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

Authors: Tran, Duc Quynh | Nguyen, Xuan Thao | Nguyen, Doan Dong | Nguyen, Quang Thuan

Article Type: Research Article

Abstract: In this paper, we propose a new formula for the entropy based on similarity measures of intuitionistic fuzzy sets (IFS). The contribution of this work is the proof that the new formula satisfies all the conditions of entropy. The experimentation on some examples shows that the new entropy is useful. Besides, we use the new entropy and similarity measures to design an algorithm for ranking assets in stock markets. The numerical results on 5 benchmark data sets were reported. It points out that the entropy and the similarity measures of IFS may provide an alternative tool for solving portfolio selection …problems. Show more

Keywords: Intuitionistic fuzzy set, similarity measures, stock markets, assets ranking, entropy

DOI: 10.3233/JIFS-211563

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

Authors: Gupta, Bhavna | Kaur, Harmeet | Bedi, Punam

Article Type: Research Article

Abstract: A robust collaborative system of active products (a product is called active when its ownership does not get transferred from provider to requestor at the time of its usage) should have an in-built mechanism which can make entities (service provider(s) and requestor(s)) to decide with whom to collaborate. In the absence of such a mechanism, the system is bound to have high job failure rate, resulting in wastage of resources. This paper proposes a Trust based Multi-Agent Framework (TbMAF) for collaborative systems of active products which enable only trustworthy entities to collaborate, safeguarding both users’ sensitive applications and providers’ resources. …The trustworthiness of service provider(s) and requestor(s) is computed using Fuzzy Inference System (FIS) and Radial Basis Function Neural Network (RBFNN) methodologies, respectively. A prototype based on the proposed system has been tested using real time data of a collaborative system namely, EGEE (Enabling Grids for E-science). This paper finds evidence that the job failure rate is lower when collaborations take place only between trustworthy entities. Further, the proposed framework is found to be robust against malicious entities and can capture the evolving behavior of entities as well. Show more

Keywords: Trust, reputation, recommendation, active product, collaborative system

DOI: 10.3233/JIFS-212691

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2022

Authors: Yang, Shaojun | Huang, Xinyi

Article Type: Research Article

Abstract: The numbers of Helly, Carathéodory and Radon are a central theme in classic convex spaces and each of them is invariant under isomorphism. The theory of M -fuzzifying convex spaces is an active research field since it is presented. This paper focus on convex invariants of M -fuzzifying convex spaces. The degrees of Helly independent, Carathéodory independent and Radon independent of non-empty set are defined in the framework of M -fuzzifying convex spaces. By those definitions, we introduce the Helly number, Carathéodory number and Radon number of M -fuzzifying convex spaces. Finally, we inspect M -fuzzifying topology and M -fuzzifying …JHC which are characterized by convex invariants of M -fuzzifying convex spaces. Show more

Keywords: Helly number, Carathéodory number, Radon number

DOI: 10.3233/JIFS-213081

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

Authors: Yadav, Yadavendra | Chand, Satish | Sahoo, Ramesh Ch. | Sahoo, Biswa Mohan | Kumar, Somesh

Article Type: Research Article

Abstract:  Machine learning and deep learning methods have become exponentially more accurate. These methods are now as precise as experts of respective fields, so it is being used in almost all areas of life. Nowadays, people have more faith in machines than men, so, in this vein, deep learning models with the concept of transfer learning of CNN are used to detect and classify diabetic retinopathy and its different stages. The backbone of various CNN-based models such as InceptionResNetV2, InceptionV3, Xception, MobileNetV2, VGG19, and DenceNet201 are used to classify this vision loss disease. In these base models, transfer learning has been …applied by adding some layers like batch normalization, dropout, and dense layers to make the model more effective and accurate for the given problem. The training of the resulting models has been done for the Kaggle retinopathy 2019 dataset with about 3662 fundus fluorescein angiography colored images. Performance of all six trained models have been measured on the test dataset in terms of precision, recall, F1 score, macro average, weighted average, confusion matrix, and accuracy. A confusion matrix is based on maximum class probability prediction that is the incapability of the confusion matrix. The ROC-AUC of different classes and the models are analyzed. ROC-AUC is based on the actual probability of different categories. The results obtained from this study show that InceptionResNetV2 is proven the best model for diabetic retinopathy detection and classification, among other models considered here. It can work accurately in case of less training data. Thus, this model may detect and classify diabetic retinopathy automatically and accurately at an early stage. So it would be beneficial for humans to reduce the effects of diabetes. As a result of this, the impact of diabetes on vision loss can be minimized, and that would be a blessing in the medical field. Show more

Keywords: Diabetic retinopathy, nonproliferative, proliferative, maculopathy, transfer learning

DOI: 10.3233/JIFS-212771

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2022

Authors: Gong, Weijun | Wang, Chaoqing | Jia, Jinlu | Qian, Yurong | Fan, Yingying

Article Type: Research Article

Abstract: Facial expression recognition (FER) has been one of the research focuses in recent years due to its significance in human-computer interactions. However, there are still challenges in the field of FER caused by the diversity and variation of facial expressions in real scenes, the singleness of feature type and the lack of enough discriminant features cannot effectively improve the recognition performance. To solve these problems, we propose a Multi-feature Fusion Network (MFNet) with dual-branch based on deep learning. Firstly, the MFNet uses the pyramid parallel multiscale residual network structure with progressive max-pooling of channel attention to extract multi-level facial features …and enhance the discrimination of features; In the meantime, a shallow Gabor convolutional network is designed to enhance the adaptation of learned features to the orientation and scale changes and improve the ability to capture local details features; Finally, the maximum expression features obtained by the above two networks are fused to make more effective expression recognition. Experiments on three public large-scale wild FER datasets (RAF-DB, FERPlus, and AffectNet) show that our MFNet has a superior recognition performance than other recognition methods. Show more

Keywords: Facial expression recognition, multi-feature fusion, feature extraction, deep learning

DOI: 10.3233/JIFS-211021

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2022

Authors: Liang, Tao | Zhao, Qing | Shi, Huan

Article Type: Research Article

Abstract: Wind energy, a highly popular renewable clean energy, has been increasingly valued by the international community and been leaping forward. However, the original wind speed signal characterized by intermittent fluctuations impose heavy burdens on wind speed forecasting of wind farms. This study proposed a wind speed forecasting method by complying with a model integrating the Variational Mode Decomposition (VMD) and the Improved Multi-Objective Dragonfly Optimization Algorithm (IMODA). First, the VMD was adopted to decompose the original wind speed signal, as an attempt to obtain multiple sub-sequences (IMFs) exhibiting stable frequency domain. Second, to simplify the calculation, the sample entropy (SE) …was adopted for the sequence recombination, and the respective recombined sub-sequence of the wind speed was forecasted by using four advanced neural networks. Lastly, the IMODA algorithm was adopted to fuse the forecasting results of the neural network, and the results of the optimal wind speed were forecasted. To verify the effectiveness and adaptability of the algorithm, the wind farm data in four different regions were forecasted. As indicated from the results, this algorithm could outperform other algorithms in the comprehensive forecasting accuracy and the model calculation time, and it could be effectively applied for the wind speed forecasting in wind farms. Show more

Keywords: Wind speed forecasting, variational mode decomposition, IMODA, combined model

DOI: 10.3233/JIFS-201191

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2022

Authors: Zhao, Yucheng | Liang, Jun | Chen, Long | Wang, Yafei | Gong, Jinfeng

Article Type: Research Article

Abstract: Driving behavior type is a hotspot in transportation field, but there have been few studies on free driving behavior type. The factor of current driving behavior evaluation model is single, and its environmental adaptability is insufficient, and driving behavior type is difficult to predict accurately. In addition, free driving behavior as one kind of the important driving operation behaviors lacks quantitative assessment methods and models. In view of these deficiencies, evaluation and prediction of free driving behavior based on Fuzzy Comprehensive Support Vector Machine (FC-SVM) is proposed. Firstly, a variety of individual decision-making behavior data obfuscating with environmental complexity are …collected. These obtained parameters were used as FC multi-factor evaluation parameters to quantitatively evaluate free driving behavior from multiple aspects, and to qualitatively derive the driver’s driving behavior type. Further, the SVM used the RBF kernel function to obtain the optimal parameters and train the SVM network, and it used the obtained SVM model for the prediction of driving behavior type in short time. The results of simulations using different methods show that the SD value of FC-SVM evaluation results is the lowest, only 1.273. Compared with other common methods, its MacroP reaches 89.2% . It is interesting to find that aggressive driving can be more distinct from other behavior types. Moreover, the mixed traffic flow composed of aggressive driver has a higher traffic efficiency in basic sections. This work is of great value for improving driving behavior, reducing road congestion and improving road traffic efficiency in the mixed intelligent traffic. Show more

Keywords: Free driving behavior, fuzzy comprehensive, support vector machine, evaluation and prediction, intelligent system

DOI: 10.3233/JIFS-201680

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2022

Authors: Prajna, Yellamelli | Nath, Malaya Kumar

Article Type: Research Article

Abstract: Blood vessel segmentation is an essential element of automatic retinal disease screening systems. In particular, retinal blood vessel analysis from fundus image is vital in the identification and diagnosis of cardiovascular and ophthalmological diseases (Ex: Diabetic Retinopathy, Macular degeneration, Retinal Pigmentosa, Macular Edema, and various stages of Glaucoma, etc). Wherefore, the diagnosis of these diseases by automatic vessel segmentation has become essential, especially in disclosure of premature prognosis of vision condition. In general, blood vessel extraction is divided into vessel tracking and pixel classification. In vessel tracking a vasculature model is expanded from a seed point. In pixel classification, the …classifier classifies the pixels as either a vessel or background pixel, which is demonstrated in the proposed architecture. In this paper, deep learning based 19 layer U-Net architecture is proposed for the accurate and efficient segmentation of blood vessels. Prior to segmentation, a pre-processing block of AlexNet architecture is introduced for the classification of high-quality images from the experimented databases. This pre-classification stage helps in efficiently picking high-quality images determined by clarity, field definition, and sharpness. AlexNet classification is pivotal in enhancing the overall performance of the system by segmenting fine and tiny blood vessels. The proposed U-Net architecture has an encoder-decoder framework with 9 and 5 convolutional layers in each respectively. In order to boost the efficiency of the network as well as to reduce training and testing time, a proper choice of kernel dimension and number of filters are necessary. Our architecture was investigated on popular databases such as DRIVE, ARIA_d and MESSIDOR and various performance measures (accuracy, sensitivity, specificity, sensibility, Dice coefficient, and Jaccard coefficient) have been computed along with the Receiver Operating Characteristics. It is observed that the accuracy for DRIVE, ARIA_d and MESSIDOR are 90.60%, 87.60% and 83.42%, respectively. Area under curve in Receiver Operating Characteristics plot is found to be 98.54%, 93.28% and 88.18%, for DRIVE, ARIA_d and MESSIDOR databases, respectively. Results with the proposed architecture show remarkable improvement in the performance metrics for blood vessel segmentation. Show more

Keywords: Fundus image, U-Net, MSRI, accuracy, ROC curve

DOI: 10.3233/JIFS-211479

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2022

Authors: Ding, Xiong | Gao, Jinding

Article Type: Research Article

Abstract: In addition to the tragic loss of life, precious natural and personal property, forest fires pose a huge threat to ecologically healthy forests and environmental protection. A forest-fire-identification is explored in this article. Because the color space model FCS brings a high false alarm rate in flame recognition, this paper proposes an improved flame recognition color space (IFCS) based on chaos theory and k-medoids particle swarm optimization algorithm. The use of IFCS color space for flame recognition can ensure simple and fast calculations and more prominent flame/non-flame pixel color attribute difference characteristics when it is compared to FCS. The IFCS …flame recognition color space is obtained by using methods such as initializing the particles in the chaotic sequence, adaptively adjusting the inertia weight, dynamic nonlinear adjustment of the learning factor, and jumping out of the local optimum from the chaotic search. In the IFCS flame color space, the binary image is obtained by the classic Otsu threshold method, and the Flame Recognition algorithm (IOFR) algorithm is established based on IFCS and Otsu. The experimental results show that based on the FCS flame recognition algorithm, the IOFR algorithm effectively reduces the flame misjudgment rate. Show more

Keywords: Forest fire prevention, flame recognition, fire color space, chaos, k-medoids, particle swarm optimization (PSO)

DOI: 10.3233/JIFS-211816

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2022

Authors: Ait Benali, B. | Mihi, S. | Ait Mlouk, A. | El Bazi, I. | Laachfoubi, N.

