Journal of Intelligent & Fuzzy Systems - Volume 40, issue 6

Authors: Guo, Jiong | Lei, Deming | Li, Ming

Article Type: Research Article

Abstract: Energy-efficient flexible job shop scheduling problems (EFJSP) have been investigated fully; however, energy-related objectives often have lower importance than other ones in many real-life situations and this case is hardly considered in the previous works. In this study, EFJSP with sequence-dependent setup times (SDST) is considered, in which total tardiness and makespan are given higher importance than total energy consumption. A two-phase imperialist competitive algorithm (TPICA) is proposed. The importance difference among objectives is implemented by treating all objectives equally in the first phase and making energy consumption not to exceed a diminishing threshold in the second phase. A dynamical …differentiating assimilation and a novel imperialist competition with the enforced search are implemented. Extensive experiments are conducted and the computational results show that TPICA is very competitive for EFJSP with SDST. Show more

Keywords: Flexible job shop scheduling, energy-efficient scheduling, imperialist competitive algorithm, sequence-dependent setup times

DOI: 10.3233/JIFS-210198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12125-12137, 2021

Authors: Cao, Xin-Zi | Luo, Sheng-Zhou | Li, Jing-Cong | Pan, Jia-Hui

Article Type: Research Article

Abstract: The grade and stage of bladder tumors is an essential key for diagnosing and treating bladder cancer. This study proposed an automated bladder tumor prediction system to automatically assess the bladder tumor grade and stage automatically on Magnetic Resonance Imaging (MRI) images. The system included three modules: tumor segmentation, feature extraction and prediction. We proposed a U-ResNet network that automatically extracts morphological and texture features for detecting tumor regions. These features were used in support vector machine (SVM) classifiers to predict the grade and stage. Our proposed method segmented the tumor area and predicted the grade and stage more accurately …compared to different methods in our experiments on MRI images. The accuracy of bladder tumor grade prediction was about 70%, and the accuracy of the data set was about 77.5%. The extensive experiments demonstrated the usefulness and effectiveness of our method. Show more

Keywords: Bladder tumor segmentation, U-ResNet network, grade and stage, feature extraction, support vector machine

DOI: 10.3233/JIFS-210263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12139-12150, 2021

Authors: Xu, Zhixuan | Chen, Caikou | Han, Guojiang | Gao, Jun

Article Type: Research Article

Abstract: As a successful improvement on Low Rank Representation (LRR), Latent Low Rank Representation (LatLRR) has been one of the state-of-the-art models for subspace clustering due to the capability of discovering the low dimensional subspace structures of data, especially when the data samples are insufficient and/or extremely corrupted. However, the LatLRR method does not consider the nonlinear geometric structures within data, which leads to the loss of the locality information among data in the learning phase. Moreover, the coefficients of the learnt representation matrix can be negative, which lack the interpretability. To solve the above drawbacks of LatLRR, this paper introduces …Laplacian, sparsity and non-negativity to LatLRR model and proposes a novel subspace clustering method, termed latent low rank representation with non-negative, sparse and laplacian constraints (NNSLLatLRR), in which we jointly take into account non-negativity, sparsity and laplacian properties of the learnt representation. As a result, the NNSLLatLRR can not only capture the global low dimensional structure and intrinsic non-linear geometric information of the data, but also enhance the interpretability of the learnt representation. Extensive experiments on two face benchmark datasets and a handwritten digit dataset show that our proposed method outperforms existing state-of-the-art subspace clustering methods. Show more

Keywords: Subspace clustering, low rank representation, latent low rank representation, non-negative sparse laplacian constraints

DOI: 10.3233/JIFS-210274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12151-12165, 2021

Authors: Liu, Luping | Wang, Meiling | He, Xiaohai | Qing, Linbo | Zhang, Jin

Article Type: Research Article

Abstract: Joint extraction of entities and relations from unstructured text is an essential step in constructing a knowledge base. However, relational facts in these texts are often complicated, where most of them contain overlapping triplets, making the joint extraction task still challenging. This paper proposes a novel Sequence-to-Sequence (Seq2Seq) framework to handle the overlapping issue, which models the triplet extraction as a sequence generation task. Specifically, a unique cascade structure is proposed to connect transformer and pointer network to extract entities and relations jointly. By this means, sequences can be generated in triplet-level and it speeds up the decoding process. Besides, …a syntax-guided encoder is applied to integrate the sentence’s syntax structure into the transformer encoder explicitly, which helps the encoder pay more accurate attention to the syntax-related words. Extensive experiments were conducted on three public datasets, named NYT24, NYT29, and WebNLG, and the results show the validity of this model by comparing with various baselines. In addition, a pre-trained BERT model is also employed as the encoder. Then it comes up to excellent performance that the F1 scores on the three datasets surpass the strongest baseline by 5.7%, 5.6%, and 4.4%. Show more

Keywords: Information extraction, sequence to sequence, transformer network, pointer network, syntax-guided attention network

DOI: 10.3233/JIFS-210281

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12167-12183, 2021

Authors: Gao, Xiue | Jiang, Panling | Xie, Wenxue | Chen, Yufeng | Zhou, Shengbin | Chen, Bo

