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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: Mohankumar, B. | Karuppasamy, K.
Article Type: Research Article
Abstract: Congestion and security plays a most important key role in the wireless sensor network. In our previous work, energy balances routing is ensured by introducing the Energy Balancing and Optimal Routing Based Secured Data Transmission (EBORDT). But in this research work, congestion and security is not focused. This will lead to increased data loss rate along with entire network collision. These issues are focused and resolved in the proposed research work by introducing the method namely Honest aware Congestion concerned Secured Edge Disjoint Multipath Routing (HC-SEDMR) Scheme. In this work cluster head selection is done using hybridized simulated annealing with …fuzzy rule descriptors. Base on these selected cluster head, clustering is done using Normalized optimal clustering algorithm. After clustering Energy and Edge disjoint aware optimal routing is done using Congestion and Collision aware Edge Disjoint multipath routing. Here secured routing is ensured by choosing honest nodes for the forwarding process. This is done by using honest forwarding node selection method. The performance analysis of the research work is done in the NS2 simulation environment from which it is proved that the proposed method attains better performance than the existing methodology. Show more
Keywords: Honesty, congestion, edge disjoint routing, clustering, simulated annealing, multipath routing
DOI: 10.3233/JIFS-212841
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2219-2229, 2022
Authors: Kheirollahi, Hooshang | Rostamzadeh, Mahfouz | Marzang, Soran
Article Type: Research Article
Abstract: Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable …is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions. Show more
Keywords: Data envelopment analysis, fuzzy-random, most productive scale size, hospitals
DOI: 10.3233/JIFS-202456
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2231-2241, 2022
Authors: Vijayalakshmi, P. | Muthumanickam, K. | Karthik, G. | Sakthivel, S.
Article Type: Research Article
Abstract: Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability …of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility. Show more
Keywords: Medical image processing, adenomyosis, endometriosis, infertility, BIWDNN
DOI: 10.3233/JIFS-212866
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2243-2251, 2022
Authors: Xu, Meiling | Tian, Boping | Fu, Yongqiang
Article Type: Research Article
Abstract: Credit scoring is widely used by financial institutions for default prediction, however, a significant portion of online credit loan customers have inadequate or unverifiable credit histories, making it difficult for financial institutions to make effective credit decisions. Since the widespread use of smartphones and the popularity of mobile applications, it is worth investigating whether mobile application usage behaviors (App behaviors) of customers can effectively predict online loan defaults. This paper proposes a combined algorithm of CNN and LightGBM, and establishes credit scoring models with App behaviors to evaluate the default risk of online credit loans based on logistic regression, LightGBM, …CNN and the combined algorithm, respectively. The experimental results suggest that App behaviors have an obvious effect on the default prediction of customers applying for online credit loans, and the combined model outperforms the other models in terms of the area under the curve (AUC). Furthermore, integrated credit scoring models are developed by combining App behaviors with traditional scoring features. A comparison of the integrated models and the traditional scoring model indicates that the integrated models have achieved a significant improvement in classification performance and App behaviors can be a powerful complement to the traditional credit scoring model. Show more
Keywords: Credit scoring, online credit loans, mobile application usage behaviors, logistic regression, combined model
DOI: 10.3233/JIFS-211825
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2253-2264, 2022
Authors: Chen, Yan | Yu, Ying | Wang, Ya-Meng | Lou, Jun-He
Article Type: Research Article
Abstract: Probabilistic Uncertain Linguistic Term Set (PULTS), as an emerging and effective linguistic expression tool, can appropriately describe the complex evaluation information of decision makers. The cloud model is powerful in handling complex cognitive linguistic information, based on which, this paper proposes two new Multicriteria Decision-Making (MCDM) Methods with PULTSs. Firstly, to avoid the problem of information loss in traditional linguistic conversion methods, Probabilistic Uncertainty Trapezium Cloud (PUTC) is proposed to quantify linguistic evaluation information. Secondly, the Probabilistic Uncertainty Trapezium Cloud Weighted Bonferroni mean (PUTCWBM) operator is defined, while presenting a new cloud score function and similarity measures. Additionally, two ranking …methods are proposed, one on the basis of the similarity measures of PUTCs and ideal solutions, the other on the basis of the PUTCWBM operator and the cloud score function. Finally, the two methods are verified with an example of evaluation on masks, and the effectiveness and superiority of the methods are further illustrated through sensitivity analysis and method comparison. Show more
Keywords: Multicriteria decision-making, probabilistic uncertain linguistic term set, probabilistic uncertain Trapezium cloud, similarity measure, cloud score function
DOI: 10.3233/JIFS-213001
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2265-2282, 2022
Authors: Pirzad Mashak, Neda | Akbarizadeh, Gholamreza | Farshidi, Ebrahim
Article Type: Research Article
Abstract: Prostate cancer is one of the most common cancers in men, which takes many victims every year due to its latent symptoms. Thus, early diagnosis of the extent of the lesion can help the physician and the patient in the treatment process. Nowadays, detection and labeling of objects in medical images has become especially important. In this article, the prostate gland is first detected in T2 W MRI images by the Faster R-CNN network based on the AlexNet architecture and separated from the rest of the image. Using the Faster R-CNN network in the separation phase, the accuracy will increase as …this network is a model of CNN-based target detection networks and is functionally coordinated with the subsequent CNN network. Meanwhile, the problem of insufficient data with the data augmentation method was corrected in the preprocessing stage, for which different filters were used. Use of different filters to increase the data instead of the usual augmentation methods would eliminate the preprocessing stage. Also, with the presence of raw images in the next steps, it was proven that there was no need for a preprocessing step and the main images could also be the input data. By eliminating the preprocessing step, the response speed increased. Then, in order to classify benign and malignant cancer images, two deep learning architectures were used under the supervision of ResNet18 and GoogleNet. Then, by calculating the Confusion Matrix parameters and drawing the ROC diagram, the capability of this process was measured. By obtaining Accuracy = 95.7%, DSC = 96.77% and AUC = 99.17%, The results revealed that this method could outperform other well-known methods in this field (DSC = 95%) and (AUC = 91%). Show more
Keywords: Prostate cancer, data augmentation, filtering, feature extraction, localization, deep learning
DOI: 10.3233/JIFS-212990
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2283-2298, 2022
Authors: Venkatasamy, B. | Kalaivani, L.
Article Type: Research Article
Abstract: Solar photovoltaic industry is continuously thriving to improve the performance of power management systems with technology advancement. In the power system, balancing active and reactive power injection and absorption plays a pivotal role in maintaining Grid regulation and power factor and thus improving the efficiency and power handling capability. The Grid-tied solar PV inverters can be the cost-effective solution for reactive power compensation. Solar photovoltaic (SPV) system with the Grid-tied inverter is to produce active and reactive power in various ecological conditions. The Grid-tied PV inverter injects active and reactive power into the grid according to the desired value by …the Grid requirements. The Proposed system uses decoupled P-Q theory with Interval type-2 Fuzzy Logic Controller (IT2-FLC). Also, the proposed controller minimizes the Total Harmonic Distortion (THD) and produces regulated output power compared to the other control techniques. The proposed IT2-FLC is compared with a Type-1 Fuzzy Logic Controller (T1-FLC) and conventional mathematical PI controller for performance analysis using simulation. A 75 kW Grid-tied inverter is designed in simulation to prove the effectiveness of the system through Matlab/Simulink. A smaller rating prototype model is developed to verify the simulation results. Show more
Keywords: Solar Photovoltaic, grid-tied inverter, interval type-2 fuzzy logic controller, reactive power
DOI: 10.3233/JIFS-212721
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2299-2314, 2022
Authors: Haouassi, Hichem | Mahdaoui, Rafik | Chouhal, Ouahiba | Bekhouche, Abdelaali
Article Type: Research Article
Abstract: Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our …present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle’s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class “Normal” and 12 rules for the class “CAD”. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models. Show more
Keywords: Coronary artery disease, medical diagnosis, machine learning, rule-based diagnosis, rule discovery, population-based optimization, discrete equilibrium optimization algorithm
DOI: 10.3233/JIFS-213257
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2315-2331, 2022
Authors: Indira, K. | Karki, Maya V. | Mallika, H.