Article Type: Research Article

Abstract: Named Entity Recognition (NER) is a vitally important task of Natural Language Processing (NLP), which aims at finding named entities in natural language text and classifying them into predefined categories such as persons (PER), places (LOC), organizations (ORG), and so on. In the Arabic context, the current NER approaches based on deep learning are mainly based on word embedding or character-level embedding as input. However, using a single granularity representation has problems with out-of-vocabulary (OOV), word embedding errors, and relatively simple semantic content. This paper presents a multi-headed self-attention mechanism implemented in the BiLSTM-CRF neural network structure to recognize Arabic …named entities on social media using two embeddings. Unlike other state-of-the-art approaches, this approach combines character and word embedding at the embedding layer, and the attention mechanism calculates the similarity over the entire sequence of characters and captures local context information. The proposed approach better recognized NEs in Dialect Arabic, reaching an F1 value of 74.15% on Darwish’s dataset (a publicly available Arabic NER benchmark for social media). According to our knowledge, our findings outperform the current state-of-the-art models for Arabic Named Entity Recognition on social media. Show more

Keywords: Arabic named entity recognition (ANER), natural language processing (NLP), multi-head self-attention, BiLSTM, CRF, dialect arabic, social media

DOI: 10.3233/JIFS-211944

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

Authors: Adisusilo, Anang Kukuh | Wahyuningtyas, Emmy | Saurina, Nia | Radi,

Article Type: Research Article

Abstract: Soil Tillage serious game designed as a training media has been researched based on the plowing forces using polynomial functions. However, the learning process is rare; hence the players in Serious Games (SG) are less engaged and tend to be more static in their games. The effects of vertical cutting angle, plowshare depth, and motor speed affect the soil plowing force in soil tillage. Therefore it is expected that a plow force model with a learning function will generate more actual conditions, engage the player and eventually affect the player’s behavior. The serious game design uses a Hierarchical Finite State …Machine (HFSM) in this study. HFSM state is motor speed, vertical cutting angle, and plowing depth. The learning function is based on Neural Network (NN), with a multilayer feed-forward neural network (FFNN) is chosen to estimate plowing forces. The Levenberg-Marquardt algorithm is used by NN to approach second-order training speed without computing the Hessian matrix and is the fastest backpropagation algorithm. The result of the research is a plowing force model values closer to the actual by giving players feedback as they learn. In the transition, HFSM has a feedback value to the initial state, which is helpful as part of measuring one game cycle that is run, thus providing a learning experience in a serious game. Show more

Keywords: Neural network, plowing forces, serious game, soil tillage, HFSM

DOI: 10.3233/JIFS-212419

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

Authors: Wang, Huidong | Zhang, Yao | Bai, Chuanzheng

Article Type: Research Article

Abstract: As an effective tool for three-way decisions (3WD) problems, decision-theoretic rough sets (DTRSs) have raised increasing attention recently. In view of the advantages of q-rung orthopair uncertain linguistic variables (q-ROULVs) in depicting uncertain information, a new DTRSs model based on q-ROULVs is proposed to solve three-way group decision-making (3WGDM) problems. Firstly, the loss function of DTRSs is depicted by q-ROULVs and a q-rung orthopair uncertain linguistic DTRSs model is constructed subsequently. Secondly, to aggregate different experts’ evaluation results on loss function in group decision-making (GDM) scenario, the q-rung orthopair uncertain linguistic geometric Heronian mean (q-ROULGHM) operator and the q-rung orthopair …uncertain linguistic weighted geometric Heronian mean (q-ROULWGHM) operator are presented. Related properties of the proposed operators are investigated. Thirdly, to compare the expected loss of each alternative, a new score function of q-ROULVs is defined and the corresponding decision rules for 3WGDM are deduced. Finally, an illustrative example of venture capital in high-tech projects is provided to verify the rationality and effectiveness of our method. The influence of different conditional probabilities and parameter values on decision results is comprehensively discussed. Show more

Keywords: q-rung orthopair uncertain linguistic variable, decision-theoretic rough set, three-way group decision-making, geometric Heronian mean operator

DOI: 10.3233/JIFS-212327

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2022

Authors: Zhang, Qiang | Liu, Jianping | Hu, Junhua | Yao, Zhihai | Yang, Jian

Article Type: Research Article

Abstract: With the increasing complexity of decision making (DM) problems, powerful mathematical tools are needed to represent and process fuzzy and uncertain DM information, and Pythagorean fuzzy set (PFS) is such a mathematical tool. PFS has been successfully applied in the field of fuzzy multiple criteria decision making (MCDM). Correlation coefficient is an information measure of PFS, and plays an important role in the application of PFS. At present, there is a problem that the existing correlation coefficients cannot moderately measure the correlation degree between PFSs, so this paper proposes the new correlation coefficients of PFS. The TODIM method has been …proved to be effective in dealing with MCDM problems that consider the psychological behavior of decision makers. This paper extends the TODIM method with the new correlation coefficients of PFS, and the extended TODIM method is called Pythagorean fuzzy CC-TODIM method. By numerical examples, it is verified that the new correlation coefficients of PFS are more reasonable and valid. By case analysis, it is verified that the Pythagorean fuzzy CC-TODIM method can effectively solve the MCDM problems, and the Pythagorean fuzzy CC-TODIM method based on the new correlation coefficients is more accurate and reliable. Show more

Keywords: Pythagorean fuzzy set, correlation coefficient, TODIM method, multiple criteria decision making

DOI: 10.3233/JIFS-212323

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2022

Authors: Wong, Shi-Ting | Too, Chian-Wen | Yap, Wun-She | Khor, Kok-Chin

Article Type: Research Article

Abstract: With technological advancement, visual search has become an effective tool for searching important information by providing images. We propose a practical medical equipment recognition that can be used in visual search through deep transfer learning. We evaluated three deep learning models, i.e., VGG-16, ResNet-50, and Inception-v3, to recognise ten different classes of medical equipment. A data set consisting of 2,666 images had been collected and augmented to measure the models’ effectiveness. The models pre-trained with the ImageNet data set were transferred to the final models, and the last layers were replaced and trained with the collected data set. A grid …search method was then used to find the best combination of hyperparameters, such as optimiser, batch size, epoch number, dropout rate, and learning rate. We tested the models using photos captured using smartphones. The results showed that Inception-v3 outperformed the other two models with the highest accuracy of 0.9454. This is the first study that uses deep transfer learning for recognising medical equipment to our best knowledge. Such recognition technology can potentially be implemented in visual search for helping consumers to check the validity of medical equipment. Show more

Keywords: Medical equipment, object recognition, deep transfer learning

DOI: 10.3233/JIFS-212786

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

Authors: Yin, Longjun | Zhang, Qinghua | Zhao, Fan | Mou, Qiong | Xian, Sidong

Article Type: Research Article

Abstract: In uncertain information processing, new knowledge can be discovered by measuring the proximity between discovered and undiscovered knowledge. Pythagorean Fuzzy Sets (PFSs) is one of the important tools to describe the natural attributes of uncertain information. Therefore, how to appropriately measure the distance between PFSs is an important topic. The earth mover’s distance (EMD) is a real distance metric that can be used to describe the difference between two distribution laws. In this paper, a new distance measure for PFSs based on EMD is proposed. It is a new perspective to measure the distance between PFSs from the perspective of …distribution law. First, a new distance measure namely D EMD is presented and proven to satisfy the distance measurement axiom. Second, an example is given to illustrate the advantages of D EMD compared with other distance measures. Third, the problem statements and solving algorithms of pattern recognition, medical diagnosis and multi-criteria decision making (MCDM) problems are given. Finally, by comparing the application of different methods in pattern recognition, medical diagnosis and MCDM, the effectiveness and practicability of D EMD and algorithms presented in this paper are demonstrated. Show more

Keywords: Pythagorean Fuzzy Sets, Intuitionistic Fuzzy Sets, Pattern recognition, Medicinal diagnosis, Multi-criteria decision making

DOI: 10.3233/JIFS-210800

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

Authors: Liu, Xuwang | Li, Huihui | Qi, Wei

Article Type: Research Article

Abstract: With recent developments in information technology and the extensive promotion of Internet Plus, the use of online centralized procurement by governments and enterprise groups has become progressively more common, and the winning bid evaluation decision making method is particularly important in this context. However, experts might not be completely rational during the process of bid evaluation, which may induce the enhancement or repression of bid scores. To address such behaviors during the process of bid evaluation, an automatic mechanism to identify and correct such tendencies is proposed in this study. Because experts have different preferences for different alternatives, which are …directly reflected in the evaluation of attribute values. Based on selection preference, this paper proposes a selection preference method for solving the subjective weight of attributes. Firstly, the weights of relevant attributes are first determined via the entropy weight method and the selection preference method, and the weights corresponding to groups are determined according to the differences between the scores assigned by experts. Then, a grouped multi-attribute bid evaluation decision making method is proposed based on the selection preference. Finally, an example is used to verify the effectiveness of the method and its superiority over existing methods. Thus, a theoretical basis and a decision support mechanism are provided in this study for centralized procurement departments of governments and enterprises. Further, it also provides guidance for multi-attribute decision making problems with identical grouped features. Show more

Keywords: Centralized procurement, multi-attribute decision making, selection preference, entropy weight

DOI: 10.3233/JIFS-210748

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2022

Authors: Zhang, Xuewu | Jing, Wenfeng

Article Type: Research Article

Abstract:  Overhead contact systems are important power supply equipments for electrified railway locomotives. If there are faults in the equipment, it will threaten the safety of train operation. Dropper faults and foreign matter are two common types of Overhead contact systems faults, the fault regions of which occupy a relatively small area in the whole image, so it is difficult to detect the faults accurately by capturing detailed features using original Faster R-CNN. To solve the above problem, an improved Faster R-CNN based on convolutional neural networks, named multi-view Faster R-CNN, is proposed in order to extract more details of the …fault regions by combining deep and shallow feature maps. Experiments on the images collected from the Lanzhou-Xinjiang Railway line show that the feature fusion can significantly enhance the mean average precision. The precision of our model on the test set was 89.53% , increasing by 3.5% , 9.65% , and 5.8% compared with YOLOv3, SSD, and Faster R-CNN, and the detection accuracies of our model for unforced droppers and foreign matter were 88.17% and 90.60% , respectively, under a recall rate of almost 1.0. Because the multi-view feature fusion model in our method can flexibly detect faults with various sizes, it has an important application value in the fault detection of an overhead contact system. Show more

Keywords: Overhead contact system, dropper, fault detection, multi-view Faster R-CNN

DOI: 10.3233/JIFS-212257

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

Authors: Elayaraja, P. | Kumarganesh, S. | Martin Sagayam, K. | Dang, Hien | Pomplun, Marc

Article Type: Research Article

Abstract: Cervical cancer can be cured if it is initially screened and giving timely treatment to the patients. This paper proposes an optimization technique for exposing and segmenting the cancer portion in cervical images using transform and windowing technique. The image processing steps are preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation involved in the proposed work. Initially, Gabor transform is enforced on the cervical test image to modify the pixels associated with the spatial domain into multi-resolution domain. Subsequently, the parameters of the multi-level features are extracted from the Gabor transformed cervical image. Then, the extracted features are optimized …using the Genetic Algorithm (GA), and the optimistic prominent part is classified by the Convolutional Neural Networks (CNN). Finally, the Finite Segmentation Algorithm (FSA) is used to detect and segment the cancer region in cervical images. The proposed GA based CNN classification method describes the effectual detection and classification of cervical cancer by the parameters such as sensitivity, specificity and accuracy. The experimental results are shown 99.37% of average sensitivity, 98.9% of average specificity and 99.21% of average accuracy, 97.8% of PPV, 91.8% of NPV, 96.8% of FPR and 90.4% of FNR. Show more

Keywords: Cervical cancer, Gabor, features, optimization, ANFIS, classification, Artificial Neural Network

DOI: 10.3233/JIFS-212871

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

Authors: Saravanan, C. | Anbalagan, P.