Article Type: Research Article

Abstract: Decision fusion is an effective way to resolve the conflict of diagnosis results. Aiming at the problem that Dempster-Shafer (DS) theory deals with the high conflict of evidence and produces wrong results, a decision fusion algorithm for fault diagnosis based on closeness and DS theory is proposed. Firstly, the relevant concepts of DS theory are introduced, and the normal distribution membership function is used as the evidence closeness. Secondly, the harmonic average is introduced, and the weight of each evidence is established according to the product of closeness of each evidence and its harmonic average. Thirdly, the weight of conflicting …evidence is regularized, and the final decision fusion result is obtained by using the Dempster’s rule. Lastly, the simulation and application examples are designed. Simulation and application results show that the method can effectively reduce the impact of diagnostic information conflicts and improve the accuracy of decision fusion. What’s more, the method considers the overall average distribution of evidence in the identification framework, it can reduce evidence conflicts while preserving important diagnostic information. Show more

Keywords: Fault diagnosis, decision fusion, DS theory, closeness, harmonic average

DOI: 10.3233/JIFS-210283

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12185-12194, 2021

Authors: Chen, Ting-Yu

Article Type: Research Article

Abstract: The purpose of this paper is to evolve a novel area-based Pythagorean fuzzy decision model via an approach-oriented measure and an avoidance-oriented measure in support of multiple criteria decision analysis involving intricate uncertainty of Pythagorean fuzziness. Pythagorean membership grades embedded in a Pythagorean fuzzy set is featured by tensible functions of membership, non-membership, indeterminacy, strength, and direction, which delivers flexibility and adaptability in manipulating higher-order uncertainties. However, a well-defined ordered structure is never popular in real-life issues, seldom seen in Pythagorean fuzzy circumstances. Consider that point operators can make a systematic allocation of the indeterminacy composition contained in Pythagorean fuzzy …information. This paper exploits the codomains of the point operations (i.e., the quantities that express the extents of point operators) to launch new measurements of approach orientation and avoidance orientation for performance ratings. This paper employs such measurements to develop an area-based performance index and an area-based comprehensive index for conducting a decision analysis. The applications and comparative analyses of the advanced area-based approach to some decision-making problems concerning sustainable recycling partner selection, company investment decisions, stock investment decisions, and working capital financing decisions give support to methodological advantages and practical effectiveness. Show more

Keywords: Area-based Pythagorean fuzzy decision model, approach-oriented measure, avoidance-oriented measure, multiple criteria decision analysis, Pythagorean fuzziness

DOI: 10.3233/JIFS-210290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12195-12213, 2021

Authors: Ghafarokhi, Omid Izadi | Moattari, Mazda | Forouzantabar, Ahmad

Article Type: Research Article

Abstract: With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this …paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods. Show more

Keywords: Composite load modeling, deep attention neural network, encoder-decoder, long short-term memory, convolutional neural network, wide-area monitoring system

DOI: 10.3233/JIFS-210296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12215-12226, 2021

Authors: Gasmi, Ibtissem | Azizi, Mohamed Walid | Seridi-Bouchelaghem, Hassina | Azizi, Nabiha | Belhaouari, Samir Brahim

Article Type: Research Article

Abstract: Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from …the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques. Show more

Keywords: Collaborative filtering, context, topic modeling, PSO, LDA, sparsity problem

DOI: 10.3233/JIFS-210331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12227-12242, 2021

Authors: Xu, Kaijie | E, Hanyu | Quan, Yinghui | Cui, Ye | Nie, Weike

Article Type: Research Article

Abstract: In this study, we develop a novel clustering with double fuzzy factors to enhance the performance of the granulation-degranulation mechanism, with which a fuzzy rule-based model is designed and demonstrated to be an enhanced one. The essence of the developed scheme is to optimize the construction of the information granules so as to eventually improve the performance of the fuzzy rule-based models. In the design process, a prototype matrix is defined to express the Fuzzy C-Means based granulation-degranulation mechanism in a clear manner. We assume that the dataset degranulated from the formed information granules is equal to the original numerical …dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme. Show more

Keywords: Partition matrix, granulation-degranulation mechanism, information granules, fuzzy clustering, rule-based models, prototype matrix

DOI: 10.3233/JIFS-210336

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12243-12252, 2021

Authors: Ahkouk, Karam | Machkour, Mustapha | Majhadi, Khadija | Mama, Rachid

Article Type: Research Article

Abstract: In the last decade, many intelligent interfaces and layers have been suggested to allow the use of relational databases and extraction of the content using only the natural language. However most of them struggle when exposed to new databases. In this article, we present SQLSketch, a sketch-based network for generating SQL queries to address the problem of automatically translate Natural Languages questions to SQL using the related databases schemas. We argue that the previous models that use full or partial sequence-to-sequence structure in the decoding phase can, in fact, have counter-effect on the generation operation and came up with more …loss of the context or the meaning of the user question. In this regard, we use a full sketch-based structure that decouples the generation process into many small prediction modules. The SQLSketch is evaluated against GreatSQL, a new cross-domain, large-scale and balanced dataset for the Natural Language to SQL translation task. For a long-term aim of making better models and contributing in adding more improvements to the semantic parsing tasks, we propose the GreatSQL dataset as the first balanced cross-domain corpus that includes 45,969 pairs of natural language questions and their corresponding SQL queries in addition to simplified and well structured ground-truth annotations. We establish results for SQLSketch using GreatSQL dataset and compare the performance against two popular types of models that represent the sequential and partial-sketch based approaches. Experimental result shows that SQLSketch outperforms the baseline models by 13% in exact matching accuracy and achieve a score of 23.9% to be the new state-of-the-art model on GreatSQL. Show more

Keywords: Natural language processing, text to SQL translation, database interfaces, natural language translation, machine translation

DOI: 10.3233/JIFS-210359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12253-12263, 2021