Article Type: Research Article
Abstract: Recognition of Kannada Characters is a complex task as the number of classes in Kannada language by considering all combinations of vowels and consonants is 623,893. In this paper, the complexity is reduced from 623,893 to just having 313 classes as Main aksharas (Vowel, Consonants,Vowel modifiers and Consonant modifiers) and 30 classes as vattu aksharas(conjuncts) by using two line segmentation. A novel CNN model for recognition of printed and handwritten Kannada characters is proposed. CNN model with two, three and four layers are designed for Main akshara and Vattu aksharas with different filter size. The database consists of total of …31,300 samples and 3000 samples of printed and handwritten characters of Main akshara and Vattu aksharas respectively. Simulation result revealed that CNN model with four layer architecture is the best model for recognition of Kannada characters. This model achieved a recognition accuracy of 98.83% and 99.29% for printed Main akshara and Vattu aksharas and 82.50% and 80.92% for handwritten main and vattu akshara respectively. Show more
Keywords: Deep learning, convolution neural network, SVM classifier, horizontal projection profile, vertical projection profile
DOI: 10.3233/JIFS-212680
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2333-2346, 2022
Authors: Dhamija, Ashutosh | Dubey, R.B.
Article Type: Research Article
Abstract: Age invariant face recognition (AIFR) is currently a study topic with several potential uses. It offers a variety of real-world applications, including passport renewal, driver’s license renewal, locating missing children, locating criminals, providing security to VIPs etc. In the field of AIFR, scientific efforts have increased. Matching faces of big age differences is thus a challenge, owing to the significant variation in appearance between young and elderly age. The appearance and form of the face deteriorate with age, making facial recognition the most difficult task. AIFR has become a highly common and difficult chore in recent years. In this discipline, …the set of feature extraction and classification algorithms is crucial. This paper addresses the issues raised above by proposing an enhanced ASM approach for extracting features from 2D search regions using handcrafted and deep image features in conjunction with a 7-layer CNN architecture and a smaller image size of 32x32 pixels to reduce delay time and space complexity. Using the standard dataset LAG, the study approach entails running many tests to evaluate the proposed system’s performance. The results show that the suggested method beats state-of-the-art algorithms in face recognition and achieves good accuracy throughout the age spectrum. The presented methodology achieves a maximum accuracy of 91.76 percent for the LAG database, outperforming all existing state-of-the-art methodologies. Show more
Keywords: Age invariant face recognition, CNN architecture, enhanced active shape model, handcrafted and deep features, and the features reduction
DOI: 10.3233/JIFS-212789
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2347-2362, 2022
Authors: Azimirad, Ehsan
Article Type: Research Article
Abstract: Edge Detection is the first stage in the image division into separate parts. Image division is the partitioning of a digital image to the different zones or the set of pixels. Edge detection is one of the techniques applied in digital image processing often. The purpose of detecting pixels is to match the edges in the image. Filtering, Enhancement, and Detection are three steps in edge detection. Images are usually destroyed by casual changes in intensity intervals called noise or confusion. some noise variations include salt and pepper, pulse, and Gaussian. However, there is a relation between edge detection power …and noise reduction. Using filters to the noise reduction causes the loss of edge detection power. For facilitating the edges detection, it is essential for the determination of pixels’ intensity constraints in their neighborhood. Many points in an image have a nontransparent slope, and all of them are not the edges of the joint space. Therefore, some of the linear and nonlinear methods such as Sobel, Prewitt, and Robert have to be used to determine the edge points. The fuzzy logic and the system based on it, is one of the most effective methods for edge detection. This paper presents an optimized rule-based fuzzy inference system and designs the efficiency mask matric. The simulation results for edge detection are presented using the traditional edge detection techniques, including Binary Filter, Sobel Filter, Prewitt Filter, and Robert Filter. Also, it is presented using the fuzzy approach. The simulation results show that the designed fuzzy system has been able to detect the edges of the image more accurately and help to increase the sharpness and quality of the edges. Therefore, the proposed method has more accurate and more reliable results and reduces false edge detection comparison to the traditional methods. Show more
Keywords: Edge detection, fuzzy sets, optimized fuzzy system, membership functions
DOI: 10.3233/JIFS-213008
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2363-2373, 2022
Authors: Mystica, A. | Senthil kumar, V.S. | Sakthi abirami, B.
Article Type: Research Article
Abstract: AA2014 is an Al-Cu alloy friction stir welded under different combinations of rotational speed (800, 1000 and 1200 rpm) and transverse speed (44, 60, 72 mm/min) under minimum quantity lubrication condition with graphene nanofluid as coolant. Design of experiments is performed using Taguchi L9 orthogonal array. Analysis of variance technique is adapted to find the most influencing input parameter (rotational speed, transverse speed) of each output response (ultimate tensile strength, % elongation, microhardness and grain size). Regression and fuzzy logic based models are developed to predict the output responses. The reliability of the predicted results is tested by calculating the correlation coefficient. …The predicted results from regression and fuzzy logic are then compared with the experimental results. The results of trend analysis exhibit the substantial influence of both the input parameters on the output responses. The results from ANOVA reveals that the rotational speed highly influences ultimate tensile strength and grain size while transverse speed majorly influences microhardness. The error in prediction using fuzzy model is observed to be significantly limited with correlation coefficients in the range of 0.70–0.96. The developed models are observed to be highly efficient and therefore can be used for prediction in any uncertain engineering applications. Show more
Keywords: Friction stir welding, minimum quantity lubrication, graphene nanofluid, regression modelling, fuzzy logic
DOI: 10.3233/JIFS-213032
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2375-2390, 2022
Authors: Bokir, Abdullah | Narasimha, V.B
Article Type: Research Article
Abstract: High utility mining is gaining prominence, and with the increasing set of business intelligence models, the scope of such significant practices is high. Rather than focusing only on profitability as one key utility metric, today’s organizations believe in having more robust levels of the multi-objective filtering process. In this manuscript, a contemporary model of the high utility mining process is proposed, wherein the multiple averages are used for grading the recommendation of the itemsets for merchandise. The model’s key advantage is its dynamic approach. The goods-related period of the average time interval can be flexible, alongside the fusion of multiple …utility thresholds of diversified features chosen for itemsets recommendation. The performance analysis has been carried out by using a multi-fold cross-validation strategy. The results obtained for cross-validation show that the proposed model is outperforming the contemporary models with significant precision, specificity, sensitivity, and accuracy having values 97%, 95%, 98%, and 97% in respective order. Whereas, the contemporary models HUPM-MUO and MOEA-FHUI have obtained 93% and 90%, 88% and 82%, 89%, and 84%, and 89% and 83% in respective order of the corresponding metrics. The experimental study of the model denotes the effectiveness and ease with which the solution can generate results and produce significant output in the real-time environment for more dynamic and periodic decisions by different organizations. Show more
Keywords: High Utility Mining, multiple-utility factors, economic order quantity, exponential moving average, inventory storage cost
DOI: 10.3233/JIFS-213037
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2391-2405, 2022
Authors: Jiang, Huimin | Guo, Gaicong | Sabetzadeh, Farzad | Chan, Kit Yan
Article Type: Research Article