Article Type: Research Article

Abstract: Congestion not only affects the power flow, but also leads certain issues, like market power, market inefficiency and security. When the transmission line exceeds their limits congestion is occurred (voltage, thermal, stability). Congestion management is a technique that helps to deal the issue corresponding to congestion. Many methods have been developed to manage congestion, and also several countries execute various strategies for the smooth functioning of their network. In this manuscript, the rescheduling of congestion management in a deregulated environment using DA-MRFO is proposed. The proposed hybrid technique is the combined execution of both the dragonfly algorithm (DA) and manta …ray foraging optimization (MRFO). Dragonfly algorithm is enhanced using Manta ray Foraging optimization (MRFO), hence it is named DA-MRFO technique. The proposed method is used to alleviate transmission grid congestion on group-based electricity market via reprogramming active power of generators and also to reprogram the generator power. Congestion is the major Independent System Operator (ISO) concern on deregulated electricity market that is traditionally controlled by reprogramming generator output power. However, the effects of changes in the generator output power on the overloaded line flow are not identical. All the generators do not represent a desirable approach for congestion management. Here, a generator sensitivity factor is adapted for supporting the optimal generator selection in a congestion management (CM). In a congestion relief process, it is provided at the lowest possible cost. The reduction of power flow with collection of congested lines is probable through coordinated response of reactive energy dispatch as wind farms. The proposed approach is executed in modified IEEE 30 bus system and IEEE 57 bus system, then the efficiency is compared with the various existing optimization approaches. Show more

Keywords: Congestion management, rescheduling, deregulated environment, dragonfly, manta ray foraging optimization

DOI: 10.3233/JIFS-213138

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

Authors: Thangamuthu, Logeswaran | Albert, Johny Renoald | Chinnanan, Kalaivanan | Gnanavel, Banu

Article Type: Research Article

Abstract: To minimize real-time errors in a Photovoltaic (PV) system performances must be forecasted through precise simulation design before continuing into a practical application. However, due to the scarcity of data in datasheets and the inherent transcendental connections are between PV current and PV voltage, to determining the Single Diode Model (SDM) parameters becomes a more challenging problems. This paper offers a simulated study of a SDM and Double Diode Model (DDM) solar PV system under various irradiation represents, and the performance was developed by incorporating an optimization-based Maximum Power Point (MPP) tracking techniques. According to the present simulation presented in …this article, a mathematical model for a SDM/DDM as well as optimization methodologies has been estimated MATLAB platform. The present MPP circuit model designed and compared with BAT optimization algorithms. The nonlinear relationship between Voltage (V) - Current (I) and Voltage (V) –Power (W) acknowledged as characteristic curves for different temperature (∘c) and irradiance (W/m2 ) values are verified in numerical simulation results. MPP tracking power and efficiency are examined for maximum power (Pmax ) to test the optimization based system. The simulation results show that the BAT optimization model was achieved the highest tracking efficiency better than other heuristic algorithms. Show more

Keywords: Photovoltaic, maximum power point, BAT Optimization, firefly algorithm, solar irradiations

DOI: 10.3233/JIFS-213241

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

Authors: Carreón-Díaz de León, Carlos Leopoldo | Vergara-Limon, Sergio | Vargas-Treviño, María Aurora D. | González-Calleros, Juan Manuel

Article Type: Research Article

Abstract: This paper presents a novel methodology to identify the dynamic parameters of a real robot with a convolutional neural network (CNN). Conventional identification methodologies use continuous motion signals. However, these signals are quantized in their amplitude and are discrete in time. Therefore, the time required to identify the parameters of a robot with a limited measurement system is related to an optimized motion trajectory performed by the robot. The proposed methodology consists of an algorithm that uses a trained CNN with the data created by the dynamical model of the case study robot. A processing technique is proposed to transform …the position, velocity, acceleration, and torque robot signals into an image whose characteristics are extracted by the CNN to determine their dynamic parameters. The proposed algorithm does not require any optimal trajectory to find the dynamic parameters. A proposed time-spectral evaluation metric is used to validate the robot data and the identification data. The validation results show that the proposed methodology identifies the parameters of a Cartesian robot in less than 1 second, exceeding 90% of the proposed evaluation metric and 98% for the simulation results. Show more

Keywords: Identification, dynamic parameters, CNN, robotics, signals

DOI: 10.3233/JIFS-219246

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

Authors: Poornima, R. | Elangovan, Mohanraj | Nagarajan, G.

Article Type: Research Article

Abstract: The evolving new and modern technologies raise the risks in the network which will be affected by several attacks and thus give rise to developing efficient network attack detection and classification methods. Here in this article for predicting and classifying the network attacks, the LSTM neural network with XGBoost is suggested in which the NSL-KDD dataset was utilized to train the LSTM in the study. In the beginning, the unnecessary data and the noisy data will be eliminated using the dataset and the feature subset with the most compelling features will be selected using the feature selection. By utilizing the …essential data, the proposed system will be trained and the training parameter values will be modified for maximizing the functionality of the proposed system. Then, the result of the proposed system will be evaluated with some of the existing machine learning and deep learning algorithms such as SVM, LR, RF, DNN, and CNN with the performance metrics like Accuracy, F1 score, Recall, and Precision. It was found that the proposed model outperforms better than the other algorithms as this model is trained with the most important features and due to this, the training time and overfitting of the learning model was reduced thereby increasing the model effectiveness Show more

Keywords: Deep learning, feature selection, LSTM, network attack, XGBoost

DOI: 10.3233/JIFS-212731

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

Authors: Li, Jie | Song, Li | Cao, Lianglin

Article Type: Research Article

Abstract:  In this paper, to reduce the redundant attractions and incorrect directions of firefly algorithm (FA), a distance-guided selection approach (DSFA) is proposed, which consists of a distance-guided mechanism and selection strategy. Where the designed distance-guided mechanism reduces the attractions and plays as a classifier for global search and local search, the suggested selection strategy can avoid local search falling into traps, thereby increasing the probability of correct direction. With the good cooperation of these two approaches, DSFA obtains a good balance of exploration and exploitation. To confirm the performance of the proposed algorithm, excessive experiments are conducted on CEC2013 benchmark …functions, large-scale optimization problems CEC2008, and software defect prediction (SDP). In the comparison with the 5 advanced FA variants, DSFA provides the optimal solutions to most CEC2013 problems. Besides, when facing the problems of class imbalance and the dimensional explosion of datasets, DSFA greatly improves the performance of machine learning classifiers employed by SDP. It can be concluded that DSFA is an effective method for global continuous optimization problems. Show more

Keywords: Firefly algorithm, distance guided mechanism, selection strategy, global continuous optimization, software defect prediction

DOI: 10.3233/JIFS-212587

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

Authors: Wei, Mingrun | Wang, Hongjuan | Cheng, Ru | Yu, Yue

Article Type: Research Article

Abstract:  Clear images are generally desirable in high-level computer vision algorithms which are mostly deployed outdoors. However, affected by the changeable weather in the real world, images are inevitably contaminated by rain streaks. Deep convolutional neural networks (CNNs) have shown significant potential in rain streaks removal. The performance of most existing CNN-based deraining methods is often enhanced by stacking vanilla convolutional layers and some other methods use dilated convolution which can only model local pixel relations to provide the necessary but limited receptive field. Therefore, long-range contextual information is rarely considered for this specific task, thus, deraining a single image remains …challenging problem. To address the above problem, an effective residual deep attention network (RDANet) for single image rain removal is proposed. Specifically, we design a strong basic unit that contains dilated convolution, spatial and channel attention module (SCAM) simultaneously. As contextual information is very important for rain removal, the proposed basic unit can capture global long-distance dependencies among pixels in feature maps and model feature relations across channels. Compared with a single dilated convolution, the spatial and channel attention enhance the feature expression ability of the network. Moreover, some previous works have proven that the no-rain information in a rain image will be missing during deraining. To enrich the detailed information in the clean images, we present a residual feature processing group (RFPG) that contains several source skip connections to inject rainy shallow source information into each basic unit. In summary, our model can effectively handle complicated long rain streaks in spatial and the outputs of the network can retain most of the details of the original rain images. Experiments demonstrate the superiority of our RDANet over state-of-the-art methods in terms of both quantitative metrics and visual quality on both synthetic and real rainy images. Show more

Keywords: Single image deraining, convolutional neural network, spatial and channel attention, source skip connection

DOI: 10.3233/JIFS-212571

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

Authors: Lin, Yang | Ling, Yiqun | Yang, Zhe | Wang, Chunli | Li, Chuankun

Article Type: Research Article

Abstract: In the modern industrial process, a complete production process is achieved by requiring a variety of equipment to cooperate with each other. The abnormality in any equipment will have a large or small impact on process safety or product quality, resulting in increased risk. In recent years, many data-driven early-warning methods have been developed in academia. However, most of the methods need to be implemented on the support of normal and fault data. In order to overcome the problem, this paper establishes a new early-warning model based on negative selection algorithm (NSA) for centrifugal compressor unit without fault data. Firstly, …a nearest neighbor fixed boundary negative selection algorithm (NFB-NSA) is proposed by optimizing detector generation mechanism and matching rules for test samples. Secondly, the performance of NFB-NSA is tested by Iris dataset. The experimental results among NFB-NSA, V-detector, and other anomaly detection methods for Iris dataset shows that NFB-NSA can achieve the highest detection accuracy and the lowest false alarm rate in most cases. Finally, the early-warning of centrifugal compressor unit under normal samples is carried on by NFB-NSA in this paper. Validated by field data, NFB-NSA is demonstrated to be of excellent accuracy and robustness by results of experiments. Moreover, the influence of size of training sample on performance of NFB-NSA is obtained. Show more

Keywords: Early-warning, centrifugal compressor unit, fault data, NFB-NSA

DOI: 10.3233/JIFS-213075

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

Authors: Wei, Xiaolong | Huang, Xianglin | Yang, LiFang | Cao, Gang | Tao, Zhulin | Wang, Bing | An, Jing

Article Type: Research Article

Abstract: Structural models based on Attention can not only record the relationships between features’ position, but also can measure the importance of different features based on their weights. By establishing dynamically weighted parameters for choosing relevant and irrelevant features, the key information can be strengthened, and the irrelevant information can be weakened. Therefore, the efficiency of Deep Learning algorithms can be significantly elevated and improved. Although Transformer have been performed very well in many fields including Reinforcement Learning (RL). We tried to integrate Transformers into RL, however there are some challenge in this task. Especially, MARL (known as Multi-Agent Reinforcement Learning), …which can be recognized as a set of independent agents trying to adapt and learn through their way to reach the goal. In order to emphasize the relationship between each MDP decision in a certain time period, we applied the hierarchical coding method and validated the effectiveness of this method. This paper proposed a Hierarchical Transformer MADDPG based on recurrent neural network(RNN) which we call it Hierarchical RNNs-Based Transformers MADDPG(HRTMADDPG). It consists of a lower level encoder based on RNNs that encodes multiple step sizes in each time sequence, and it also consists of an upper sequence level encoder based on Transformer for learning the correlations between multiple sequences. Then we can capture the causal relationship between sub-time sequences and make HRTMADDPG more efficient. Show more

Keywords: MADDPG, Attention, RNN

DOI: 10.3233/JIFS-212795

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

Authors: Ajilisa, O. A. | Raj, V. P. Jagathy | Sabu, M. K.

Article Type: Research Article

Abstract: Thyroid nodule segmentation is an indispensable part of the computer-aided diagnosis of thyroid nodules from ultrasound images. However, it remains challenging to segment the nodules from ultrasound images due to low contrast, high noise, diverse appearance, and complex thyroid nodules structure. So it requires high clinical experience and expertise for proper detection of nodules. To alleviate the doctor’s tremendous effort in the diagnosis stage, we utilized several convolutional neural network architectures based on encoder-decoder architecture, U-Net architecture, Res-UNet architecture. To handle the complexity of the residual blocks, we also proposed three hybrid Res-UNet architectures by reducing the number of residual …connections. The experimental analysis of the segmentation models proves the viability of residual learning in the UNet architecture. Hybrid models which use minimum residual connections provide efficient segmentation frameworks similar to ResUNet architecture with a minimum computational requirement. The experimental results indicate that all the segmentation models based on residual learning and UNet can accurately delineate nodules without human intervention. This model helps to reduce dependencies on operators and acts as a decision tool for the radiologist. Show more

Keywords: Semantic segmentation, thyroid nodules, ultrasound images, U-Net, residual learning

DOI: 10.3233/JIFS-212398

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

Authors: Mirvakili, M. | Allahviranloo, T. | Soltanian, F.

Article Type: Research Article

Abstract: Fractional order differential equations accurately model dynamic systems and processes. In some of the fractional optimal control problems (FOCPs), due to the ambiguity in the initial conditions and the transfer of ambiguity to the solution, it is necessary to use fuzzy mathematics. In this paper, a numerical method is presented to approximate the solution for a class of Fuzzy Fractional Optimal Control Problems (FFOCPs) using the Legendre basis functions. The fuzzy fractional derivative is described in the Caputo sense. The performance index of an FFOCP is considered as a function of both the state and the control variables, and the …dynamic constraints are expressed by a set of Fuzzy Fractional Differential Equations (FFDEs). After obtaining Euler–Lagrange equations for FFOCPs and the necessary and sufficient conditions for the existence of solutions, using the definition of generalized Hukuhara differentiability (types I, II), the problem is considered in two cases. Then the distance function and an approach similar to the variational type along with the Lagrange multiplier method are used to formulate and solve the equations in a system. Time-invariant and time-varying examples are provided to assess the presented method. Numerical results show a similar trend for the state and control variables for various numbers of Legendre polynomials. Also, the convergence of state and control variables for the time-invariant system can be seen, and the same is true for control variables for the time-varying system. Show more

Keywords: Fuzzy fractional optimal control problem, Caputo derivative, Legendre basis function, Euler–Lagrange equations, Generalized Hukuhara differentiability, Numerical method

DOI: 10.3233/JIFS-210583

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-31, 2021

Authors: Heim, Isaac | Fonseca, Daniel J. | Houser, Rick | Cook, Ryan | O’Donnell, John

Article Type: Research Article

Abstract: This paper deals with the design and development of a novel approach, centered on the creation and development of a fuzzy controller to analyze electroencephalogram (EEG) data. The fuzzy controller makes use of the functions associated with the different regions of the brain to correlate multiple Brodmann areas to several outputs, where a normal analysis would associate only one region to one output. This controller was designed to quickly adapt to any data imported into it. The current implemented framework supports a math study. The math subjects’ outputs were attuned to their related study which involved transcranial direct current stimulation …(tDCS), which is a form of neurostimulation. Anode affinity, cathode affinity, calculation, memory, and decision making were the outputs focused on for the math study. This task is best suited to a fuzzy controller since interactions between Brodmann areas can be analyzed and the contributions of each area accounted for by indicating which regions have stronger and weaker effects on any given output. Show more

