Journal of Intelligent & Fuzzy Systems - Volume 41, issue 6

Authors: Zhang, Nian | Pan, Qian | Wei, Guiwu

Article Type: Research Article

Abstract: In order to effectively solve the decision-making problems with the diversity of evaluation information, the dynamics of research objects, the limitations of subjective authorization, and the irrational behavior of decision-makers, this paper extends catastrophe progression method to solve hybrid multiple attribute decision-making problems based on regret theory. Firstly, some basic theories are introduced. Secondly, the original catastrophe progression method is extended by using the regret theory, which is employed to solve the multiple attribute decision-making problems with hybrid evaluation information. Finally, a real-life case study of selecting a fresh cold chain logistics service provider is used to verify the practicality …and effectiveness of the proposed method, and a comparative analysis with the TOPSIS method and the sensitivity of the regret avoidance coefficient is analyzed in this article. Show more

Keywords: Regret theory, catastrophe progression, hybrid multi-attribute decision-making

DOI: 10.3233/JIFS-210515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6641-6654, 2021

Authors: Du, Wen Sheng

Article Type: Research Article

Abstract: Aggregation of q -rung orthopair fuzzy information serves as an important branch of the q -rung orthopair fuzzy set theory, where operations on q -rung orthopair fuzzy values (q -ROFVs) play a crucial role. Recently, aggregation operators on q -ROFVs were established by employing the Einstein operations rather than the algebraic operations. In this paper, we give a further investigation on operations and aggregation operators for q -ROFVs based on the Einstein operational laws. We present the operational principles of Einstein operations over q -ROFVs and compare them with those built on the algebraic operations. The properties of the q …-rung orthopair fuzzy Einstein weighted averaging (q -ROFEWA) operator and q -rung orthopair fuzzy Einstein weighted geometric (q -ROFEWG) operator are investigated in detail, such as idempotency, monotonicity, boundedness, shift-invariance and homogeneity. Then, the developed operators are applied to multiattribute decision making problems under the q -rung orthopair fuzzy environment. Finally, an example for selecting the design scheme for a blockchain-based agricultural product traceability system is presented to illustrate the feasibility and effectiveness of the proposed methods. Show more

Keywords: Aggregation operator, Einstein operation, multiattribute decision making, q-rung orthopair fuzzy Einstein aggregation operator, q-rung orthopair fuzzy value

DOI: 10.3233/JIFS-210548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6655-6673, 2021

Authors: Wang, Xiaomin | Liu, Yang | Zhou, Rui

Article Type: Research Article

Abstract: A new model named multi-granularity belief interval-valued soft set is introduced in this paper. Some basic properties about it are presented and illustrated. The improved concepts of the soft belief value and soft belief degree are proposed, which provided an easier and better compared horizontally and vertically method among the different objects and different parameters. An algorithm for decision-making problems on multi-granularity belief interval-valued soft set is put forward and its validity is proved by the application of an example. Moreover, the newly proposed algorithm is compared with existing method to indicate its extensive application.

Keywords: Belief interval-valued soft set, soft belief value, soft belief degree, decision making

DOI: 10.3233/JIFS-210565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6675-6684, 2021

Authors: Cui, Xiaohui | Ying, Yongzhi | Chen, Zhibo

Article Type: Research Article

Abstract: The identification and classification of plant diseases is of great significance to ecological protection and deep learning methods have made a great of progress in the common plant diseases identification for specific plant. While faced with the same plant disease of other plants, due to the insufficient or low quality training data, current deep learning methods will be difficult to identify the diseases effectively and accurately. Inspired by the advantages of GAN in dataset expansion, we propose the CycleGAN based confusion model in this paper. In this paper, GAN framework is improved by adding noise label and learn together during …training stage, which migrates the data of common plant diseases to the plants with insufficient or low quality data. In order to evaluate the quality of the migrated training dataset among different GAN approaches, we introduce the quality indicators of the migration images such as MMD, FID, EMD etc. We compare our model with other GANs model, and the experimental results show that the proposed model obtains better results in the migration process, which make it more effective for the identification of cross species plant diseases. Show more

Keywords: Deep learning, generative adversarial nets, CycleGAN, image translation

DOI: 10.3233/JIFS-210585

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6685-6696, 2021

Authors: Janakiramaiah, B. | Kalyani, G. | Prasad, L.V. Narasimha | Karuna, A. | Krishna, M.

