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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: 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
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