Keywords: Neurological activation, transcranial direct current stimulation, EEG, fuzzy controller, math enhancement

DOI: 10.3233/JIFS-212315

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

Authors: Tripathy, Santosh Kumar | Sudhamsh, Repala | Srivastava, Subodh | Srivastava, Rajeev

Article Type: Research Article

Abstract: Crowd panic detection (CPD) is crucial to control crowd disasters. The recent CPD approaches fail to address crowd shape change due to perspective distortion in the frame and across the frames. To this end, we are motivated to design a simple but most effective model known as multiscale spatial-temporal atrous-net and principal component analysis (PCA) guided one-class support vector machine (OC-SVM), i.e., MuST-POS for the CPD. The proposed model utilizes two multiscale atrous-net to extract multiscale spatial and multiscale temporal features to model crowd scenes. Then we adopted PCA to reduce the dimension of the extracted multiscale features and fed …them into an OC-SVM for modeling normal crowd scenes. The outliers of the OC-SVM are treated as crowd panic behavior. Three publicly available datasets: the UMN, the MED, and the Pets-2009, are used to show the effectiveness of the proposed MuST-POS. The MuST-POS achieves the detection accuracy of 99.40%, 97.61%, and 98.37% on the UMN, the MED, and the Pets-2009 datasets, respectively, and performs better to recent state-of-the-art approaches. Show more

Keywords: Atrous (Dilated)-CNN, multiscale spatial-temporal features, dimension reduction, PCA, OC-SVM

DOI: 10.3233/JIFS-211556

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

Authors: Gong, Zengtai | Xiao, Zhiyong

Article Type: Research Article

Abstract: It plays an important role in the calculation of fuzzy numbers using some special fuzzy numbers to approximate general fuzzy numbers in fuzzy analysis, similar to fuzzy complex analysis. A method is proposed to seek an approximation operator that produces a special kind of fuzzy complex number which is the nearest one to a given fuzzy complex number in this paper. The concept of prismoid fuzzy complex number is introduced, and the nearest prismoid fuzzy complex number of fuzzy complex number which preserve the centroid of the core for the fuzzy complex number with respect to the weighted pseudometric are …discussed. Finally, some properties of the approximation operator including scale invariance, identity and nearness are presented. Show more

Keywords: Fuzzy numbers, fuzzy complex number, rectangular fuzzy complex number

DOI: 10.3233/JIFS-211306

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

Authors: Ziquan, Xiang | Jiaqi, Yang | Naseem, Muhammad Hamza | Zuquan, Xiang

Article Type: Research Article

Abstract: In view of the extremely complex logistics level, long logistics cycle, high-risk coefficient, extremely fuzzy and uncertain evaluation information of cruise construction logistics allocation, an improved intuitionistic fuzzy TOPSIS method is proposed to evaluate the risk of cruise construction logistics allocation. Firstly, on the basis of empirical data analysis and research interviews, risk sources and risk assessment criteria are determined, and different weights are given to experts according to their importance. Then, in order to reduce the fuzziness and uncertainty of risk source information, an intuitionistic fuzzy weighted arithmetic average (IFWAA) operator is used to aggregate the experts’ opinions, and …the intuitionistic fuzzy aggregation decision risk matrix is obtained. The objective weight of risk assessment criteria is introduced to balance the impact of subjective weight on the final results, to improve the accuracy of the evaluation results. Finally, according to the relative closeness coefficient between each risk source and the positive-ideal solution, the priority of the risk sources of cruise construction logistics allocation is obtained, and the corresponding risk control measures are put forward. Compared with other methods and sensitivity analysis, the effectiveness and applicability of this method are verified. Show more

Keywords: Cruise construction, intuitionistic fuzzy number, TOPSIS method, risk assessment, logistics allocation

DOI: 10.3233/JIFS-211163

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

Authors: Fathy, E. | Ammar, E. | Helmy, M.A.

Article Type: Research Article

Abstract: In real-world problems, the parameters of optimization problems are uncertain. A class of multilevel linear programming (MLLP) with uncertainty problem models cannot be determined exactly. Hence, in this paper, we are concerned with studying the uncertainty of MLLP problems. The main motivation of this paper is to obtain the solution to a multilevel rough interval linear programming (MLRILP) problem. To obtain that, we start turning the problem into its competent crisp equivalent using the interval method. Moreover, we rely on three methods to address the problem of multiple levels. First, by applying the constraint method in which upper levels give …satisfactory solutions that are reasonable in rank order to the lower levels, second, by an interactive approach that uses the satisfaction test function, and third, by the fuzzy approach that is based on the concept of the tolerance membership function. A numerical example is given for illustration and to examine the validity of the approach. An application to deduce the optimality for the cost of the solid MLLP transportation problem in rough interval environment is presented. Show more

Keywords: Multilevel linear programming, constraint method, interactive approach, fuzzy approach, rough interval programming

DOI: 10.3233/JIFS-210694

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

Authors: Li, Jiwen | Lan, Fengchong | Chen, Jiqing

Article Type: Research Article

Abstract: In view of the disadvantages of the existing pose estimation algorithm, which has low real-time performance and the positioning accuracy will be greatly reduced in dynamic scene, a compound deep learning and parallel computing algorithm (DP-PE) is proposed. The detection algorithm based on deep learning is used to detect dynamic objects in the environment, and the dynamic feature points are removed before the matching of feature points to reduce the impact of dynamic objects on the positioning accuracy; A method for distinguishing “pseudo-dynamic objects” is proposed to solve the problem that the stationary vehicles and pedestrians in the environment are …regarded as dynamic objects. The parallel computing framework for feature point extraction and matching is established on CPU-GPU heterogeneous platform to speed up DP-PE; In the localization part of DP-PE, we propose a 3D interior point detection strategy to achieve parallel search of map points, and the saturated linear kernel function is used to act on reprojection error to realize the parallelization of pose optimization. We verify the algorithm on KITTI dataset, the experimental results show that average speedup ratio of feature point extraction and matching is 6.5 times, and the overall computational efficiency of DP-PE is about 7 times higher than that before acceleration, which can realize high precision and efficient pose estimation in dynamic scene. Show more

Keywords: Intelligent vehicle, visual pose estimation, deep learning, parallel computing

DOI: 10.3233/JIFS-211771

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

Authors: Li, Feng

Article Type: Research Article

Abstract: Many experts and scholars focus on the Maclaurin symmetric mean (MSM) equation, which can reflect the interrelationship among the multi-input arguments. It has been generalized to different fuzzy environments and put into use in various actual decision problems. The fuzzy data intuitionistic fuzzy numbers (FNIFNs) could well depict the uncertainties and fuzziness during the security evaluation of Wireless Sensor Network (WSN). And the WSN security evaluation is frequently viewed as the multiple attribute decision-making (MADM) issue. In this paper, we expand the generalized Maclaurin symmetric mean (GMSM) equation with FNIFNs to propose the fuzzy number intuitionistic fuzzy generalized MSM (FNIFGMSM) …equation and fuzzy number intuitionistic fuzzy weighted generalized MSM (FNIFWGMSM) equation in this study. A few MADM tools are developed with FNIFWGMSM equation. Finally, taking WSN security evaluation as an example, this paper illustrates effectiveness of the depicted approach. Moreover, by comparing and analyzing the existing methods, the effectiveness and superiority of the FNIFWGMSM method are further certified. Show more

Keywords: Multiple attribute decision making (MADM), fuzzy number intuitionistic fuzzy sets (FNIFSs), fuzzy number intuitionistic fuzzy GMSM (FNIFGMSM) equation, fuzzy number intuitionistic fuzzy weighted GMSM (FNIFWGMSM) equation, Wireless Sensor Network (WSN)

DOI: 10.3233/JIFS-211731

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

Authors: Shan, Chuanhui | Chen, Xiumei

Article Type: Research Article

Abstract: Because of the advantages of deep learning and information fusion technology, it has drawn much attention for researchers to combine them to achieve target recognition, positioning, and tracking. However, when the existing neural network process multichannel images (e.g., color images), multiple channels as a whole input into neural networks, which makes it hard for networks to fully learn information in R, G, and B channels of images. Therefore, it is not conducive to the final learning effect of the networks. To solve the problem, using different combinations of R, G, and B channels of color images for feature-level fusion, this …paper proposes three fusion types as “R/G/B”, “R+G/G+B/B+R”, and “R+G+B/R+G+B/R+G+B” multichannel concat-fusional convolutional neural networks. Experimental results show that multichannel concat-fusional convolutional neural networks with fusional types of “R+G/G+B/B+R” and “R+G+B/R+G+B/R+G+B” achieve better performance than the corresponding non-fusional convolutional neural networks on different datasets. It shows that networks with fusion types of “R+G/G+B/B+R” and “R+G+B/R+G+B/R+G+B” can learn more fully information of R, G, and B channels of color images and improve the learning performance of networks. Show more

Keywords: Information fusion technology, “R+G/G+B/B+R” fusional type, “R+G+B/R+G+B/R+G+B” fusional type, multichannel concat-fusional convolutional neural network, convolutional neural network

DOI: 10.3233/JIFS-212718

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

Authors: Shi, Yucai | Li, Weiqing | Lu, Pengfei | Chen, Fuxu | Qi, Xiaochen | Xiong, Changxin

Article Type: Research Article

Abstract: The precise motion control of a hydraulic motor system has some problems due to uncertain disturbance, complex nonlinear dynamics. Traditional methods are difficult to obtain the desired control performance. In this paper, a new fuzzy neural network (FNN) combined with terminal sling mode control (TSMC) and time delay estimation (TDE) is proposed. FNN is used to adjust the parameter of TSMC to reduce the time for the system to reach the equilibrium point and chatting. To increase the accuracy of the system, TDE is used to compensate the error caused by uncertain disturbance. This controller was simulated in Amesim and …Simulink, and the results showed that the control scheme proposed in this paper has the smallest angular displacement error, angular velocity error and variance than other control schemes, such as PID and sliding mode control (SMC). Furthermore, the designed controller was implemented on a drill pipe automatic handling manipulator, and its control performance was verified. Show more

Keywords: Motion control, hydraulic motor, fuzzy neural network, terminal sliding mode control, time delay estimation

DOI: 10.3233/JIFS-211398

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

Authors: Wuqiang, Zhu | Yang, Lu | Yongliang, Zhang | Xing, Wei | Zhen, Wei

Article Type: Research Article

Abstract: End-to-end deep learning has gained considerable interests in autonomous driving vehicles. End-to-end autonomous driving uses the deep convolutional neural network to establish input-to-output mapping. However, existing end-to-end driving models only predict steering angle with front-facing camera data and poorly extract spatial-temporal information. Based on deep learning and attention mechanism, we propose an end-to-end driving model which combines the multi-stream attention module with the multi-stream network. As a multimodal multitask model, the proposed end-to-end driving model not only fully extracts spatial-temporal information from multimodality, but also adopts the multitask learning method with hard parameter sharing to predict the steering angle and …speed. Furthermore, the proposed multi-stream attention module predicts the attention weights of streams based on the multimodal feature fusion, which encourages the proposed end-to-end driving model to pay attention to streams that positively impact the prediction result. We demonstrate the efficiency of the proposed driving model on the public Udacity dataset compared to existing models. Experimental results show that the proposed driving model has better performances than other existing methods. Show more

Keywords: End-to-end autonomous driving, attention mechanism, multimodal, multitask

DOI: 10.3233/JIFS-211206

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

Authors: Kumar, Arvind | Sodhi, Sartaj Singh

Article Type: Research Article

Abstract: We increase the power of the Artificial Neural Networks with the help of the Activation Function (AF). The tansig and logsig are widely used AF. But there is still requires some improvement in the AF. So, in this paper, we have proposed a NewSigmoid AF in the neural network. NewSigmoid is also as powerful as tansig and logsig. In multiple cases, the NewSigmoid function gives a better or equivalent performance as compared with both these AF. Like these AF, NewSigmoid is also a smooth S-shape, bounded, continuously differentiable, and zero-centered function. Therefore the NewSigmoid is also suitable for solving non-linear …problems. We have tested this AF on iris, cancer, glass, chemical, bodyfat, wine, and ovarian datasets. We use Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), and Bayesian Regularization (BR) algorithms during the optimization of the neural network. Maximum 100% accuracy in the iris dataset while using LM, and BR; 99.9% accuracy in the cancer dataset using BR; 100% accuracy in the glass dataset using BR; 100% accuracy in the chemical and bodyfat dataset using SCG, LM, and BR; 100% accuracy in the wine dataset using LM, and BR; and 99.1% accuracy in the ovarian dataset using BR has been found while working with multilayer neural networks. The NewSigmoid also achieves 100% training and validation accuracy on the mathework-cap image dataset using SCG. Show more

Keywords: Logsigmoid, tanigmoid, neural network, activation function, multilayer network.

