Journal of Intelligent & Fuzzy Systems - Volume 42, issue 6

Authors: Cheng, Ru | Wang, Lukun | Wei, Mingrun

Article Type: Research Article

Abstract: Finer-grained local features play a supplementary role in the description of pedestrian global features, and the combination of them has been an essential solution to improve discriminative performances in person re-identification (PReID) tasks. The existing part-based methods mostly extract representational semantic parts according to human visual habits or some prior knowledge and focus on spatial partition strategies but ignore the significant influence of channel information on PReID task. So, we proposed an end-to-end multi-branch network architecture (MCSN) jointing multi-level global fusion features, channel features and spatial features in this paper to better learn more diverse and discriminative pedestrian features. It …is worth noting that the effect of multi-level fusion features on the performance of the model is taken into account when extracting global features. In addition, to enhance the stability of model training and the generalization ability of the model, the BNNeck and the joint loss function strategy are applied to all vector representation branches. Extensive comparative evaluations are conducted on three mainstream image-based evaluation protocols, including Market-1501, DukeMTMC-ReID and MSMT17, to validate the advantages of our proposed model, which outperforms previous state-of-the-art in ReID tasks. Show more

Keywords: Person re-identification, multi-branch deep network, multi-level global fusion feature, spatial-channel partition

DOI: 10.3233/JIFS-212656

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5987-6001, 2022

Authors: Qiu, Chenye | Liu, Ning

Article Type: Research Article

Abstract: This paper proposes a novel two layer differential evolutionary algorithm with multi-mutation strategy (TLDE) for solving the economic emission dispatch (EED) problem involving random wind power. In recent years, renewable energy such as wind power is more and more participated in the power systems to address the problems of fossil energy shortage and environmental pollution. Hence, the EED problem with the availability of random wind power is investigated in this paper. Due to the uncertain nature of wind speed, the Weibull probability distribution function is used to model the random wind power. In order to improve the search ability, TLDE …divides the population into two layers according to the fitness ranking, and individuals in the two layers are treated differently to fully investigate their own potential. The two layers can cooperate with each other to further enhance the search performance by utilizing an information sharing strategy. Also, an adaptive restart scheme is introduced to avoid falling into stagnation. The performance of the proposed TLDE is testified on the 40 units system with 2 modified wind turbines. The experimental results demonstrate that the TLDE method can achieve precise dispatch strategy in EED problem with random wind power. Show more

Keywords: Economic emission dispatch, wind power, differential evolution, mutation operator, two layer structure

DOI: 10.3233/JIFS-212735

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6003-6016, 2022

Authors: Wang, Zhi | Song, Shufang | Wei, Hongkui

Article Type: Research Article

Abstract: When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution of solution set simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed …for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils. Show more

Keywords: Teaching-learning-based optimization (TLBO), multi-objective optimization, convergence, distribution, airfoils

DOI: 10.3233/JIFS-212743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6017-6026, 2022

Authors: Shen, Yonghong

Article Type: Research Article

Abstract: In the present paper, the notion of the linearly correlated difference for linearly correlated fuzzy numbers is introduced. Especially, the linearly correlated difference and the generalized Hukuhara difference are coincident for interval numbers or even symmetric fuzzy numbers. Accordingly, an appropriate metric is induced by using the norm and the linearly correlated difference in the set of linearly correlated fuzzy numbers. Based on the symmetry of the basic fuzzy number, the linearly correlated derivative is proposed by the linearly correlated difference of linearly correlated fuzzy number-valued functions. In both non-symmetric and symmetric cases, the equivalent characterizations of the linearly correlated …differentiability of a linearly correlated fuzzy number-valued function are established, respectively. Moreover, it is shown that the linearly correlated derivative is consistent with the generalized Hukuhara derivative for interval-valued functions. Show more

Keywords: Fuzzy numbers, Linearly correlated difference, Linearly correlated derivative, Canonical form, Linearly correlated fuzzy number-valued functions

DOI: 10.3233/JIFS-212908

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6027-6043, 2022

Authors: Lu, Haishu | Li, Rong

Article Type: Research Article

Abstract: In this paper, based on the KKM method, we prove a new fuzzy fixed-point theorem in noncompact CAT(0) spaces. As applications of this fixed-point theorem, we obtain some existence theorems of fuzzy maximal element points. Finally, we utilize these fuzzy maximal element theorems to establish some new existence theorems of Nash equilibrium points for generalized fuzzy noncooperative games and fuzzy noncooperative qualitative games in noncompact CAT(0) spaces. The results obtained in this paper generalize and extend many known results in the existing literature.

Keywords: CAT(0) space, fuzzy fixed point, fuzzy maximal element, fuzzy noncooperative game, Nash equilibrium point

DOI: 10.3233/JIFS-212194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6045-6062, 2022

Authors: Sindhiya Devi, R. | Perumal, B. | Pallikonda Rajasekaran, M.

Article Type: Research Article

Abstract: In today’s world, Brain Tumor diagnosis plays a significant role in the field of Oncology. The earlier identification of brain tumors increases the compatibility of treatment of patients and offers an efficient diagnostic recommendation from medical practitioners. Nevertheless, accurate segmentation and feature extraction are the vital challenges in brain tumor diagnosis where the handling of higher resolution images increases the processing time of existing classifiers. In this paper, a new robust weighted hybrid fusion classifier has been proposed to identify and classify the tumefaction in the brain which is of the hybridized form of SVM, NB, and KNN (SNK) classifiers. …Primarily, the proposed methodology initiates the preprocessing technique such as adaptive fuzzy filtration and skull stripping in order to remove the noises as well as unwanted regions. Subsequently, an automated hybrid segmentation strategy can be carried out to acquire the initial segmentation results, and then their outcomes are compiled together using fusion rules to accurately localize the tumor region. Finally, a Hybrid SNK classifier is implemented in the proposed methodology for categorizing the type of tumefaction in the brain. The hybrid classifier has been compared with the existing state-of-the-art classifier which shows a higher accuracy result of 99.18% while distinguishing the benign and malignant tumors from brain Magnetic Resonance (MR) images. Show more

Keywords: Adaptive fuzzy filter, brain MR images, tumor diagnosis, hybrid classifier, segmentation

DOI: 10.3233/JIFS-212200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6063-6078, 2022

Authors: Cao, Yukun | Miao, Zeyu

Article Type: Research Article

Abstract: Knowledge graph link prediction uses known fact links to infer the missing link information in the knowledge graph, which is of great significance to the completion of the knowledge graph. Generating low-dimensional embeddings of entities and relations which are used to make inferences is a popular way for such link prediction problems. This paper proposes a knowledge graph link prediction method called Complex-InversE in the complex space, which maps entities and relations into the complex space. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. The Complex-InversE effectively captures the …antisymmetric relations and introduces Dropout and Early-Stopping technologies into deal with the problem of small numbers of relationships and entities, thus effectively alleviates the model’s overfitting. The results of comparison experiment on the public knowledge graph datasets show that the Complex-InversE achieves good results on multiple benchmark evaluation indicators and outperforms previous methods. Complex-InversE’s code is available on GitHub at https://github.com/ZeyuMiao97/Complex-InversE . Show more

DOI: 10.3233/JIFS-212374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6079-6089, 2022

Authors: Gayathri, R. | Babitha Lincy, R.

Article Type: Research Article

Abstract: The paper describes the excellent method to get first-rate accuracy and performance in the discipline of Tamil character recognition in a handwritten mode. However, the subject is still at a nascent stage and grossly lacks adequate accuracy in the Tamil language, even though several studies have been conducted within the discipline of handwritten character recognition. This paper draws the attention to the offline handwritten recognition for the Tamil language using the Inception-v3 based transfer learning method. The proposed work is conducted on the readily available HP Tamil handwritten character offline dataset (Hewlett-Packard Lab: hpl-tamil-iso-char-offline-1.0.). It reveals that with the suitable …arrangement of transfer learning approach with Inception-v3, the pre-trained model can achieve the recognition accuracy of 93.1%, overtaking the former deep learning designs. The achieved accuracy is due to the use of a pre-trained version with transfer learning that regularly hastens the method of the training process on a new task. Overall, this results in higher accuracy and a more capable version. Show more

Keywords: Handwritten character recognition, inception-v3, tamil language, transfer learning

DOI: 10.3233/JIFS-212378

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6091-6102, 2022

Authors: Vaishnavi, V. | Suveetha Dhanaselvam, P.

Article Type: Research Article

Abstract: The study of neonatal cry signals is always an interesting topic and still researcher works interminably to develop some module to predict the actual reason for the baby cry. It is really hard to predict the reason for their cry. The main focus of this paper is to develop a Dense Convolution Neural network (DCNN) to predict the cry. The target cry signal is categorized into five class based on their sound as “Eair”, “Eh”, “Neh”, “Heh” and “Owh”. Prediction of these signals helps in the detection of infant cry reason. The audio and speech features (AS Features) were exacted …using Mel-Bark frequency cepstral coefficient from the spectrogram cry signal and fed into DCNN network. The systematic DCNN architecture is modelled with modified activation layer to classify the cry signal. The cry signal is collected in different growth phase of the infants and tested in proposed DCNN architecture. The performance of the system is calculated through parameters accuracy, specificity and sensitivity are calculated. The output of proposed system yielded a balanced accuracy of 92.31%. The highest accuracy level 95.31%, highest specificity level 94.58% and highest sensitivity level 93% attain through proposed technique. From this study, it is concluded that the proposed technique is more efficient in detecting cry signal compared to the existing techniques. Show more

Keywords: Infant cry signal, spectrogram images, audio and speech features, mel-bark frequency cepstral domain, dense convolution neural network

DOI: 10.3233/JIFS-212473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6103-6116, 2022

Authors: Fan, Jianping | Zhou, Wei | Wu, Meiqin

Article Type: Research Article

Abstract: Handing uncertain information is one of the research focuses currently. For the sake of great ability of handing uncertain information, Dempster-Shafer evidence theory (D-S theory) has been widely used in various fields of uncertain information processing. However, when highly contradictory evidence appears, the results of the classical Dempster combination rules (DCR) can be counterintuitive. Aiming at this defect, by considering the relationship between the evidence and its own characteristics, the proposed method is a new method of conflicting evidence management based on non-extensive entropy and Lance distance in uncertain scenarios. Firstly, the Lance distance function is used to measure the …degree of discrepancy and conflict between evidences, and the credibility of evidence is expressed by matrix. Introducing non-extensive entropy to measure the amount of information about evidence and express the uncertainty of evidence. Secondly, the discount coefficient of the final fusion evidence is measured by considering the credibility and uncertainty of the evidence, and the original evidence is modified by the discount coefficient. Then, the final result is obtained by evidence fusion with DCR. Finally, two numerical examples are provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application of the active lane change to avoid obstacles to the autonomous driving of new energy vehicles. The proposed method has a better identification accuracy, reaching 0.9811. Show more

Keywords: Dempster-Shafer evidence theory, conflicting evidences, information fusion, Lance distance, non-extensive entropy

DOI: 10.3233/JIFS-212489

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 6117-6129, 2022