Abstract: Previous studies developed consumer preference models mainly through customer surveys, ignoring the variability of consumer preferences over time. In addition, it is difficult to obtain time series data based on the customer surveys. In recent years, some previous studies tried to analyse consumer preferences based on online reviews. However, they have not solved the problems of modelling variational consumer preference based on time series data with the consideration of the ambiguity of emotions expressed by customers in online reviews. To solve the above problems, this article proposes the particle swarm optimization (PSO) based dynamic evolving neural-fuzzy inference system (DENFIS) method …to model variational consumer preferences based on online customer reviews. Using the time series data mined by the sentiment analysis method and the product attribute settings of the review products, the PSO-based DENFIS method is offered to dynamically model consumer preferences, in which PSO is used to adjust DENFIS parameters adaptively. Show more
Keywords: Consumer preference, opinion mining, DENFIS, particle swarm optimization, new product development
DOI: 10.3233/JIFS-213057
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2407-2418, 2022
Authors: Chotikunnan, Phichitphon | Panomruttanarug, Benjamas
Article Type: Research Article
Abstract: Iterative Learning Control (ILC) is an intelligent control algorithm that can effectively handle a tracking error in any system that operates in a repetitive manner. In practice, it is hardly possible to implement a single gain learning control law to improve the tracking performance due to the existence of large transient growth. To prevent the growth, this paper proposes a time-varying learning control design using the unique concept of fuzzy logic control to track the desired trajectory as well as the desired control input signal. The proposed control design is developed on both serial and parallel ILC configurations. The two …configurations are initially constructed and implemented on a robotic manipulator with the use of a single gain learning control law. To avoid bad transients, the gain adjustment mechanism based on fuzzy logic control is introduced to vary the learning gain in each time step for enhancing the robustness of the system. According to the simulation and experiment on a robotic manipulator, both ILC structures with the proposed mechanism achieve the desired learning performance without bad transients. Show more
Keywords: Iterative learning control, serial ILC, parallel ILC, fuzzy logic control, robotic manipulator
DOI: 10.3233/JIFS-213082
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2419-2434, 2022
Authors: Han, Meng | Shan, Zhihui | Han, Qiang
Article Type: Research Article
Abstract: High utility quantitative itemsets (HUQI) mining is a new research topic in the field of data mining. It not only provides high utility itemset (HUI), but also provides quantitative information of individual item in the itemset. HUQI can provide decision makers with information about items and their purchase quantities. However, the currently proposed HUQI mining algorithms assume that the datasets are static. In order to solve this problem, an incremental quantitative utility list (IQUL) data structure is proposed to store item information, including item name, item number, transaction weight utility of item, each entry in the list stores the transaction …identifier, the utility of the original data, the remaining utility, the utility of the incremental data, the remaining utility, and the sum of the utility and the remaining utility. When data is inserted, the item information will be updated. Based on IQUL, an incrementally updating HUQI (IHUQI) mining algorithm is proposed to mine HUQI on incremental update data. A large number of experiments on real datasets show that the IHUQI algorithm can effectively mine HUQI Experimental results show better performance on sparse datasets. Show more
Keywords: Incremental mining, high utility quantitative itemsets, high utility itemsets, utility list, itemsets mining
DOI: 10.3233/JIFS-213136
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2435-2448, 2022
Authors: Ge, Liang | Lin, Yongquan | Li, Senwen | Zeng, Bo
Article Type: Research Article
Abstract: Urban traffic flow prediction is a critical problem in the intelligent transportation system, and it’s very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the complex road network and dynamic traffic conditions. However, existing methods either rely too much on prior knowledge or the data itself when modeling spatial-temporal dependency and few researchers consider them in combination. In this paper, a new spatial-temporal network for traffic flow prediction, which can comprehensively capture the complex spatial and temporal dependency based on prior knowledge and data-driven, is proposed. In particular, in the perspective of local and global …spatial dependency in road networks, we construct a dynamic weighted graph by finding the spatial and semantic neighborhoods of road nodes based on road networks and the similarities between traffic data on different roads. Besides, the temporal trend module and implicit temporal dependency module are combined to capture the temporal transitivity of traffic flow and implicit dependencies between time point pairs. The experiment results of our proposed model outperform the state-of-the-art baselines. Show more
Keywords: Traffic forecasting, spatial-temporal network, prior knowledge, data-driven
DOI: 10.3233/JIFS-213317
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2449-2462, 2022
Authors: Chai, Shaolong | Wang, Zeng
Article Type: Research Article
Abstract: In view of the shortcomings of the existing evaluation methods in fully considering the fuzziness and randomness of product design evaluation, a novel product design evaluation method based on FAHP and cloud model is proposed. First, a hierarchical structure model of product design evaluation is established. Second, fuzzy pairwise comparison of criteria is constructed through questionnaire survey, and the digital characteristics of weight cloud model are acquired by the proposed fuzzy weight model. Then, based on the factor scores, the digital characteristics of scoring cloud model are obtained by backward cloud. Finally, the digital characteristics of comprehensive evaluation cloud model …are obtained by using the proposed improved fuzzy composite operator, and the forward cloud is used to get the cloud picture for evaluation. Taking reading lamp as an example, the feasibility and effectiveness of the proposed method are verified. The results show that compared with the other two methods, the Kendall rank correlation coefficients of entropy of the method are increased by 0.17 and 0.33, respectively, which proves that the method achieves more accurate evaluation result under the complex criteria, and provides a more effective tool for decision makers and designers to evaluate and optimize design schemes. Show more
Keywords: FAHP, cloud model, design evaluation, reading lamp
DOI: 10.3233/JIFS-213331
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2463-2483, 2022
Authors: Cebi, Selcuk | Gündoğdu, Fatma Kutlu | Kahraman, Cengiz
Article Type: Research Article
Abstract: Risk assessment takes place depending on the expertise and subjective linguistic assessments of experts. Expert judgements are collected via a questionnaire or an interview including qualitative data. Pessimistic or optimistic status of experts can affect their perceptions on risk. Furthermore, expert judgments are affected by questions’ structure based on whether it is a positive type question (e.g., ‘What is the occurrence probability of the accident?) or a negative type question (e.g., ‘What is the non-occurrence probability of the accident?). All of these cases create uncertainties in the risk assessment process. For this reason, there are various studies using fuzzy risk …analysis models to address these uncertainties in risk assessment. However, there is not any risk assessment tool that considers the uncertainties caused by the factors mentioned above, simultaneously. Therefore, in this paper, we introduce the concept of decomposed fuzzy sets (DFS) to model human thoughts and perceptions in a more realistic and detailed way through optimistic and pessimistic membership functions. We present the basic operations on decomposed fuzzy sets and their properties. To demonstrate the utility of the proposed method, the method is applied to operational risk analysis in business processes. The data used in the application are collected from the managerial board of a construction company. The application results and advantages of the proposed method are presented together with a comparative analysis. Show more
Keywords: Intuitionistic fuzzy sets, fuzzy set theory, decomposed fuzzy sets, risk assessment, business management
DOI: 10.3233/JIFS-213385
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2485-2502, 2022
Authors: Thillai Rani, M. | Sai Pradeep, K.P. | Sivakumar, R. | Suresh Kumar, S.