Article Type: Research Article

Abstract: Horticulture crops take a crucial part of the Indian economy by creating employment, supplying raw materials to different food processing industries. Mangoes are one of the major crops in horticulture. General Infections in Mango trees are common by various climatic and fungal infections, which became a cause for reducing the quality and quantity of the mangos. The most common diseases with bacterial infection are anthracnose and Powdery Mildew. In recent years, it has been perceived that different variants of deep learning architectures are proposed for detecting and classifying the problems in the agricultural domain. The Convolutional Neural Network (CNN) based …architectures have performed amazingly well for disease detection in plants but at the same time lacks rotational or spatial invariance. A relatively new neural organization called Capsule Network (CapsNet) addresses these limitations of CNN architectures. Hence, in this work, a variant of CapsNet called Multilevel CapsNet is introduced to characterize the mango leaves tainted by the anthracnose and powdery mildew diseases. The proposed architecture of this work is validated on a dataset of mango leaves collected in the natural environment. The dataset comprises both healthy and contaminated leaf pictures. The test results approved the undeniable level of exactness of the proposed framework for the characterization of mango leaf diseases with an accuracy of 98.5%. The outcomes conceive the higher-order precision of the proposed Multi-level CapsNet model when contrasted with the other classification algorithms such as Support Vector Machine (SVM) and CNNs. Show more

Keywords: Deep learning, disease detection, machine learning, capsule networks, mango leaf diseases, convolutional neural network

DOI: 10.3233/JIFS-210593

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6697-6713, 2021

Authors: Liu, Yikun | Yang, Gongping | Huang, Yuwen | Yin, Yilong

Article Type: Research Article

Abstract: Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard. Firstly, the squeeze-and-excitation block is introduced into the ResNet-50 backbone, which can distribute the available computational resources to the most informative feature map in channel-wise. Secondly, the aspect ratio is introduced into the bounding box regression loss, which can promote the regression of bounding boxes by deforming the shape of …bounding boxes to the apple boxes. Finally, we replace the NMS operation in Mask R-CNN by Soft-NMS, which can remove the redundant bounding boxes and obtain the correct detection results reasonably. The experimental result on the Minneapple dataset demonstrates that our method overperform several state-of-the-art on apple detection and segmentation. Show more

Keywords: Apple detection and segmentation, complex background, squeeze-and-excitation block, aspect ratio, soft-NMS

DOI: 10.3233/JIFS-210597

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6715-6725, 2021

Authors: Deng, Shangju | Qin, Jiwei

Article Type: Research Article

Abstract: Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our …approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse. Show more

Keywords: Recommendation system, tensor factorization, similarity, user-project context interaction

DOI: 10.3233/JIFS-210641

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6727-6738, 2021

Authors: Ji, Ying | Jin, Xiaowan | Xu, Zeshui | Qu, Shaojian

Article Type: Research Article

Abstract: In practical multiple attribute decision making (MADM) problems, the interest groups or individuals intentionally set attribute weights to achieve their own benefits. In this case, the rankings of different alternatives are changed strategically, which is called the strategic weight manipulation in MADM. Sometimes, the attribute values are given with imprecise forms. Several theories and methods have been developed to deal with uncertainty, such as probability theory, interval values, intuitionistic fuzzy sets, hesitant fuzzy sets, etc. In this paper, we study the strategic weight manipulation based on the belief degree of uncertainty theory, with uncertain attribute values obeying linear uncertain distributions. …It allows the attribute values to be considered as a whole in the operation process. A series of mixed 0-1 programming models are constructed to set a strategic weight vector for a desired ranking of a particular alternative. Finally, an example based on the assessment of the performance of COVID-19 vaccines illustrates the validity of the proposed models. Comparison analysis shows that, compared to the deterministic case, it is easier to manipulate attribute weights when the attribute values obey the linear uncertain distribution. And a further comparative analysis highlights the performance of different aggregation operators in defending against the strategic manipulation, and highlights the impacts on ranking range under different belief degrees. Show more

