Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Gowthami, S. | Harikumar, R.

Article Type: Research Article

Abstract: Melanoma is one of the widespread skin cancers that has affected millions in past decades. Detection of skin cancer at preliminary stages may become a source of reducing mortality rates. Hence, it is required to develop an autonomous system of reliable type for the detection of melanoma via image processing. This paper develops an independent medical imaging technique using Self-Attention Adaptation Generative Adversarial Network (SAAGAN). The entire processing model involves the process of pre-processing, feature extraction using Scale Invariant Feature Transform (SIFT), and finally, classification using SAAGAN. The simulation is conducted on ISIC 2016/PH2 datasets, where 10-fold cross-validation is undertaken …on a high-end computing platform. The simulation is performed to test the model efficacy against various images on several performance metrics that include accuracy, precision, recall, f-measure, percentage error, Matthews Correlation Coefficient, and Jaccard Index. The simulation shows that the proposed SAAGAN is more effective in detecting the test images than the existing GAN protocols. Show more

Keywords: Autonomous, melanoma, generative adversarial network, scale invariant feature transform, synthetic datasets

DOI: 10.3233/JIFS-220015

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2022

Authors: Liu, Yuyang | Ma, Tinghuai | Huang, Xuejian | Li, Ting

Article Type: Research Article

Abstract: As the latest and most popular concept in the world, metaverse as well as its application and technology integration has attracted the attention of all walks of life including information, economics, management, design and education, etc. However, the definition of metaverse as a technology or an intelligent scene still has no unified consensus in the academic and scientific fields. We believe that the metaverse should be a key concept and emerging theory in the future field of wisdom. This research focuses on the evaluation of the importance of college teaching courses for future education in the context of the metaverse, …and discusses which courses may be greatly affected by the concept of the metaverse. First, on the basis of analyzing the scholars’ understanding of the concept of the metaverse and related application research literature, we give the specific framework of this paper and the definition of the edu-metaverse, and propose a future intelligent teaching environment construction model based on the metaverse. It should be noted that our research is under the framework of the metaverse intelligent teaching construction model, and mainly focuses on the in-depth analysis of the teaching evaluation problem in colleges, which is a multi-attribute decision-making problem in the field of systems science. We propose an improved Pythagorean fuzzy multi-attribute decision-making method based on cumulative prospect theory, including improved scoring function, improved distance measure method, improved combination weighting method, etc., and construct a cumulative prospect value function. The proposed theory and method were applied to teaching courses of 10 majors in Chinese colleges to construct an importance evaluation indicator system. The importance of the courses was ranked, verifying the applicability and scientificity of the proposed method. The research content of this paper can provide a reference for the decision-making of Chinese education authorities. More importantly, the method proposed in this research is also universal, and can also provide theoretical support and experience reference for multi disciplines and fields, such as financial investment, engineering construction evaluation, enterprise management decision-making, and emergency management, etc. Show more

Keywords: Metaverse, intelligent teaching environment, teaching importance evaluation, cumulative prospect theory, Pythagorean fuzzy set theory

DOI: 10.3233/JIFS-221671

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2022

Authors: Li, Xu-Dong | Wang, Jie-Sheng | Hao, Wen-Kuo | Song, Hao-Ming | Zhao, Xiao-Rui

Article Type: Research Article

Abstract: With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. The arithmetic optimization algorithm (AOA) design is inspired by the distribution behavior of the main arithmetic operators in mathematics, including multiplication (M), division (D), subtraction (S) and addition (A). In order to improve the global search ability and local development ability of the AOA, the Lorentz triangle search variable step coefficient was proposed based on the broad-spectrum trigonometric functions combined with the Lorentz chaotic mapping strategy, which include a total of 24 search functions …in four categories, such as regular trigonometric functions, inverse trigonometric functions, hyperbolic trigonometric functions, and inverse hyperbolic trigonometric functions. The position update was used to improve the convergence speed and accuracy of the algorithm. Through test experiments on benchmark functions and comparison with other well-known meta-heuristic algorithms, the superiority of the proposed improved AOA was proved. Show more

Keywords: Arithmetic optimization algorithm, trigonometric function, function optimization

DOI: 10.3233/JIFS-221098

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-33, 2022

Authors: Fu, Chengcai | Lu, Fengli | Wu, Fan | Zhang, Guoying

Article Type: Research Article

Abstract: The estimation of gangue content is the main basis for intelligent top coal caving mining by computer vision, and the automatic segmentation of gangue is crucial to computer vision analysis. However, it is still a great challenge due to the degradation of images and the limitation of computing resources. In this paper, a hybrid connected attentional lightweight network (HALNet) with high speed, few parameters and high accuracy is proposed for gangue intelligent segmentation on the conveyor in the top-coal caving face. Firstly, we propose a deep separable dilation convolution block (DSDC) combining deep separable convolution and dilation convolution, which can …provide a larger receptive field to learn more information and reduce the size and computational cost of the model. Secondly, a bridging residual learning framework is designed as the basic unit of encoder and decoder to minimize the loss of semantic information in the process of feature extraction. An attention fusion block (AFB) with skip pathway is introduced to capture more representative and distinctive features through the fusion of high-level and low-level features. Finally, the proposed network is trained through the expanded dataset, and the gangue image segmentation results are obtained by pixel-by-pixel classification method. The experimental results show that the proposed HALNet reduces about 57 percentage parameters compared with U-Net, and achieves state-of-the art performance on dataset. Show more

