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

Authors: Srivastava, Sangeeta | Varshney, Ashwani | Katyal, Supriya | Kaur, Ravneet | Gaur, Vibha

Article Type: Research Article

Abstract: The government has established special schools to cater to the needs of children with disabilities but they are often segregated rather than receiving equitable opportunities. Artificial Intelligence has opened new ways to promote special education with advanced learning tools. These tools enable to adapt to a typical classroom set up for all the students with or without disabilities. To ensure social equity and the same classroom experience, a coherent solution is envisioned for inclusive education. This paper aims to propose a cost-effective and integrated Smart Learning Assistance (SLA) tool for Inclusive Education using Deep Learning and Computer Vision techniques. It …comprises speech to text and sign language conversion for hearing impaired students, sign language to text conversion for speech impaired students, and Braille to text for communicating with visually impaired students. The tool assists differently-abled students to make use of various teaching-learning opportunities conferred to them and ensures convenient two-way communication with the instructor and peers in the classroom thus makes learning easier. Show more

Keywords: Inclusive classroom, image processing, computer vision, deep learning, artificial intelligence

DOI: 10.3233/JIFS-210075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11981-11994, 2021

Authors: Jin, Jiulin | Zhu, Fuyang | You, Taijie

Article Type: Research Article

Abstract: In this paper, picture fuzzy tensor is proposed, and some related properties are studied. In the meantime, the decomposition theorem of picture fuzzy tensors is established by using picture fuzzy cutting tensors and picture fuzzy t -norm. Moreover, we propose the generalized picture fuzzy weighted interaction aggregation (GPFWIA) operator and the generalized picture fuzzy weighted interaction geometric (GPFWIG) operator. Finally, an application of picture fuzzy tensor in multi-attribute decision making (MADM) problems is presented, that is, a method is suggested to solve picture fuzzy MADM problems with multi-dimensional data characteristics. It is found that our proposed method is feasible and …effective by a typical application example. Show more

Keywords: Picture fuzzy tensor, Multi-attribute decision making (MADM), Decomposition theorem, Generalized picture fuzzy weighted interaction aggregation (GPFWIA) operator, Generalized picture fuzzy weighted interaction geometric (GPFWIG) operator

DOI: 10.3233/JIFS-210093

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11995-12009, 2021

Authors: Liu, Yaning | Han, Lin | Wang, Hexiang | Yin, Bo

Article Type: Research Article

Abstract: Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, …in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images. Show more

Keywords: papillary thyroid carcinoma, histological image classification, convolutional neural network, deep learning

DOI: 10.3233/JIFS-210100

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12011-12021, 2021

Authors: Wei, Qianjin | Wang, Chengxian | Wen, Yimin

Article Type: Research Article

Abstract: Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy …was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR. Show more

Keywords: Intelligent optimization, rough set theory, attribute reduction, social spider optimization, opposition-based learning

DOI: 10.3233/JIFS-210133

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12023-12038, 2021

Authors: Liu, Jinpei | Shao, Longlong | Zhou, Ligang | Jin, Feifei

Article Type: Research Article

Abstract: Faced with complex decision problems, distribution linguistic preference relation (DLPR) is an effective way for decision-makers (DMs) to express preference information. However, due to the complexity of the decision-making environment, DMs may not be able to provide complete linguistic distribution for all linguistic terms in DLPRs, which results in incomplete DLPRs. Therefore, in order to solve group decision-making (GDM) with incomplete DLPRs, this paper proposes expected consistency-based model and multiplicative DEA cross-efficiency. For a given incomplete DLPRs, we first propose an optimization model to obtain complete DLPR. This optimization model can evaluate the missing linguistic distribution and ensure that the …obtained DLPR has a high consistency level. And then, we develop a transformation function that can transform DLPRs into multiplicative preference relations (MPRs). Furthermore, we design an improved multiplicative DEA model to obtain the priority vector of MPR for ranking all alternatives. Finally, a numerical example is provided to show the rationality and applicability of the proposed GDM method. Show more

Keywords: Group decision making, distribution linguistic preference relation, incomplete distribution linguistic preference relation, expected consistency, multiplicative DEA cross-efficiency

