Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Purchase individual online access for 1 year to this journal.
Price: EUR 315.00Impact Factor 2024: 1.7
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: 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
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
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl