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

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

Authors: Mi, Xiao | Wang, Xue-Ping

Article Type: Research Article

Abstract: This paper investigates minimal solutions of fuzzy relation inequalities with addition-min composition. It first shows the conditions that an element is a minimal solution of the inequalities, and presents the conditions that the inequalities have a unique minimal solution. It then proves that every solution of the inequalities has a minimal one and proposes an algorithm to searching for a minimal solution with computational complexity O  (n 2 ) where n is the number of unknown variables of the inequalities. This paper finally describes all minimal solutions of the inequalities.

Keywords: Fuzzy relation inequality, addition-min composition, minimal solution, Algorithm

DOI: 10.3233/JIFS-202590

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2021

Authors: Chen, Kuen-Suan | Yu, Chun-Min

Article Type: Research Article

Abstract: Industry 4.0 has fostered innovation in industries around the world. Manufacturing industries in particular are advancing towards smart manufacturing by integrating and applying relevant technologies. The output value of machine tools in Taiwan is among the top of the world and the central region is a key area for this industry chain, which supplies manufacturers in Taiwan and their international downstream customers. To support innovation in this industry, the current study used the Six Sigma quality indices for smaller-the-better, larger-the-better, and nominal-the-best quality characteristics to construct a fuzzy decision-making model. Based on this model, we propose a process quality fuzzy …analysis chart (PQFAC) for process quality improvement. Our use of fuzzy decision values to replace lower confidence limits decreases the probability of misjudgment made by sampling errors. The proposed fuzzy model also offers a more accurate assessment of process improvement requirements. We provide a real-world example to demonstrate the applicability of the proposed approach. Machine tool manufacturers can apply the platform and proposed model to evaluate their process capabilities for the vital parts suppliers and downstream customers, determine optimal machine parameter settings for processes with inadequate accuracy or precision, establish more suitable machine repair and maintenance systems, and combine the improvement experiences of customers to create an improvement knowledge base. This will enhance product value and industry competitiveness for the entire machine tool industry chain. Show more

Keywords: Fuzzy decision-making model, six sigma quality index, quality characteristic, production data, process quality fuzzy analysis chart

DOI: 10.3233/JIFS-210868

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

Authors: Murugesan, Malathi | Kaliannan, Kalaiselvi | Balraj, Shankarlal | Singaram, Kokila | Kaliannan, Thenmalar | Albert, Johny Renoald

Article Type: Research Article

Abstract: Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the …lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities. Show more

Keywords: Lung cancer, pre-processing, support vector machine, deep learning, U-Net, classification accuracy

DOI: 10.3233/JIFS-212189

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Li, Chenliang | Yu, Xiaobing | Zhao, Wen-Xuan

Article Type: Research Article

Abstract: In today’s economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the “Specification for Design of Data Center” in GB50174-2017 of China: economic factors, natural climate environment …factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China. Show more

Keywords: Big data center, PROMETHEE, MCGP model, MCDM method

DOI: 10.3233/JIFS-210319

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Liu, Chaojie | Lu, Jie | Fu, Wenjing | Zhou, Zhuoyi

Article Type: Research Article

Abstract: How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier …Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model. Show more

Keywords: Housing price, big data, MGWR, GTWR, BLAAP, batch evaluation model

DOI: 10.3233/JIFS-210917

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2021

Authors: Han, Guojiang | Chen, Caikou | Xu, Zhixuan | Zhou, Shengwei

Article Type: Research Article

Abstract: Ensemble learning using a set of deep convolutional neural networks (DCNNs) as weak classifiers has become a powerful tool for face expression. Nevertheless, training a DCNNS-based ensemble is not only time consuming but also gives rise to high redundancy due to the nature of DCNNs. In this paper, a novel DCNNs-based ensemble method, named weighted ensemble with angular feature learning (WDEA), is proposed to improve the computational efficiency and diversity of the ensemble. Specifically, the proposed ensemble consists of four parts including input layer, trunk layers, diversity layers and loss fusion. Among them, the trunk layers which are used to …extract the local features of face images are shared by diversity layers such that the lower-level redundancy can be largely reduced. The independent branches enable the diversity of the ensemble. Rather than the traditional softmax loss, the angular softmax loss is employed to extract more discriminant deep feature representation. Moreover, a novel weighting technique is proposed to enhance the diversity of the ensemble. Extensive experiments were performed on CK+ and AffectNet. Experimental results demonstrate that the proposed WDEA outperforms existing ensemble learning methods on the recogntion rate and computational efficiency. Show more

Keywords: Facial expression recognition, ensemble-based CNN, end to end learning, weight matrix unit

DOI: 10.3233/JIFS-210762

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 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. Pre-press, no. Pre-press, pp. 1-11, 2021

Authors: She, Chunyan | Zeng, Shaohua

Article Type: Research Article

Abstract: Outlier detection is a hot issue in data mining, which has plenty of real-world applications. LOF (Local Outlier Factor) can capture the abnormal degree of objects in the dataset with different density levels, and many extended algorithms have been proposed in recent years. However, the LOF needs to search the nearest neighborhood of each object on the whole dataset, which greatly increases the time cost. Most of these extended algorithms only consider the distance between an object and its neighborhood, but ignore the local distribution of an object within its neighborhood, resulting in a high false-positive rate. To improve the …running speed, a rough clustering based on triple fusion is proposed, which divides a dataset into several subsets and outlier detection is performed only on each subset. Then, considering the local distribution of an object within its neighborhood, a new local outlier factor is constructed to estimate the abnormal degree of each object. Finally, the experimental results indicate that the proposed algorithm has better performance and lower running time than the others. Show more

Keywords: Outlier detection, local outlier factor, rough Clustering

DOI: 10.3233/JIFS-211433

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Rajagopal, Sureshkumar | Umapathy, Prabha

Article Type: Research Article

Abstract: As the move towards Grid Integrated-Photovoltaic (GI-PV) system is proposed to improve the power quality development. A novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based on improved Moth Flame Optimization (MFO) algorithm is described for grid integrated approach. The solar integration of Maximum Power Point (MPP) fed into modified Switched Boost Inverter (SBI) is presented, this GI-PV connected circuit has become prominent research in a recent scenario for energy demand. Proposed MFOA-ANFIS controller has generated the duty cycle pulses to each converter circuit. The benefit of grid-tied SBI is direct control outer-loop employed to obtain MFO-ANFIS techniques. To maintain a constant …voltage DC-link is employed for inner-loop, this presence of constant DC-power to grid loads with support of MFO-ANFIS assists Proportional Integral Differential (PID) method. The results acquired by the simulation expressed that the proposed controller is addressed to maintain active and reactive power exchange, regulate DC bus-link voltages, grid voltage, and grid current. The effectiveness of the practical implication research is achieved by the output as represented as minimum grid harmonics, load current, and compensator current as verified in MATLAB/Simulink platform. Show more

Keywords: Grid Integrated-Photovoltaic, Maximum Power Point, Adaptive Neuro-Fuzzy Inference System, Moth Flame Optimization Algorithm

DOI: 10.3233/JIFS-211748

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: An, Qingxian | Zhang, Ruiyi | Shen, Yongchang

Article Type: Research Article

Abstract: Data envelopment analysis (DEA) is widely used to evaluate the performance of a group of homogeneous decision making units (DMUs). Considering the uncertainty, interval DEA has been introduced to fit into more situations. In this paper, an interval efficiency method based on slacks-based measure is proposed to solve the uncertain problems in DEA. Firstly, the maximum and minimum efficiency values of the evaluated DMU are calculated by the furthest and closest distance from the evaluated DMU to the projection points on the Pareto-efficient frontier, respectively. Then, the AHP method is used for the full ranking of DMUs. The paper uses …the pairwise comparison relationship between each pair of DMUs to construct the interval multiplicative preference relations (IMPRs) matrix. If the matrix does not meet the consistency condition, a method to obtain consistency IMPRs is introduced. According to the consistency judgment matrix, the full ranking of DMUs can be obtained. Finally, we apply our method to the performance evaluation of 12 tourist hotels in Taipei in 2019. Show more

Keywords: Performance measurement, data envelopment analysis, interval efficiency, interval multiplicative preference relations, full ranking

DOI: 10.3233/JIFS-211292

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Wang, Jianfeng | Wang, Ruomei | Liu, Shaohui

Article Type: Research Article

Abstract: Session-based recommendation is an overwhelming task owing to the inherent ambiguity in anonymous behaviors. Graph convolutional neural networks are receiving wide attention for session-based recommendation research for the sake of their ability to capture the complex transitions of interactions between sessions. Recent research on session-based recommendations mainly focuses on sequential patterns by utilizing graph neural networks. However, it is undeniable that proposed methods are still difficult to capture higher-order interactions between contextual interactions in the same session and has room for improvement. To solve it, we propose a new method based on graph attention mechanism and target oriented items to …effectively propagate information, HOGAN for brevity. Higher-order graph attention networks are used to select the importance of different neighborhoods in the graph that consists of a sequence of user actions for recommendation applications. The complementarity between high-order networks is adopted to aggregate and propagate useful signals from the long distant neighbors to solve the long-range dependency capturing problem. Experimental results consistently display that HOGAN has a significantly improvement to 71.53% on precision for the Yoochoose1_64 dataset and enhances the property of the session-based recommendation task. Show more

Keywords: Long-range dependency, higher-order network, context-aware, intelligent recommendation

DOI: 10.3233/JIFS-211155

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Dai, Tianhong | Cong, Shijie | Huang, Jianping | Zhang, Yanwen | Huang, Xinwang | Xie, Qiancheng | Sun, Chunxue | Li, Kexin

Article Type: Research Article

Abstract: In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and …image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%. Show more

Keywords: Deep learning, plant seedlings classification, machine learning, U-Net

DOI: 10.3233/JIFS-211507

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

Authors: Zhang, Yaling | Liu, Hongwei

Article Type: Research Article

Abstract: A new projection neural network approach is presented for the linear and convex quadratic second-order cone programming. In the method, the optimal conditions of the linear and convex second-order cone programming are equivalent to the cone projection equations. A Lyapunov function is given based on the G-norm distance function. Based on the cone projection function, the descent direction of Lyapunov function is used to design the new projection neural network. For the proposed neural network, we give the Lyapunov stability analysis and prove the global convergence. Finally, some numerical examples and two kinds of grasping force optimization problems are used …to test the efficiency of the proposed neural network. The simulation results show that the proposed neural network is efficient for solving some linear and convex quadratic second-order cone programming problems. Especially, the proposed neural network can overcome the oscillating trajectory of the exist projection neural network for some linear second-order cone programming examples and the min-max grasping force optimization problem. Show more

Keywords: Second-order cone programming, G-norm distance function, Neural network, Dynamic differential equation

DOI: 10.3233/JIFS-210164

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Sharma, Sonali | Singh, Uday Pratap | Raj, Kuldip

Article Type: Research Article

Abstract: The purpose of this article is to study deferred Cesrào statistical convergence of order (ξ , ω ) associated with a modulus function involving the concept of difference sequences of fuzzy numbers. The study reveals that the statistical convergence of these newly formed sequence spaces behave well for ξ  ≤ ω and convergence is not possible for ξ  > ω . We also define p -deferred Cesàro summability and establish several interesting results. In addition, we provide some examples which explain the validity of the theoretical results and the effectiveness of constructed sequence spaces. Finally, with the help of MATLAB software, …we examine that if the sequence of fuzzy numbers is bounded and deferred Cesàro statistical convergent of order (ξ , ω ) in (Δ , F , f ), then it need not be strongly p -deferred Cesàro summable of order (ξ , ω ) in general for 0 < ξ  ≤ ω  ≤ 1. Show more

Keywords: Statistical convergence, fuzzy sequence, deferred Cesàro mean, modulus function, difference sequence space

DOI: 10.3233/JIFS-211201

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Rafi, Muhammad | Wahab, Mohammad Taha | Khan, Muhammad Bilal | Raza, Hani

Article Type: Research Article

Abstract: Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There …are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs. Show more

Keywords: Time series analysis, amazon deep ar, recurrent neural network, ATM cash prediction

DOI: 10.3233/JIFS-201953

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Zhang, Juan | Guo, Wenbin

Article Type: Research Article

Abstract: This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network …and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network. Show more

Keywords: Rain removal, raindrop detection, semi-supervised learning, image restoration, shared weight

DOI: 10.3233/JIFS-212342

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

Authors: Li, Qiaoyang | Chen, Guiming | Li, Ziqi | Zhang, Yi | Xu, Lingliang

Article Type: Research Article

Abstract: To solve the problems of strong infrared radiation, poor continuous combat capability of the system, serious ablation of the launching device, and environmental pollution of the existing missile launching system, electromagnetic launch system (EMLS) has been studied for missile launch system. Combining the situation that the current research on missile electromagnetic launch system (MEMLS) mainly focuses on the key technical points and the deficiencies in the previous research on MEMLS, this paper establishes an effectiveness prediction model based on GRA-PCA-LSSVM, and discusses the investment efficiency of the system based on DEA. The experimental results prove that the established model is …reasonable, effective and superior, and provides a reference for the further improvement and development of MEMLS. Show more

Keywords: MEMLS, Grey relation analysis (GRA), Principal component analysis (PCA), Least square support vector machine (LSSVM), Data Envelopment Analysis (DEA)

DOI: 10.3233/JIFS-210353

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhou, Lintao | Wu, Qinge | Chen, Hu | Hu, Tao

Article Type: Research Article

Abstract: Accurately diagnosing power transformer faults is critical to improving the operational reliability of power systems. Although some researchers have made great efforts to improve the accuracy of transformer fault diagnosis, accurate diagnosis of multiple faults is still a difficult problem. In order to improve the accuracy of transformer multiple faults diagnosis, a multiple fault diagnosis method based on interval fuzzy probability is proposed. Different from the previous methods which provide single-value probability, this method use probability interval to represent the occurrence degree of various possible faults, which can objectively predict the potential faults that occurring in a transformer and provide …a more reasonable explanation for the diagnosis results. In the proposed method, the interval fuzzy set is used to describe the evaluation of state variables and the interval fuzzy probability model based on interval weighted average is applied to integrate the fault information. The representative matrix of fault types based on fuzzy preference relationship is established to estimate the relative importance of each gas in the dissolved gases. The proposed method can provide the probability of probable faults in transformer, help engineers quickly determine the type and location of faults, and improve the accuracy of diagnosis and maintenance efficiency of transformer. The effectiveness of the method is verified with case studies. Show more

Keywords: Multiple faults diagnosis, Power transformer, Interval fuzzy probability, Interval weighted average

DOI: 10.3233/JIFS-202083

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Bose, S. Rubin | Kumar, V. Sathiesh

Article Type: Research Article

Abstract: The real-time perception of hand gestures in a deprived environment is a demanding machine vision task. The hand recognition operations are more strenuous with different illumination conditions and varying backgrounds. Robust recognition and classification are the vital steps to support effective human-machine interaction (HMI), virtual reality, etc. In this paper, the real-time hand action recognition is performed by using an optimized Deep Residual Network model. It incorporates a RetinaNet model for hand detection and a Depthwise Separable Convolutional (DSC) layer for precise hand gesture recognition. The proposed model overcomes the class imbalance problems encountered by the conventional single-stage hand action …recognition algorithms. The integrated DSC layer reduces the computational parameters and enhances the recognition speed. The model utilizes a ResNet-101 CNN architecture as a Feature extractor. The model is trained and evaluated on the MITI-HD dataset and compared with the benchmark datasets (NUSHP-II, Senz-3D). The network achieved a higher Precision and Recall value for an IoU value of 0.5. It is realized that the RetinaNet-DSC model using ResNet-101 backbone network obtained higher Precision (99.21 %for AP0.5 , 96.80%for AP0.75 ) for MITI-HD Dataset. Higher performance metrics are obtained for a value of γ= 2 and α= 0.25. The SGD with a momentum optimizer outperformed the other optimizers (Adam, RMSprop) for the datasets considered in the studies. The prediction time of the optimized deep residual network is 82 ms. Show more

DOI: 10.3233/JIFS-210875

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Surekha, S. | Rahman, Md. Zia Ur

Article Type: Research Article

Abstract: In medical telemetry networks, cognitive radio technology is mostly used to avoid licensed spectrum underutilization and by providing access to unlicensed spectrum users without causing interference to primary users, this concept is widely used in development of smart hospitals and smart cities. In medical telemetry networks frequency spectrum concept is used for providing treatment to patients who are far away from hospitals. In cognitive radios, spectrum sensing concept is used in which energy detection method is mostly used because it is simple to implement. While measuring health care environments using cognitive radios probability detection, false alarm probability and threshold parameters …are calculated. In this paper for identifying spectrum holes in spectrum sensing using energy detection, distributed diffusion non-negative least mean square algorithm is proposed. It gives better results compared to energy detection concept alone in terms of probability detection converged earlier. If number of nodes are increasing probability detection is decreased from one and move towards left and its SNR is around 1.5-2 dB with proposed method. Hence simulation results give better results in terms of sensing ability while measuring patient condition. Show more

Keywords: Cognitive sensors, energy detection, spectrum holes, spectrum sensing, medical telemetry

DOI: 10.3233/JIFS-202673

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Li, Lin | Yu, Xiaolei | Liu, Zhenlu | Zhao, Zhimin | Wu, Chao | Zhang, Ke | Zhou, Shanhao

Article Type: Research Article

Abstract: As a non-contact automatic identification technology, Radio Frequency Identification (RFID) is of great significance to improve the simultaneous identification of multi-target. This paper designs a more efficient and accurate multi-tag reading performance measurement system based on the fusion of YOLOv3 and Elman neural network. In the machine vision subsystem, multi-tag images are collected by dual CCD and detected by neural network algorithm. The reading distance of 3D distributed multi-tag is measured by laser ranging to evaluate the reading performance of RFID system. Firstly, the multi-tag are detected by YOLOv3, which realizes the measurement of 3D coordinates, improves the prediction accuracy, …enhances the recognition ability of small targets, and improves the accuracy of 3D coordinate detection. Secondly, the relationship between the 3D coordinates and the corresponding reading distance of RFID multi-tag are modelled by Elman recurrent neural network. Finally, the reading performance of RFID multi-tag is optimized. Compared with the state-of-the-arts, the multi-tag detection rate of YOLOv3 is 17.4% higher and the time is 3.27 times higher than that of the previous template matching algorithm. In terms of reading performance, the MAPE of Elman neural network is 1.46 %, which is at least 21.43 % higher than other methods. In running time, Elman only needs 1.69s, which is at least 28.40% higher than others. Thus, the system not only improves the accuracy, but also improves the speed, which provides a new insight for the measurement and optimization of RFID performance. Show more

Keywords: RFID, neural network, elman, YOLOv3, performance optimization

DOI: 10.3233/JIFS-211838

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

Authors: Hao, Dong | Zhang, Runtong | Bai, Kaiyuan

Article Type: Research Article

Abstract: Online health communities (OHCs) have emerged as a significant platform for people communicating health information and self-healthcare management. In recent, the researches focusing on its performance measurement and the service quality evaluation have drawn intensive attention. Although some qualitative methods have made evaluation and analyses for the OHCs performance, the studies based on fuzzy multi-attribute decision making theory are rarely developed in the service quality evaluation of OHCs. In view of the complexity and uncertainty of evaluation mission, this paper develops an integrated evaluation approach of the OHC service quality based on the q-rung orthopair fuzzy linguistic aggregation operators. Firstly, …we propose the cross-entropy of q-rung orthopair fuzzy numbers, which is applied in solving the optimal weight of indicators by a linear programming model. Next, the q-rung orthopair fuzzy linguistic power average (q-ROFLPA) and q-rung orthopair fuzzy linguistic partitioned dual Maclaurin symmetric mean (q-ROFLPDMSM) operators are developed for aggregating the assessment information and ranking the OHCs. Based on the proposed aggregation operators, the evaluation indicator system and an evaluation framework are constructed to accomplish the service quality evaluation of OHCs. Finally, a practical evaluation case of OHCs is provided to demonstrate the reliability and advantages of the proposed approach. Show more

Keywords: Online health communities, q-rung orthopair fuzzy linguistic sets, partitioned dual Maclaurin symmetric mean, multi-attribute decision making, service quality evaluation

DOI: 10.3233/JIFS-211257

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Sheng, Wen

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. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Zhang, Zhaojun | Lu, Rui | Zhao, Minglong | Luan, Shengyang | Bu, Ming

Article Type: Research Article

Abstract: The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in …this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments. Show more

Keywords: Path planning, mobile robot, genetic algorithm, initial population

DOI: 10.3233/JIFS-211423

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

Authors: Wei, Jinpeng | Qu, Shaojian | Jiang, Shan | Feng, Can | Xu, Yuting | Zhao, Xiaohui

Article Type: Research Article

Abstract: Individual opinion is one of the vital factors influencing the consensus in group decision-making, and is often uncertain. The previous studies mostly used probability distribution, interval distribution or uncertainty distribution function to describe the uncertainty of individual opinions. However, this requires an accurate understanding of the individual opinions distribution, which is often difficult to satisfy in real life. In order to overcome this shortcoming, this paper uses a robust optimization method to construct three uncertain sets to better characterize the uncertainty of individual initial opinions. In addition, we used three different aggregation operators to obtain collective opinions instead of using …fixed values. Furthermore, we applied the numerical simulations on flood disaster assessment in south China so as to evaluate the robustness of the solutions obtained by the robust consensus models that we proposed. The results showed that the proposed models are more robust than the previous models. Finally, the sensitivity analysis of uncertain parameters was discussed and compared, and the characteristics of the proposed models were revealed. Show more

Keywords: Group decision making, aggregation operator, consensus models, uncertainty set, robust optimization

DOI: 10.3233/JIFS-211704

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Liang, Pei | Hu, Junhua | Chin, KwaiSang

Article Type: Research Article

Abstract: The use of probabilistic linguistic preference relations (PLPRs) in pairwise comparisons enhances the flexibility of quantitative decision making. To promote the application of probabilistic linguistic term sets (PLTSs) and PLPRs, this paper introduces the consistency and consensus measures and adjustment strategies to guarantee the rationality of preference information utilized in the group decision making process. First of all, a novel entropy-based similarity measure is developed with PLTSs. Hereafter an improved consistency measure is defined on the basis of the proposed similarity measure, and a convergent algorithm is constructed to deal with the consistency improving process. Furthermore, a similarity-based consensus measure …is developed in a given PLPR, and the consensus reaching process is presented to deal with the unacceptable consensus degree. The proposed consistency improving and consensus reaching processes follow a principle of minimum information loss, called a local adjustment strategy. In particular, the presented methods not only overcome the deficiencies in existing studies but also enhance the interpretation and reduce the complexity of the group decision making process. Finally, the proposed consistency measure and improving process, as well as consensus measure and reaching process are verified through a numerical example for the medical plan selection issue. The result and in-depth comparison analysis validate the feasibility and effectiveness of the proposed methods. Show more

Keywords: Group decision making, PLTSs, PLPRs, consistency, consensus

DOI: 10.3233/JIFS-211371

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-25, 2021

Authors: Jha, Sunil Kumar | Marina, Ninoslav | Wang, Jinwei | Ahmad, Zulfiqar

Article Type: Research Article

Abstract: Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach …is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12%of the primary tumor, 96.45%of breast cancer Wisconsin, 94.44%of cryotherapy, 93.81%of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively. Show more

Keywords: Hybrid machine learning, fuzzy nearest neighbor, disease diagnosis prediction, feature generation and selection

DOI: 10.3233/JIFS-211820

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

Authors: Zhao, Shuai | You, Fucheng | Chang, Wen | Zhang, Tianyu | Hu, Man

Article Type: Research Article

Abstract: The BERT pre-trained language model has achieved good results in various subtasks of natural language processing, but its performance in generating Chinese summaries is not ideal. The most intuitive reason is that the BERT model is based on character-level composition, while the Chinese language is mostly in the form of phrases. Directly fine-tuning the BERT model cannot achieve the expected effect. This paper proposes a novel summary generation model with BERT augmented by the pooling layer. In our model, we perform an average pooling operation on token embedding to improve the model’s ability to capture phrase-level semantic information. We use …LCSTS and NLPCC2017 to verify our proposed method. Experimental data shows that the average pooling model’s introduction can effectively improve the generated summary quality. Furthermore, different data needs to be set with varying pooling kernel sizes to achieve the best results through comparative analysis. In addition, our proposed method has strong generalizability. It can be applied not only to the task of generating summaries, but also to other natural language processing tasks. Show more

Keywords: Summary generation, fine-tuning bert, average pooling, transformer

DOI: 10.3233/JIFS-211229

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Cui, Xiaoning | Wang, Qicai | Zhang, Rongling | Dai, Jinpeng | Li, Sheng

Article Type: Research Article

Abstract: The compressive strength of concrete can be predicted by machine learning. One thousand thirty samples of concrete compressive strength data were used as the dataset. Machine learning was applied to prediction of concrete compressive strength with seven machine learning algorithms. To improve data utilization and generalization ability of machine learning model, ten data sets were constructed by feature reorganization for data augmentation. Compared with other machine learning models, the XGBoost model based on Boosting tree algorithm had the highest prediction accuracy and the most robust generalization ability. With different multi-feature combination input conditions, the R2 score of the XGBoost …algorithm was 0.9283, the MAE score was 3.4292, the MAPE score was 12.5656, and the RMSE score was 5.2813. The error accumulation curve of the XGBoost algorithm was analyzed. When the compressive strength of concrete is at 5–20MPa, the error contribution rate is higher. When the concrete compressive strength is at 20–40MPa, the prediction result error of the model drops sharply. When the strength reaches 40MPa, the error contribution rate of the model tends to converge and the error contribution rate is stable between 1 and 1.2, which indicates that the model has high prediction accuracy when the compressive strength is higher than 40 MPa. Show more

Keywords: Machine learning, prediction of Compressive strength, feature reorganization, XGBoost, data enhancement

DOI: 10.3233/JIFS-211088

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Tan, Chao | Liu, Jie

Article Type: Research Article

Abstract: The prime focus of knowledge distillation (KD) seeks a light proxy termed student to mimic the outputs of its heavy neural networks termed teacher, and makes the student run real-time on the resource-limited devices. This paradigm requires aligning the soft logits of both teacher and student. However, few doubts whether the process of softening the logits truly give full play to the teacher-student paradigm. In this paper, we launch several analyses to delve into this issue from scratch. Subsequently, several simple yet effective functions are devised to replace the vanilla KD. The ultimate function can be an effective alternative to …its original counterparts and work well with other skills like FitNets. To claim this point, we conduct several visual tasks on individual benchmarks, and experimental results verify the potential of our proposed function in terms of performance gains. For example, when the teacher and student networks are ShuffleNetV2-1.0 and ShuffleNetV2-0.5, our proposed method achieves 40.88%top-1 error rate on Tiny ImageNet. Show more

Keywords: Neural network compression, knowledge distillation, knowledge transfer

DOI: 10.3233/JIFS-211549

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Qarehkhani, Azam | Golsorkhtabaramiri, Mehdi | Mohamadi, Hosein | Tabari, Meisam Yadollahzadeh

Article Type: Research Article

Abstract: Directional sensor networks (DSNs) are classified under wireless networks that are largely used to resolve the coverage problem. One of the challenges to DSNs is to provide coverage for all targets in the network and, at the same time, to maximize the lifetime of network. A solution to this problem is the adjustment of the sensors’ sensing ranges. In this approach, each sensor adjusts its own sensing range dynamically to sense the corresponding target(s) and decrease energy consumption as much as possible through forming the best cover sets possible. In the current study, a continuous learning automata-based method is proposed …to form such cover sets. To assess the proposed algorithm’s performance, it was compared to the results obtained from a greedy algorithm and a learning automata algorithm. The obtained results demonstrated the superiority of the proposed algorithm regarding the maximization of the network lifetime. Show more

Keywords: Directional sensor networks, continuous learning automata, target-coverage, cover set formation

DOI: 10.3233/JIFS-210759

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

Authors: Guo, Dayong | Hu, Qing

Article Type: Research Article

Abstract: Aiming at the problems of low precision, slow data transmission speed and long response time of silk quality and temperature control in tobacco intelligent production line, a multi-index testing system is designed. According to the characteristics of PROFIBUS fieldbus technology, combined with PROFIBUS transmission technology, a factory level information network is formed with PROFIBUS-DP as the exchange mode. Based on the PROFIBUS technology, the dual redundancy structure of control ring network and management information ring network is adopted, and the whole network architecture is constructed by logic layering. From the point of view of building enterprise MES system, it locates …real-time production monitoring, production task receiving and production line related data collection, integrates equipment control layer, centralized monitoring layer and production management layer, and designs system function structure. The functional structure of the system, and the establishment of a number of data tables, to achieve a tobacco intelligent production line silk quality detection system design. Experimental results show that this method can effectively speed up the data transmission speed and shorten the system response time. Show more

Keywords: PROFIBUS fieldbus technology, tobacco intelligent production line, silk quality, multi-index integrated test system

DOI: 10.3233/JIFS-211936

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Wang, Sha | Li, Teng | Liu, Zifeng | Pan, Dongbo | Zhang, Yu

Article Type: Research Article

Abstract: The embedding capacity and steganography quality are two important performance indicators of data hiding which has practical application value for copyright and intellectual property protection, public information protection and online elections. Many researches presented hiding methods to improve the performance. However, the existing data hiding methods have problems such as low embedding capacity or poor stego-image quality. This paper proposes a new method (Single Pixel Modification, SPM) to improve the performance further. The SPM (Single Pixel Modification) method embeds k secret bits into a cover-pixel with the idea that minimizing the change to cover-pixel and adopting modulus operation based …on 2 k . The experimental results show that the proposed method has better performance than methods compared and the highest hiding capacity can reach 4 bits per pixel and the average PSNR of stego-images is 34.83 dB . The source code and related materials are made to public to make it easy for researchers to verify the work and stimulate further research. Show more

Keywords: Data hiding, modulus calculation, cover image, stego-image

DOI: 10.3233/JIFS-211606

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Jin, Wenbin | Cui, Wenxia | Wang, Zhenjie

Article Type: Research Article

Abstract: Finite-time synchronization is concerned for the fractional-order complex-valued fuzzy cellular neural networks (FOCVFCNNs) with leakage delay and time-varying delays. Without using the usual complex-valued system decomposition method, this paper designs the different forms of the controllers by using 2-norm. And we construct the appropriate Lyapunov functional and apply inequality analytical techniques, some new sufficient conditions are obtained to ensure finite-time synchronization of the FOCVFCNNs. The upper bound of setting-time function is obtained. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results.

Keywords: Finite-time synchronization, fractional-order, complex-valued, time-varying delays

DOI: 10.3233/JIFS-211183

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

Authors: Yuchong, Huang | Ning, Xu | Nan, Wang | Jie, Li

Article Type: Research Article

Abstract: Through innovatively introducing the receding horizon into probabilistic model checking, an online strategy synthesis method for multi-robot systems from local automatons is proposed to complete complex tasks that are assigned to each robot. Firstly, each robot is modeled as a Markov decision process which models both probabilistic and nondeterministic behavior. Secondly, the task specification of each robot is expressed as a linear temporal logic formula. For some tasks that robots cannot complete by themselves, the collaboration requirements take the form of atomic proposition into the LTL specifications. And the LTL specifications are transformed to deterministic rabin automatons over which a …task progression metric is defined to determine the local goal states in the finite-horizon product systems. Thirdly, two horizons are set to determine the running steps in automatons and MDPs. By dynamically building local finite-horizon product systems, the collaboration strategies are synthesized iteratively for each robot to satisfy the task specifications with maximum probability. Finally, through simulation experiments in an indoor environment, the results show that the method can synthesize correct strategies online for multi-robot systems which has no restriction on the LTL operators and reduce the computational burden brought by the automaton-based approach. Show more

Keywords: Receding horizon, linear temporal logic, Markov decision process, probabilistic model checking, multi-robot collaboration

DOI: 10.3233/JIFS-211427

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Wu, Jian | Jin, Yuting | Zhou, Mi | Cao, Mingshuo | Liu, Yujia

Article Type: Research Article

Abstract: Sustainable supplier selection (SSS) plays an increasingly critical role in the stability and development of the organization with increasing environmental awareness. This article proposes a linguistic multiple attribute group decision-making method to select the appropriate sustainable supplier by combing Decision Making and Trial Evaluation Laboratory(DEMATEL) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). To do that, a distribution linguistic based DEMATEL technique is developed to deal with the complexity in criteria of SSS. To eliminate the inconsistency among multiple decision-makers providing the preference information of evaluation criteria, a minimum adjusting cost feedback mechanism is utilized to reach group consensus. Therefore, the …proposed weights obtaining method can not only deal with the subjectivity of evaluation criterion but also satisfy group decision-makers with different profits and backgrounds. Then, based on the evaluation matrices of supplier performance, it calculates the ranking of alternative suppliers by the VIKOR method. Hence, it can deal with the ambiguity of decision makers’ evaluation and provide the best solution for decision-makers, as a consequence, it makes the final evaluation result more feasible and operable. Finally, the effectiveness and efficiency of this method are verified based on the actual situation of ABC Company. This study proposed a linguistic multiple attribute group decision-making method to select the appropriate sustainable supplier by combing Decision Making and Trial Evaluation Laboratory(DEMATEL) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). What’s more, the proposed method considered the group consensus reaching processes. Show more

Keywords: Sustainable supplier selection, , Group decision making, DEMATEL, VIKOR, Consensus

DOI: 10.3233/JIFS-211929

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Shabbir, Wasif | Aijun, Li | Taimoor, Muhammad | Yuwei, Cui

Article Type: Research Article

Abstract: The problem of quick and accurate fault estimation in nonlinear systems is addressed in this article by combining the technique of radial basis function neural network (RBFNN) and global fast terminal sliding mode control (GFTSMC) concept. A new strategy to update the neural network weights, by using the global fast terminal sliding surface instead of conventional error back propagation method, is introduced to achieve real time, quick and accurate fault estimation which is critical for fault tolerant control system design. The combination of online learning ability of RBFNN, to approximate any nonlinear function, and finite time convergence property of GFTSMC …ensures quick detection and accurate estimation of faults in real time. The effectiveness of the proposed strategy is demonstrated through simulations using a nonlinear model of a commercial aircraft and considering a wide range of sensors and actuators faults. The simulation results show that the proposed method is capable of quick and accurate online fault estimation in nonlinear systems and shows improved performance as compared to conventional RBFNN and other techniques existing in literature. Show more

Keywords: Fault estimation, neural networks, global fast terminal sliding mode control

DOI: 10.3233/JIFS-211547

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Zhang, Yanhan | Tian, Shengwei | Yu, Long | Ren, Yuan | Gao, Zhongyu | Hou, Long

Article Type: Research Article

Abstract: In recent years, the incidence of skin diseases has increased significantly, and some malignant tumors caused by skin diseases have brought great hidden dangers to people’s health. In order to help experts perform lesion measurement and auxiliary diagnosis, automatic segmentation methods are very needed in clinical practice. Deep learning and contextual information extraction methods have been applied to many image segmentation tasks. However, their performance is limited due to insufficient training of a large number of parameters and these parameters sometimes fail to capture long-term dependencies. In addition, due to the many interfering factors of the skin disease image, the …complex boundary and the uncertain size and shape of the lesion, the segmentation of the skin disease image is still a challenging problem. To solve these problems, we propose a long-distance contextual attention network(LCA-Net). By connecting the non-local module and the channel attention (CAM) in parallel to form a non-local operation, the long-term dependence is captured from the two dimensions of space and channel to enhance the network’s ability to extract features of skin diseases. Our method has an average Jaccard index of 0.771 on the ISIC2017 dataset, which represents a 0.6%improvement over the ISIC2017 Challenge Champion model. The average Jaccard index of 5-fold cross-validation on the ISIC2018 dataset is 0.8256. At the same time, we also compared with some advanced methods of image segmentation, the experimental results show our proposed method has a competitive performance. Show more

Keywords: Skin lesion segmentation, attentional mechanism, artificial intelligence, deep learning

DOI: 10.3233/JIFS-211182

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Narendiranath Babu, T. | Singh, Prabhu Pal | Somesh, M. | Jha, Harshit Kumar | Rama Prabha, D. | Venkatesan, S. | Ramesh Babu, V.

Article Type: Research Article

Abstract: The planetary gearbox works on an epicyclic gear train consisting of sun gear meshed with planets gears and ring gear. It got advantages due to its large torque to weight ratio and reduced vibrations. It is mostly employed in analog clocks, automobile automatic gearbox, Lathe machines, and other heavy industries. Therefore, it was imperative to analyze the various faults occurring in a gearbox. Furthermore, come up with a method so that failures can be avoided at the early stage. It was also a reason why it became the field of intensive research. Moreover, the technology of neural networks emerged recently, …where machine learning models are trained to detect uneven vibrations on their own. This attracted many researchers to perform the study to devise their own methods of prediction. The central concept of fault prediction by the neural network without human beings’ interference inspired this study. Most industries always wanted to know if their operation line is working fine or not. In this study, an attempt was made to apply the method of deep learning on one of the most critical gearboxes because of its components and functionality. A significant part of the study also involved filtering the vibration data obtained while testing. Comparative analysis of the variation of the peak of acceleration was performed for healthy and faulty conditions. Show more

Keywords: Planetary gearbox, neural networks, deep learning

DOI: 10.3233/JIFS-210229

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Authors: Zhao, Yun-Tao | Gan, Lei | Li, Wei-Gang | Liu, Ao

Article Type: Research Article

Abstract: The path planning of traditional spot welding mostly uses manual teaching method. Here, a new model of path planning is established from two aspects of welding length and welding time. Then a multi-objective grey wolf optimization algorithm with density estimation (DeMOGWO) is proposed to solve multi-object discrete problems. The algorithm improves the coding method and operation rules, and sets the density estimation mechanism in the environment update. By comparing with other five algorithms on the benchmark problem, the simulation results show that DeMOGWO is competitive which takes into account both diversity and convergence. Finally, the DeMOGWO algorithm is used to …solve the model established of path planning. The Pareto solution obtained can be used to guide the welding sequence of body-in-white(BIW) workpieces. Show more

Keywords: Spot welding robot, multi-objective grey wolf optimization algorithm, density estimation, path planning

DOI: 10.3233/JIFS-202810

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

Authors: Xiong, Pingping | Xiao, Lushuang | Liu, Yuchun | Yang, Zhuo | Zhou, Yifan | Cao, Shuren

Article Type: Research Article

Abstract: Faced with serious growing global warming problem, it is important to predict carbon emissions. As there are a lot of factors affecting carbon emissions, a novel multi-variable grey model (GM(1,N) model) based on linear time-varying parameters discrete grey model (TDGM(1,N)) has been established. In this model, linear time-varying function is introduced into the traditional model, and dynamic optimization of fixed parameters which can only be used for static analysis is carried out. In order to prove the applicability and effectiveness of the model, this paper compared the model with the traditional model and simulated the carbon emissions of Anhui Province …from 2005 to 2015. Carbon emissions in the next two years are also predicted. The results show that the TDGM(1,N) model has better simulation effect and higher prediction accuracy than the traditional GM(1,N) model and the multiple regression model(MRM) in practical application of carbon emissions prediction. In addition, the novel model of this paper is also used to predict the carbon emissions in 2018–2020 of Anhui Province. Show more

Keywords: Linear time-varying parameters, grey system theory, multi-variable model, carbon emissions, forecasting

DOI: 10.3233/JIFS-202711

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Kumar, Manish | Kumar, Bhavnesh | Rani, Asha

Article Type: Research Article

Abstract: The primary objective of this work is to optimize the induction motor rotor flux so that maximum efficiency is attained in the facets of parameter and load variations. The conventional approaches based on loss model are sensitive to modelling accuracy and parameter variations. The problem is further aggravated due to nonlinear motor parameters in different speed regions. Therefore, this work introduces an adaptive neuro-fuzzy inference system-based rotor flux estimator for electric vehicle. The proposed estimator is an amalgamation of fuzzy inference system and artificial neural network, in which fuzzy inference system is designed using artificial neural network. The training data …for neuro-fuzzy estimator is generated offline by acquiring rotor flux for different values of torque. The conventional fuzzy logic and differential calculation methods are also developed for comparative analysis. The efficacy of developed system is established by analyzing it under varying load conditions. It is revealed from the results that suggested methodology provides an improved efficiency i.e. 94.51% in comparison to 82.68% for constant flux operation. Show more

Keywords: Loss minimization, torque estimation, adaptive neuro fuzzy inference system (ANFIS), electrical vehicle (EV)

DOI: 10.3233/JIFS-189885

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

Authors: Prakash, R | Ayyar, K

Article Type: Research Article

Abstract: This paper presents an Enhanced Whale Optimization Algorithm (EWO) approach for tuning to perfection of Fractional Order Proportional Integral and integral order Controller (FOPI λ ) is used to sensorless speed control of permanent magnet Brushless DC (PMBLDC) motor under the operating dynamic condition such as (i) speed change by set speed command signal (ii) varying load conditions, (iii) integrated conditions and (iv) controller parameters uncertainty. On the other hand, it deals with a reduced THD (Total Harmonic Distortion) under dynamic operating conditions to improve the power quality for the above control system. Here present are three optimization techniques, …namely (i) Enhanced Whale Optimization (EWO), (ii) Invasive Weed Optimization (IWO), and (iii) Social Spider Optimization (SSO) for fine-tuning of the FOPI λ controller parameters with reduction of THD. The proposed optimization algorithm optimized FOPI λ controller are compared under various BLDC motor operating conditions. Based on the results of MATLAB/Simulink models, the proposed algorithms are evaluated. Here, both the simulation and the results of the experiments are validated for the proposed controller technique. It demonstrates that the effectiveness of the proposed controllers is completely validated by comparing the three intelligent optimization techniques mentioned above. The EWO optimized FOPI λ controller for speed control of sensorless PMBLDC motor clearly outperforms the other two intelligent controllers by minimizing the time domain parameters, THD, performance Indices error, convergence time, control efforts, cost function, mean and standard deviation. Show more

Keywords: BLDC motor drives, fractional order PID controller (FOPIλ), whale optimization algorithm, sensorless speed control techniques

DOI: 10.3233/JIFS-212167

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2021

Authors: Li, Shugang | Lu, Hanyu | Dou, Qian | Wang, Ru | Yu, Zhaoxu

Article Type: Research Article

Abstract: In the social business platform, continuous marketing to consumers can fully explore the consumer purchasing potential. However, since consumers can be influenced by their social friends, their tastes often change, which resulting in the cold start problem of familiar users (CSPFU), and the traditional product recommendation methods are difficult to achieve satisfactory results because they focus on identifying the preferable new products instead of boring familiar products. Therefore, a consumer multi-stage compensation product evaluation model (CMCPEM) based on the multidimensional correlation of products and customers to identify the products that consumers may feel tired is proposed. Specifically, the multidimensional correlation …indexes are firstly proposed to depict the preferences of the consumer for the target product to be identified, other consumers who have social contagion and structural equivalence relationships with the consumer and other consumers of homogeneous products. After the direct linear, non-linear and indirect fusion of these multidimensional correlation indexes, the compensation indexes (CIs) are proposed to comprehensively describe the first stage of product evaluation process of consumers. Then, J test in the non-nested model is used to screen out the non-nested CIs that consumers focus on. Finally, in the third stage, the final decision result is given by comprehensively considering CIs that consumers focus on and the indexes that represent consumers’ favorite. Experiment results on YELP data confirm the effectiveness of CMCPEM in successfully launching the continuous marketing campaign. Show more

Keywords: Social business network, consumer boring products, J test, non-nested model, continuous marketing

DOI: 10.3233/JIFS-201980

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Zhou, Yinfeng | Li, Jinjin | Wang, Hongkun | Sun, Wen

Article Type: Research Article

Abstract: In knowledge space theory (KST), knowledge structure is an effective feature to evaluate individuals’ knowledge and guide future learning. How to construct knowledge structures is one of the key research problems in KST. At present, the knowledge structure has been generalized to the polytomous knowledge structure. This article mainly focuses on the special polytomous knowledge structures delineated by Skills, which are called fuzzy knowledge structures. We consider how to construct fuzzy knowledge structures based on the relationship between items and skills, and how to find the learning paths for specific knowledge domains. First, we construct knowledge structures in four models, …which are the conjunctive model of skill maps, the disjunctive and conjunctive models of fuzzy skill maps, and the competency model of fuzzy skill multimaps. Second, we assess individuals’ skills and find the learning paths for the specific knowledge domains in the first three models. Finding the learning paths for a specific knowledge domain can guide learning and improve the learning efficiency of individuals. Finally, we analyze some data sets to show that the algorithms proposed are effective and applicable. These works can be applied to adaptive learning systems, which bring great convenience for assessing individuals’ knowledge and guiding future learning. Show more

Keywords: Fuzzy knowledge structure, learning path, disjunctive model, conjunctive model, competency model

DOI: 10.3233/JIFS-212018

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Akbar, Sumaiya Begum | Govindarajan, Valarmathi | Thanupillai, Kalaiselvi

Article Type: Research Article

Abstract: Bitcoin is an innovative decentralized digital currency without intermediaries. Bitcoin price prediction is a demanding need in the present situation. This paper makes an investigation on the Bitcoin price forecast with a Bi-directional Gated Recurrent Unit (GRU) time series method, combined with opinion mining based on Twitter and Reddit feeds. An hourly basis sentimental analysis through the implementation of Natural Language Processing presents a positive impact of sentimental analysis on the Bitcoin price prediction. For prediction, RNN, long-short memory, GRU has been utilized. Unidirectional and Bi-directional versions of all three networks with and without sentimental analysis were implemented for comparison. …Of all the techniques implemented Bi-directional GRU along with sentimental analysis gives a minimum RMSE and Minimum absolute percentage error of 1108.33 and 7.384%. Thus, the framework including Bi-Directional GRU along with Sentimental Analysis provides better results than the State-of-art methods. Show more

Keywords: Bitcoin, neural network, mining, GRU, RMSE, MAPE

DOI: 10.3233/JIFS-211217

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

Authors: Abdelhamid, Abdelaziz A. | Alotaibi, Sultan R.

Article Type: Research Article

Abstract: Internet of things (IoT) plays significant role in the fourth industrial revolution and attracts an increasing interest due to the rapid development of smart devices. IoT comprises factors of twofold. Firstly, a set of things (i.e., appliances, devices, vehicles, etc.) connected together via network. Secondly, human-device interaction to communicate with these things. Speech is the most natural methodology of interaction that can enrich user experience. In this paper, we propose a novel and effective approach for building customized voice interaction for controlling smart devices in IoT environments (i.e., Smart home). The proposed approach is based on extracting customized tiny decoding …graph from a large graph constructed using weighted finite sates transducers. Experimental results showed that tiny decoding graphs are very efficient in terms of computational resources and recognition accuracy in clean and noisy conditions. To emphasize the effectiveness of the proposed approach, the standard Resources Management (RM1) dataset was employed and promising results were achieved when compared with four competitive approaches. Show more

Keywords: Intelligent systems, internet-of-things, voice interaction

DOI: 10.3233/JIFS-201781

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

Authors: Xiang, Chen | Xing, Wang | Hubiao, Zhang | Yuheng, Xu | You, Chen | Xiaotian, Wu

Article Type: Research Article

Abstract: Threat evaluation (TE) is essential in battlefield situation awareness and military decision-making. The current processing methods for uncertain information are not effective enough for their excessive subjectivity and difficulty to obtain detailed information about enemy weapons. In order to optimize TE on uncertain information, an approach based on interval Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and the interval SD-G1 (SD standard deviation) method is proposed in this article. By interval SD-G1 method, interval number comprehensive weights can be calculated by combining subjective and objective weights. Specifically, the subjective weight is calculated by interval G1 method, …which is an extension of G1 method into interval numbers. And the objective weight is calculated by interval SD method, which is an extension of SD method with the mean and SD of the interval array defined in this paper. Sample evaluation results show that with the interval SD-G1 method, weights of target threat attributes can be better calculated, and the approach combining interval TOPSIS and interval SD-G1 can lead to more reasonable results. Additionally, the mean and SD of interval arrays can provide a reference for other fields such as interval analysis and decision-making. Show more

Keywords: Interval number, multiple attribute decision making (MADM), interval TOPSIS, comprehensive weight, threat evaluation

DOI: 10.3233/JIFS-210945

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2021

Authors: Xing, Yuping

Article Type: Research Article

Abstract: The recently proposed q -rung orthopair fuzzy set (q -ROFS) whose main feature is that the qth power of membership degree (MD) and the qth power of non-membership degree (NMD) is equal to or less than 1, is a powerful tool to describe uncertainty. The major contribution of this paper lies to investigate power point average (PPA) aggregation operators with q -rung orthopair fuzzy information based on Frank t-conorm and t-norm. Since the existing power average (PA) operators all rely on the traditional distance measures to measure support degree between the input values, it cannot reflect decision makers’ attitude. In …response, this paper introduces firstly a series of distance measures for q -rung orthopair fuzzy numbers (q -ROFNs) based on point operators, from which the corresponding support measures can be obtained. Secondly, based on the proposed point distance measures, new Frank power point average aggregation operators are proposed to aggregate q -rung orthopair fuzzy information. Finally, a novel multiple attribute decision making (MADM) technique is presented based on the proposed Frank power point average aggregation operators. The developed MADM method not only can get more objective information, but also avoid the influence of unduly high or low attribute values on the decision result, providing a new way for decision makers (DMs) under q -rung orthopair fuzzy environment. Show more

Keywords: Multi-attribute decision making, q-Rung orthopair fuzzy set, frank operational laws, q-Rung orthopair fuzzy Frank power point aggregation operators

DOI: 10.3233/JIFS-211152

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2021

Authors: Tao, Yujie | Suo, Chunfeng | Wang, Guijun

Article Type: Research Article

Abstract: Piecewise linear function (PLF) is not only a generalization of univariate segmented linear function in multivariate case, but also an important bridge to study the approximation of continuous function by Mamdani and Takagi-Sugeno fuzzy systems. In this paper, the definitions of the PLF and subdivision are introduced in the hyperplane, the analytic expression of PLF is given by using matrix determinant, and the concept of approximation factor is first proposed by using m -mesh subdivision. Secondly, the vertex coordinates and their changing rules of the n -dimensional small polyhedron are found by dividing a three-dimensional cube, and the algebraic cofactor …and matrix norm of corresponding determinants of piecewise linear functions are given. Finally, according to the method of solving algebraic cofactors and matrix norms, it is proved that the approximation factor has nothing to do with the number of subdivisions, but the approximation accuracy has something to do with the number of subdivisions. Furthermore, the process of a specific binary piecewise linear function approaching a continuous function according to infinite norm in two dimensions space is realized by a practical example, and the validity of PLFs to approximate a continuous function is verified by t -hypothesis test in Statistics. Show more

Keywords: Piecewise linear function, mesh subdivision, approximation factor, mamdani fuzzy system, matrix norm

DOI: 10.3233/JIFS-210770

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Qihang, Ma | Jian, Zh | Jiahao, Zhang

Article Type: Research Article

Abstract: Local information coding helps capture the fine-grained features of the point cloud. The point cloud coding mechanism should be applicable to the point cloud data in different formats. However, the local features of the point cloud are directly affected by the attributes, size and scale of the object. This paper proposes an Adaptive Locally-Coded point cloud classification and segmentation Network coupled with Genetic Algorithm(ALCN-GA), which can automatically adjust the size of search cube to complete network training. ALCN-GA can adapt to the features of 3D data at different points, whose adjustment mechanism is realized by designing a robust crossover and …mutation strategy. The proposed method is tested on the ModelNet40 dataset and S3DIS dataset. Respectively, the overall accuracy and average accuracy is 89.5% and 86.5% in classification, and overall accuracy and mIoU of segmentation is 80.34% and 51.05%. Compared with PointNet, average accuracy in classification and mIoU of segmentation is improved about 10% and 11% severally. Show more

Keywords: Genetic algorithm, 3D classification, segmentation, deep learning, local coding

DOI: 10.3233/JIFS-211541

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

Authors: Xing, Shixiong | Chen, Guohua | Yu, Guoming | Chen, Xiaolan | Sun, Chuan

Article Type: Research Article

Abstract: According to the characteristics of NC milling, an approach for optimization of milling parameters considering high efficiency and low carbon based on gravity search algorithm is proposed. Taking the carbon emission and processing time as the objectives, the cutting rate, feed per tooth, and cutting width as the optimization variables. A multi-objective optimization model of NC milling parameters is established. An non-dominated sorting gravity search algorithm (NSGSA) is used to solve the multi-objective model, and the position update backoff operation is introduced. Finally, taking NC machining process as an example, the multi-objective optimization results and the single objective optimization results …are compared respectively, the actual data show that when the optimization objective is high efficiency and low carbon, the processing time and carbon emissions are 173 and 192 respectively. The comparison results show that the combination of processing parameters obtained by multi-objective optimization is the best, the optimal parameter combination obtained by NSGSA algorithm is verified by grey correlation analysis, and the grey correlation degree of the optimal solution set is 0.81, which is the largest in all solution sets. This approach can help the decision-makers flexibly select the corresponding milling parameters, and provide decision-makers with flexible selection decisions suitable for various scenarios. Show more

Keywords: NC milling, multi-objective model, milling parameter optimization, NSGSA, Grey relational analysis

DOI: 10.3233/JIFS-210059

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Muhiuddin, G. | Mahboob, A. | Khan, N. M. | Al-Kadi, D.

Article Type: Research Article

Abstract: In this paper, we introduce new types of fuzzy (m , n )-ideals in ordered semigroups. In fact, the notion of (∈ , ∈ ∨ (κ * , q κ ))-fuzzy (m , n )-ideals of the ordered semigroups is introduced. Further, we present the characterzations of this notion in different ways. Then the (κ * , κ )-lower part of the (∈ , ∈ ∨ (κ * , q κ ))-fuzzy (m , n )-ideals is defined and its associated properties are investigated. After that, (m , n )-regular ordered semigroups are characterized in terms of its (∈ , ∈ ∨ (κ * , q κ …))-fuzzy (m , n )-ideals and their (κ * , κ )-lower parts. Show more

Keywords: Ordered semigroups, fuzzy sets, (∈ , ∈ ∨ (κ*, qκ))-fuzzy (m, n)-ideals, (m, n)-regular ordered semigroups

DOI: 10.3233/JIFS-210378

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2021

Authors: Chu, Xiaolin | Zhao, Ruijuan

Article Type: Research Article

Abstract: Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s …building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity). Show more

Keywords: Building carbon emission, prediction model, support vector regression (SVR), particle swarm optimization (PSO), low-carbon economy development

DOI: 10.3233/JIFS-211435

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Xiao, Lu | Wei, Guiwu | Guo, Yanfeng | Chen, Xudong

Article Type: Research Article

Abstract: Interval-valued intuitionistic fuzzy set (IVIFS) is a flexible method to deal with uncertainty and fuzziness. For the past few years, extensive researches about the multi-attribute group decision making (MAGDM) problems based on IVIFSs has been extensively studied in many fields. In this study, the Taxonomy method based on IVIFSs (IVIF-Taxonomy) was proposed for MAGDM problems. For the sake of the objectivity of attribute weight, entropy is introduced into the proposed model. The IVIF-Taxonomy method fully considers the weight of the decision makers (DMs) and the homogeneity of the chosen alternatives, making it more realistic. In addition, we apply IVIF-Taxonomy method …to fund selection to verify the validity of IVIF-Taxonomy method. Finally, the trustworthy of IVIF-Taxonomy method is proved by comparing with the aggregate operator, IVIF-TOPSIS method, IVIF-GRA method and modified IVIF-WASPAS method. Show more

Keywords: Multiple attribute group decision making (MAGDM), Interval-valued intuitionistic fuzzy sets, taxonomy method, entropy

DOI: 10.3233/JIFS-210918

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Saif, Shahela | Tehseen, Samabia

Article Type: Research Article

Abstract: Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being …designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution. Show more

Keywords: Video forgery, forgery detection, deepfakes, deepfake videos, deepfake detection

DOI: 10.3233/JIFS-210625

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2021

Article Type: Retraction

DOI: 10.3233/JIFS-219217

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

Authors: Zhao, Yifan | Li, Kai

Article Type: Research Article

Abstract: In the recent years, several new construction methods of fuzzy implications have been proposed. However, these construction methods actually care about that the new implication could preserve more properties. In this paper, we introduce a new method for constructing fuzzy implications based on an aggregation function with F  (1,  0) =1, a fuzzy implication I and a non-decreasing function φ , called FI φ -construction. Specifically, some logical properties of fuzzy implications preserved by this construction are studied. Moreover, it is studied how to use the FI φ -construction to produce a new implication satisfying a specific property. Furthermore, we …produce two new subclasses of fuzzy implications such as UI φ -implications and G p I φ -implications by this method and discuss some additional properties. Finally, we provide a way to generate fuzzy subsethood measures by means of FI φ -implications. Show more

Keywords: Fuzzy implication, aggregation function, uninorm, nullnorm, fuzzy subsethood measure

DOI: 10.3233/JIFS-202385

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

Authors: Tang, Han | Dalin,

Article Type: Research Article

Abstract: Uncertain time series analysis has been developed for studying the imprecise observations. In this paper, we propose a nonlinear model called uncertain max-autoregressive (UMAR) model. The unknown parameters in model are estimated by the least squares estimation. Then the residual analysis is presented. In many cases, there are some outliers in the time series due to short-term change in the underlying process. The UMAR model offers an alternative for detecting outliers in the imprecise observations. Based on the previous theoretical results, the UMAR model is used to forecast the future. Finally, an example suggests that the new proposed time series …model works well compared to the uncertain autoregressive (UAR) model. Show more

Keywords: Uncertain time series analysis, principle of least squares, residual analysis, outlier detection, confidence interval

DOI: 10.3233/JIFS-210848

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

Authors: Du, Yuqin | Ren, Weijia | Du, Yuhong | Hou, Fujun

Article Type: Research Article

Abstract: A Hamacher operator in a q-rung orthopair trapezoidal fuzzy linguistic environment is studied based on the definition of the q-rung orthopair fuzzy set and the Hamacher aggregation operator. First, we define a new fuzzy variable called q-rung orthopair trapezoidal fuzzy linguistic sets, and the operational laws, score function, accuracy function, comparison rules, and distance measures of the IVPFLVS are defined. Second, based on the Hamacher operator and the q-rung orthopair trapezoidal fuzzy linguistic sets, we propose several q-rung trapezoidal fuzzy linguistic Hamacher operator information aggregation operators, such as the generalized q-rung orthopair trapezoidal fuzzy linguistic Hamacher weighted averaging (q-GROTrFLHWA) operator, …and the generalized q-rung orthopair trapezoidal fuzzy linguistic Hamacher weighted geometric (q-GROTrFLHWG) operator. Third, some desirable properties of the correlation operators, such as idempotency, boundedness, and monotonicity are discussed. Finally, there are two group decision schemes based on q-rung orthopair trapezoidal fuzzy information with known attribute weights. The decision-making scheme is applied to the evaluation of school teaching quality, and the practicability and effectiveness of the scheme are demonstrated by different methods. Show more

Keywords: The q-rung trapezoidal fuzzy linguistic set, multi-attribute decision making, Hamacher operator, application

DOI: 10.3233/JIFS-210056

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Yu, Shujuan | Liu, Danlei | Zhang, Yun | Zhao, Shengmei | Wang, Weigang

Article Type: Research Article

Abstract: As an important branch of Nature Language Processing (NLP), how to extract useful text information and effective long-range associations has always been a bottleneck for text classification. With the great effort of deep learning researchers, deep Convolutional Neural Networks (CNNs) have made remarkable achievements in Computer Vision but still controversial in NLP tasks. In this paper, we propose a novel deep CNN named Deep Pyramid Temporal Convolutional Network (DPTCN) for short text classification, which is mainly consisting of concatenated embedding layer, causal convolution, 1/2 max pooling down-sampling and residual blocks. It is worth mentioning that our work was highly inspired …by two well-designed models: one is temporal convolutional network for sequential modeling; another is deep pyramid CNN for text categorization; as their applicability and pertinence remind us how to build a model in a special domain. In the experiments, we evaluate the proposed model on 7 datasets with 6 models and analyze the impact of three different embedding methods. The results prove that our work is a good attempt to apply word-level deep convolutional network in short text classification. Show more

Keywords: Deep convolution network, causal convolution, shortcut connection, short text classification

DOI: 10.3233/JIFS-210970

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

Authors: Li, Chao | Yan, Yeyu | Zhao, Zhongying | Luo, Jun | Zeng, Qingtian

Article Type: Research Article

Abstract: Owing the continuous enrichment of mobile application resources, mobile applications carry almost all user behaviors and preferences. The analysis of user behavior regarding mobile terminals has become an important research direction. The frequency with which users click on mobile applications reflects their preferences to a certain extent. In this study, we propose a mobile application click-frequency prediction model based on heterogeneous information network representation. This model first constructs a heterogeneous information network between users’ mobile devices and mobile applications. To generate a meaningful sequence of network-embedded nodes, we perform a random walk on a specified meta-path. Finally, the prediction of …users’ mobile application click frequency is completed using representation fusion and matrix factorization. Experiments show that our method outperforms other baseline methods in terms of the mean absolute error and root mean square error. Therefore, the application of a heterogeneous information network representation method to the prediction model is effective. This study is significant to the behavior research of mobile terminal users. Show more

Keywords: Heterogeneous information network, network representation learning, prediction algorithm, mobile application

DOI: 10.3233/JIFS-211488

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

Authors: Liao, Ningna | Gao, Hui | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: Facing with a sea of fuzzy information, decision makers always feel it difficult to select the optimal alternatives. Probabilistic hesitant fuzzy sets (PHFs) utilize the possible numbers and the possible membership degrees to describe the behavior of the decision makers. though this environment has been introduced to solve problems using different methods, this circumstance can still be explored by using different method. This paper’ s aim is to develop the MABAC (Multi-Attributive Border Approximation area Comparison) decision-making method which based on cumulative prospect theory (CPT) in probabilistic hesitant fuzzy environment to handle multiple attributes group decision making (MAGDM) problems. Then …the weighting vector of attributes can be calculated by the method of entropy. Then, in order to show the applicability of the proposed method, it is validated by a case study for buying a house. Finally, through comparing the outcome of comparative analysis, we conclude that this designed method is acceptable. Show more

Keywords: Multiple attribute group decision making (MAGDM), probability hesitant fuzzy sets (PHFs), cumulative prospect theory (CPT); MABAC method

DOI: 10.3233/JIFS-210889

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

Authors: Bhatia, Tanveen Kaur | Kumar, Amit | Appadoo, S.S.

