Journal of Intelligent & Fuzzy Systems - Volume 43, issue 1

Authors: Duong, T.H. | Le, T.-T. | Nguyen, S.X. | Le, M.V.

Article Type: Research Article

Abstract: This study is devoted to the development of an Adaptive-Neuro-Fuzzy-Inference-System (ANFIS) model for the prediction of ultimate load of rectangular concrete-filled steel tubular structural members. The learning process of the model is performed by conducting a combination of backpropagation gradient descent and least-squares techniques. The performance of the model is examined by several quality metrics such as coefficient of determination (R2 ), Root-Mean-Squared-Error (RMSE), Mean-Absolute-Error (MAE), Index of Agreement (IA) and Slope of linear regression. Monte Carlo random sampling technique is employed to propagate input variations to the output response. Moreover, the performance of ANFIS is also compared with other …machine learning models including Artificial Neural Network (ANN), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Ensemble. Results show that the ANFIS model yields higher prediction performance than other machine learning models, for both training and testing data points and regarding all quality metrics. For instance, using training data points, the ANFIS model exhibits a RMSE of 0.0283 compared to 0.0342, 0.0588, 0.0291, and 0.0464 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding gain values are+17.3%,+51.9%,+2.8%, and+39.0%, respectively). On the other hand, using testing data points, the ANFIS model exhibits a RMSE of 0.0276 compared to 0.0393, 0.0987, 0.0403, and 0.0460 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding values of gain are+29.8%,+72.1%,+31.5%, and+40.0%, respectively). The same observation can be made for other quality metrics. It can be concluded that the ANFIS model outperforms other models for both training and testing datasets. The ANFIS model is also compared with existing works in the past, showing its improvement in prediction results. Finally, sensitivity analysis is performed to determine the degree of effect of the input parameters on the ultimate load. Show more

Keywords: Concrete-filled steel tubular members, adaptive-neuro-fuzzy-inference-system, ultimate load, sensitivity analysis, machine learning

DOI: 10.3233/JIFS-201628

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1-19, 2022

Authors: Ren, Chunhua | Sun, Linfu | Gao, Yunhui | Yu, Yang

Article Type: Research Article

Abstract: The density peaks clustering algorithm (DPC) has been widely concerned since it was proposed in 2014. There is no need to specify in advance and only one parameter required. However, some disadvantages are still witnessed in DPC: (1) Requiring repeated experiments for choosing a suitable calculation method of the local density due to the variations in the scale of the dataset, which will lead to additional time cost. (2) Difficulty in finding an optimal cutoff distance threshold, since different parameters not only impact the selection of cluster centers but also directly affect the quality of clusters. (3) Poor fault tolerance …of the allocation strategy, especially in manifold datasets or datasets with uneven density distribution. Targetting solutions to these problems, a density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy (LF-DPC) is proposed in this paper. First, to obtain a more balanced local density, two classic local density calculation methods are combined in the algorithm to calculate the local fair density through the optimization function with the smallest local density difference. Second, a robust two stage remaining points allocation strategy is designed. In the first stage, k-nearest neighbors are used to quickly and accurately allocate points from the cluster center. In the second stage, to further improve the accuracy of allocation, a fuzzy k-nearest neighbors membership method is designed to allocate the remaining points. Finally, the LF-DPC algorithm has been experimented based on several synthetic and real-world datasets. The results prove that the proposed algorithm has obvious advantages compared with the other five ones. Show more

Keywords: Density peaks clustering, local fair density, fuzzy k-nearest neighbors, membership allocation strategy

DOI: 10.3233/JIFS-202449

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 21-34, 2022

Authors: Mishra, Amit Kumar | Bhardwaj, Ramakant | Joshi, Nisheeth | Mathur, Iti

Article Type: Research Article

Abstract: This paper aims to select the appropriate node(s) to effectively destabilize the terrorist network in order to reduce the terrorist group’s effectiveness. Considerations are introduced in this literature as fuzzy soft sets. Using the weighted average combination rule and the D–S theory of evidence, we created an algorithm to determine which node(s) should be isolated from the network in order to destabilize the terrorist network. The paper may also prove that if its power and foot soldiers simultaneously decrease, terrorist groups will collapse. This paper also proposes using entropy-based centrality, vote rank centrality, and resilience centrality to neutralize the network …effectively. The terrorist network considered for this study is a network of the 26/11 Mumbai attack created by Sarita Azad. Show more

