstract: This paper proposes a novel Takagi–Sugeno fuzzy model identification method by combining fuzzy c-regression model clustering (FCRM), least squares support vector machine (LSSVM) and intelligent optimization algorithm. Firstly, in order to improve the performance of FCRM for the complex nonlinear dataset, in this paper the method of FCRM based on LSSVM (FCRM-LSSVM) is proposed to discover the data structure and obtain the antecedent parameters. And then, a newly developed intelligent optimization algorithm by hybridizing Harris hawks optimization and moth-flame optimization algorithm (IHHOMFO) is proposed to further optimize the antecedent membership function parameters obtained by the FCRM-LSSVM. Finally, the proposed novel …T-S fuzzy model identification combines FCRM, LSSVM and IHHOMFO for solving actual model identification problems. Experiments on five different datasets demonstrate that the proposed method is more efficient than conventional methods, such as T-S model identification based on fuzzy c-means (FCM), FCRM and FCRM-LSSVM, in standard measurement indexes. This study thus demonstrates that the proposed method is a credible and competitive fuzzy model identification method. The novel method contributes not only to the theoretical aspects of fuzzy model, but is also widely applicable in data mining, image recognition and prediction problems. Show more
Keywords: T-S fuzzy model, fuzzy c-regression model, least squares support vector machine, hybrid optimization algorithm
DOI: 10.3233/JIFS-211093
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3575-3598, 2022
Authors: Yang, Junfeng | Huang, Yuwen | Guo, Yubin | Huang, Fuxian | Li, Jing
Article Type: Research Article
Abstract: Although some methods of feature extraction for photoplethysmography (PPG) biometric recognition have been extensively studied, effectiveness of local features, time cost of feature extraction, and robust identification for small-scale data remain challenging. To address these issues, we proposed a feature-extraction method of PPG biometrics combining singular value decomposition with local mean decomposition and time-domain parameters. First, we used the singular-value-decomposition method to de-noise the original PPG data. Second, we extracted the local-mean-decomposition-based and time-domain features, which are fused into a concatenated feature. Finally, we combined the concatenated feature with four classifiers for classification and decision-making. Extensive experiments on the three …datasets have shown that the waveform of the PPG signal de-noised by singular value decomposition was smoother and more regular, the concatenated feature had strong inter-subject distinguishability and intra-subject similarity, and the concatenated feature combined with a random-forest classifier was the best and could achieve 99.40%, 99.88%, and 99.56% recognition rates on the respective datasets. The method is competitive with several state-of-the-art methods. Show more
Keywords: PPG biometrics, singular value decomposition, local mean decomposition, time-domain parameters
DOI: 10.3233/JIFS-212086
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3599-3610, 2022
Authors: Jiang, Weiwei | Luo, Jiayun
Article Type: Research Article
Abstract: Drought is a serious natural disaster that has a long duration and a wide range of influences. To decrease drought-induced losses, drought prediction is the basis of corresponding drought prevention and disaster reduction measures. While this problem has been studied in the literature, it remains unknown whether drought can be precisely predicted with machine learning models using weather data. To answer this question, a real-world public dataset is leveraged in this study, and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors. In a comprehensive approach, 16 machine learning models and 16 …deep learning models are evaluated and compared. The results show that no single model can achieve the best performance for all evaluation metrics simultaneously, which indicates that the drought prediction problem is still challenging. As benchmarks for further studies, the code and results are publicly available in a GitHub repository. Show more
Keywords: Drought prediction, weather data, machine learning, deep learning
DOI: 10.3233/JIFS-212748
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3611-3626, 2022
Authors: Yue, Xiaofeng | Ma, Guoyuan | Gao, Xueliang | Lu, Yucheng
Article Type: Research Article
Abstract: The surface inspection of strip steel defects plays a vital role in the industry, and it has attracted widespread attention in the industry. In this paper, an improved sparrow search algorithm (WMR-SSA) with intelligent weighting factors and mutation operators is proposed, WMR-SSA can balance the development capability of the algorithm based on the number of iterations. In addition, WMR-SSA enhances the local search capability of the algorithm through mutation operators. At the same time, the algorithm determines the initial position of the population by random walk to enhance the diversity of the population. The WMR-SSA algorithm is compared with GA, …PSO, CS, GWO, BSA, and original SSA, and the experiment proves that the WMR-SSA algorithm is better than other algorithms. In this study, WMR-SSA is combined with BP neural network and implemented for the classification of defective strip images. The accuracy and stability of WMR-SSA-BP are effectively demonstrated experimentally by comparing it with classifiers optimized by other intelligent algorithms. Show more
Keywords: Defect detection, sparrow search algorithm (SSA), intelligent weighting factor, mutation operator, random walk
DOI: 10.3233/JIFS-212883
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3627-3653, 2022
Authors: Zheng, Ting | Li, Shangze | Zhang, Luyan
Article Type: Research Article
Abstract: The silicon dioxide is the hardest part to melt among the iron tailing components, the melting behavior of iron tailing can be represented by the melting behavior of silicon dioxide. Estimating the real-time melting rate of silicon dioxide in the time sequence provide guidance for the tailing addition and heat compensation in the process of slag cotton preparation, also indirectly improved the direct fiber forming technology of blast furnace slag. The position of silicon dioxide particles in the high-temperature molten pool during the melting process is changing constantly, using a strong weighted distance centroid algorithm to rack the centroid position …of silicon dioxide particles during the melting process, and present the motion trail of centroid of silicon dioxide. In the paper, extracting indexes which represent the edge outline characteristics of silicon dioxide during the melting process of silicon dioxide using Snake active contour algorithm combined with Sobel operator, include shape, perimeter and area. Using the extracted skeleton characteristics, a three-dimensional skeleton generation model is created. From the skeleton data, estimating the volume of silicon dioxide and determine the parameter formula for the actual melting rate of silicon dioxide. The silicon dioxide melting rate at each moment is calculated by numerical simulation. The results of the Hough test circle and the silicon dioxide melting rate are verified. The rationality of the model is further determined. Show more