Article Type: Research Article

Abstract: Enayattabr et al. (Journal of Intelligent and Fuzzy Systems 37 (2019) 6865– 6877) claimed that till now no one has proposed an approach to solve interval-valued trapezoidal fuzzy all-pairs shortest path problems (all-pairs shortest path problems in which distance between every two nodes is represented by an interval-valued trapezoidal fuzzy number). Also, to fill this gap, Enayattabr et al. proposed an approach to solve interval-valued trapezoidal fuzzy all-pairs shortest path problems. In this paper, an interval-valued trapezoidal fuzzy shortest path problem is considered to point out that Enayattabr et al.’s approach fails to find correct shortest distance between two fixed nodes. Hence, it …is inappropriate to use Enayattabr et al.’s approach in its present from. Also, the required modifications are suggested to resolve this inappropriateness of Enayattabr et al.’s approach. Show more

Keywords: Interval-valued trapezoidal fuzzy all-pairs shortest path problem, interval-valued trapezoidal fuzzy numbers, signed distance ranking

DOI: 10.3233/JIFS-210182

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 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. Pre-press, no. Pre-press, pp. 1-16, 2021

Authors: Ding, Ling | Chen, Xiaojun | Xiang, Yang

Article Type: Research Article

Abstract: Few-shot text classification aims to learn a classifier from very few labeled text data. Existing studies on this topic mainly adopt prototypical networks and focus on interactive information between support set and query instances to learn generalized class prototypes. However, in the process of encoding, these methods only pay attention to the matching information between support set and query instances, and ignore much useful information about intra-class similarity and inter-class dissimilarity between all support samples. Therefore, in this paper we propose a negative-supervised capsule graph neural network (NSCGNN) which explicitly takes use of the similarity and dissimilarity between samples to …make the text representations of the same type closer with each other and the ones of different types farther away, leading to representative and discriminative class prototypes. We firstly construct a graph to obtain text representations in the form of node capsules, where both intra-cluster similarity and inter-cluster dissimilarity between all samples are explored with information aggregation and negative supervision. Then, in order to induce generalized class prototypes based on those node capsules obtained from graph neural network, the dynamic routing algorithm is utilized in our model. Experimental results demonstrate the effectiveness of our proposed NSCGNN model, which outperforms existing few-shot approaches on three benchmark datasets. Show more

Keywords: Graph neural networks, negative supervision, dynamic routing, few-shot learning

DOI: 10.3233/JIFS-210795

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Yang, Songyue | Yu, Guizhen | Meng, Zhijun | Wang, Zhangyu | Li, Han

Article Type: Research Article

Abstract: In the intelligent unmanned systems, unmanned aerial vehicle (UAV) obstacle avoidance technology is the core and primary condition. Traditional algorithms are not suitable for obstacle avoidance in complex and changeable environments based on the limited sensors on UAVs. In this article, we use an end-to-end deep reinforcement learning (DRL) algorithm to achieve the UAV autonomously avoid obstacles. For the problem of slow convergence in DRL, a Multi-Branch (MB) network structure is proposed to ensure that the algorithm can get good performance in the early stage; for non-optimal decision-making problems caused by overestimation, the Revise Q-value (RQ) algorithm is proposed to …ensure that the agent can choose the optimal strategy for obstacle avoidance. According to the flying characteristics of the rotor UAV, we build a V-Rep 3D physical simulation environment to test the obstacle avoidance performance. And experiments show that the improved algorithm can accelerate the convergence speed of agent and the average return of the round is increased by 25%. Show more

Keywords: UAV, obstacle avoidance, DQN, overestimation, convergence rate

DOI: 10.3233/JIFS-211192

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Wang, Heng | Ye, Xiang | Li, Yong

Article Type: Research Article

Abstract: Model pruning aims to reduce the parameter amount of deep neural networks while retaining the performance. Existing strategies often treat all layers equally and all layers simply share the same pruning rate. However, it is observed from our experiments that the redundancy degree differs from layer to layer. Based on this observation, this work proposes a pruning strategy depending on the layer-wise redundancy degree. Firstly, we define the redundancy degree for each layer by the norm and similarity redundancy of filters. Then a novel layer-wise strategy, Redundancy-dependent Filter Pruning (RedFiP), is proposed which prunes different proportion of filters at different …layers according to the defined redundancy degree. Since the redundancy analysis and experimental results of RedFiP show that deeper layers need fewer filters, a phase-wise strategy, Phased Filter Pruning (PFP), is proposed that divides the layers into three phases and layers in each phase share the same pruning rate. The phase-wise PFP allows the layer-wise RedFiP to be easily implemented in existing structures of deep neural networks. Experimental results show that when total parameters are pruned by 40%, RedFiP outperforms the state-of-the-art strategy FPGM-Mixed by 1.83% on CIFAR-100, and even slightly outperforms the non-pruned model by 0.11% on CIFAR-10. On ImageNet-1k, RedFiP (30%) and PFP (30%) outperform FPGM-Mixed (30%) by 1.3% and 0.8% with ResNet-18. Show more

Keywords: Filter pruning, redundancy, phase, importance

DOI: 10.3233/JIFS-211346

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

Authors: Yuan, Ling | Pan, Zhuwen | Sun, Ping | Wei, Yinzhen | Yu, Haiping

Article Type: Research Article

Abstract: Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad, is a critical task in online advertising systems. The problem is very challenging since(1) an effective prediction relies on high-order combinatorial features, and(2)the relationship to auxiliary ads that may impact the CTR. In this paper, we propose Deep Context Interaction Network on Attention Mechanism(DCIN-Attention) to process feature interaction and context at the same time. The context includes other ads in the current search page, historically clicked and unclicked ads of the user. Specifically, we use the attention mechanism to learn the interactions between …the target ad and each type of auxiliary ad. The residual network is used to model the feature interactions in the low-dimensional space, and with the multi-head self-attention neural network, high-order feature interactions can be modeled. Experimental results on Avito dataset show that DCIN outperform several existing methods for CTR prediction. Show more

Keywords: Click-through rate, attention mechanism, residual network, feature interaction, context

DOI: 10.3233/JIFS-210830

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

Authors: Zhang, Jinping | Deng, Xiaoping | Li, Chengdong | Su, Guanqun | Yu, Yulong

Article Type: Research Article

Abstract: Building energy consumption (BEC) prediction often requires constructing a corresponding model for each building based historical data. However, the constructed model for one building is difficult to be reused in other buildings. Recent approaches have shown that cloud-edge collaboration architecture is promising in realizing model reuse. How to complete the reuse of cloud energy consumption prediction models at the edge and reduce the computational cost of the model training is one of the key issues that need to be solved. To handle the above problems, a cloud-edge collaboration based transferring prediction method for BEC is proposed in this paper. Specifically, …a model library stored prediction models for different types of buildings is constructed based the historical energy consumption data and the long short-term memory (LSTM) network in the cloud firstly; then, the similarity measurement strategies of time series with different granularity are given, and the model to be transferred from the model library is matched by analyzing the similarity between observation data uploaded to the cloud and the historical data collected in the cloud; finally, the fine-tuning strategy of the matching prediction model is given, and this model is fine-tuned at the edge to achieve its reuse in concrete application scenarios. Experiments on practical datasets reveal that compared with the prediction model which doesn’t utilize the transfer strategy, the proposed prediction model has better performance according to MAE and RMSE. Experimental results also confirm that the proposed method effectively reduces the computational cost of the network training at the edge. Show more

Keywords: Cloud-edge collaboration, transfer learning, data driven, similarity analysis, energy consumption prediction

DOI: 10.3233/JIFS-211607

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Vo, Tham

Article Type: Research Article

Abstract: Recently, many pre-trained text embedding models have been applied to effectively extract latent features from texts and achieve remarkable performance in various downstream tasks of sentiment analysis domain. However, these pre-trained text embedding models also encounter limitations related to the capability preserving the syntactical structure as well as the global long-range dependent relationships of words. Thus, they might fail to recognize the relevant syntactical features of words as valuable evidences for analyzing sentiment aspects. To overcome these limitations, we proposed a novel deep semantic contextual embedding technique for sentiment analysis, called as: SE4SA. Our proposed SE4SA is a multi-level text …embedding model which enables to jointly exploit the long-range syntactical and sequential representations of texts. Then, these achieved rich semantic textual representations can support to have a better understanding on the sentiment aspects of the given text corpus, thereby resulting the better performance on sentiment analysis task. Extensive experiments in several benchmark datasets demonstrate the effectiveness or our proposed SE4SA model in comparing with recent state-of-the-art model. Show more

Keywords: Sentiment analysis, GCN, BERT, attention, masked language model

DOI: 10.3233/JIFS-211535

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2021

Authors: Wang, Fen | Ali, Zeeshan | Mahmood, Tahir | Zeng, Shouzhen

Article Type: Research Article

Abstract: The Muirhead mean (MM) operators offer a flexible arrangement with its modifiable factors because of Muirhead’s general structure. On the other hand, MM aggregation operators perform a significant role in conveying the magnitude level of options and characteristics. In this manuscript, the complex spherical fuzzy uncertain linguistic set (CSFULS), covering the grade of truth, abstinence, falsity, and their uncertain linguistic terms is proposed to accomplish with awkward and intricate data in actual life dilemmas. Furthermore, by using the MM aggregation operators with the CSFULS, the complex spherical fuzzy uncertain linguistic MM (CSFULMM), complex spherical fuzzy uncertain linguistic weighted MM (CSFULWMM), …complex spherical fuzzy uncertain linguistic dual MM (CSFULDMM), complex spherical fuzzy uncertain linguistic dual weighted MM (CSFULDWMM) operators, and their important results are also elaborated with the help of some remarkable cases. Additionally, multi-attribute decision-making (MADM) based on the Multi-MOORA (Multi-Objective Optimization Based on a Ratio Analysis plus full multiplicative form), and proposed operators are developed. To determine the rationality and reliability of the elaborated approach, some numerical examples are illustrated. Finally, the supremacy and comparative analysis of the elaborated approaches with the help of graphical expressions are also developed. Show more

Keywords: Complex spherical fuzzy uncertain linguistic sets, Muirhead mean Aggregation operators, Dual Muirhead mean Aggregation operators, Multi-attribute decision-making methods.

DOI: 10.3233/JIFS-211455

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-26, 2021

Authors: Yu, Xiaobing | Wu, Xuejing | Chen, Hong | Wang, Xuming | Li, Chenliang | Ji, Zhonghui

Article Type: Research Article

Abstract: Social vulnerability assessment is of great significance for risk management and reduction. Carrying out the assessment is beneficial to the sustainability of the development of society and the economy. For this purpose, Jiangsu province in China is taken as the study area to explore the social vulnerability assessment at a city level. A framework has been constructed from three dimensions of demographics, economics, and social security. In our study, a new approach based on the maximizing deviation method and TODIM model is proposed to evaluate social vulnerability in Jiangsu province. For the sake of analysis, we divide 13 cities of …Jiangsu province into three parts, namely the southern part, central part, and northern part, according to the geographical location. As a result, the north part performance of social vulnerability is the worst among the three regions. The average of the northern part has always obviously exceeded the others of Jiangsu province from 2012 to 2017, which indicates that the north part is the most vulnerable to natural hazards. In addition, the performance of the southern part is relatively better than that of the central region. Especially, Suqian has always been at the bottom from 2012 to 2017, which reveals the ability to withstand natural disasters is the most insufficient. Our findings also imply that social vulnerability is related to local economic development to some extent. Show more

Keywords: Social vulnerability, maximizing deviation method, TODIM model, risk management

DOI: 10.3233/JIFS-211428

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Tilli, Tuomo | Espinosa-Leal, Leonardo

Article Type: Research Article

Abstract: Online advertisements are bought through a mechanism called real-time bidding (RTB). In RTB, the ads are auctioned in real-time on every webpage load. The ad auctions can be of two types: second-price or first-price auctions. In second-price auctions, the bidder with the highest bid wins the auction, but they only pay the second-highest bid. This paper focuses on first-price auctions, where the buyer pays the amount that they bid. This research evaluates how multi-armed bandit strategies optimize the bid size in a commercial demand-side platform (DSP) that buys inventory through ad exchanges. First, we analyze seven multi-armed bandit algorithms on …two different offline real datasets gathered from real second-price auctions. Then, we test and compare the performance of three algorithms in a production environment. Our results show that real data from second-price auctions can be used successfully to model first-price auctions. Moreover, we found that the trained multi-armed bandit algorithms reduce the bidding costs considerably compared to the baseline (naïve approach) on average 29%and optimize the whole budget by slightly reducing the win rate (on average 7.7%). Our findings, tested in a real scenario, show a clear and substantial economic benefit for ad buyers using DSPs. Show more

Keywords: Bid shading, bid optimization, multi-armed bandits, reinforcement learning

DOI: 10.3233/JIFS-202665

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Bhuvaneswari, R. | Ganesh Vaidyanathan, S.