Article Type: Research Article
Abstract: Electronics industry has attained huge development in last few decades due to the rapid increase in system design applications. With the growth of very large scale integration (VLSI) design, integrated circuits (ICs) are employed in many applications. VLSI design comprises many steps like system-level design, high-level synthesis (HLS), logic design, test generation, and physical design. HLS interprets behavior description and create digital hardware that executes the behavior. But, the power-process-voltage-temperature (PPVT) variation can causee many issues and reduce the performance of VLSI design circuits. In order to address these problems, Recurrent Deep Neural Learning Classification based High Level Synthesis (RDNLC-HLS) …Model is designed for better runtime adaptability with minimal time consumption. VLSI circuits are designed with the behavioral input and the output performance is measured at runtime. The behavioral description of the circuit is taken as input. Then, source code compilation process translates high level specification into Intermediate Representation (IR) and converts to control/data flow graph (CDFG). CDFG reveals data and control dependencies between operations. The proposed Recurrent Deep Neural Learning Classification based High Level Synthesis (RDNLC-HLS) Model is designed for providing better runtime adaptability with minimal time consumption. Finally, Register Transfer Level Generation is carried out to yield better runtime adaptability with minimal time. Simulation results on ISCAS’89 Benchmark Dataset, shows that the RDNLC-HLS model increases the FUSA with minimal error rate and CAT. Show more
Keywords: Deep learning, neural network, optimization techniques, VLSI circuits
DOI: 10.3233/JIFS-213406
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2503-2514, 2022
Authors: Jindaluang, Wattana
Article Type: Research Article
Abstract: A class imbalance problem is a problem in which the number of majority class and minority class varies greatly. In this article, we propose an oversampling method using GA and k -Nearest Neighbors (k NN) to deal with a network intrusion, a class imbalance problem. We use GA as the main algorithm and use a k NN as its fitness function. We compare the proposed method with a very popular oversampling technique which is a SMOTE family. The experimental results show that the proposed method provides better Accuracy, Precision, and F-measure values than a SMOTE family in almost all datasets …with almost all classifiers. Moreover, in some datasets with some classifiers, the proposed method also gives a better Recall value than a SMOTE family as well. This is because the proposed method can generate new intruders in a more independent area than a SMOTE family. Show more
Keywords: Oversampling, class imbalanced problem, genetic algorithm, k-nearest neighbors
DOI: 10.3233/JIFS-213430
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2515-2528, 2022
Authors: M, Devi Sri Nandhini | Gurunathan, Pradeep
Article Type: Research Article
Abstract: Since people express their opinions and feelings more openly than ever before, sentiment analysis proves to be a promising research area that effectively analyses the opinion expressed over the entities. In this context, Sentiment analysis is utilized to gather valuable insights from users’ opinions. These insights would benefit a lot for the business concerns and institutions to improve their respective products/services. Aspect-based sentiment analysis (ABSA) is the most robust technique that offers a more fine-grained analysis. The objective of this paper is to improve the efficacy of ABSA by framing a robust and enhanced set of rules. Several experiments were …carried out to detect explicit and implicit aspects. The hybrid approach comprising of enhanced rule-based approach (ERBA) and domain-specific lexicon (DSL) is used to improve the solution of the aspect-based sentiment analysis problem. The proposed approach employs a domain-specific adjective-noun collocation list(DSANCL) tailored to the domain for fine-tuning the process of implicit aspect detection(IAD). The proposed model frames a new nine-point scale for measuring the sentiment strength by introducing a ternary classification of intensifiers based on their degree of intensification. The performance of the proposed model is evaluated using the university reviews dataset. Show more
Keywords: Aspect-based sentiment analysis, rule-based approach, implicit aspect detection, adjective-noun collocation, domain-specific lexicon
DOI: 10.3233/JIFS-213584
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2529-2547, 2022
Authors: Jeyasingh, Dani Abraham | Rajamanickam Manickaraj, Sasiraja | Govindhan Radhakrishnan, Rajesh Kanna
Article Type: Research Article
Abstract: Fault detection and identification in a solar Photovoltaic (PV) systems are one of the crucial task in recent days for ensuring both reliability and safety measures. The fault occurrence in the PV cell will affect the output power, and can reduce the efficiency of its characteristics. The fault in PV cell can identify by using the thermal scan method manually. Arrangement of the proposed setup regularly is not possible to monitor due to the hardware installation of several equipment, it took more time to test, and validate the affected PV cells prediction less accuracy while doing in manual testing. In …order to solve these issues, this paper intends to propose a novel algorithm, named as Truncated Arrangement of Active Cell (TAAC) structure for accurately detecting the PV faults. This technique is used to analyze the PV cell aging condition and to enhance the PV characteristics. Typically, the improvement in a cell arrangement provides an optimal solution for efficient fault detection. Moreover, the TAAC architecture computes the optimal solution for a PV output terminal based on the PV cell parameters and variation of temperature measures. Also, a Kalman filtering technique is employed to extract the features that are used to improve the detection process. The major advantages of this structure are, it enhance the lifetime of PV cell and stores the maximum power for a long time usage. The experimental results evaluate the performance of this technique by using various measures such as false alarm rate, misclassification rate, misdetection rate, and prediction rate. Furthermore, some of the existing techniques are compared with the proposed technique for proving its superiority. Show more
Keywords: Renewable Energy Source (RES), Photovoltaic (PV), fault detection, Truncated Arrangement of Active Cell (TAAC), Maximum Power Point Tracking (MPPT)
DOI: 10.3233/JIFS-213040
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2549-2565, 2022
Authors: Idris, Nur Farahaina | Ismail, Mohd Arfian
Article Type: Research Article
Abstract: Globally, the second most common cause of death for female cancer patients is breast cancer. In the United States, about 11,000 females aged below 40 are diagnosed with invasive breast cancer each year. Early detection of breast cancer is the foundation for preventing the progression of the disease, and the diagnosis can be conducted using intelligent systems for quicker detection. Based on the FUZZYDBD method and bootstrap aggregation (bagging) technique, the Bagging fuzzy-ID3 algorithm (BFID3) was proposed for this study. This method combined the techniques of the fuzzy system, ID3 algorithm and bagging. For BFID3’s data fuzzification, the automatic fuzzy …database definition method, known as the FUZZYDBD method, would assist in developing the fuzzy database. One of the weaknesses of the ID3 algorithm is its incapability to handle continuous data. The problem was resolved via the linguistic variable replacement and data fuzzification in the BFID3. Meanwhile, this paper’s implementation of the bagging technique improved the generalization ability and reduced overfitting. Additionally, BFID3 was verified through an extensive comparison with several existing methods to investigate the competency of the proposed method. The study identified that BFID3 was proficient in breast cancer classification. Show more
Keywords: Fuzzy system, ID3 algorithm, bagging, breast cancer
DOI: 10.3233/JIFS-212842
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2567-2577, 2022
Authors: Lather, Mansi | Singh, Parvinder
Article Type: Research Article
Abstract: Due to the complexity of the task involved in extracting and segmenting the tumor area from the images, it is very challenging to be successful in detecting the disorders. This paper presents a method that can handle the various issues related to brain tumor segmentation, such as noise reduction, artifact removal, and visual interpretation. In this paper, an advanced brain tumor segmentation approach is proposed that is working in different phases such as pre-processing that includes image enhancement and noise removal from the input image, Stationary Wavelet Transform (SWT) based feature extraction and Sine Tree-Seed Algorithm (STSA) based modified K-means …clustering algorithm for segmentation. In addition to this, the proposed approach is analyzed for its effectiveness by considering the impact of Gaussian and speckle noise on the original image. The experimental results have been evaluated in three different cases of the input noise in terms of accuracy, precision, recall, F-score, and Jaccard. Finally, a comparative analysis is performed with different conventional approaches to prove the effectiveness of the proposed scheme. The result analysis shows an improvement of approximately 1% in terms of accuracy, 4%, and 5% in terms of precision and recall respectively when compared to the other techniques. Show more
Keywords: Image segmentation, medical image processing, image analysis, K-means clustering
DOI: 10.3233/JIFS-212709
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2579-2595, 2022
Authors: Li, Dongjie | Zhang, Zilei | Zhao, Hongyue
Article Type: Research Article
Abstract: The dynamic gesture trajectory recognition results are low accurate and poor real-time due to the problems of occlusion, complex background and fast gesture movement. In this paper, we take advantage of the advantages of machine vision to extract the video keyframes by the three-frame differential method and use the annotation software to produce the dataset. The you only look once 4 (YOLOv4) algorithm is improved to reduce the redundancy of the network structure and enhance the applicability of the feature map for hand gesture recognition. Combined with the Deep-sort real-time tracking feature, the hand motion trajectory is obtained by introducing …the epiphenomenal features to effectively avoid the situation that the object is not tracked when it is obscured. To avoid the problem of gradient disappearance during deep network training, the DenseNet-BC-169 network is used to balance the recognition rate and training time for gesture trajectory classification. Compared with FLIXT, the winner of the dynamic gesture recognition challenge, the final results showed a 6.13% improvement in accuracy and video processing with the IsoGD dataset reached 31fps, validating the effectiveness of this method. Show more
Keywords: Gesture recognition, convolutional neural network, YOLOv4, trajectory tracking
DOI: 10.3233/JIFS-212766
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2597-2607, 2022
Authors: Kala, A. | Ganesh Vaidyanathan, S. | Sharon Femi, P.