Keywords: Silicon dioxide melting, active contour, star skeleton, depth estimation, machine vision
DOI: 10.3233/JIFS-212971
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3655-3677, 2022
Authors: Almseidin, Mohammad | Al-Sawwa, Jamil | Alkasassbeh, Mouhammd
Article Type: Research Article
Abstract: Nowadays, with the rapid increase in the number of applications and networks, the number of cyber multi-step attacks has been increasing exponentially. Thus, the need for a reliable and acceptable Intrusion Detection System (IDS) solution is becoming urgent to protect the networks and devices. However, implementing a robust IDS needs a reliable and up-to-date dataset in order to capture the behaviors of the new types of attacks especially a multi-step attack. In this paper, a new benchmark Multi-Step Cyber-Attack Dataset (MSCAD) is introduced. MSCAD includes two multi-step scenarios; the first scenario is a password cracking attack, and the second attack …scenario is a volume-based Distributed Denial of Service (DDoS) attack. The MSCAD was assessed in two manners; firstly, the MSCAD was used to train IDS. Then, the performance of IDS was evaluated in terms of G-mean and Area Under Curve (AUC). Secondly, the MSCAD was compared with other free open-source and public datasets based on the latest keys criteria of a dataset evaluation framework. The results show that IDS-based MSCAD achieved the best performance with G-mean 0.83 and obtained good accuracy to detect the attacks. Besides, the MSCAD successfully passing twelve keys criteria. Show more
Keywords: Intrusion detection system (IDS), multi-step cyber-attacks, machine learning, resampling algorithms, intrusion datasets
DOI: 10.3233/JIFS-213247
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3679-3694, 2022
Authors: Subarna, T.G. | Sukumar, P.
Article Type: Research Article
Abstract: Earlier detection of cervical cancer in women can save their lives before a chronic development. The accurate detection in cancer tissues of cervix in the human body is very important. In this article, cervical images were classified into either affected or healthy images using deep learning architecture. The proposed approach was designed with the modules of Edge detector, complex wavelet transform, feature derivation and Convolutional Neural Networks (CNN) architecture with segmentation. The edge pixels in the source cervical image were detected using Kirsch’s edge detector, the Complex Wavelet Transform (CWT) was there used to decompose the edge detected cervical images …into number of sub bands. Local Derivative Pattern (LDP) and statistical features were computed from the decomposed sub bands and feature map was constructed using the computed features. The featured map along with the source cervical image was fed into the Cervical Ensemble Network (CEENET) model for classifying of cervical images into the classes healthy or cancer (affected). Show more
Keywords: Cervix, deep learning, CNN, cervical image, cancer
DOI: 10.3233/JIFS-220173
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3695-3707, 2022
Authors: Cao, Jing | Xu, Xuan-hua | Chen, Yudi | Ji, Wenying
Article Type: Research Article
Abstract: During and after an emergency event, multiple organizations with various specialties are involved in consensus decision-making to reduce the loss of lives and property in a timely manner. However, timely, high-consensus decision-making is challenging due to communication barriers between participating organizations. Thus, this study generalizes a conceptual communication network considering communication barriers by reviewing multiple historical emergencies and proposes a quantitative communication network model by integrating an opinion dynamics model and social network analysis (SNA). An illustrative example is provided by simulating two emergency decision-making scenarios to verify the proposed model. A case study of the 2013 Qingdao oil pipeline …explosion is presented to demonstrate the feasibility and applicability of the proposed model. The results of the case study indicate that the proposed model can accurately quantify the impact of communication barriers on the opinion formation time. This research provides a quantitative toolkit for understanding and improving decision-making performance in various emergencies. Show more
Keywords: Interorganizational communication network, communication barriers, opinion formation, emergency decision-making
DOI: 10.3233/JIFS-212102
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3709-3726, 2022
Authors: Parimala, V. | Devarajan, K.
Article Type: Research Article
Abstract: The recent decade has seen a rapid evolution of communication technologies and standards with the ultimate goal of providing global users with seamless connectivity a data access. Conventional methods of communication have been completely replaced by state-of-the-art hand-held gadgets and portable devices that enable users to communicate at high transmission rates. However, as high-end devices and gadgets become more popular and their demand for operating frequency which is essentially the Radio Frequency (RF) band in the EM (Electro Magnetic) spectrum tends to force the limits to the higher end of the RF spectrum. Due to the limitation of RF band …availability, a spectrum is constructed for the requesting user for promising solution, and a difficult task. The emerging cognitive radio networks are a set of intelligent tools and scheme of identify the vacant spots in the band through effective sensing and allocating the spectrum to the requesting users. A modified cluster-based model has been proposed as part of extensive research on spectrum sensing. In the proposed work, a two-phase clustering model in the form of modified Fuzzy C-Means (FCM), and K-Means clustering is used, in which FCM is used as a training module on the channel features. K-Means is effectively used as an unsupervised classifier model. The proposed classification model was tested in a densely populated cognitive radio network compared to standard methods such as SVM (Support Vector Machine), FCM, and K-Means. Superior performance in terms of quality metrics like 90% classification accuracy, 91% spectral utility 90% are notable findings of this research work. Show more
Keywords: Cognitive radio network, clustering, spectrum utilization, support vector machine, fuzzy C-means K-means
DOI: 10.3233/JIFS-212863
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3727-3740, 2022
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