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

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. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Jiang, Chunmao | Guo, Doudou | Sun, Lijuan

Article Type: Research Article

Abstract: The basic idea of the three-way decisions (3WD) is ‘thinking in threes.’ The TAO (trisecting-acting-outcome) model of 3WD includes three components, trisect a whole into three reasonable regions, devise a corresponding strategy on the trisection, and measure the effectiveness of the outcome. By reviewing existing studies, we found that only a few papers touch upon the third component, i.e., measure the effect. This paper’s principal aim is to present an effectiveness measure framework consisting of three parts: a specific TAO model - Change-based TAO model, interval sets, and utility functions with unique characteristics. Specifically, the change-based TAO model provides a …method to measure effectiveness based on the difference before and after applying a strategy or an action. First, we use interval sets to represent these changes when a strategy or an action is applied. These changes correspond to three different intervals. Second, we use the utility measurement method to figure out three change intervals. Namely, different utility measures correspond to the different intervals, concave utility metric, direct utility metric, and convex utility metric, respectively. Third, it aggregates the toll utility through the joint of the three utilities mentioned above. The weights among these three are adjusted by a dual expected utility function that conveys the decision-makers’ preferences. We give an example and experiment highlighting the validity and practicability of the utility measure method in the change-based TAO model of three-way decisions. Show more

Keywords: Three-way decisions, change-based TAO model, interval set, utility measurement, dual expected utility

DOI: 10.3233/JIFS-202207

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Li, Chunhua | Xu, Baogen | Huang, Huawei

Article Type: Research Article

Abstract: In this paper, the notion of a fuzzy *–ideal of a semigroup is introduced by exploiting generalized Green’s relations L * and R * , and some characterizations of fuzzy *–ideals on an arbitrary semigroup are obtained. Our main purpose is to establish the relationship between fuzzy *–ideals and abundance for an arbitrary semigroup. As an application of our results, we also give some new necessary and sufficient conditions for an arbitrary semigroup to be regular and inverse, respectively.

Keywords: fuzzy*–ideals, abundant semigroups, abundance, 20M20

DOI: 10.3233/JIFS-202759

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2021

Authors: Wang, Guan | Wu, Lingjiu | Liu, Yusheng | Ye, Xiaoping

Article Type: Research Article

Abstract: With the rise of group decision-making and the increasingly complex decision-making environment, preference modeling for decision makers has become more and more important, and many preference modeling methods have emerged. Based on the fuzzy theory, researchers have proposed a large number of preference models to express the subjective uncertainty of decision makers. These methods based on fuzzy theory are collectively referred to as fuzzy preference modeling methods. The fuzzy sets preference model is the first practice of fuzzy theory used in the field of preference modeling, and it is still widely used by researchers until now. Subsequently, based on fuzzy …theory, the researchers also proposed linguistic term sets and cloud model. These methods have different representation domains, and are applicable to different decision-making environment. In this paper we give a review of classical fuzzy preference modeling methods and its latest extensions and variants. After the presentation of comparative analyses on the existing methods, we figure out some current challenges and possible future development directions in the field of fuzzy preference modeling. Show more

Keywords: Preference modeling, fuzzy preference modeling, fuzzy sets, group decision-making, decision-making modeling

DOI: 10.3233/JIFS-201529

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Dai, Ziwei | Zhang, Zhiyong | Chen, Mingzhou

Article Type: Research Article

Abstract: Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for …determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms. Show more

Keywords: Cloud manufacturing, task scheduling, multi-supply chain collaboration, new task arrival, biogeography-based optimization

DOI: 10.3233/JIFS-201066

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2021

Authors: Zhu, Jia-Nian | Liu, Xu-Chong | Liu, Chong

Article Type: Research Article

Abstract: Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k ) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k ) model, the NENGM (1,1, k ) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k ) model, and a NENGM (1,1, k ) model based on double optimization is established (abbreviated as FBNENGM (1,1, k ) …model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k ) model, the FBNENGM (1,1, k ) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k ) model based on double optimization is better than other prediction models. Show more

Keywords: Grey system theory, fractional-order accumulation, grey prediction model, FBNENGM (1, 1, k) model, ·whale optimization algorithm

DOI: 10.3233/JIFS-210023

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Nagaballi, Srinivas | Kale, Vijay S.