DOI: 10.3233/JIFS-212333

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

Authors: Jia, Zhifu | Liu, Xinsheng

Article Type: Research Article

Abstract: Uncertain delay differential system is an important mathematical model. Stability is a basic problem of uncertain delay differential system. Delay and uncertain interference often lead to changes in the stability of the system. Establishing the judgment of the stability of uncertain delay differential system conditions is very important. Based on the strong Lipschitz condition, the judgment of p -th moment stability for uncertain delay differential equations (UDDEs) has been investigated. Actually, the strong Lipschitz condition is assumed that it only relates to the current state, it is difficult to be employed to determine the stability in p -th moment for …the UDDEs. In this paper, we consider two kinds of new Lipschitz conditions containing the current state and the past state, which are more weaker than the strong Lipschitz condition. Meanwhile, new sufficient theorems and corollaries under the new Lipschitz conditions as the tools to judge the p -th moment stability for the UDDEs are proved. Some examples explain the rationality of the corresponding theorems and corollaries. Show more

Keywords: Liu process, stability in p-th moment, new Lipschitz conditions, uncertain delay differential equations

DOI: 10.3233/JIFS-212288

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

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

Article Type: Research Article

Abstract: To the best of author’s knowledge, only one approach is proposed in the literature to solve fuzzy linear fractional minimal cost flow problems (minimal cost flow problems in which each known arc cost is represented either by a triangular fuzzy number or a trapezoidal fuzzy number). In this paper, the mathematical incorrect assumptions, considered in the existing approach to solve fuzzy linear fractional minimal cost flow problems, are pointed out. Also, by generalizing an existing approach for solving fuzzy linear fractional programming problems, an approach (named as Mehar approach) is proposed to solve fuzzy linear fractional minimal cost flow problems. …Furthermore, two numerical examples are solved to illustrate the proposed Mehar approach. Show more

Keywords: Linear fractional minimal cost flow problem, triangular fuzzy numbers, trapezoidal fuzzy numbers, lexicographic approach

DOI: 10.3233/JIFS-212909

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

Authors: Zhang, Feng | Luo, Xiaoying | Li, Fengling | Li, Yun | Li, Yanbin | Zhang, Pengyu

Article Type: Research Article

Abstract: Although smart grids are characterized by self-healing, economy, high efficiency, and security, many hidden dangers exist in the development of smart grids due to a gradually expanding power grid and the continuous access of new energy to the power grid. Therefore, the development of smart grids, especially their reliability, security, and vulnerability, warrants further investigation. In this study, the vulnerability of smart grids is identified, and the vulnerability elements of smart grids are selected. Based on relevant theories, such as credibility and the combination of the credibility-based moment-generating function and the distortion function, a calculation model and framework of the …vulnerability index of a smart grid are constructed. An empirical analysis is also conducted. This study provides a scientific basis for analyzing the vulnerability of smart grids and suggesting reasonable preventive measures and auxiliary decision-making information for relevant planning, design, and operation personnel, which contributes to the sustainable and healthy development of smart grids. Show more

Keywords: Smart grids, vulnerability index, moment-generating function, distortion function

DOI: 10.3233/JIFS-212575

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

Authors: Muhiuddin, G. | Talebi, A. A. | Sadati, S. H. | Rashmanlou, Hossein

Article Type: Research Article

Abstract: The cubic set, introduced as a combination of a fuzzy set and an interval-valued fuzzy set, provided researchers with more flexibility than the previous two sets in dealing with complex and uncertain problems. Fuzzy graphs, based on this type of set, are among the emerging fuzzy graphs that have a great potential to model the surrounding phenomena. Consistent with the special role that cubic graphs play in decision-making and selecting superior options, dominating these graphs is of great importance and value. In this paper, we introduce the domination of the cubic graphs in terms of strong edges and examine their …properties. In addition, we examine domination in terms of independent sets and since many of the phenomena surrounding us are hybrid, we also discuss the domination concept on its fuzzy operations. Finally, we present an application of this graph on the subject of domination. Show more

Keywords: Cubic graph, dominating set, independent cubic set, cubic operations

DOI: 10.3233/JIFS-212534

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

Authors: Wang, Haolun | Zhang, Faming

Article Type: Research Article

Abstract: The interaction operation laws (IOLs) between membership functions can effectively avoid the emergence of counterintuitive situations. The power average (PA) operator can eliminate the negative effect of extremely or improperly assessments on the decision results. The Heronian mean (HM) operator is capable of examining the interrelationship between the two attributes. To synthesize the powers of the IOLs, PA and HM operators in this paper, the PA and HM operators are extended to process T-spherical fuzzy evaluation information perfectly based on the IOLs, and the T-spherical fuzzy interaction power Heronian mean (T-SFIPHM) operator and its weighted form are proposed. We further …present some properties of these proposed AOs and discuss several special cases. Moreover, a novel method to T-spherical fuzzy multiple attribute decision making (MADM) problems applying the proposed AO is developed. Lastly, we present a numerical example to validate its feasibility and reasonableness, and the superiority of the developed method is further illustrated by sensitivity analysis of parameters and comparison with existing methods. The results show that proposed AOs not only can capture the interactivity among membership degree (MD), abstinence degree (AD) and non-membership degree (NMD) of T-spherical fuzzy numbers (T-SFNs), bust also ensure the overall balance of variable values in the process of information fusion and realize the interrelationship between attribute variables, so the decision results can be closer to reality and more reliable. Show more

Keywords: Multiple attribute decision making, T-spherical fuzzy sets, Heronian mean operator, interaction operation laws, power average operator

DOI: 10.3233/JIFS-212149

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

Authors: Mohankumar, B. | Karuppasamy, K.

Article Type: Research Article

Abstract: Congestion and security plays a most important key role in the wireless sensor network. In our previous work, energy balances routing is ensured by introducing the Energy Balancing and Optimal Routing Based Secured Data Transmission (EBORDT). But in this research work, congestion and security is not focused. This will lead to increased data loss rate along with entire network collision. These issues are focused and resolved in the proposed research work by introducing the method namely Honest aware Congestion concerned Secured Edge Disjoint Multipath Routing (HC-SEDMR) Scheme. In this work cluster head selection is done using hybridized simulated annealing with …fuzzy rule descriptors. Base on these selected cluster head, clustering is done using Normalized optimal clustering algorithm. After clustering Energy and Edge disjoint aware optimal routing is done using Congestion and Collision aware Edge Disjoint multipath routing. Here secured routing is ensured by choosing honest nodes for the forwarding process. This is done by using honest forwarding node selection method. The performance analysis of the research work is done in the NS2 simulation environment from which it is proved that the proposed method attains better performance than the existing methodology. Show more

Keywords: Honesty, congestion, edge disjoint routing, clustering, simulated annealing, multipath routing

DOI: 10.3233/JIFS-212841

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

Authors: Ullah Khan, Izaz | Aftab, Muhammad

Article Type: Research Article

Abstract: This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. Dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan …comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable. Show more

Keywords: Fuzzy Linear Programming (FLP), fuzzy mathematical programming, dynamic programming, education, fuzzy sets and fuzzy modeling, resource allocation

DOI: 10.3233/JIFS-211577

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

Authors: Tingting, Zhang | Zhenpeng, Tang | Linjie, Zhan | Xiaoxu, Du | Kaijie, Chen

Article Type: Research Article

Abstract: An important feature of the outbreak of systemic financial risk is that the linkage and contagion of risk amongst the various sub-markets of the financial system have increased significantly. In addition, research on the prediction of systemic financial risk plays a significant role in the sustainable development of the financial market. Therefore, this paper takes China’s financial market as its research object, considers the risks co-activity among major financial sub-markets, and constructs a financial composite indicator of systemic stress (CISS) for China, describing its financial systemic stress based on 12 basic indicators selected from the money market, bond market, stock …market, and foreign exchange market. Furthermore, drawing on the decomposition and integration technology in the [email protected] complex system research methodology, this paper introduces advanced variational mode decomposition (VMD) technology and extreme learning machine (ELM) algorithms, constructing the VMD-DE-ELM hybrid model to predict the systemic risk of China’s financial market. According to e RMSE , e MAE , and e MAPE , the prediction model’s multistep-ahead forecasting effect is evaluated. The empirical results show that the China’s financial CISS constructed in this paper can effectively identify all kinds of risk events in the sample range. The results of a robustness test show that the overall trend of China’s financial CISS and its ability to identify risk events are not affected by parameter selection and have good robustness. In addition, compared with the benchmark model, the VMD-DE-ELM hybrid model constructed in this paper shows superior predictive ability for systemic financial risk. Show more

Keywords: Systemic financial risk, financial stress indicator, artificial intelligence model, VMD, DE-ELM

DOI: 10.3233/JIFS-212178

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

Authors: Muhiuddin, G. | Grace John, J. Catherine | Elavarasan, B. | Porselvi, K. | Al-Kadi, D.

Article Type: Research Article

Abstract:  The notions of hybrid ideals and k -hybrid ideals in a ternary semiring are introduced in this paper, and a substantial amount of effort has been made to study some of their features. In terms of characteristic function, we show some properties of k -hybrid ideals and give some characterizations of hybrid intersection with respect to these k -hybrid ideals. Finally, results based on a k -hybrid ideal’s homomorphic hybrid preimage are provided. With respect to k-hybrid ideals, we give certain characterizations of hybrid intersection.

Keywords: Semirings, Ternary semirings, k-hybrid ideals, Homomorphism, ψ-invariant

DOI: 10.3233/JIFS-212311

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

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

Article Type: Research Article

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

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

DOI: 10.3233/JIFS-212743

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

Authors: Mahmood, Tahir | Izatmand, | Ali, Zeeshan | Panityakul, Thammarat

Article Type: Research Article

Abstract: In the real decision process, an important problem is how to express the attribute value more efficiently and accurately. In the real world, because of the complexity of decision-making problems and the fuzziness of decision-making environments, it is not enough to express attribute values of alternatives by exact values. For this managing with such sorts of issues, the principle of Linear Diophantine uncertain linguistic set is a valuable and capable technique to manage awkward and inconsistent information in everyday life problems. In this manuscript, we propose the original idea of Linear Diophantine uncertain linguistic set and elaborated their essential laws. …Additionally, to determine the association among any numbers of attributes, we elaborated the Linear Diophantine uncertain linguistic arithmetic Heronian mean operator, Linear Diophantine uncertain linguistic weighted arithmetic Heronian mean operator, Linear Diophantine uncertain linguistic geometric Heronian mean operator, Linear Diophantine uncertain linguistic weighted geometric Heronian mean operator, and their properties are also discovered. By using these operators, we utilize the multi-attribute decision-making procedure by using elaborated operators. To determine the consistency and validity of the elaborated operators, we illustrate some examples by using explored operators. Finally, the superiority and comparative analysis of the elaborated operators with some existing operators are also determined and justified with the help of a graphical point of view. Show more

Keywords: Linear Diophantine uncertain linguisticsets, arithmetic/geometric Heronian mean operators, decision-making methods

DOI: 10.3233/JIFS-211839

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-29, 2021

Authors: Ali, Zeeshan | Mahmood, Tahir | Ullah, Kifayat | Chinram, Ronnason

Article Type: Research Article

Abstract: The major contribution of this analysis is to analyze the confidence complex q-rung orthopair fuzzy weighted averaging (CCQROFWA) operator, confidence complex q-rung orthopair fuzzy ordered weighted averaging (CCQROFOWA) operator, confidence complex q-rung orthopair fuzzy weighted geometric (CCQROFWG) operator, and confidence complex q-rung orthopair fuzzy ordered weighted geometric (CCQROFOWG) operator and invented their feasible properties and related results. Future more, under the invented operators, we diagnosed the best crystalline solid from the family of crystalline solids with the help of the opinion of different experts in the environment of decision-making strategy. Finally, to demonstrate the feasibility and flexibility of the invented …works, we explored the sensitivity analysis and graphically shown of the initiated works. Show more

Keywords: Complex q-rung orthopair fuzzy sets, aggregation operators, confidence levels, decision-making methods

DOI: 10.3233/JIFS-211840

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

Authors: Ammar, E. | Al-Asfar, A.

