Journal of Intelligent & Fuzzy Systems - Volume 44, issue 2

Authors: Li, Zepeng | Huang, Rikui | Zhang, Yufeng | Zhu, Jianghong | Hu, Bin

Article Type: Research Article

Abstract:  Knowledge Graph Embedding (KGE), which aims to embed the entities and relations of a knowledge gxraph into a low-dimensional continuous space, has been proven to be an effective method for completing a knowledge graph and improving the quality of the knowledge graph. The translation-based models represented by TransE, TransH, TransR and TransD have achieved great success in this regard. There is still potential for improvement in dealing with complex relations. In this paper, we find that the lack of flexibility in entity embedding limits the model’s ability to model complex relations. Therefore, we propose single-directional-flexible (sdf) models and multi-directional-flexible (mdf) …models to increase the flexibility and expressiveness of entity embeddings. These two methods can be applied to the TransD model and its variant models without increasing any time cost and space cost. We conduct experiments on benchmarks such as WN18 and FB15k. The experimental results show that the models significantly surpasses the classical translation models in both tasks of triplet classification and link prediction. In particular, for Hits@1 of link prediction of WN18, we get 71.7% after applying our method to TransD, which is much better than 24.1% of TransD. Show more

Keywords: Knowledge graph embedding, translation model, complex relation, single-directional-flexible model, multi-directional-flexible model

DOI: 10.3233/JIFS-211553

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3093-3105, 2023

Authors: Wan, Chenxia | Fang, Liqun | Cao, Shaodong | Luo, Jiaji | Jiang, Yijing | Wei, Yuanxiao | Lv, Cancan | Si, Weijian

Article Type: Research Article

Abstract: The investigation on brain magnetic resonance imaging (MRI) of cerebral small vessel disease (CSVD) classification algorithm based on deep learning is particularly important in medical image analyses and has not been reported. This paper proposes an MRI classification algorithm based on convolutional neural network (MRINet), for accurately classifying CSVD and improving the classification performance. The working method includes five main stages: fabricating dataset, designing network model, configuring the training options, training model and testing performance. The actual training and testing datasets of MRI of CSVD are fabricated, the MRINet model is designed for extracting more detailedly features, a smooth categorical-cross-entropy …loss function and Adam optimization algorithm are adopted, and the appropriate training parameters are set. The network model is trained and tested in the fabricated datasets, and the classification performance of CSVD is fully investigated. Experimental results show that the loss and accuracy curves demonstrate the better classification performance in the training process. The confusion matrices confirm that the designed network model demonstrates the better classification results, especially for luminal infarction. The average classification accuracy of MRINet is up to 80.95% when classifying MRI of CSVD, which demonstrates the superior classification performance over others. This work provides a sound experimental foundation for further improving the classification accuracy and enhancing the actual application in medical image analyses. Show more

Keywords: Cerebral small vessel disease, brain magnetic resonance imaging, convolutional neural network, feature extraction, classification accuracy

DOI: 10.3233/JIFS-213212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3107-3114, 2023

Authors: Zhang, Xinyu | Yu, Long | Tian, Shengwei

Article Type: Research Article

Abstract: In today’s social media and various frequently used lifestyle applications, the phenomenon that people express their sentiment via comments or instant barrage is common. People not only show their joys and sorrows in the process of expression but also present their opinions to one thing in many aspects which include. Nowadays, aspect-based sentiment analysis has become a mature and wildly-used technology. There are many public datasets considered as a benchmark to test model performance, such as Laptop2014, Restaurant2014, Twitter, etc. In our work, we also use these public datasets as the test criteria. Current mainstream models generally use the methods …of stacking multi-RNNs layers or combining neural networks and BERT or other pre-trained models. On account of the importance displayed by the dependence between aspect words and sentiment words, we investigate a novel model (BGAT) blending bidirectional gated recurrent unit (BiGRU) and relational graph attention network (RGAT) to learn dependencies information. Extensive experiments have been conducted on five datasets, the results demonstrate the great capability of our model. Show more

Keywords: Aspect-based sentiment analysis, graph attention network, BiGRU, dependency information, natural language processing

DOI: 10.3233/JIFS-213020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3115-3126, 2023

Authors: Mythrei, S. | Singaravelan, S.