Keywords: Multiple attribute decision making, strategic weight manipulation, uncertainty theory, ranking range, belief degree

DOI: 10.3233/JIFS-210650

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6739-6754, 2021

Authors: Wang, Dan | Wang, Jie-Sheng | Wang, Shao-Yan | Xing, Cheng | Li, Xu-Dong

Article Type: Research Article

Abstract: Aiming at predicting the purity of the extract and raffinate components in the simulated moving bed (SMB) chromatographic separation process, a soft-sensor modeling method was proposed by adoptig the hybrid learning algorithm based on an improved particle swarm optimization (PSO) algorithm and the least means squares (LMS) method to optimize the adaptive neural fuzzy inference system (ANFIS) parameters. The hybrid learning algorithm includes a premise parameter learning phase and a conclusion parameter learning phase. In the premise parameter learning stage, the input data space division of the SMB chromatographic separation process and the initialization of the premise parameters are realized …based on the fuzzy C-means (FCM) clustering algorithm. Then, the improved PSO algorithm is used to calculate the excitation intensity and normalized excitation intensity of all the rules for each individual in the population. In the conclusion parameter learning phase, these linear parameters are identified by the LMS method. In order to improve population diversity and convergence accuracy, the population evolution rate function was defined. According to the relationship between population diversity, population fitness function and particle position change, a new adaptive population evolution particle swarm optimization (NAPEPSO) algorithm was proposed. The inertia weight is adaptively adjusted according to the evolution of the population and the change of the particle position, thereby improving the diversity of the particle swarm and the ability of the algorithm to jump out of the local optimal solution. The simulation results show that the proposed soft-sensor model can effectively predict the key economic and technical indicators of the SMB chromatographic separation process so as to meet the real-time and efficient operation of the SMB chromatographic separation process. Show more

Keywords: Keywords: SMB chromatographic separation, soft sensing, adaptive neural fuzzy inference system, PSO algorithm, inertia weight

DOI: 10.3233/JIFS-210663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6755-6780, 2021

Authors: Zhang, Wen-Ran

Article Type: Research Article

Abstract: The road from bipolar fuzzy sets to equilibrium-based mathematical abstraction is surveyed. A continuing historical debate on bipolarity and isomorphism is outlined. Related literatures are critically reviewed to counter plagiarism, distortion, renaming, and sophistry. Based on the debate, the term “isomorphistry ” is coined. It is concluded that if isomorphism is used correctly it can be helpful in mathematics. If abused it may become isomorphistry—a kind of historical, socially constructed, entrenched, and “noble” hypocrisy hindering major scientific advances. It is shown that isomorphistry can be motivated by “denying” the originality of bipolar fuzzy sets and aimed at “justifying” plagiarism …and distortion. Thus, isomorphistry is sophistry on isomorphism . Some (-,+)-bipolar isomorphistry behaviors are critiqued. YinYang vs. YangYin are distinguished. The geometrical and logical basis of equilibrium-based AI&QI computing machinery is introduced as a new computing paradigm with logically definable causality for mind-body unity. A philosophical joke on sophistry is appended. Show more

Keywords: Bipolar fuzzy sets and mathematics, isomorphistry and plagiarism, YinYang vs. YangYin, equilibrium-based mathematical abstraction, logically definable causality for mind-body unity AI&QI, philosophical joke

DOI: 10.3233/JIFS-210692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 6781-6799, 2021