Article Type: Research Article
Abstract: The risks of severe weather events due to climate changes, including droughts and floods require accurate and timely forecasting of rainfall. But, the rainfall time series contains nonlinear and non-stationary data which lowers the model performance. This paper attempts to solve the nonlinear and non-stationary challenges imposed by the rainfall forecasting models by building a hybrid model based on complete ensemble empirical mode decomposition with Adaptive Noise(CEEMDAN) combined with long short-term memory (LSTM) for forecasting All India monthly rainfall. For monthly rainfall forecasting, homogeneous Indian monthly rainfall time series dataset (1871–2016) is used. Complete ensemble empirical mode decomposition decomposes the …rainfall time series data into Intrinsic Mode Functions (IMF) and residual element. Each IMF and residual is forecasted using the LSTM after determining the significant lags. The forecasted intrinsic mode functions and the residual elements are reconstructed to obtain the forecasted rainfall value. The proposed model performance has been verified against existing models. Compared with single LSTM model, the forecasted values prove that the model achieves good performance in predicting monthly rainfall time series. Show more
Keywords: Rainfall forecast, CEEMDAN, LSTM, IMF
DOI: 10.3233/JIFS-213064
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2609-2617, 2022
Authors: Ganesan, Balaraman | Raman, Sundareswaran | Pal, Madhumangal
Article Type: Research Article
Abstract: Let H = (V , E ) be a graph and xy ∈ E (H ). Then x strongly dominates y if deg(x ) ⩾ deg(y ). A subset S of V is said to be a strong dominating set if every node y ∈ V – S is strongly dominated by some node x in H and is denoted by sd -set. The strong domination number γ s (H ) is the minimum cardinality of a strong dominating set. In this paper, we introduce a new vulnerability parameter called strong domination integrity in graphs. Strong domination integrity …of some families of graphs are determined and its bounds are also obtained. The proposed parameter is applied in water distribution network system to identify the influential group of nodes within the network. Fuzzy graphs can be used to model uncertain networks. By using membership values of strong arcs, strong domination integrity is extended to fuzzy graphs as a new vulnerability parameter. In this study, we investigate the strong domination integrity for complete bipartite fuzzy graphs, complete fuzzy graphs and bounds are also derived. Some basic results and theorems are obtained. This vulnerability parameter is also applied in the transportation network systems. Show more
Keywords: Strong dominating set, strong domination number, domination integrity, strong domination integrity, fuzzy graphs, strong arcs, weight of strong arcs, efficient fuzzy graphs.
DOI: 10.3233/JIFS-213189
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2619-2632, 2022
Authors: Nguyen, Long H. B. | Pham, Nghi T. | Duc, Le D. C. | Hoang, Cong Duy Vu | Dinh, Dien
Article Type: Research Article
Abstract: In recent years, Neural Machine Translation (NMT), which harnesses the power of neural networks, has achieved astonishing achievements. Despite its promise, NMT models can still not model prior external knowledge. Recent investigations have necessitated the adaptation of past expertise to both training and inference methods, resulting in translation inference issues. This paper proposes an extension of the moment matching framework that incorporates advanced prior knowledge without interfering with the inference process by using a matching mechanism between the model and empirical distributions. Our tests show that the suggested expansion outperforms the baseline and effectively over various language combinations.
Keywords: Neural machine translation, moment matching, objective function
DOI: 10.3233/JIFS-213240
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2633-2645, 2022
Authors: Tian, Yu | Guo, Zixue
Article Type: Research Article
Abstract: A risky large group decision-making method based on FCM clustering and cloud models is proposed for risky large group decision-making problems with linguistic evaluation scales, unknown attribute weights, and many decision members with unknown weights, considering the psychological behavioral characteristics of decision makers’ regret avoidance. The method first uses the golden partition method to improve the cloud model to transform the uncertain linguistic evaluation matrix into a comprehensive cloud model, which quantifies the fuzziness and randomness of linguistic values. The cloud model expectation values are then extracted to determine the attribute weights using the entropy weighting method. Secondly, the three …numerical features of the cloud model are extracted as sample features for FCM clustering to obtain the decision maker’s preference clustering information, and the initial weights of decision-makers are determined according to the majority principle, which improves the existing studies that simply use the expected value of the cloud model for clustering analysis, ignoring the entropy and super entropy for portraying the ambiguity and randomness. On this basis, the Hamming distance is introduced to calculate the closeness to adjust the initial weights of decision-makers, improving the way that the weights of aggregation members are equally distributed in previous studies. Finally, considering the influence of the decision maker’s psychological behavior on decision information in the risky decision-making process, regret theory is introduced to construct a decision maker’s perceived utility matrix, which is combined with the decision maker’s weights to determine and rank the combined perceived utility. Through comparison with existing methods, it is found that the proposed method of recalibration of decision-maker preference clustering, while considering the psychological behavior of decision-maker regret avoidance, not only solves the situation of large group decision making in which expert information is easily distorted but also satisfies the convenience of the calculation process and is more suitable for the situation where there are many decision-makers and their preferences are complicated. Show more
Keywords: Cloud model, fuzzy C-mean clustering, regret theory, large group decision-making
DOI: 10.3233/JIFS-213216
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2647-2665, 2022
Authors: Nair, Kavya R. | Sunitha, M. S.