Article Type: Research Article

Abstract: In this web era, entity linking plays a major role. In the web the information’s are associated with different kinds of data and objects. Heterogeneous information networks (HIN) involved multi composed interlinked interconnected objects with various types of connections which is more prominent in this real world. Most of the research work focused towards processing homogeneous networks as well as linking entities with Wikipedia as knowledge base. In this paper we proposed a probabilistic based domain specific entity linking system that will link named entity mentions detected from unstructured web text corpus with corresponding entity in the existing domain specific …Heterogeneous information networks as knowledge base. This work is most challenging due to entity name ambiguity as well as knowledge in the network that are limited one. The proposed model framework presents a model that will link named entity from unstructured web text with domain specific Heterogeneous information network mainly focuses on to learn the weight of meta path. The experiments are done over real world dataset such as DBLP and IMDB dataset. Show more

Keywords: DBLP, IMDB dataset, homogeneous networks

DOI: 10.3233/JIFS-220331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3127-3135, 2023

Authors: Xu, Huiyan

Article Type: Research Article

Abstract: The diagnosis cycle of schizophrenia is long, there is no objective diagnostic basis. The over-energy entropy product of the speech fluency rectangular parameter is designed in the paper, the fuzzy clustering is used to double locate speech pause areas and to assist in the diagnosis of schizophrenia. The pause area of speech is located based on the low speech fluency and flat energy in schizophrenia patients, an extraction algorithm is given for speech fluency quantification parameters, support vector machine (SVM) classifier is used in the approach. The fluency acoustic features of speech are taken from 28 schizophrenia patients and 28 …normal controls, these are used to verify the effect of the method in schizophrenia recognition, there is a correct rate of over 85%. The automatic schizophrenia identification based on energy entropy product and fuzzy clustering can provide objective, effective and non-invasive auxiliary for clinical diagnosis of schizophrenia. Show more

Keywords: Schizophrenia, speech fluency rectangle parameter, fuzzy clustering, hyperenergy entropy product, speech pauses in schizophrenia

DOI: 10.3233/JIFS-220248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3137-3151, 2023

Authors: Purohit, Amit | Patheja, Pushpinder Singh

Article Type: Research Article

Abstract: Sentiment analysis is a natural language processing (NLP) technique for determining emotional tone in a body of text. Using product reviews in sentiment analysis and opinion mining various methods have been developed previously. Although, existing product review analyzing techniques could not accurately detect the product aspect and non-aspect. Hence a novel Detach Frequency Assort is proposed to detect the product aspect term using TF-ISF (Term frequency-inverse sentence frequency) with Part of Speech (POS) tags for sentence segmentation and additionally using Feedback Neural Network to combine product aspect feedback loop. Furthermore, decision-making problem occurs during classification of sentiments. Hence, to solve …this problem a novel technique named, Systemize Polarity Shift is proposed in which flow search based Support Vector Machine (SVM) with Bag of Words model classifies pre-trained review comments as positive, negative, and neutral sentiments. Moreover, the identification of specific products is not focused in sentiment analysis. Hence, a novel Revival Extraction is proposed in which a specific product is extracted based on thematic analysis method to obtain accurate data. Thus, the proposed Product Review Opinion framework gives effective optimized results in sentiment analysis with high accuracy, specificity, recall, sensitivity, F1-Score, and precision. Show more

Keywords: Sentiment analysis, opinion mining, support vector machine, thematic analysis

DOI: 10.3233/JIFS-213296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3153-3169, 2023

Authors: Hernández, Sergio | López, Juan Luis | López-Cortés, Xaviera | Urrutia, Angelica

Article Type: Research Article

Abstract: Recommendations analysis of road safety requires decision-making tools that accommodate weather uncertainties. Operation and maintenance of transport infrastructure have been one of the sub-areas that require attention due to its importance in the quality of the road. Several investigations have proposed artificial neural networks and Bayesian networks to assess the risk of the road. These methods make use of historic accident records to generate useful road safety metrics; however, there is less information on how climatic factors and road surface conditions affect the models that generate recommendations for safe traffic. In this research, Bayesian Network, as a Hidden Markov Models, …and Apriori method are proposed to evaluate the open and closed state of the road. The weather and road surface conditions are explicitly written as a sequence of latent variables from observed data. Different weather variables were studied in order to evaluate both road states (open or close) and the results showed that the Hidden Markov Model provides explicit insight into the sequential nature of the road safety conditions but does not provide a directly interpretable result for human decision making. In this way, we complement the study with the Apriori algorithm using categorical variables. The experimental results show that combining the Hidden Markov Model and the Apriori algorithm provides an interpretable rule for decision making in recommendations of road safety to decide an opening or closing of the road in extreme weather conditions with a confidence higher than 90%. Show more