Article Type: Research Article

Abstract: The advent of distributed energy resources is undoubtedly transforming the nature of the electric power system. The crisis of conventional energy sources and their environmental effects resulted in the integration of Distributed Generators (DGs) into the distribution system. Simultaneous application of optimum network reconfiguration, DGs, and Distribution Static Compensator (DSTATCOM) unit’s placement in the Radial Distribution Systems (RDS) comes with a raft of technical, economic, and environmental benefits. Benefits include improved power quality, reliability, stability, mitigation of power losses, and voltage profile improvement. In this paper, the combinational process of optimal deployment of DGs and DSTATCOM units in RDS with …suitable network reconfiguration to achieve positive benefits has been analyzed. A recent metaphor-less based Artificial intelligence (AI) technique named the Rao-1 method is employed to overcome this combinational nonlinear optimization problem. The objective functions are to mitigate the power losses, enhance the voltage profile, and voltage stability index of the RDS considering the net economic cost-benefit to the distribution utility. The simulation study of this pragmatic approach problem is carried out on IEEE 33-bus RDS. The comparison of the results obtained by the Rao-1 method with other existing meta-heuristic optimization methods has been made to show its efficacy. Show more

Keywords: Artificial intelligence techniques, distributed generation, DSTATCOM, power losses, reconfiguration, voltage profile, stability, techno-economic benefits

DOI: 10.3233/JIFS-189891

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: Dat, Nguyen Quang | Ngoc Anh, Nguyen Thi | Nhat Anh, Nguyen | Solanki, Vijender Kumar

Article Type: Research Article

Abstract: Short-term electricity load forecasting (STLF) plays a key role in operating the power system of a nation. A challenging problem in STLF is to deal with real-time data. This paper aims to address the problem using a hybrid online model. Online learning methods are becoming essential in STLF because load data often show complex seasonality (daily, weekly, annual) and changing patterns. Online models such as Online AutoRegressive Integrated Moving Average (Online ARIMA) and Online Recurrent neural network (Online RNN) can modify their parameters on the fly to adapt to the changes of real-time data. However, Online RNN alone cannot handle …seasonality directly and ARIMA can only handle a single seasonal pattern (Seasonal ARIMA). In this study, we propose a hybrid online model that combines Online ARIMA, Online RNN, and Multi-seasonal decomposition to forecast real-time time series with multiple seasonal patterns. First, we decompose the original time series into three components: trend, seasonality, and residual. The seasonal patterns are modeled using Fourier series. This approach is flexible, allowing us to incorporate multiple periods. For trend and residual components, we employ Online ARIMA and Online RNN respectively to obtain the predictions. We use hourly load data of Vietnam and daily load data of Australia as case studies to verify our proposed model. The experimental results show that our model has better performance than single online models. The proposed model is robust and can be applied in many other fields with real-time time series. Show more

Keywords: Hybrid online, RNN online, multi time series, multi seasonal decompose, Electricity forecasting

DOI: 10.3233/JIFS-189884

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2021

Authors: Wu, Yang

Article Type: Research Article

Abstract: In the non-medical model physiological parameter monitoring system, learning the monitoring parameters can improve the diagnostic and prediction accuracy. Aiming at the problems of insufficient information mining and low prediction accuracy in multi-task time series, the supervised and semi-supervised learning methods in machine learning are combined to predict the physiological status of remote health monitoring objects. This method uses the K-means algorithm to cluster the same type of data and use the Multitasking Least Squares Support Vector Machine (MTLS-SVM) to train historical data for trend prediction. In order to evaluate the effectiveness of the method, the MTLS-SVM method is compared …with the K-means and MTLS-SVM methods. It can be seen from the experimental results that the body temperature data measured by the GY-MCU90615 is close to that of the digital thermometer. Moreover, the body temperature speed collected by the GY-MCU90615 can reach the millisecond level, which can well meet the needs of the system. The research shows that the method has higher prediction accuracy and has a breakthrough significance for the monitoring of athletes’ physiological parameters. Show more

Keywords: K-means algorithm, MTLS-SVM algorithm, athlete, physiological parameters, monitoring

DOI: 10.3233/JIFS-189915

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2021

Authors: Wang, Man | Zhou, Jia | Lin, Huazhi

Article Type: Research Article

Abstract: User generated content on web serves as a valuable source of information for both companies and consumers. Scholars have analyzed emotional polarity of the reviews to study customer satisfaction, but the dominant factors are not explained accurately by numerical ratings solo and the simplistic-categories of emotional polarity. This paper investigates the service attributes and detailed emotions effecting consumer satisfaction using deep learning, to explore how consumption satisfaction is influenced by emotions and what factors arouse the certain emotion. First, more than 120,000 online hotel reviews related were retrieved. Second, a novel and dataset-based seven-dimensional evaluation system, applying the BERT model …was proposed. This solves the problem of polysemous words, and can more accurately reflect the service attributes consumers really care about. In particular, the analysis reveals that the overall consumer satisfaction is affected by key service attributes including service, cleanliness, equipment, price, location, internet and catering, among which the cleanliness attributes has the greatest impact. Lastly, the latest Kismet emotional recognition method was adopted to effectively identify the emotional polarity and 11 detailed emotions. The regression relationship between emotion and overall satisfaction was also verified, which enabled a more accurate analysis for consumption emotions and satisfaction. Show more

Keywords: User generated content, service attributes, hotels, sentiment analysis, consumer satisfaction

DOI: 10.3233/JIFS-201207

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2021