Article Type: Research Article
Abstract: Fuzzy Incidence graph (FIG) is one of the most suitable ways to model real life problems when there is an influence of the vertices on the edges. Domination in FIG is a novel concept which has many applications. The study aims to introduce a new concept of domination in fuzzy incidence graphs using strong pairs and define strong incidence domination number (SIDN) using weight of strong pairs. Minimal strong incidence dominating set (MSIDS) is defined and some of its properties are discussed. Bounds for the SIDN and the properties of strong incidence dominating sets (SIDS) of some FIGs are investigated. …Also a social application of the SIDN is obtained. Show more
Keywords: Fuzzy incidence graphs, strong incidence domination, strong incidence neighborhood degree, strong pair degree, complete bipartite fuzzy incidence graph
DOI: 10.3233/JIFS-213060
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2667-2678, 2022
Authors: Sadhasivam, Saranya | Murugasamy, Rajalakshmi
Article Type: Research Article
Abstract: Voluminous graph data management is a daunting problem in every real world application of a kind, besides the advancements in computation and storage technology. Efficient graph summarization techniques were contributed to achieve the substantial need for preserving novelty in social graphs. A GraceOutZip compression friendly graph reordering technique using graceful labeling strategy is adopted. User defined probabilistic selection method that provides unique labels for every identified outlier for potential use. Proposed method exploits unicycle-star based community representation rendering assignment of both node and edge labels based on graceful property. A novel mathematical programming model GraceOutZip is proposed to perform lossless …compression with graph decomposition and unique label arrangement with intention for futuristic graph reconstruction. The experimental study on different real world network datasets demonstrates that GraceOutZip shows better scalable performance in perspective of interactive large-scale visual analytics and query optimization with 4 times better compression than the state-of-the-art representative method. Show more
Keywords: Graceful labeling, graph compression, graph theory, anomaly detection, star graph, cycle graph, mutual friend suggestion
DOI: 10.3233/JIFS-212942
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2679-2691, 2022
Authors: Kaur, Gaganjot | Gupta, Prinima
Article Type: Research Article
Abstract: In today’s world, Software-Defined Networking (SDN) plays a significant role in the advancement of next-generation network architecture that offers vast control to the network operators. However, the control layer is vulnerable to Distributed Denial of Service (DDoS) attacks where DDoS is one of the most powerful and devastating cyber-attacks. Thus, the development of a DDoS attack detection mechanism is very essential since these kinds of attacks have a direct impact on the overall performance of the SDN. In this paper, a new robust Tuned support vector machine-based DDoS attack detection methodology has been proposed to categorize the benign traffic from …DDoS attack traffic on the SDN. Primarily, the network is created with controller and OpenFlow switch and the communication can be carried out through secure channels among different benign users and also attackers. Afterward, the multi-characteristic values are extracted by the effective extraction strategy which consists of the six-tuple characteristic values matrix. Finally, the tuned classifier has been implemented with the aid of optimization algorithm for differentiating the abnormal traffic and the normal traffic. The performance results manifest that the proposed detection framework achieves a higher accuracy of 98% and precision of 99% when compared with existing classifiers. Show more
Keywords: Denial of service attack, cyber security, hybrid classifier, software-defined network, quality of service, machine learning, optimization algorithm
DOI: 10.3233/JIFS-212946
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2693-2710, 2022
Authors: Anusha, B. | Geetha, P. | Kannan, A.
Article Type: Research Article
Abstract: The identification of Parkinson’s Disease (PD) is a necessary concern for reducing the occurrences of nervous disorders and brain death. The prediction of PD based on symptoms is depending on the body conditions of patients as the symptoms differ for every individual. Doctors preferably use ionized radiation-free MRI scans since they offer more precise images of soft tissues in the brain. In the recent years, deep learning is the prominently used method for performing image analysis and classification. However, the systems developed using deep learning are not able to predict the PD accurately. In order to bridge the gaps present …in the existing systems, we propose a hybrid model based on neuro-fuzzy classification to detect PD more accurately. For enhancing the accuracy of PD identification, we used the ResNet-18 deep learning architecture for the classification of MRI images. In addition to this, a hybrid framework is also proposed in this paper where the softmax layer of ResNet-18 is modified using non-linear SVM and Fuzzy SVM (fSVM) classifiers. The convolution and max-pooling layers of ResNet-18 are able to learn more objective features for classification. The proposed hybrid model of ResNet-fSVM is evaluated on the neuro-MRI images from the PPMI dataset and achieved 4.4% higher accuracy than the ResNet-18 model and 2.8% higher accuracy than hybrid ResNet-SVM model. The age group based results obtained in this work has proved that the accuracy of the proposed ResNet-fSVM hybrid model is better when it is compared with ResNet-18 and hybrid ResNet-SVM models. This system effectively detects Early-onset PD through its efficiency in classification. Show more
Keywords: Parkinson’s disease (PD), magnetic resonance imaging (MRI), pre-trained ResNet-18, support vector machine, fuzzy support vector machine
DOI: 10.3233/JIFS-220271
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2711-2729, 2022
Authors: Zhai, Yuejing | Liu, Haizhong
Article Type: Research Article
Abstract: Recent studies have shown that the evolution of infinitely wide neural networks satisfying certain conditions can be described by a kernel function called neural tangent kernel (NTK). We introduce NTK into a one-class support vector machine model and select data from different domains in UCI for a small-sample outlier detection task, demonstrate that NTK-OCSVM generally outperforms a variety of commonly used classification models, with more than 20% improvement in accuracy for similar models. When the kernel function parameters are varied, the experiments show that the model has strong robustness within a certain parameter range. Finally, we experimentally compare the time …complexity of different models and the decision boundaries, and demonstrate that NTK-OCSVM improves accuracy at the expense of operational efficiency and has linear decision boundaries. Show more
Keywords: One class SVM, neural tangent kernel, anomaly detection
DOI: 10.3233/JIFS-213088
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2731-2746, 2022
Authors: Abadlia, Houda | Smairi, Nadia | Ghedira, Khaled
Article Type: Research Article
Abstract: Distributed evolutionary computation has been efficiently used, in last decades, to solve complex optimization problems. Island model (IM) is considered as a distributed population paradigm employed by evolutionary algorithms to preserve the diversification and, thus, to improve the local search. In this article, we study different island model techniques integrated in to particle swarm optimization (PSO) algorithm in order to overcome its drawbacks: premature convergence and lack of diversity. The first IMPSO approach consists in using the migration process in a static way to enhance the police migration strategy. On the other hand, the second approach, called dynamic-IMPSO, consists in …integrating a learning strategy in case of migration. The last version called constrained-IMPSO utilizes a stochastic technique to ensure good communication between the sub-swarms. To evaluate and verify the effectiveness of the proposed algorithms, several standard constrained and unconstrained benchmark functions are used. The obtained results confirm that these algorithms are more efficient in solving low-dimensional problems (CEC’05), large-scale optimization problems (CEC’13) and constrained problems (CEC’06), compared to other well-known evolutionary algorithms. Show more
Keywords: Particle swarm optimization, Island model, diversity, constrained optimization, unconstrained optimization
DOI: 10.3233/JIFS-213380
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2747-2763, 2022
Authors: Zhang, Liqiang | Yu, Long | Tian, Shengwei | Yang, Qimeng
Article Type: Research Article
Abstract: Metaphor plays an indispensable role in human life. Although sequence tagging models took advantage of linguistic theories of metaphor identification, the usage of metaphor in common words is not considered, when choosing the literal meaning of the target verbs. We present a novel approach to express the literal meaning subtly, combining the common usage and the inherent visualizability properties of words, termed GloVe embedding and visual embedding. Meanwhile, we import position information of the target verbs to gain the contextual meaning more accurately. Both two DNN models use these embeddings as inputs in this paper, which are inspired by two …human metaphor identification procedures augmented with contextualized word representations (ELMo embedding). By testing on two public datasets, the results show improvement over previous state-of-the-art approaches. In addition, we also verify the universality of the approach by testing the examples that the target words were adjectives, adverbs, and nouns, and the results show the approach is applicable to the above three parts of speech. Show more
Keywords: Metaphor detection, sequence tagging, recurrent neural network models, natural language processing
DOI: 10.3233/JIFS-210381
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2765-2775, 2022
Authors: Sun, Hong | Wei, Gui-Wu | Chen, Xu-Dong | Mo, Zhi-Wen
Article Type: Research Article
Abstract: In multiple attribute decision making (MADM) issues, the ambiguity, imprecision, and imperfection of assessment information may lead to inadequate decision-making results. However, the Z-number suggested by Zadeh in 2011 could somehow prevent this problem. For MADM issues with unknown attributes weights, an extended Distance from the Average Solution (EDAS) method is proposed under a mixture Z-number environment. In addition, the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights of the criterion, which is easy to calculate and avoids subjective forecasts. A novel illustrative example is provided to demonstrate the feasibility, validity, and practicability of the …presented method, and is compared with existing decision methods. The outcome indicates that the suggested method can solve complicated decision-making problems. Show more
Keywords: MADM, Z-number, EDAS method, CRITIC
DOI: 10.3233/JIFS-212954
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2777-2788, 2022
Authors: Fan, Jianping | Han, Dongshuai | Wu, Meiqin
Article Type: Research Article
Abstract: In this manuscript, we introduce a multi-criteria decision-making (MCDM) method under T-spherical fuzzy set environment. Firstly, we propose a method to use the correlation coefficient and standard deviation (CCSD) method to determine the attribute weight under T-spherical fuzzy environment, when the attribute weight information is completely unknown or partially unknown. Secondly, we introduce a T-spherical fuzzy complex proportional assessment (COPRAS) method. Finally, a numerical example is given to illustrate the application of the T-spherical fuzzy COPRAS method, and some comparative analysis is carried out to verify the feasibility and effectiveness of the proposed method.