Article Type: Research Article

Abstract: Diabetic Retinopathy (DR) is one of the most common diabetic diseases that affect the retina’s blood vessels. Too much of the glucose level in blood leads to blockage of blood vessels in the retina, weakening and damaging the retina. Automatic classification of diabetic retinopathy is a challenging task in medical research. This work proposes a Mixture of Ensemble Classifiers (MEC) to classify and grade diabetic retinopathy images using hierarchical features. We use an ensemble of classifiers such as support vector machine, random forest, and Adaboost classifiers that use the hierarchical feature maps obtained at every pooling layer of a convolutional …neural network (CNN) for training. The feature maps are generated by applying the filters to the output of the previous layer. Lastly, we predict the class label or the grade for the given test diabetic retinopathy image by considering the class labels of all the ensembled classifiers. We have tested our approaches on the E-ophtha dataset for the classification task and the Messidor dataset for the grading task. We achieved an accuracy of 95.8% and 96.2% for the E-ophtha and Messidor datasets, respectively. A comparison among prominent convolutional neural network architectures and the proposed approach is provided. Show more

Keywords: Diabetic retinopathy, convolutional neural network(CNN), feature extraction, ensemble of classifiers

DOI: 10.3233/JIFS-211364

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Yao, Jianrong | Wang, Zhongyi | Wang, Lu | Zhang, Zhebin | Jiang, Hui | Yan, Surong

Article Type: Research Article

Abstract: With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal …feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model. Show more

Keywords: Credit scoring, hybrid model, feature selection, machine learning, ensemble learning

DOI: 10.3233/JIFS-211828

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Xu, Jie | Lv, Jian | Yang, Hong-Tai | Li, Yan-Lai

Article Type: Research Article

Abstract: The video conferencing software is regarded as a significant tool for social distancing and getting incorporations up and going. Due to the indeterminacy of epidemic evolution and the multiple criteria, this paper proposes a video conferencing software selection method based on hybrid multi-criteria decision making (HMCDM) under risk and cumulative prospect theory (CPT), in which the criteria values are expressed in various mathematical forms (e.g., real numbers, interval numbers, and linguistic terms) and can be changed with natural states of the epidemic. Initially, the detailed description of video conferencing software selection problem under an epidemic are given. Subsequently, a whole …procedure for video conferencing software selection is conducted, the approaches for processing and normalizing the multi-format evaluation values are presented. Furthermore, the expectations provided by DMs under different natural states of the epidemic are considered as the corresponding reference points (RP). Based on this, the matrix of gains and losses is constructed. Then, the prospect values of all criteria and the perceived probabilities of natural states are calculated according to the value function and the weighting function in CPT respectively. Finally, the proposed method is illustrated by an empirical case study, and the comparison analysis and the sensitivity analysis for the loss aversion parameter are conducted to prove the effectiveness and robustness. The results show that considering the psychological characteristics of DMs in selection decision is beneficial to avoid the unacceptable and potential loss risks. This study could provide a useful guideline for managers who intend to select appropriate video conferencing software. Show more

Keywords: Epidemic, video conferencing software selection, cumulative prospect theory, hybrid multi-criteria decision making under risk

DOI: 10.3233/JIFS-211054

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Alshammari, Ibtesam | Parimala, Mani | Jafari, Saeid

Article Type: Research Article

Abstract: Imprecision in the decision-making process is an essential consideration. In order to navigate the imprecise decision-making framework, measuring tools and methods have been developed. Pythagorean fuzzy soft sets are one of the new methods for dealing with imprecision. Pythagorean fuzzy soft topological spaces is an extension of intuitionistic fuzzy soft topological spaces. These sets generalizes intuitionistic fuzzy sets for a broader variety of implementations. This work is a gateway to study such a problem. The concept of Pythagorean fuzzy soft topological spaces(PyFSTS), interior, closure, boundary, neighborhood of Pythagorean fuzzy soft spaces PyFSS, base and subspace of PyFSTSs are presented and …its properties are figured out. We established an algorithm under uncertainty based on PyFSTS for multi-attribute decision-making (MADM) and to validate this algorithm, a numerical example is solved for suitable brand selection. Finally, the benefits, validity, versatility and comparison of our proposed algorithms with current techniques are discussed.The advantage of the proposed work is to detect vagueness with more sizably voluminous valuation space than intuitionistic fuzzy sets. Show more

Keywords: Pythagorean fuzzy soft sets, Pythagorean fuzzy soft topology, Pythagorean fuzzy soft interior and soft closure, multi-attribute decision making

DOI: 10.3233/JIFS-210805

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

Authors: More, Sujeet | Singla, Jimmy

Article Type: Research Article

Abstract: Knee rheumatoid arthritis (RA) is the highly prevalent, chronic, progressive condition in the world. To diagnose this disease in the early stage in detail analysis with magnetic resonance (MR) image is possible. The imaging modality feature allows unbiased assessment of joint space narrowing (JSN), cartilage volume, and other vital features. This provides a fine-grained RA severity evaluation of the knee, contrasted to the benchmark, and generally used Kellgren Lawrence (KL) assessment. In this research, an intelligent system is developed to predict KL grade from the knee dataset. Our approach is based on hybrid deep learning of 50 layers (ResNet50) with …skip connections. The proposed approach also uses Adam optimizer to provide learning linearity in the training stage. Our approach yields KL grade and JSN for femoral and tibial tissue with lateral and medial compartments. Furthermore, the approach also yields area under curve (AUC) of 0.98, accuracy 96.85%, mean absolute error (MAE) 0.015, precision 98.31%, and other commonly used parameters for the existence of radiographic RA progression which is improved than the existing state-of-the-art. Show more

Keywords: Magnetic resonance imaging, ResNet50, MultiResUNet, Sparse aware noise reduction Convolutional neural network (SANR_CNN), Adam optimizer

DOI: 10.3233/JIFS-212015

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2021

Authors: Du, Cheng | Ye, Jun

Article Type: Research Article

Abstract: The ambiguity and uncertainty of human cognition about actual engineering problems are very challenging and indispensable issues in the information expression and aggregation. However, existing various cubic (hesitant) concepts may not reasonably represent the hybrid information of both an interval-valued fuzzy value and a fuzzy sequence with identical and/or different fuzzy values, which commonly occurs in engineering fields. To express the hybrid information, this paper first proposes the notion of a cubic fuzzy multi-valued set as a new extension of existing cubic (hesitant) notions and defines operational relations of cubic fuzzy multi-valued elements. To obtain reasonable operations between different fuzzy …sequence lengths in cubic fuzzy multi-valued elements, cubic fuzzy multi-valued elements are transformed into cubic fuzzy-consistency elements based on the average value and consistency degree/level (complement of standard deviation) of a fuzzy sequence in a cubic fuzzy multi-valued element. Next, we present operations of cubic fuzzy-consistency elements and an expected value of a cubic fuzzy-consistency element for ranking cubic fuzzy-consistency elements. Further, we propose a cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator, and then develop a multi-attribute group decision-making model using the cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator and expected value of cubic fuzzy-consistency elements to solve group decision-making problems under the cubic fuzzy multi-valued environment. To reflect the feasibility and effectiveness of the developed group decision-making model, the developed group decision-making model is utilized in an example on the selection problem of slope design schemes regarding an open pit mine in the cubic fuzzy multi-valued environment. Comparative analysis indicates the flexibility and rationality of the developed group decision-making model. Show more

Keywords: Cubic fuzzy multi-valued set, expected value, cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator, group decision making, slope design scheme

DOI: 10.3233/JIFS-211205

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Ngo, Quoc-Dung | Nguyen, Huy-Trung | Nguyen, Le-Cuong

Article Type: Research Article

Abstract: Over the last decade, due to exponential growth in IoT devices and weak security mechanisms, the IoT is now facing more security challenges than ever before, especially botnet malware. There are many security solutions in detecting botnet malware on IoT devices. However, detecting IoT botnet malware, particularly multi-architecture botnets, is challenging. This paper proposes a graphically structured feature extraction mechanism integrated with reinforcement learning techniques in multi-architecture IoT botnet detection. We then evaluate the proposed approach using a dataset of 22849 samples, including actual IoT botnet malware, and achieve a detection rate of 98.03 with low time consumption. The proposed …approach also achieves reliable results in detecting the new IoT botnet (has a new architecture-processor) not appearing in the training dataset at 96.69. To promote future research in the field, we share relevant datasets and source code. Show more

Keywords: IoT security, IoT botnet, reinforcement learning, static analysis, PSI-walk

DOI: 10.3233/JIFS-210699

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

Authors: Revathy, K. | Thenmozhi, K. | Praveenkumar, Padmapriya | Amirtharajanr, Rengarajan

Article Type: Research Article

Abstract: In Today’s pandemic situation, ‘Spectrum accessing and smart usage’ is the sacred Mantra uttered by every individual citizen in the world. Work from home for techies, online classes for students, games for kids, webinar for teaching fraternity etc., are going almost on indoor coverage without any limit in pace because of the smart spectrum coverage by the network service providers. This paper provides an add-on facility to the existing wireless infrastructure to provide a better user experience in this highly regrettable routine. In this paper, a cognitive domain unused spectrum holes are efficiently handled by (i) adaptive spectrum management technique; …(ii) Fuzzy Inference System based spectrum administration and (iii) Hybrid Cognitive Femtocell approaches based on the user demand and their applications. The proposed integrated cognitive femtocell and Fuzzy-based approach reduces the indoor coverage problems and enhances the throughput of the macrocell users by allowing adaptive spectrum management based on the demand, thereby eliminating spectrum underlay and overlay problems during critical conditions. In cognitive femtocell networks, the access points are prepared and installed with Cognitive Radio which can determine spectrum dynamically by macrocells and nearby Femto Access Points. It adjusts its radiating parameters to evade the macrocells’ interferences and the neighbouring femtocells, thereby maximising the spectrum band’s overall utility. Show more

Keywords: Spectrum, cognitive, fuzzy, femtocell

DOI: 10.3233/JIFS-202540

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Wang, Y. | Chu, Y.M. | Khan, Y.A. | Khan, Z.Y. | Liu, Q. | Malik, M.Y. | Abbas, S.Z.

Article Type: Research Article

Abstract: This paper addressed the prediction of heart sicknesses from hazard elements through a decision-making tree. We introduced the facts mining technique in public fitness to extract high-degree knowledge from raw data, which facilitates predicting heart diseases from risk factors and their prevention. The existing work intends to introduce a new risk element in heart diseases using novel data mining strategies. Latest actual international affected person’s information (e.g., smoking, area of residence, age, weight, blood stress, chest pain, low-density lipoproteins (LDL), high-density lipoproteins (HDL), block arteries became accrued by way of the use of questionnaire through direct interview technique from patients. …Novel two-variable decision trees are constructed for coronary heart illness records primarily based on chance factors and ranking of risk elements. The results show a correct prediction of cardiovascular disease (CVD) from the risk factor if records on chance factors are available as direct results of this study, tobacco, loss of physical exercise, and weight-reduction plan play a vital role in predicting heart diseases, which is the most important reason for mortality in developing countries, especially in my country. Show more

Keywords: Machine learning, heart diseases, prevention, decision tree, risk factors, prediction, hybrid technique, low-density lipoproteins (LDL), high-density lipoproteins (HDL)

DOI: 10.3233/JIFS-202226

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2021

Authors: Farooq, Muhammad | Qamar-uz-zaman, | Ijaz, Muhammad

Article Type: Research Article

Abstract: The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of …FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW. Show more

Keywords: FEW, MLE, statistical properties, applications, and simulation

DOI: 10.3233/JIFS-211519

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

Authors: Yihong, Li | Yunpeng, Wang | Tao, Li | Xiaolong, Lan | Han, Song

Article Type: Research Article

Abstract: DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used density-based clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts . Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new …density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN. Show more

Keywords: Density-based clustering algorithm, Grid, The nearest neighbor, DBSCAN

DOI: 10.3233/JIFS-211922

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Hamed, Nadir O. | Samak, Ahmed H. | Ahmad, Mostafa A.