Article Type: Research Article

Abstract: In real conditions, the parameters of multi-objective nonlinear programming (MONLP) problem models can’t be determined exactly. Hence in this paper, we concerned with studying the uncertainty of MONLP problems. We propose algorithms to solve rough and fully-rough-interval multi-objective nonlinear programming (RIMONLP and FRIMONLP) problems, to determine optimal rough solutions value and rough decision variables, where all coefficients and decision variables in the objective functions and constraints are rough intervals (RIs). For the RIMONLP and FRIMONLP problems solving methodology are presented using the weighting method and slice-sum method with Kuhn-Tucker conditions, We will structure two nonlinear programming (NLP) problems. In the …first one of this NLP problem, all of its variables and coefficients are the lower approximation (LAI) it’s RIs. The second NLP problems are upper approximation intervals (UAI) of RIs. Subsequently, both NLP problems are sliced into two crisp nonlinear problems. NLP is utilized because numerous real systems are inherently nonlinear. Also, rough intervals are so important for dealing with uncertainty and inaccurate data in decision-making (DM) problems. The suggested algorithms enable us to the optimal solutions in the largest range of possible solution. Finally, Illustrative examples of the results are given. Show more

Keywords: Multi-objective nonlinear programming (MONLP), rough-interval (RI), Fully-rough-interval, Slice-sum method and Kuhn-Tucker (KT) conditions

DOI: 10.3233/JIFS-202586

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

Authors: Wei, Dongmei | Rong, Yuan | Garg, Harish

Article Type: Research Article

Abstract: Teaching quality evaluation (TQE) can not only improve teachers’ teaching skills, but also provide an important reference for school teaching management departments to formulate teaching reform measures and strengthen teaching management. TQE is a process of grading and ranking a given teachers based on the comprehensive consideration of multiple evaluation criteria by expert. The Maclaurin symmetric mean (MSM), as a powerful aggregation function, can capture the correlation among multiple input data more efficient. Although multitude weighted MSM operators have been developed to handle the Pythagorean fuzzy decision issues, these above operators do not possess the idempotency and reducibility during the …procedure of information fusion. To conquer these defects, we present the Pythagorean fuzzy reducible weighted MSM (PFRWMSM) operator and Pythagorean fuzzy reducible weighted geometric MSM (PFRWGMSM) operator to fuse Pythagorean fuzzy assessment information. Meanwhile, several worthwhile properties and especial cases of the developed operators are explored at length. Afterwards, we develop a novel Pythagorean fuzzy entropy based upon knowledge measure to ascertain the weights of attribute. Furthermore, an extended weighted aggregated sum product assessment (WASPAS) method is developed by combining the PFRWMSM operator, PFRWGMSM operator and entropy to settle the decision problems of unknown weight information. The efficiency of the proffered method is demonstrated by a teaching quality evaluation issue, as well as the discussion of sensitivity analysis for decision outcomes. Consequently, a comparative study of the presented method with the extant Pythagorean fuzzy approaches is conducted to display the superiority of the propounded approach. Show more

Keywords: Teaching quality evaluation, Pythagorean fuzzy set, information fusion, Reducible weighted MSM, WASPAS

DOI: 10.3233/JIFS-210821

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-32, 2021

Authors: Li, Xiaofei | Ye, Binhua | Liu, Xinwang

Article Type: Research Article

Abstract: Linear programming is an important branch of operations research. The model is mature in theory and widely used in real life. However, various complex realistic scenarios involve fuzzy information. In this paper, we consider a fuzzy linear programming (FLP) model in which all parameters are trapezoidal interval type-2 fuzzy numbers (IT2FNs) and propose a solution method based on the nearest interval approximation and the best-worst cases (BWC) method. We prove the nearest interval approximation interval of trapezoidal IT2FNs, then the trapezoidal IT2FNs in the model are transformed into interval numbers which both upper and lower limits are interval numbers. With …the help of best-worst cases (BWC) method, the sub-models of the transformed interval linear programming model are proposed, and four sub-solutions with different specific meanings can be obtained by solving them respectively. Finally, an application example is presented to show the rationality and practical significance of the method. Show more

Keywords: Fuzzy linear programming (FLP), Trapezoidal interval type-2 fuzzy numbers (IT2FNs), Best-worst cases (BWC) method, The nearest interval approximation

DOI: 10.3233/JIFS-211568

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

Authors: Kheirollahi, Hooshang | Rostamzadeh, Mahfouz | Marzang, Soran

Article Type: Research Article

Abstract: Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable …is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions. Show more

Keywords: Data envelopment analysis, fuzzy-random, most productive scale size, hospitals

DOI: 10.3233/JIFS-202456

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

Authors: Vijayalakshmi, P. | Muthumanickam, K. | Karthik, G. | Sakthivel, S.

Article Type: Research Article

Abstract: Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability …of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility. Show more

Keywords: Medical image processing, adenomyosis, endometriosis, infertility, BIWDNN

DOI: 10.3233/JIFS-212866

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

Authors: Ferrari, Allan Christian Krainski | Silva, Carlos Alexandre Gouvea da | Osinski, Cristiano | Pelacini, Douglas Antonio Firmino | Leandro, Gideon Villar | Coelho, Leandro dos Santos

Article Type: Research Article

Abstract: The Whale Optimization Algorithm (WOA) is a recent approach to the swarm intelligence field that can be explored in many global optimization applications. This paper proposes a new mechanism to tune the control parameters that influence the hunting process in the WOA to improve its convergence rate. This schema adjustment is made by a fuzzy inference system that uses the normalized fitness value of each whale and the hunting mechanism control parameters of WOA. The method proposed was tested and compared with the conventional WOA and another version that uses a fuzzy inference system as input information on the ratio …of the current iteration number and the maximum number of iterations. For performance analysis of the method proposed, all optimizers were evaluated with twenty-three benchmark optimization functions in the continuous domain. The algorithms were also implemented in the identification process of two real control system that are a boiler system and water supply network. For identification process, it is used the value of MSE (mean squared error) to available each algorithm. The simulation results show that the proposed fuzzy mechanism improves the convergence of the conventional WOA and it is competitive in relation to another fuzzy version adopted in the WOA design. Show more

Keywords: Humpback whale, Metaheuristics, optimization, identification process, Whale Optimization Algorithm

DOI: 10.3233/JIFS-210781

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

Authors: Zhang, Huiyuan | Wei, Guiwu | Wei, Cun

Article Type: Research Article

Abstract: Nowadays, how to choose a comfortable and relatively satisfactory residence is one of the multiple attribute group decision making (MAGDM) issues which people are paying more and more attention. However, since the inaccuracy and fuzziness of the information are given by decision makers (DMs) in practical decision-making and psychological factors of DMs should be considered in the decision-making process, this paper presents TOPSIS approach based on cumulative prospect theory (CPT) to deal with the MAGDM issues under the spherical fuzzy environment. Furthermore, considering the objective relationship between the attributes, the combined weights are used to get attribute weights in spherical …fuzzy sets (SFSs). Finally, an example of residential location is introduced to prove the validity of our proposed approach by comparing with spherical fuzzy TOPSIS(SF-TOPSIS) method and spherical fuzzy WASPAS (SF-WASPAS) method. Show more

Keywords: Multiple attribute group decision making (MAGDM), spherical fuzzy sets (SFSs), TOPSIS, cumulative prospect theory (CPT), combined weights, residential location

DOI: 10.3233/JIFS-210267

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

Authors: Varatharaj, Nagaraj | Ramalingam, Sumithira Thulasimani

Article Type: Research Article

Abstract: Most revolutionary applications extending far beyond smartphones and high configured mobile device use to the future generation wireless networks’ are high potential capabilities in recent days. One of the advanced wireless networks and mobile technology is 5G, where it provides high speed, better reliability, and amended capacity. 5 G offers complete coverage, which is accommodates any IoT device, connectivity, and intelligent edge algorithms. So that 5 G has a high demand in a wide range of commercial applications. Ambrosus is a commercial company that integrates block-chain security, IoT network, and supply chain management for medical and food enterprises. This paper proposed a …novel framework that integrates 5 G technology, Machine Learning (ML) algorithms, and block-chain security. The main idea of this work is to incorporate the 5 G technology into Machine learning architectures for the Ambrosus application. 5 G technology provides continuous connection among the network user/nodes, where choosing the right user, base station, and the controller is obtained by using for ML architecture. The proposed framework comprises 5 G technology incorporate, a novel network orchestration, Radio Access Network, and a centralized distributor, and a radio unit layer. The radio unit layer is used for integrating all the components of the framework. The ML algorithm is evaluated the dynamic condition of the base station, like as IoT nodes, Ambrosus users, channels, and the route to enhance the efficiency of the communication. The performance of the proposed framework is evaluated in terms of prediction by simulating the model in MATLAB software. From the performance comparison, it is noticed that the proposed unified architecture obtained 98.6% of accuracy which is higher than the accuracy of the existing decision tree algorithm 97.1% . Show more

Keywords: 5 G Technology, internet of things, block-chain security, wireless communication, machine learning models

DOI: 10.3233/JIFS-211745

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

Authors: Jiang, Nan-Yun | Yan, Hong-Sen

Article Type: Research Article

Abstract: For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level …model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm. Show more

Keywords: Uncertain re-entrance, fixed-position assembly workshop, integrated optimization of production planning and scheduling, improved genetic algorithm

DOI: 10.3233/JIFS-211159

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

Authors: Li, Shuailong | Zhang, Wei | Zhang, Huiwen | Zhang, Xin | Leng, Yuquan

Article Type: Research Article

Abstract: Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency. To improve the policy performance and sample efficiency of model-free reinforcement learning, we propose proximal policy optimization with model-based methods (PPOMM), a fusion method of both model-based and model-free reinforcement learning. PPOMM not only considers the information of past experience but also the prediction information of the future state. PPOMM adds the information of the next state to the objective function of the proximal policy optimization (PPO) …algorithm through a model-based method. This method uses two components to optimize the policy: the error of PPO and the error of model-based reinforcement learning. We use the latter to optimize a latent transition model and predict the information of the next state. For most games, this method outperforms the state-of-the-art PPO algorithm when we evaluate across 49 Atari games in the Arcade Learning Environment (ALE). The experimental results show that PPOMM performs better or the same as the original algorithm in 33 games. Show more

Keywords: Model-based reinforcement learning, model-free reinforcement learning, policy optimization method

DOI: 10.3233/JIFS-211935

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

Authors: Li, Huipeng | Lu, Lin | Yang, Liguo

Article Type: Research Article

Abstract: The rapid development of the Internet has accelerated the expansion of e-commerce sacle of fresh agricultural products. The actual audience of smart logistics distribution of fresh agricultural products is customers, and customers enjoy the process and results of distribution services. However, the current research mainly selects indicators from the aspects of enterprise performance, cost and technical level based on the perspective of managers and technicians, which make it difficult to truly reflect customers’ feelings in the evaluation results. At the same time, the evaluation methods mainly focus on the comprehensive evaluation method and fuzzy evaluation method. These evaluation methods are …greatly affected by subjective factors in the evaluation grade distribution, and the assignment is often relatively complete and inaccurate. To solve these problems, this paper constructs the evaluation index system of intelligent logistics distribution of fresh agricultural products from the perspective of customers, so that the selection of indicators is more in line with the real wishes of customers. And we use the extension function to construct the correlation function for multi-level extension evaluation to ensure the accuracy of the evaluation results. Taking X logistics enterprise as an example, this paper verifies the scientificity of the evaluation index system of intelligent logistics distribution of fresh agricultural products through empirical research, which has reference significance for further improving the intelligent logistics distribution of fresh agricultural products. Show more

Keywords: Smart logistics distribution, fresh agricultural products, customer perspective, extenics

DOI: 10.3233/JIFS-212362

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

Authors: Xie, Rongjian | Jia, Yucai | Wu, Yuanmei | Zhang, Peiyun

Article Type: Research Article

Abstract: During major epidemics, monitoring vaccine quality can ensure the public health and social stability. Considering that social media has become an important way for the public to obtain external information during the epidemic. We developed a dual regulatory system of vaccine quality with the government in the leading role and the participation of We Media, and constructed a four-party evolutionary game model (government regulatory agency, We Media, vaccine industry groups, and the public) and analyzed the stability of each game player’s strategy choice. The system’s possible equilibrium points are identified using Lyapunov’s first law. Then the game trajectory between stakeholders …is simulated by MATLAB, the effects of initial intention and parameters on the evolution process and results are analyzed. The results show that to ensure the quality and safety of vaccines and stabilize network public opinion during epidemics, the government should invest in an effective supervision mechanism. By strengthening responsibility, increasing penalties, and reducing supervision costs, the probability of vaccine industry groups providing high-quality vaccines is effectively enhanced. Restricting the behavior of We Media and supervising vaccine industry groups to reduce speculation reduces the cost of government supervision and improves its efficiency. Show more

Keywords: Major epidemics, vaccine quality, dual regulatory system, four-party evolutionary game

DOI: 10.3233/JIFS-212146

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

Authors: Shen, Yonghong

Article Type: Research Article

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

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

DOI: 10.3233/JIFS-212908

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

Authors: Zhong, Xianyou | Xia, Tianyi | Zhao, Yankun | Zhao, Xiao

Article Type: Research Article

Abstract: The weak fault characteristics of rolling bearings are difficult to identify due to strong background noise. To address this issue, a bearing fault detection scheme combining swarm decomposition (SWD) and frequency-weighted energy operator (FWEO) is presented. First, SWD is applied to decompose the bearing fault signal into single mode components. Then, a new evaluation index termed LEP is constructed by combining the advantages of envelope entropy, Pearson correlation coefficient and L-kurtosis, and it is utilized to choose the sensitive component containing the richest bearing fault characteristics. Finally, FWEO is employed for extracting the bearing fault features from the sensitive component. …Simulation and experimental analyses indicate that the LEP index has better performance than the L-kurtosis index in determining the sensitive component. The method has the effect of suppressing noise and enhancing impulse characteristics, which is superior to the SWD-based envelope demodulation method. Show more