Keywords: T-spherical fuzzy sets (T-SFSs), T-spherical fuzzy set numbers (T-SFSNs), the correlation coefficient and standard deviation (CCSD) method, complex proportional assessment (COPRAS)
DOI: 10.3233/JIFS-213227
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2789-2801, 2022
Authors: Ye, Jun | Du, Shigui | Yong, Rui
Article Type: Research Article
Abstract: Modern decision-making (DM) systems are becoming more and more complex and sophisticated in their demands for information expressions and credibility levels. In the existing literature, a trapezoidal fuzzy neutrosophic value (TFNV) that combines trapezoidal fuzzy numbers with neutrosophic values can be better depicted by truth, indeterminacy, and falsity membership functions. Unfortunately, TFNV implies its defect since it lacks a measure of credibility. To make TFNV more creditable, TFNV should be related to its credibility level. Regarding the motivation for combining TFNV with its credibility level, this paper first proposes the concept of a credibility TFNV (C-TFNV) as a new framework …of TFNV associated with the measure of credibility. The advantage of its information expression is that C-TFNV has a more creditable ability to describe indeterminate and inconsistent knowledge and judgments of human beings by the mixed information of a TFNV and a related credibility level (an ordered pair of TFNVs). Next, we propose the operational laws of C-TFNVs and the score function of C-TFNV. Furthermore, we present a C-TFNV weighted arithmetic averaging (C-TFNVWAA) and a C-TFNV weighted geometric averaging (C-TFNVWGA) operators and their properties. Then, a multicriteria DM model based on the C-TFNVWAA and C-TFNVWGA operators and the score function is established in the case of C-TFNVs. Finally, an actual DM example of slope decision schemes is provided to show the applicability and efficiency of the established DM model in the case of C-TFNVs. Show more
Keywords: Credibility trapezoidal fuzzy neutrosophic value, credibility trapezoidal fuzzy neutrosophic value weighted arithmetic averaging operator, credibility trapezoidal fuzzy neutrosophic value weighted geometric averaging operator, decision making
DOI: 10.3233/JIFS-212782
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2803-2817, 2022
Authors: Rajkumar, K. | Dhanakoti, V.
Article Type: Research Article
Abstract: Storage consumption is increasing significantly these days, with consumers trying to find an effective approach to safe storage space. In these situations, a deduplication in cloud storage services is a significant way to reduce bandwidth and service space by omitting unnecessary information and keeping only a single copy of the information. This raises computational, privacy and storage issues when large numbers of handlers outsource the similar data to cloud service storage. To overcome these problems, an effective Fuzzy-Dedup framework is designed in this research by integrating four steps namely is introduced, which breaks down the data into fixed size chunks …and is immediately fingerprinted by a hashing algorithm for ensuring data authentication and then indexing is done with the help of traditional b-tree indexing, similarity function is calculated to compute the similarity value in the documents. After calculating the similar values, the fuzzy interference system is designed by formulating appropriate rules for the decision-making process that determines duplicate and non-duplicate files by obtaining an effective de-duplication ratio over existing methods. After detecting duplicate files, the inline based deduplication policy checks that the new data is ready to send for storage against existing data and does not store any redundant data it discovers. The proposed model is implemented in MATLAB software is carried out several performance metrics and these parameter attained better performance such as, deduplication ratio of 1.2, memory utilization of 12500 bytes in inline and 9550 bytes in offline, throughput of 32500 Mb/s in inline and 25500 Mb/s in offline and processing time of 0.4494 s in inline and 0.1139 s in offline. Thus when compared to previous methods, such as Two Thresholds Two Divisors deduplication (TTTD) approach proposed design shows high range of performance. Show more
Keywords: De-duplication, Fuzzy-Dedup, cosine similarity, chunking, fingerprinting, indexing, fuzzy interference system, cloud storage, inline, encryption, decryption
DOI: 10.3233/JIFS-210511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2819-2832, 2022
Authors: Yuan, Zhizhu | Hou, Lijuan | Gao, Zihuan | Wu, Meiqin | Fan, Jianping
Article Type: Research Article
Abstract: Single-valued neutrosophic sets can efficiently depict a great deal of imprecise, uncertain and discordant information. Hamy mean operator can consider the interrelationships among multiple integrated arguments and Schweizer-Sklar operations express great flexibility in the process of information aggregation. To give full consideration to these advantages, we merge the Hamy mean operator with the Schweizer-Sklar operations in single-valued neutrosophic environment, proposing a single-valued neutrosophic Schweizer-Sklar Hamy mean operator and a single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator. Besides, we illustrate some specific cases and attributes of the two operators. Moreover, based on the entropy weight method and the single-valued neutrosophic Schweizer-Sklar …weighted Hamy mean operator, this paper presents a single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method to tackle multi-attribute decision making problems. At last, the method and other three existing methods are applied to solve a practical multi-attribute decision making problem, which validates the credibility and validity of the single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method by comparing the differences among them. Show more
Keywords: Single-valued neutrosophic sets, Schweizer-Sklar operations, Hamy mean operator, the entropy weight method, multi-attribute decision making
DOI: 10.3233/JIFS-212818
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2833-2851, 2022
Authors: Ramachandraarjunan, Senthilkumar | Perumalsamy, Venkatakrishnan | Narayanan, Balaji
Article Type: Research Article
Abstract: Monitoring indoor air quality stays needed for human health because people use more than 95% of air in their indoor rooms. An Intelligent Internal Air Quality Monitoring (IIAQM) system built on the Internet of Things (IoT) devices has been developed and tested in Quantanics Techserv Private Limited, Tamilnadu, India. To monitor the levels of CO2 , PM2.5 (Particle Matters 2.5), and moisture measurement, the IIAQM model has been used to monitor the present level of air quality. The gateway collects IIAQM sensor data in a few seconds and transfers data to cloud server. Approved users can incorporate the cloud …systems through mobile applications or web servers. Installation of sensor networks, instrument transformers, and IoT-powered microcontrollers will provide air quality monitoring for buildings. The proposed window controller configuration is designed with the help of a Recurrent Neural Network (RNN) to predict the air quality level in advance. If the air quality level is above the normal level, the window controller automatically will open with the help of sensor activity control system. After the AQI (Air Quality Index) becomes normal, hence the window controller is closed automatically. The air quality index, CO2, and humidity data are visualized on the Grafana dashboard. Show more
Keywords: Internet of things, machine learning, recurrent neural networks humidity sensor, intelligent internal air quality monitoring system
DOI: 10.3233/JIFS-212955
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2853-2868, 2022
Authors: Zhang, Shuai | Chen, Qian | Zeng, Wenhua | Guo, Shanshan | Xu, Jiyuan
Article Type: Research Article
Abstract: The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic …behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic. Show more
Keywords: COVID-19, deep learning, load forecasting, reinforcement learning, transfer learning
DOI: 10.3233/JIFS-213103
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2869-2882, 2022
Authors: Mohammed Hashim, B.A. | Amutha, R.