Article Type: Research Article

Abstract: The evolution of technology has brought new challenges and opportunities for the different dimensions of feature space. The higher dimension of the feature space is one of the most critical issues in e-mail classification problems due to accuracy considerations. The problem of finding the subset features that significantly influence the performance of e-mail spam classification has become one of the important challenges. This paper proposes to overcome such a problem, an intelligent approach to Binary Differential Evolution Support Vector Machine (BDE-SVM). The proposed approach enhances the Binary Differential Evolution (BDE) algorithm based on the correlation coefficient as a fitness function …to select the significant subset feature evaluated by an SVM classifier. To our best of knowledge, the correlation coefficient as the fitness function has not been used in the differential evolution algorithm before. The selected subset feature is used to assess the most features that contribute to the reliability of the email spam classification. The finding of the enhanced BDE is to present a powerful accuracy. The tests were conducted using “Spambase” and “SpamAssassin.” Identified benchmark datasets are to assess the feasibility of the proposed solution. The result with full-feature accuracy was 93.55 percent compared to the proposed BDE-SVM approach, which is 93.99 percent. Empirical findings also show that our method is capable of effectively increasing the number of features required to enhance the reliability of the email spam classification. Show more

Keywords: Feature selection, e-mail, e-mail classification, differential evolution (DE), support vector machine (SVM)

DOI: 10.3233/JIFS-201990

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

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

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

Authors: Shu, Lei | Huang, Kun | Jiang, Wenhao | Wu, Wenming | Liu, Hongling

Article Type: Research Article

Abstract: It is easy to lead to poor generalization in machine learning tasks using real-world data directly, since such data is usually high-dimensional dimensionality and limited. Through learning the low dimensional representations of high-dimensional data, feature selection can retain useful features for machine learning tasks. Using these useful features effectively trains machine learning models. Hence, it is a challenge for feature selection from high-dimensional data. To address this issue, in this paper, a hybrid approach consisted of an autoencoder and Bayesian methods is proposed for a novel feature selection. Firstly, Bayesian methods are embedded in the proposed autoencoder as a special …hidden layer. This of doing is to increase the precision during selecting non-redundant features. Then, the other hidden layers of the autoencoder are used for non-redundant feature selection. Finally, compared with the mainstream approaches for feature selection, the proposed method outperforms them. We find that the way consisted of autoencoders and probabilistic correction methods is more meaningful than that of stacking architectures or adding constraints to autoencoders as regards feature selection. We also demonstrate that stacked autoencoders are more suitable for large-scale feature selection, however, sparse autoencoders are beneficial for a smaller number of feature selection. We indicate that the value of the proposed method provides a theoretical reference to analyze the optimality of feature selection. Show more

Keywords: Autoencoder, Bayesian method, feature selection, high-dimensional data

DOI: 10.3233/JIFS-211348

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Mahmood, Asma | Abbas, Mujahid

Article Type: Research Article

Abstract: A group decision-making process is introduced by utilizing the influence model together with a matrix of interpersonal influences and an opinion matrix. The opinion matrix is constructed with the opinions/advice from one group of experts towards the other. Experts are divided into two groups, one which has more experienced, skilled and qualified persons is known as the group of opinion leaders and the other is known as the group of opinion followers. Sometimes, decision-makers are ordinary agents and their opinion formation is profoundly influenced by opinion leaders. The truthfulness of opinion leaders and the interpersonal influences of decision-makers is also …taken into account. Also, a modified definition of trust score evaluation is presented with the understanding of the fact that the maximum trust which a decision-maker can do upon some opinion leader is his/her truthfulness. On the basis of this definition, a trust score matrix is constructed and the influence model is modified to take into account that matrix. Show more

Keywords: Group decision making, opinion dynamics, trust score evaluations, influence model

DOI: 10.3233/JIFS-210161

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

Authors: Guan, Li | Zhang, Jinping | Zhou, Jieming

Article Type: Research Article

Abstract: This work proposes the concept of uncorrelation for fuzzy random variables, which is weaker than independence. For the sequence of uncorrelated fuzzy variables, weak and strong law of large numbers are studied under the uniform Hausdorff metric d H ∞ . The results generalize the law of large numbers for independent fuzzy random variables.

Keywords: Fuzzy random variable, uncorrelated, law of large numbers

DOI: 10.3233/JIFS-210099

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

Authors: Malik, Manisha | Gupta, S. K. | Ahmad, I.

Article Type: Research Article

Abstract: In many real-world problems, one may encounter uncertainty in the input data. The fuzzy set theory fits well to handle such situations. However, it is not always possible to determine with full satisfaction the membership and non-membership degrees associated with an element of the fuzzy set. The intuitionistic fuzzy sets play a key role in dealing with the hesitation factor along-with the uncertainity involved in the problem and hence, provides more flexibility in the decision-making process. In this article, we introduce a new ordering on the set of intuitionistic fuzzy numbers and propose a simple approach for solving the fully …intuitionistic fuzzy linear programming problems with mixed constraints and unrestricted variables where the parameters and decision variables of the problem are represented by intuitionistic fuzzy numbers. The proposed method converts the problem into a crisp non-linear programming problem and further finds the intuitionistic fuzzy optimal solution to the problem. Some of the key significance of the proposed study are also pointed out along-with the limitations of the existing studies. The approach is illustrated step-by-step with the help of a numerical example and further, a production planning problem is also demonstrated to show the applicability of the study in practical situations. Finally, the efficiency of the proposed algorithm is analyzed with the existing studies based on various computational parameters. Show more

Keywords: Intuitionistic fuzzy linear programming problem, accuracy function, crisp non-linear programming problem, triangular intuitionistic fuzzy number

DOI: 10.3233/JIFS-202398

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

Authors: Lin, Shaopei | Zhu, Wei

Article Type: Research Article

Abstract: This paper summarizes the relationship of subjective information with artificial intelligence (AI) technology and points out how the role of subjective information and its position in AI. Eventually, the characteristic of digital era is the “softening of the theories and hardening of the experiences”. Subjective information is widely used in digital revolution for transforming the qualitative estimations into quasi-quantitative solutions, such as the empirical methods in decision making for quantitative management, etc., it will be the transferor for realizing it. The theoretical formulation of how subjective information is digitized through “Fuzzy-AI Model” for digital revolution is presented in this paper; …it has becoming a universal problem solver of utilizing AI technology for quantizing the degree uncertainties in decision-making and fuzzy estimation. Besides, the “Big Data” searching will heavily depend on the completeness of its source information, yet “subjective information” approach can directly predict human thinking or the internal law of complicated objective events into an explicit digital form, for the completeness of source information to make the correct and comprehensive “Big Data” prediction possible. Practical case studies are presented. Show more

Keywords: Subjective information, AI application, mathematical operator, fuzzy-AI model, intelligent design

DOI: 10.3233/JIFS-211624

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

Authors: Hu, Dongping | Yin, Aihua

Article Type: Research Article

Abstract: In cloud computing, enabling search directly over encrypted data is an important technique to effectively utilize encrypted data. Most of the existing techniques are focusing on fuzzy keyword search as it helps achieve more robust search performance by tolerating misspelling or typos of data users. Existing works always build index without classifying keywords in advance. They suffer from efficiency issue. Furthermore, Euclidean distance or Hamming distance is often chosen to evaluate strings’ similarity, ignoring prefixes matching and the influence of strings’ length on the accuracy. We propose an efficient fuzzy keyword search scheme with lower computation cost and higher accuracy …to address the aforementioned problems. We employ the sub-dictionaries technique and the Bed -tree structure to build an index with three layers for achieving better search efficiency. With this index structure, the server could locate the keyword and could narrow the search scope quickly. The Jaro-Winkler distance is introduced to qualify the strings’ similarity by considering the prefixes matching and string length. The secure privacy mechanism is incorporated into the design of our work. Security analysis and performance evaluation demonstrate our scheme is more efficient compared to the existing one while guaranteeing security. Show more

Keywords: Fuzzy keyword search, jaro-winkler distance, Bed tree-based index, searchable encryption

DOI: 10.3233/JIFS-202844

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Raipurkar, Abhijeet R. | Chandak, Manoj B.

Article Type: Research Article

Abstract: A query application for On-Line Analytical Processing (OLAP) examines various kinds of data stored in a Data Warehouse (DW). There have been no systematic studies that look at the impact of query optimizations on performance and energy consumption in relational and NoSQL databases. Indeed, due to a lack of precise power calculation techniques in various databases and queries, the energy activity of several basic database operations is mostly unknown, as are the queries themselves, which are very complicated, extensive, and exploratory. As a result of the rapidly growing size of the DW system, query response times are regularly increasing. To …improve decision-making performance, the response time of such queries should be as short as possible. To resolve these issues, multiple materialized views from individual database tables have been collected, and queries have been handled. Similarly, due to overall maintenance and storage expenses, as well as the selection of an optimal view set to increase the data storage facility’s efficacy, materializing all conceivable views is not viable. Thus, to overcome these issues, this paper proposed the method of energy-aware query optimization and processing, on materialized views using enhanced simulated annealing (EAQO-ESA). This work was carried out in four stages. First, a Simulated Annealing (SA) based meta-heuristic approach was used to pre-process the query and optimize the scheduling performance. Second, the optimal sets of views were materialized, resulting in enhanced query response efficiency. Third, the authors assessed the performance of the query execution time and computational complexity with and without optimization. Finally, based on processing time, efficiency, and computing cost, the system’s performance was validated and compared to the traditional technique. Show more

Keywords: Simulated annealing, EAQO-ESA, materialized view selection, OLAP queries

DOI: 10.3233/JIFS-202821

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2021

Authors: Xiao, Huimin | Wang, Meiqi

Article Type: Research Article

Abstract: In this paper, we mainly extended the study of fuzzy matroid related problems to research the fuzzy decision method. Considering the ambiguity of actual event information and evaluation, we chose hesitant fuzzy set as the extended data set. To construct the hesitant fuzzy matroid, we defined the satisfaction function of hesitant fuzzy set combining hesitant fuzzy index entropy and score function, and defined the mapping function of fuzzy matroid through this function. We also defined the algorithm of hesitant fuzzy matroid and proved the theory of rank, basis of hesitant fuzzy matroid.

Keywords: Fuzzy entropy, fuzzy matroid, hesitant fuzzy matroid, rank

DOI: 10.3233/JIFS-211213

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2021

Authors: Zhou, Qing | Shi, Xi | Ge, Liang

Article Type: Research Article

Abstract: The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation …of question significance. Experiments demonstrate the effectiveness of our proposed methods. Show more

Keywords: Mental health, text analysis, interpretability, TF-IDF, Likert scale

DOI: 10.3233/JIFS-211044

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2021

Authors: Ren, Shengbing | Zuo, Xing | Chen, Jun | Tan, Wenzhao

Article Type: Research Article

Abstract: The existing Software Fault Localization Frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of fault localization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software fault localization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these …extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as fault localization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software fault localization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth. Show more

Keywords: Fault localization framework, program spectrum, feature extraction, RBF neural network, ridge regression

DOI: 10.3233/JIFS-202931

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

Authors: Alqurashi, Fahad A. | Alsolami, F. | Abdel-Khalek, S. | Sayed Ali, Elmustafa | Saeed, Rashid A.

Article Type: Research Article

Abstract: Recently, there were much interest in technology which has emerged greatly to the development of smart unmanned systems. Internet of UAV (IoUAV) enables an unmanned aerial vehicle (UAV) to connect with public network, and cooperate with the neighboring environment. It also enables UAV to argument information and gather data about others UAV and infrastructures. Applications related to smart UAV and IoUAV systems are facing many impairments issues. The challenges are related to UAV cloud network, big data processing, energy efficiency in IoUAV, and efficient communication between a large amount of different UAV types, in addition to optimum decisions for intelligence. …Artificial Intelligence (AI) technologies such as Machine Learning (ML) mechanisms enable to archives intelligent behavior for unmanned systems. Moreover, it provides a smart solution to enhance IoUAV network efficiency. Decisions in data processing are considered one of the most problematic issues related to UAV especially for the operations related to cloud and fog based network levels. ML enables to resolve some of these issues and optimize the Quality of UAV network experience (QoE). The paper provides theoretical fundamentals for ML models and algorithms for IoUAV applications and recently related works, in addition to future trends. Show more

Keywords: IoUAV, machine learning, deep learning, QoE, network optimization, smart unmanned systems

DOI: 10.3233/JIFS-211009

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

Authors: Xuejian, Zhang | Xiaobin, Hu | Hang, Li

Article Type: Research Article

Abstract: To ensure the cutting speed during the cutting operation, this paper proposes a groove cutting speed inference planning system that relies on production experience and set parameters and is based on machine vision and a two-level fuzzy neural hybrid network. The overall structure of the inference system is designed, including the mechanical body, vision system, and fuzzy neural hybrid network. The contour information of the part is obtained using industrial cameras and digital image processing systems. The cutting speed of the trajectory segment is inferred based on the related processing parameters and the secondary fuzzy neural hybrid network. Finally, all …of the processing parameters are transmitted to the PLC, so that the robot can work according to the predetermined displacement and speed. Simulations verify that the speed inference planning system offers certain advantages compared to the traditional one. The appearance of the speed inference planning realises independent design and planning of the cutting speed, and further ensures the unity of the cutting quality and cutting speed. This proposed method provides a new direction for the development and transformation of machining processes that rely on manual experience and in which expert systems cannot be used. Show more

Keywords: groove cutting speed, machine vision, fuzzy neural network, MATLAB simulation

DOI: 10.3233/JIFS-211116

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