Keywords: Swarm decomposition, frequency-weighted energy operator, fault diagnosis, rolling bearing

DOI: 10.3233/JIFS-212305

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

Authors: Peng, Zhenxing | He, Lina | Xie, Yushi | Song, Wenyan | Liu, Jue | Ming, Xinguo | Goh, Mark

Article Type: Research Article

Abstract: A sustainable supply chain (SSC) is vital for company’s sustainability success, so it is imperative to identify and prioritize SSC’s design requirements (DRs) for better SSC planning. For customer-centric markets, the customer requirements (CRs) need to be integrated into SSC’s DRs. This paper thus proposes a customer-centric approach based on Analytic Network Process (ANP), Quality Function Deployment (QFD), Grey Relational Analysis (GRA), and Pythagorean Fuzzy Set (PFS) to rank SSC’s DRs, considering CRs and information ambiguity. The PFS is combined with ANP, QFD, and GRA to better handle uncertainty in the SSC. The Pythagorean fuzzy ANP is applied to analyze …the correlations among the sustainable CRs and determine the corresponding weights. The sustainable CRs are transformed into the DRs using the Pythagorean fuzzy QFD. The relationships among the resulting DRs are analyzed through Pythagorean fuzzy GRA to prioritize DRs. The approach is validated through a case study. The results obtained in this paper shows that the proposed method is efficient to prioritize DRs of SSC with the consideration of sustainable CRs under uncertain environment. The novelties of proposed method are that it not only offers a customer-oriented SSC planning method through the integration of ANP, QFD and GRA, but also can reflect the uncertain information with a broader membership representation space via PFSs. Based on the proposed method, the decision-maker can conduct comprehensive analysis to prioritize DRs and design appropriate SSC to fulfill CRs under uncertain environment. Show more

Keywords: Sustainable supply chain, design requirements, analytic network process, pythagorean fuzzy set, quality function deployment, grey relational analysis

DOI: 10.3233/JIFS-212131

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

Authors: Yang, Luting | Li, Yan

Article Type: Research Article

Abstract: Online shopping has gradually become an important way of consumption, and consumers are paying more and more attention to negative reviews. In order to avoid the massive amount of negative review information leading to loss of useful information, this paper proposes a method for evaluating the usefulness of negative online reviews. Firstly, the method constructs an evaluation index system for the usefulness of negative online reviews from three aspects: the form feature, text feature, and reviewer feature of negative reviews, and uses a combination weighting method based on fuzzy analytic hierarchy process (FAHP) and entropy method to determine the weight …of each index. Secondly, the usefulness ranking results of negative online reviews are obtained through the improved TOPSIS method based on the combined weighting method. Finally, the empirical analysis of the proposed model is carried out by crawling the negative online reviews of JD.com Fresh Food platform, and the improved model is compared with the traditional TOPSIS model, which proves the feasibility and effectiveness of the model. Show more

Keywords: Negative reviews, usefulness ranking, FAHP, entropy method, improved TOPSIS

DOI: 10.3233/JIFS-211928

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

Authors: Li, Yang | Chen, Simeng | Bai, Ke | Wang, Hao

Article Type: Research Article

Abstract: Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with …the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots. Show more

Keywords: Safety, fire risk level, path optimisation, discrete electrostatic discharge algorithm, improved discrete electrostatic discharge algorithm

DOI: 10.3233/JIFS-212464

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

Authors: Padmapriya, V. | Kaliyappan, M.

Article Type: Research Article

Abstract: In this paper, we develop a mathematical model with a Caputo fractional derivative under fuzzy sense for the prediction of COVID-19. We present numerical results of the mathematical model for COVID-19 of most three infected countries such as the USA, India and Italy. Using the proposed model, we estimate predicting future outbreaks, the effectiveness of preventive measures and potential control strategies of the infection. We provide a comparative study of the proposed model with Ahmadian’s fuzzy fractional mathematical model. The results demonstrate that our proposed fuzzy fractional model gives a nearer forecast to the actual data. The present study can …confirm the efficiency and applicability of the fractional derivative under uncertainty conditions to mathematical epidemiology. Show more

Keywords: Fuzzy triangular number, fuzzy fractional derivative, Caputo derivative, COVID-19, Mathematical model

DOI: 10.3233/JIFS-211173

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

Authors: Yu, Hui | Li, Jun-qing | Chen, Xiao-Long | Zhang, Wei-meng

Article Type: Research Article

Abstract:  During recent years, the outpatient scheduling problem has attracted much attention from both academic and medical fields. This paper considers the outpatient scheduling problem as an extension of the flexible job shop scheduling problem (FJSP), where each patient is considered as one job. Two realistic constraints, i.e., switching and preparation times of patients are considered simultaneously. To solve the outpatient scheduling problem, a hybrid imperialist competitive algorithm (HICA) is proposed. In the proposed algorithm, first, the mutation strategy with different mutation probabilities is utilized to generate feasible and efficient solutions. Then, the diversified assimilation strategy is developed. The enhanced global …search heuristic, which includes the simulated annealing (SA) algorithm and estimation of distribution algorithm (EDA), is adopted in the assimilation strategy to improve the global search ability of the algorithm.?Moreover, four kinds of neighborhood search strategies are introduced to?generate new?promising?solutions.?Finally, the empires invasion strategy?is?proposed to?increase the diversity of the population. To verify the performance of the proposed HICA, four efficient algorithms, including imperialist competitive algorithm, improved genetic algorithm, EDA, and modified artificial immune algorithm, are selected for detailed comparisons. The simulation results confirm that the proposed algorithm can solve the outpatient scheduling problem with high efficiency. Show more

Keywords: Flexible job shop scheduling, outpatient scheduling, hybrid imperialist competitive algorithm, neighborhood search strategies

DOI: 10.3233/JIFS-212024

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

Authors: Hamdi, Mohammed

Article Type: Research Article

Abstract: With the evaluation of the software industry, a huge number of software applications are designing, developing, and uploading to multiple online repositories. To find out the same type of category and resource utilization of applications, researchers must adopt manual working. To reduce their efforts, a solution has been proposed that works in two phases. In first phase, a semantic analysis-based keywords and variables identification process has been proposed. Based on the semantics, designed a dataset having two classes: one represents application type and the other corresponds to application keywords. Afterward, in second phase, input preprocessed dataset to manifold machine learning …techniques (Decision Table, Random Forest, OneR, Randomizable Filtered Classifier, Logistic model tree) and compute their performance based on TP Rate, FP Rate, Precision, Recall, F1-Score, MCC, ROC Area, PRC Area, and Accuracy (%). For evaluation purposes, I have used an R language library called latent semantic analysis for creating semantics, and the Weka tool is used for measuring the performance of algorithms. Results show that the random forest depicts the highest accuracy which is 99.3% due to its parametric function evaluation and less misclassification error. Show more

Keywords: Machine learning, software classification, software sustainability, data analytics

DOI: 10.3233/JIFS-212600

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

Authors: Zhou, Qin | Su, Zuqiang | Liu, Lanhui | Hu, Xiaolin | Yu, Jianhang

Article Type: Research Article

Abstract: This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each …working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions. Show more

Keywords: Rolling bearing, fault diagnosis, transfer learning, subdomain adaptation

DOI: 10.3233/JIFS-212343

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

Authors: Trevino-Sanchez, Daniel | Alarcon-Aquino, Vicente

Article Type: Research Article

Abstract: The need to detect and classify objects correctly is a constant challenge, being able to recognize them at different scales and scenarios, sometimes cropped or badly lit is not an easy task. Convolutional neural networks (CNN) have become a widely applied technique since they are completely trainable and suitable to extract features. However, the growing number of convolutional neural networks applications constantly pushes their accuracy improvement. Initially, those improvements involved the use of large datasets, augmentation techniques, and complex algorithms. These methods may have a high computational cost. Nevertheless, feature extraction is known to be the heart of the problem. …As a result, other approaches combine different technologies to extract better features to improve the accuracy without the need of more powerful hardware resources. In this paper, we propose a hybrid pooling method that incorporates multiresolution analysis within the CNN layers to reduce the feature map size without losing details. To prevent relevant information from losing during the downsampling process an existing pooling method is combined with wavelet transform technique, keeping those details "alive" and enriching other stages of the CNN. Achieving better quality characteristics improves CNN accuracy. To validate this study, ten pooling methods, including the proposed model, are tested using four benchmark datasets. The results are compared with four of the evaluated methods, which are also considered as the state-of-the-art. Show more

Keywords: Convolutional neural network, feature extraction, lifting scheme, pooling layer, wavelet transform

DOI: 10.3233/JIFS-219223

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

Authors: Wang, Xiaohan | He, Zengyu | Wang, Pei | Zha, Xinmeng | Gong, Zimin

Article Type: Research Article

Abstract: Due to the limitation of positioning devices, there is a certain error between GPS positioning data and the real location on the map, and the positioning data needs to be processed to have better usability. For example, accurate location is needed for traffic flow control, automatic driving navigation, logistics tracking, etc. There are few studies specifically for circular road sections. In addition, many existing map matching methods based on Hidden Markov model (HMM) also have the problem that GPS points are easily to be matched to tangent or non-adjacent road sections at circular road sections. Therefore, the contextual voting map …matching method for circular road sections (STDV-matching) is proposed. The method proposes multiple subsequent point direction analysis methods based on STD-matching to determine entry into the circular section, and adds candidate section frequency voting analysis to reduce matching errors. The effectiveness of the proposed method is verified at the circular section by comparing it with three existing HMM methods through experiments using two real map and trajectory datasets. Show more

Keywords: Map matching, circular road, GPS, voting

DOI: 10.3233/JIFS-213054

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

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

Article Type: Research Article

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

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

DOI: 10.3233/JIFS-212473

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

Authors: Lu, Haishu | Li, Rong

Article Type: Research Article

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

Keywords: CAT(0) space, fuzzy fixed point, fuzzy maximal element, fuzzy noncooperative game, nash equilibrium point, 91A10, 47H04, 47H10

DOI: 10.3233/JIFS-212194

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

Authors: Gunapriya, D. | Muniraj, C. | Lakshmi, K.

Article Type: Research Article

Abstract: The detection as well as analysis of faults in Induction Motor (IM) is prominent in the industrial process in recent decades, since it has been a demanding issue in industries to confirm the safe and reliable operations of IM. Though the electrical faults, mechanical faults and environmental faults cause damages in IM, as per Electric Power Research Institute (EPRI) statistical studies, the faults due to (i) rotor mass unbalance and (ii) rotor shaft bending substantially contribute 8-9% of the total motor fault. This present research work focuses on the issue of detecting and analysing the faults by studying the current …and vibration data obtained from the three-phase squirrel cage IM under healthy and faulty conditions using the experimental workbench. It also depicts the development of a fault detection model for IM which comprises the integrated approach of Principal Component Analysis (PCA) and Fuzzy Interference System (FIS) and two level decision fuzzy measures. Besides, fuzzy integral data fusion technique has been used in this work for the improvement of diagnosing accuracy. The data acquired from the workbench system are first investigated through the PCA to extricate the appropriate features that provide the major information of collected data without reducing its dimensions. The projected data space using the principal components is non-deterministic for further synthesis process of fault classification. Hence, to classify the faults in IM, the obtained feature vectors from PCA are fed into FIS as an input and the classification performance is compared finally. The work experiment has been carried out under the healthy and different faulty conditions of motor and the proposed integrated approach is executed by using MATLAB. Show more

Keywords: Fuzzy logic, fuzzy integral, fuzzy measure, induction motor faults, principal component analysis, current and vibration signals

DOI: 10.3233/JIFS-211124

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

Authors: Jiang, Zhiwei | Wei, Guiwu | Guo, Yanfeng

Article Type: Research Article

Abstract: In the garment manufacturing industry, purchasing management is an important link. The materials of making clothes often need high cost. In addition, customers put forward a request in the quality of clothes. Thus, choosing an optimal supplier is an essential part of job. Reaching cooperation with an optimal supplier not only can help garment manufacturer improve the quality of clothes but also is benefit to reduce the cost of producing. Most importantly, it can improve the competitiveness of manufacture enterprises. So, it is important for managers to find an optimal supplier and make a cooperation with it. In this paper, …we analysis an issue about choosing an optimal supplier during four different suppliers. With analyzing this problem, we can introduce an extended method under picture fuzzy environment to evaluate and choose an optimal supplier. In this article, we describe some basic knowledges about picture fuzzy sets (PFSs) and picture fuzzy numbers (PFNs). Then, we introduce the extension of MABAC method which is on the basis of prospect theory (PT) with picture fuzzy numbers (PF-PT-MABAC) and utilize the PF-PT-MABAC model to evaluate different suppliers to choose an optimal supplier. Finally, we compare the result of PF-PT-MABAC with the result of traditional MABAC, PFWG operators and traditional TODIM method to test the efficiency of PF-PT-MABAC model. Show more

Keywords: Multiple attribute group decision making (MAGDM), picture fuzzy sets (PFSs), MABAC method, prospect theory (PT), suppliers selection