Article Type: Research Article
Abstract: Human Activity Recognition (HAR) is the most popular research area in the pervasive computing field in recent years. Sensor data plays a vital role in identifying several human actions. Convolutional Neural Networks (CNNs) have now become the most recent technique in the computer vision phenomenon, but still, it is premature to use CNN for sensor data, particularly in ubiquitous and wearable computing.Deep CNN requires huge dataset and models which increases the computational complexity. Transfer learning that uses the pre trained CNNwith fine tuning is the better alternative to reduce the training cost.In this paper, we have proposed the idea of …transforming the raw accelerometer and gyroscope sensor data to the visual domain by using our novel activity image creation method (NAICM). Pre-trained CNN (AlexNet) has been used on the converted image domain information. The proposed method is evaluated on several online available human activity recognition dataset. The results show that the proposed novel activity image creation method (NAICM) has successfully created the activity images with a classification accuracy of 98.36% using pre trained CNN. Show more
Keywords: Human activity recognition, CNN, pervasive computing, NAICM, transfer learning
DOI: 10.3233/JIFS-213174
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2883-2890, 2022
Authors: Vijaya Karthik, S.V. | Arputha Vijaya Selvi, J.
Article Type: Research Article
Abstract: Information Centric Network (ICN) is a newer technology in handling web content distribution that has recently emerged in order to tackle the risk of data security. For handling content distribution, ICN provides data security via a name-based approach. Named Data Networking (NDN) is an ongoing ICN realisation that was incorporated recently. Named Data Networking (NDN) has recently grown in popularity and significance as a new internet design that solves certain limitations in traditional internet communications. NDN is perfectly adapted for the Internet of Things (IoT), which is today dominated by huge, and emerging applications. In this work, we propose an …IoT enabled hybrid cluster-based routing protocol with mitigation of content poisoning attack for information-centric Wireless Sensor Network (WSN)-NDN. In this method, hybrid K-medoids clustering is used with African Buffalo Optimization Algorithm (ABOA), which is to find an optimal shortest path between the cluster heads, and light weight encryption. It is developed by using Hyperelliptic Curve Cryptography (HCC) to mitigate content poisoning. Our proposed system has effective data security as it has encrypted data in the cluster head. The smart health care monitoring system has been used for our proposed method. The proposed method has been subjected to extensive analysis by comparing with other existing methods that should improve performance justified in terms of several metrics by introducing the malicious nodes (10%, 20%, and 30%). Show more
Keywords: Named data network, clustering algorithm, content poisoning, african buffalo optimization, hyperelliptic curve cryptography
DOI: 10.3233/JIFS-212674
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2891-2905, 2022
Authors: Abin, Deepa | Thepade, Sudeep D.
Article Type: Research Article
Abstract: In today’s digital times, the quality of video frames is ubiquitous, and the presence of shadows is undesirable in computer vision applications. Shadow suppression is of paramount significance in crucial application areas, especially in outdoor scene environments. The objects present in the environment occlude the light. Most of the work in literature focuses on single shadow regions in a frame or an image. Different methods are proposed in the literature. This challenging area of shadows suppression is addressed with the proposed method, as a novel amalgamation, with Adaptive Gamma Weighted Correction and modified Exemplar based inpainting method. The paper discusses …different single shadow scenarios and multiple distributed shadow regions. Across four datasets, and objective evaluation using three performance metrics, the obtained average Entropy of 7.032, ‘Blind Reference Image Spatial Quality Evaluator (BRISQUE)’ of 26.2031, and ‘No-Reference Image Quality Evaluator (NIQE)’ of 3.699 have demonstrated considerable results. Show more
Keywords: Shadow suppression, AGWCD, exemplar inpainting, outdoor scene, color space
DOI: 10.3233/JIFS-212823
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2907-2919, 2022
Authors: Li, Fu | Su, PeiYu | Qin, Feng
Article Type: Research Article
Abstract: In this paper, the ternary soft set is discussed based on the soft triadic (complete) formal context. The ternary soft set is a generalization of Molodsov’s soft set, which can characterize the objects universe more clearly by the attributes set, and different from type-2 soft set. The definitions of the ternary soft set and ternary formal context are given and illustrated with some examples. A mindmap is used to show the idea of ternary soft set visually. The usage of bijective soft set enables fast decision making process by our work. We demonstrate the idea with a flowchart and a …case study. Meanwhile, the soft operations among the ternary soft sets are defined and their properties are studied. Show more
Keywords: Soft set, soft (formal) context, ternary soft set, bijective soft set
DOI: 10.3233/JIFS-213155
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2921-2931, 2022
Authors: Yang, Mengyin | Chen, Junfen | Wang, Wenjie | He, Qiang
Article Type: Research Article
Abstract: Deep unsupervised learning extracts meaningful features from unlabeled images and simultaneously serves downstream tasks in computer vision. The basic process of deep clustering methods can include features learning and clustering assignment. To enhance the discriminative ability of the features and further improve the clustering performances, a new deep clustering method namely ACMEC (asymmetric convolutional denoising autoencoder with manifold spatial embedding clustering) is proposed. In this method, an asymmetric convolution denoising autoencoder is employed to extract visual features from images, and a manifold learning algorithm is used to obtain more distinctive features, followed by a Gaussian Mixture Model (GMM) is for …clustering learning. The stability of feature space is guaranteed using separately training mechanism. In addition, reconstruction from noisy images enhances the robustness of feature networks. Experimental results on nine benchmark datasets demonstrate that the proposed ACMEC method can provide the better performances such as 0.979 clustering accuracy on the MNIST dataset and 0.668 on the fashion-MNIST dataset. ACMEC is a comparable competitor to the N2D (not too deep clustering) algorithm that is with 0.979 and 0.672 clustering accuracies respectively. Moreover, it is 16.1% higher than DEC algorithm on the fashion-MNIST dataset. Show more
Keywords: Clustering analysis, feature learning, asymmetric convolutional denoising autoencoder, manifold embedding, Gaussian mixture models (GMM)
DOI: 10.3233/JIFS-213468
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2933-2944, 2022
Authors: Luo, Wenguan | Yu, Xiaobing
Article Type: Research Article
Abstract: Cuckoo search algorithm (CS) is an excellent nature-inspired algorithm that has been widely introduced to solve complex, multi-dimensional global optimization problems. However, the traditional CS algorithm has a low convergence speed and a poor balance between exploration and exploitation. In other words, the single search strategy of CS may make it easier to trap into local optimum and end in premature convergence. In this paper, we proposed a new variant of CS called Novel Enhanced CS Algorithm (NECSA) to overcome these drawbacks mentioned above inspired by the cuckoos’ behaviors in nature and other excellent search strategies employed in intelligent optimization …algorithms. NECSA introduces several enhancement strategies, namely self-evaluation operation and modified greedy selection operation, to improve the searchability of the original CS algorithm. The former is proposed to enhance the exploration ability and ensure population diversity, and the latter is employed to enhance the exploitation ability and increase search efficiency. Besides, we introduced adaptive control parameter settings based on the fitness and iteration number to increase the convergence speed and the accuracy of the search process. The experimental results and analysis on the CEC2014 test have demonstrated the reliable performance of NECSA in comparison with the other five CS algorithm variants. Show more
Keywords: Cuckoo search, self-evaluation mechanism, greedy selection
DOI: 10.3233/JIFS-220179
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2945-2962, 2022
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