Keywords: Gangue intelligent segmentation, the top-coal caving face, depthwise separable dilation convolution, attention mechanism

DOI: 10.3233/JIFS-213506

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2022

Authors: Amanullah, M. | Ramya, S.Thanga | Sudha, M. | Pushparathi, V.P. Gladis | Haldorai, Anandakumar | Pant, Bhaskar

Article Type: Research Article

Abstract: On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address …the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist. Show more

Keywords: Index Terms: Software defect prediction, improved random-SMOTE oversampling technique, linear pearson correlation, heuristic learning vector quantization (LVQ), training and test datasets

DOI: 10.3233/JIFS-220480

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2022

Authors: Zhao, Dazhi | Hao, Yunquan | Li, Weibin | Tu, Zhe

Article Type: Research Article

Abstract:  Whether the exact amount of training data is enough for a specific task is an important question in machine learning, since it is always very expensive to label many data while insufficient data lead to underfitting. In this paper, the topic that what is the least amount of training data for a model is discussed from the perspective of sampling theorem. If the target function of supervised learning is taken as a multi-dimensional signal and the labeled data as samples, the training process can be regarded as the process of signal recovery. The main result is that the least amount …of training data for a bandlimited task signal corresponds to a sampling rate which is larger than the Nyquist rate. Some numerical experiments are carried out to show the comparison between the learning process and the signal recovery, which demonstrates our result. Based on the equivalence between supervised learning and signal recovery, some spectral methods can be used to reveal underlying mechanisms of various supervised learning models, especially those “black-box” neural networks. Show more

Keywords: Machine learning, sampling theorem, frequency principle, signal recovery, neural network, Gaussian process regression

DOI: 10.3233/JIFS-211024

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2022

Authors: Wang, Yongguo | Bi, Xuewen | Zhang, Xinxin

Article Type: Research Article

Abstract: The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day …ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE % =1.68 %. Show more

Keywords: Photovoltaic systems, battery energy storage system, control method, prediction method, RBF neural network, experimental dataset

DOI: 10.3233/JIFS-221123

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2022

Authors: Du, Weidong

Article Type: Research Article

Abstract: Nowadays, the model compression method of knowledge distillation has drawn great attentions in Recommender systems (RS). The strategy of bidirectional distillation performs the bidirectional learning for both the teacher and the student models such that these two models can collaboratively improve with each other. However, this strategy cannot effectively exploit representation capabilities of each item and lack of the interpretability for the importance of items. Thus, how to develop an effective sampling scheme is still valuable for us to further study and explore. In this paper, we propose an improved rank discrepancy-aware item sampling strategy to enhance the performance of …bidirectional distillation learning. Specifically, by employing the distillation loss, we train the teacher and student models to reflect the fact that a user has partiality for the unobserved items. Then, we propose the improved rank discrepancy-aware sampling strategy based on feedback learning mechanism to transfer just the useful information which can effectively enhance each other. The key part of the multiple distillation training aims to select valuable items which can be re-distilled in the network for training. The proposed technique can effectively solve the problem of high ambiguity in nature for recommender system. Experimental results on several real-world recommender system datasets well demonstrate that the improved bidirectional distillation strategy shows better performance. Show more

Keywords: Bidirectional distillation, student-teacher learning, rank discrepancy aware items selection, recommender system

DOI: 10.3233/JIFS-222063

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2022

Authors: Guo, Xiaobin | Zhuo, Quanxiu

Article Type: Research Article

Abstract: [[[This paper considers the perturbation analysis of a class of fully fuzzy linear systems in which the coefficient matrix is a positive fuzzy matrix. The original fuzzy linear systems is extended into a brand new and simple crisp matrix equation using an embedding method. By discussing the perturbation of the extended crisp linear equation, the paper completes the perturbation analysis of the original fuzzy linear system. There are three cases of perturbation are analysed and the respective relative error bounds for solutions of fuzzy linear system are derived. Some numerical examples are given to illustrated our obtained results.]]]

Keywords: Fuzzy linear system, fuzzy solutions, matrix norm, perturbation analysis

DOI: 10.3233/JIFS-222421

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2022

Authors: Zhou, Tong | Zhang, Shuai | Zhang, Dongping | Chan, Verner | Yang, Sihan | Chen, Mengjiao

Article Type: Research Article

Abstract: With the increasing demand for express delivery and enhancement of sustainable logistics, the collaborative multi-depot delivery based on electric vehicles has gradually attracted the attention of logistics industry. However, most of the existing studies assumed that the products required by different customers could be delivered from any homogeneous depot, ignoring the limitations in facilities and environment of depots in reality. Thus, this study proposed a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows and battery swapping, which not only involves the multi-heterogeneous-depot to meet different customer demands, but also considers the constraints of mixed time windows to …ensure timely delivery. Furthermore, a customer-oriented multi-objective optimization model minimizing both travel costs and time window penalty costs is proposed to effectively improve both delivery efficiency and customer satisfaction. To solve this model, an extended non-dominated sorting genetic algorithm-II is proposed. This combines a new coding scheme, a new initial population generation method, three crossover operators, three mutation operators, and a particular local search strategy to improve the performance of the algorithm. Experiments were conducted to verify the effectiveness of the proposed algorithm in solving the proposed model. Show more

Keywords: Electric vehicle routing problem, multi-objective optimization, collaborative multi-heterogeneous-depot, mixed time windows, nondominated sorting genetic algorithm-II

DOI: 10.3233/JIFS-223298

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2022