Keywords: Terrorist network mining (TNM), destabilization, centralities, fuzzy soft set, social network analysis (SNA), global network efficiency, average clustering coefficient, Dempster–Shafer theory of evidence

DOI: 10.3233/JIFS-210425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 35-48, 2022

Authors: Oh, Ju-mok | Kim, Yong Chan

Article Type: Research Article

Abstract: Most of the research in fuzzy rough sets and fuzzy topological structures have been studied on the basis of fuzzy partially ordered sets. Instead of fuzzy partially ordered sets, the concept of distance functions in complete co-residuated lattices is introduced. Using distance functions, we define Alexandrov pretopology, Alexandrov precotopology and fuzzy interior (fuzzy closure) operators in complete co-residuated lattices, and we investigate their properties. Moreover, we prove that there exist isomorphic categories and Galois correspondence between topological categories.

Keywords: Complete co-residuated lattice, distance spaces, Alexandrov pretopologies, Alexandrov precotopology, fuzzy interior (fuzzy closure) operators, Galois correspondence

DOI: 10.3233/JIFS-210973

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 49-65, 2022

Authors: Li, Zheng | Lei, Xuemei

Article Type: Research Article

Abstract: The occlusion in the real feedlot environment is ubiquitous, and the current research based on the cattle face recognition under occlusion conditions is almost non-existent. Thus, an attention mechanism module with high accuracy and low model complexity is designed to incorporate into MobileNet so that the cattle face under occlusion can be identify accurately, which is the RGB images captured in the ranch environment. In this paper, we also construct a Simmental cattle face image dataset for data modeling and method evaluation, which contains 10,239 images of 103 cattle. The experimental results show that when the occluder is in the …upper left and lower right corner, if the occlusion rate is less than 30%, the value of Top_1 reaches more than 90%; if it is less than 50%, the value of Top_1 is more than 80%. Even if the middle part occludes lots of important information, the occlusion rate of 40% has an accuracy of more than 80%. Furthermore, comparing the proposal model with MobileNet, the parameter and model size are equal, and the amount of calculation as a cost increase a little. Therefore, the proposal model is suitable to transplant to the embedded system in the future. Show more

Keywords: Partial occlusion, attention mechanism, cattle face recognition, convolutional neural network, image classification

DOI: 10.3233/JIFS-210975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 67-77, 2022

Authors: Badr, Marwa | Sarhan, Amany | Elbasiony, Reda

Article Type: Research Article

Abstract: Over the past decade, the computer vision community has given increased attention to the development of age estimation systems. Several approaches to more accurate and robust facial age estimation have been introduced. Apparent age datasets are typically collected from uncontrolled environments, leading to a number of challenges. In this paper, a cascade model system, which we called the ‘Integrated Classification and Regression with Landmark Ratios (ICRL), is introduced. Our system uses a classification model in order to learn the age label distribution, then uses this knowledge as an auxiliary input to a regression model. ICRL is based on context facial …information and label distribution analysis. Facial context information is introduced through the extraction of precise facial landmark ratios. Extracted landmark ratios allow the system to distinguish each age label. The ICRL system uses a classification model to train the CNN network to learn the in-between relation of age labels. ICRL sufficiently models the aging process in the form of ordered and continuous imagery. The ICRL system minimizes the number of parameters needed as well as overall computational costs whilst maintaining robust and accurate results. Despite its simplicity, our system has outperformed other state-of-the-art approaches when applied onto the MORPH II, CLAP2015, AFAD and UTKFace datasets. ICRL achieved an overall superior predictive performance, reaching 99.67% with MORPH II, 99.51% with AFAD, 96.52 with CLAP2015, and 96.28% with UTKFace. Show more

Keywords: Age estimation, ordinal regression, facial context information, age label distribution

DOI: 10.3233/JIFS-211267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 79-92, 2022

Authors: Yin, Ming | Zhu, Kuiyu | Xiao, Hongli | Zhu, Dan | Jiang, Jijiao

Article Type: Research Article

Abstract: Effectively identifying self-admitted technical debt (SATD) from project source code comments helps developers quickly find and repay these debts, thereby reducing its negative impact. Previous studies used techniques based on patterns, text mining, natural language processing, and neural networks to detect SATD. Compared with these above, Convolutional Neural Networks (CNN) have the strong feature extraction ability. Deep network ensembles are demonstrated great potential for the task of sentences classification. In order to boost the performance of CNN-based SATD detecting, we propose a deep neural network ensemble contribute to ensemble learning in a simple yet effective way. Specifically, CNN, CNN-LSTM (convolutional …neural network and long short-term memory), and DPCNN (Deep Pyramid Convolutional Neural Networks) are used as individual classifiers to diversify the deep network ensembles. In order to improve the explainability, we introduce attention to measure the contribution of feature words to SATD classification. 62,285 source code comments from 10 projects were used in our experiments. The results show that our approach can effectively reduce misjudgment and detect more SATD, especially for cross-project, so as to greatly improve the detection accuracy. Show more