DOI: 10.3233/JIFS-210148

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12039-12059, 2021

Authors: Chen, Wei | Chen, Junqiu | Xian, Yantuan

Article Type: Research Article

Abstract: It is of great significance to recognize the metallurgical entity relations in order to construct the Knowledge graph of Metallurgical Literature and to further understand the metallurgical literature. However, there are few researches on the textual entity relations in metallurgical fields either few marked Corpora. The syntactic structure of the same entity relationship category is relatively simple and has strong domain characteristics. The traditional entity relationship model can not identify the domain entity relationship well. Meanwhile the syntactic structure of the same entity relations class is relatively simple, and the syntactic structure is relatively simple in the recognition of entity …relations in metallurgy field. Furthermore, the entities with similar syntactic structure often have the same entity relations and the different words in the sentence have different contribution to the entity relations. In order to solve the mentioned problems, this paper will combine the algorithm that can highlight the syntactic structure in sentences and improve the accuracy of the model with the Algorithm that can highlight the contribution of words in sentences and the loss function level integration is carried out in the framework of small sample prototype network, so as to maximize the advantages of each algorithm and improve the accuracy –firstly, in the coding layer of the prototype network, we use the CNN algorithm which can highlight the important words in the sentences and the TreeLSTM algorithm which can parse the sentences in the text so that the syntactic relations between the words in the sentences can be acted on in the relation recognition, the sentences are coded together by two algorithms, then, the EUCLIDEAN distance loss is calculated by using this high quality coding and the prototype coding, finally, the traditional entity relation recognition model with Attention Mechanism is integrated into the loss function, further highlighting the decisive role of important words in text sentences in relation recognition and improving the generalization of the model. The results showed that compared with the traditional methods such as CNN, RNN, PCNN and Bi-LSTM, the proposed method in this paper has better performance in the case of small sample data set. Show more

Keywords: Syntactic analysis, integration learning, prototype network, entity relationship recognition

DOI: 10.3233/JIFS-210163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12061-12073, 2021

Authors: Shi, Jinglei | Guo, Junjun | Yu, Zhengtao | Xiang, Yan

Article Type: Research Article

Abstract: Unsupervised aspect identification is a challenging task in aspect-based sentiment analysis. Traditional topic models are usually used for this task, but they are not appropriate for short texts such as product reviews. In this work, we propose an aspect identification model based on aspect vector reconstruction. A key of our model is that we make connections between sentence vectors and multi-grained aspect vectors using fuzzy k-means membership function. Furthermore, to make full use of different aspect representations in vector space, we reconstruct sentence vectors based on coarse-grained aspect vectors and fine-grained aspect vectors simultaneously. The resulting model can therefore learn …better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect identification and topic coherence of the extracted aspect terms. Show more

Keywords: Aspect identification, text clustering, topic coherence, membership function, aspect extraction

DOI: 10.3233/JIFS-210175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12075-12085, 2021

Authors: Chen, Zhe | Zhong, Peisi | Liu, Mei | Sun, Hongyuan | Shang, Kai

Article Type: Research Article

Abstract: This work aims to help the designers to make decisions in the early stage of new product development. Design concept evaluation is very critical in design process, it may affect the later stages. However, facing to uncertain circumstance, mostly, the raw data in early stage are subjective and imprecise. This work proposes a novel approach to solve this problem. The whole work is based on rough numbers, Shannon entropy, technique for order performance by similarity to ideal solution method and preference selection index method. Firstly, rough numbers and Shannon entropy are integrated to determine the weight of evaluation criteria based …on their interrelationships. After that, a novel technique for order performance by similarity to ideal solution method improved by rough numbers and preference selection index method is proposed to evaluate and rank the alternatives. Then, a comparative case is carried out with proposed method and two other methods in this study. The comparation of evaluation processes indicates that the proposed method’s advantage. Compared the other methods, proposed approach is objective, simple and do not need additional input. The results of three methods are similar. It means that the proposed method is not only effective and efficient in design concept evaluation, but also can save time and cost in the early stage of new product development. Show more

Keywords: Rough numbers, TOPSIS-PSI, shannon entropy, design concept evaluation

DOI: 10.3233/JIFS-210184

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12087-12099, 2021

Authors: Vivek, S. | Mathew, Sunil C.

Article Type: Research Article

Abstract: This paper studies the closure and interior operators in LM -fuzzy topological spaces. The algebraic structures associated with various collections of closed sets and open sets are identified. Further, certain lattices formed by these algebraic structures are obtained and some lattice theoretic properties of the same are investigated. Corresponding to every element in M , the study associates a lattice of monoids which is determined by various types of closed sets and open sets.

Keywords: LM-fuzzy topology, Closure operator, Lattice, Monoid, 54A40

DOI: 10.3233/JIFS-210195

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12101-12109, 2021

Authors: Wang, Rui | Jia, Zhaohong | Li, Kai

Article Type: Research Article

Abstract: In this paper, a problem of scheduling jobs with different sizes and fuzzy processing times (FPT) on non-identical parallel batch machines to minimize makespan is investigated. Moreover, the processing time (PT) of each batch is subject to the location-based learning and total-PT-based deterioration effect. Since this is an NP-hard combinatorial optimization problem, an improved intelligent algorithm based on fruit fly optimization algorithm (IFOA) is proposed. To verify the performance of the algorithm, the IFOA is compared with three state-of-the-art algorithms. The comparative results demonstrate that the proposed IFOA outperforms the other compared algorithms.

Keywords: Evolutionary algorithms, combinatorial optimization, fuzzy sets, scheduling

DOI: 10.3233/JIFS-210196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12111-12124, 2021