Keywords: Road safety analysis, hidden markov models, apriori methods

DOI: 10.3233/JIFS-211746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3171-3187, 2023

Authors: Kannan, Sridharan

Article Type: Research Article

Abstract: In today’s world, mining and learning applications play an essential role in healthcare sectors and intend to transform all the data into an understandable form. However, the healthcare sectors require an automated disease prediction system for better medical analysis and emphasize better prediction accuracy for evaluation purposes. In this paper, a new automated prediction model based on Linearly Support Vector Regression and Stacked Linear Swarm Optimization (LSVR-SLSO) has been proposed to predict heart disease accurately. Primarily, the features are analyzed in a linear and non-linear manner using LSVR feature learning approaches. The extracted features are then fed into the SLSO …model in order to extract the global optimal solutions. These global solutions will reduce the data dimensionality and computational complexity during the evaluation phase. Moreover, the optimal solution facilitates the proposed model to predict heart disease appropriately. The simulation can be carried out through the MATLAB environment by utilizing a publicly available benchmark heart disease dataset. The performance results evident that the proposed LSVR-SLSO model can efficiently predict heart disease with superior accuracy of 98%, precision of 98.76%, and recall of 99.7% when compared with conventional approaches. The better performance of the proposed model will pave the way to act as an effective clinical decision support tool for physicians during an emergency. Show more

Keywords: Heart disease prediction, feature selection, optimization, automated system, mining and learning

DOI: 10.3233/JIFS-212772

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3189-3202, 2023

Authors: Wang, Kaixiang | Yang, Ming | Yang, Wanqi | Wang, Lei

Article Type: Research Article

Abstract: Deep neural networks have been adopted in multi-label classification for their excellent performance, however, existing methods fail to comprehensively utilize the high-order correlations between instances and the high-order correlations between labels, and these methods are difficult to deal with label noise effectively. We propose a novel end-to-end deep framework named Robust Fused Hypergraph Neural Networks for Multi-Label Classification (RFHNN), which can effectively utilize the two kinds of high-order correlations and adopt them to mitigate the impact of label noise. In RFHNN, Hypergraph Neural Networks (HNNs) are adopted to mine and utilize the high-order correlations of the instances in the feature …space and the label space respectively. The high-order correlations of the instances can not only improve the accuracy of the classification and the discrimination of the proposed model, but also lay the foundation for the subsequent noise correction module. Meanwhile, a hypergraph construction method based on the Apriori algorithm is proposed to realize Hypergraph Neural Networks (HNNs), which can mine robust second-order and high-order label correlations effectively. Effective classifiers are learned based on the correlations between the labels, which will not only improve the accuracy of the model, but can also enhance the subsequent noise correction module. In addition, we have designed a noise correction module in the networks. With the help of the high-order correlations among the instances and the effective classifier, the framework can effectively correct the noise and improve the robustness of the model. Extensive experimental results on datasets demonstrate that our proposed approach is better than the state-of-the-art multi-label classification algorithms. When dealing with the multi-label training datasets with noise in the label space, our proposed method also has great performance. Show more

Keywords: Multi-label classification, fused hypergraph neural network, high-order label correlations, noise correction, robust classification framework

DOI: 10.3233/JIFS-212844

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3203-3218, 2023

Authors: Jie, Zheng | Daijun, Wei | Liming, Tang

Article Type: Research Article

Abstract: For D numbers theory, there are some drawbacks in the D numbers’ integration rule. For example, the missing information is ignored in the final decision judgment for multi-attribute decision (MADM). For this problem, some researchers have improved the D numbers’ integration rules based on optimistic criterion for overcoming the shortcoming of D numbers’ integration rule. However, optimistic and pessimistic criterion are two sides of the coin for fuzzy environment. Therefore, in this article, a new D numbers’ integration rules based on pessimistic criterion is proposed. We improve the D numbers’ integration rules to redefine the missing information distribution rules based …on pessimistic criterion. The missing information is distributed in inverse proportion to each D number according to the size of the original evidence credibility. Two examples of MADM is applied by the proposed method, the results show that the proposed method can be applied to MADM. Show more

Keywords: Uncertainty, multiple attributes decision making, D numbers, integration representation, pessimistic criterion

DOI: 10.3233/JIFS-211533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3219-3231, 2023