Keywords: Self-admitted technical debt, ensemble learning, convolutional neural network, long short-term memory

DOI: 10.3233/JIFS-211273

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 93-105, 2022

Authors: Mahendhiran, P.D. | Subramanian, Kannimuthu

Article Type: Research Article

Abstract: The refining of information from the immense amount of unstructured data on the internet can be a critical issue in identifying public opinion. It is difficult to extract relevant concepts from huge amounts of data. Concept level semantic parsing is improved over word-based investigation as it conserves the semantical data relevant to many-word articulations. The semantic proposals offer a superior comprehension of textual data and serve to altogether precision the exactness of numerous mining operations in text assignments. The extraction of concepts from textual data is a significant step forward in content analysis at the concept stage. We present a …CLSA-CapsNet method that extracts concepts from natural language text. Then the extracted concepts are applied in Capsule networks (CapsNet). Moreover, the integration of Concept Level Sentiment Analysis (CLSA) and Capsule Networks (CapsNet) has not yet been implemented on the hotel review dataset. This is the first attempt, which we researched and embraced by the capsule network, to develop classification models for hotel reviews. The developed results demonstrated excellent performance with a prediction accuracy of 86.6% for CLSA-CapsNet models, respectively. Various similarities have also been made across our techniques and they are implemented by some other deep learning algorithms, such as rnn-lstm. Overall, the outstanding success obtained by CLSA-CapsNet in this investigation highlights its ability in the hotel review dataset. We likewise show exploratory outcomes, in which the proposed system outpaced the state-of-the-art CLSA-CapsNet model. Show more

Keywords: Capsule network, sentiment analysis, CNN, RNN, LSTM, concept level

DOI: 10.3233/JIFS-211321

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 107-123, 2022

Authors: Mehmood, Arif | Al Ghour, Samer | Imran Khan, Muhammad | Afzal, Farkhanda | Ishfaq, Muhammad | Qureshi, Humera

Article Type: Research Article

Abstract: In this article, new generliased neutrosophic soft *b open set is introduced in neutrosophic soft bi-topological spaces (NSBTS) concerning soft points of the space. This new set is produced by the combination of soft semi-open and soft pre-open sets of neutrosophic soft topological space. Different results are ushered in NSBTS. Appropriate examples are provided for verification of different results. The non-validity of some results is verified with appropriate examples.

Keywords: Neutrosophic soft set (NSS), neutrosophic soft point (NSP), neutrosophic soft bi-topological space (NSBTS), neutrosophic soft *b-open set and neutrosophic soft *b-separation axioms

DOI: 10.3233/JIFS-211492

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 125-142, 2022

Authors: Jeyabalan, Saranya Devi | Yesudhas, Nancy Jane | Harichandran, Khanna Nehemiah | Sridharan, Gayathri

Article Type: Research Article

Abstract: The development of advanced technologies in variety of domains such as health care, sensor measurements, intrusion detection, motion capture, environment monitoring have directed to the emergence of large scale time stamped data that varies over time. These data are influenced by complexities such as missing values, multivariate attributes, time-stamped features. The objective of the paper is to construct temporal classification framework using stacked Gated Recurrent Unit (S-GRU) for predicting ozone level. Ozone level prediction plays a vital role for accomplishing healthy living environment. Temporal missing value imputation and temporal classification are two functions performed by the proposed system. In temporal …missing value imputation, the temporal correlated k-nearest neighbors (TCO-KNN) approach is presented to address missing values. Using attribute dependency based KNN, the nearest significant set is identified for each missing value. The missing values are imputed using the mean values from the determined closest significant set. In temporal classification, the classification model is build using stacked gated recurrent unit (S-GRU). The performance of the proposed framework investigated using ozone multivariate temporal data sets shows improvement in classification accuracy compared to other state of art methods. Show more

Keywords: Multivariate time series data, decision making, knowledge discovery, ozone level prediction, K-nearest neighbors (KNN), stacked gated recurrent unit (S-GRU)

DOI: 10.3233/JIFS-211835

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 143-157, 2022