DOI: 10.3233/JIFS-211359

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

Authors: Davids, D. Minola | Christopher, C. Seldev

Article Type: Research Article

Abstract: The visual data attained from surveillance single-camera or multi-view camera networks is exponentially increasing every day. Identifying the important shots in the presented video which faithfully signify the original video is the major task in video summarization. For executing efficient video summarization of the surveillance systems, optimization algorithm like LFOB-COA is proposed in this paper. Data collection, pre-processing, deep feature extraction (FE), shot segmentation JSFCM, classification using Rectified Linear Unit activated BLSTM, and LFOB-COA are the proposed method’s five steps. Finally a post-processing step is utilized. For recognizing the proposed method’s effectiveness, the results are then contrasted with the existent …methods. Show more

Keywords: Video summarization, Levy Flight (LF) and opposition-based learning Coyote Optimization Algorithm (LFOB-COA), Bi-directional Long Short-term Memory (BLSTM), Jaccard Similarity-centered Fuzzy C-Means (JSFCM)

DOI: 10.3233/JIFS-212800

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

Authors: Sánchez-Fernández, Manuel-Alejandro | Medina-Urrea, Alfonso | Torres-Moreno, Juan-Manuel

Article Type: Research Article

Abstract: The present work aims to study the relationship between measures, obtained from Latent Semantic Analysis (LSA) and a variant known as SPAN , and activation and identifiability states (Informative States) of referents in noun phrases present in journalistic notes from Northwestern Mexican news outlets written in Spanish. The aim and challenge is to find a strategy to achieve labelling of new / given information in the discourse rooted in a theoretically linguistic stance. The new / given distinction can be defined from different perspectives in which it varies what linguistic forms are taken into account. Thus, the focus in this …work is to work with full referential devices (n  = 2 388). Pearson’s R correlation tests, analysis of variance, graphical exploration of the clustering of labels, and a classification experiment with random forests are performed. For the experiment, two groups were used: noun phrases labeled with all 10 tags of informative states and a binary labelling, as well as the use of two bags-of-words for each noun phrase: the interior and the exterior. It was found that using LSA in conjunction with the inner bag of words can be used to classify certain informational states. This same measure showed good results for the binary division, detecting which sentences introduce new referents in discourse. In previous work using a similar method in noun phrases in English, 80% accuracy (n  = 478) was reached in their classification exercise. Our best test for Spanish reached 79%. No work on Spanish using this method has been done before and this kind of experiment is important because Spanish exhibits a more complex inflectional morphology. Show more

Keywords: Automatic tagging, activation states, latent semantic analysis, noun phrases, computational pragmatics

DOI: 10.3233/JIFS-219235

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

Authors: Wang, Zeng | Liu, Weidong | Yang, Minglang

Article Type: Research Article

Abstract: As the main part of design display and evaluation, product three-dimensional (3D) form is the core object in affective product design. However, previous research has not yet addressed the development of technical models and method involving complete 3D surface data, and thus cannot guarantee the quality of affective product design. By using the techniques of triangular mesh model, spherical harmonic and conditional variational auto-encoder, this paper proposes a data-driven affective product design method composed of several technical models using complete 3D surface data. These models include: mathematical model for quantifying 3D form, recognition model for recognizing customer’s affective responses, and …generative model for generating new 3D forms. For affective product design, the mathematical model achieves the acquisition and processing of complete 3D surface data, the recognition model improves the objectivity and accuracy of recognition by integrating the 3D form data into the calculation process of emotion recognition, and the generative model realizes the automatic generation of new 3D forms in response to emotional data based on the recognition results. Each model provides technical support for realizing the acquisition, processing and generation of complete 3D surface data of product form, and ensures the systematic and completeness of the proposed method for the affective product design involving 3D form innovation. The feasibility of the method is verified by an example of car design, and the results show that it is an effective affective product design method involving 3D form innovation. Show more

Keywords: Affective product form design, complete three-dimensional surface data, emotion recognition, spherical harmonic, conditional variational auto-encoder

DOI: 10.3233/JIFS-211947

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

Authors: Sierra, Gerardo | Hernández-García, Tonatiuh | Gómez-Adorno, Helena | Bel-Enguix, Gemma

Article Type: Research Article

Abstract: In this paper, we present authorship attribution methods applied to ¡El Mondrigo! (1968), a controversial text supposedly created by order of the Mexican Government to defame a student strike. Up to now, although the authorship of the book has been attributed to several journalists and writers, it could not be demonstrated and remains an open problem. The work aims at establishing which one of the most commonly attributed writers is the real author. To do that, we implement methods based on stylometric features using textual distance, supervised, and unsupervised learning. The distance-based methods implemented in this work are Kilgarriff and …Delta of Burrows, an SVM algorithm is used as the supervised method, and the k -means algorithm as the unsupervised algorithm. The applied methods were consistent by pointing out a single author as the most likely one. Show more

Keywords: Machine learning, stylometry, authorship attribution

DOI: 10.3233/JIFS-219236

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

Authors: García-Gorrostieta, Jesús Miguel | López-López, Aurelio | González-López, Samuel | López-Monroy, Adrián Pastor

Article Type: Research Article

Abstract: Academic theses writing is a complex task that requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill that takes time to master. In this paper, we present an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. We present a novel proposal, which combines the information in the complete paragraph with the detection of argumentative segments in order to achieve improved results for …the detection of argumentative paragraphs. We propose two approaches; a more descriptive one, which uses the decision tree classifier with indicators and lexical features; and another more efficient, which uses an SVM classifier with lexical features and a Document Occurrence Representation (DOR). Both approaches consider the detection of argumentative segments to ensure that a paragraph detected as argumentative has indeed segments with argumentation. We achieved encouraging results for both approaches. Show more

Keywords: Academic writing, argumentation analysis, machine learning, text representation, natural language processing

DOI: 10.3233/JIFS-219237

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

Authors: Xu, Guoteng | Lu, Tingjie | Chen, Xia | Liu, Yiman

Article Type: Research Article

Abstract: The paper constructs a research model mainly based on the Deng’s correlation analysis model on the convergence level measurement, the GM (1,1) coordinated development prediction model and PLS-Structural Equation Model (PLS-SEM) analysis model on the influencing factors. The data about China’s digital economy and real economy from 2005 to 2019 (totaled 2,250) is adopted to conduct an empirical analysis of the convergence level from 2005 to 2019 and predict the development trend from 2020 to 2029. The paper could further analyze the influencing factors of convergence, in an attempt to put forward relevant development suggestions. We hope the study could …provide an objective reference and theoretical basis for improving the convergence level in China in some extent. Show more

Keywords: Grey model, PLS-structural equation model, convergence of digital economy and real economy, influencing factors

DOI: 10.3233/JIFS-210981

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-31, 2021

Authors: Zhai, Longzhen | Feng, Shaohong

Article Type: Research Article

Abstract: The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the …ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning. Show more

Keywords: Emergency evacuation, path planning, ant colony algorithm (ACO), quantum evolution theory

DOI: 10.3233/JIFS-212220

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

Authors: Yang, Jie | Luo, Tian | Zeng, Lijuan | Jin, Xin

Article Type: Research Article

Abstract: Neighborhood rough sets (NRS) are the extended model of the classical rough sets. The NRS describe the target concept by upper and lower neighborhood approximation boundaries. However, the method of approximately describing the uncertain target concept with existed neighborhood information granules is not given. To solve this problem, the cost-sensitive approximation model of the NRS is proposed in this paper, and its related properties are analyzed. To obtain the optimal approximation granular layer, the cost-sensitive progressive mechanism is proposed by considering user requirements. The case study shows that the reasonable granular layer and its approximation can be obtained under certain …constraints, which is suitable for cost-sensitive application scenarios. The experimental results show that the advantage of the proposed approximation model, moreover, the decision cost of the NRS approximation model will monotonically decrease with granularity being finer. Show more

Keywords: Neighborhood rough sets, approximation model, cost-sensitive, Granular layer selection

DOI: 10.3233/JIFS-212234

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

Authors: Rashwan, Rashwan A. | Hammad, Hasanen A. | Nafea, A.

Article Type: Research Article

Abstract: In this manuscript, the concept of a cyclic tripled type fuzzy cone contraction mapping in the setting of fuzzy cone metric spaces is introduced. Also, some theoretical results concerned with tripled fixed points are given without a mixed monotone property in the mentioned space. Moreover, under this concept, some strong tripled fixed point results are obtained. Ultimately, to support the theoretical results non-trivial examples are listed and the existence of a unique solution to a system of integral equations is presented as an application.

Keywords: Strong tripled fixed point, fuzzy cone metric space, contraction condition, integral equation, cyclic tripled type fuzzy cone contraction mapping

DOI: 10.3233/JIFS-212188

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

Authors: George, Sophia Jasmine | Ramaraju, Satish Kumar | Venkataraman, Vanitha | Kaliannan, Thenmalar | Kumaravel, Umadevi | Veerasundaram, M.

Article Type: Research Article

Abstract: Conventionally in many countries, electrical power industry is organized as vertically integrated system. Under this system, large utilities are authoritative for the generation, transmission and distribution of electrical power. Such utilities are governed by the rules and regulations of the government and are forced to operate within the prescribed guidelines with minimal profit. This confirmation causes an ineffective and sluggish perspective in power industry with a lack of technical innovation, competent management and customer satisfaction. To overcome these deficiencies, power sector around the globe is getting restructured. This paper addresses an inevitable technical disputes occurring in deregulated environment i.e., transmission …congestion which has an adverse effect on system security, increase in electricity pricing and line losses. Flexible AC Transmission System (FACTS) is a boon to the power sector which helps in a better and reliable power flow through the transmission lines. The problem is articulated as a multi objective function satisfying all the operational and security limits. Three heuristic algorithms namely Particle Swarm Optimization (PSO), Symbiotic Organism Search (SOS) and hybrid Quantum based PSO-Bio-geography based krill herd optimization (Q-PSOBBKH) algorithms were applied in finding solution to this complex congestion problem. To study the effectiveness of the proposed objective, IEEE 14 bus system was considered as the test system. In order to validate the proposed methodology three congestion cases i.e. bilateral transaction, multilateral transaction and overloading were imposed on the test bus system. Simulation was carried out in MATLAB. Show more

Keywords: Deregulated power system, particle swarm optimization, symbiotic organism search algorithm, hybrid quantum based PSO, bio-geography based Krill Herd Algorithm

DOI: 10.3233/JIFS-212717

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

Authors: Senthilkumar, D. | George Washington, D. | Reshmy, A.K. | Noornisha, M.

Article Type: Research Article

Abstract: Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a prominent complex non-linear multi-target learning problem and extracting a relevant subset of features from a large number of features with multiple targets is a challenging task. Existing water quality prediction model not focused on multi-target learning process simultaneously and not identifying the non-linear relationship between the features and target variables. Therefore, this study proposes a multi-task learning method dealing with multi-target regression using non-linear machine learning technique. Finally, experiments …are conducted to build a prediction model based on the proposed methods to evaluate accuracy on water quality dataset. The experimental results indicate that our method increases the overall accuracy of the experimental dataset compared with the existing methods with the reduced number of significant features. Show more

Keywords: Water quality prediction, multi-target, non-linear, MARS, CART

DOI: 10.3233/JIFS-212117

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

Authors: Li, Xin | Li, Xiaoli | Wang, Kang

Article Type: Research Article

Abstract: The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the …the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs. Show more

Keywords: Niche selection, multi-objective optimization, diversity, irregular Pareto front

DOI: 10.3233/JIFS-212426

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

Authors: Ruiz Alonso, Dorian | Zepeda Cortés, Claudia | Castillo Zacatelco, Hilda | Carballido Carranza, José Luis

Article Type: Research Article

Abstract: In this work, we propose the extension of a methodology for the multi-label classification of feedback according to the Hattie and Timperley feedback model, incorporating a hyperparameter tuning stage. It is analyzed whether the incorporation of the hyperparameter tuning stage prior to the execution of the algorithms support vector machines, random forest and multi-label k-nearest neighbors, improves the performance metrics of multi-label classifiers that automatically locate the feedback generated by a teacher to the activities sent by students in online courses on the Blackboard platform at the task, process, regulation, praise and other levels proposed in the feedback model by …Hattie and Timperley. The grid search strategy is used to refine the hyperparameters of each algorithm. The results show that the adjustment of the hyperparameters improves the performance metrics for the data set used. Show more

Keywords: Text mining, multi-label classification, hyperparameter tuning, online courses

DOI: 10.3233/JIFS-219238

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

Authors: Huang, Rui-Lu | Deng, Min-hui | Li, Yong-yi | Wang, Jian-qiang | Li, Jun-Bo

Article Type: Research Article

Abstract: With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval …2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods. Show more

Keywords: Cloud model theory, decision support framework, interval 2-tuple linguistic information, health-care waste management

DOI: 10.3233/JIFS-212065

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

Authors: Lv, Zhaoming | Peng, Rong

Article Type: Research Article

Abstract: The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population; the second stage merges the learned population into the gregarious …population and updates each grasshopper; and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems. Show more

Keywords: Meta-heuristic algorithms, grasshopper optimization algorithm, solitarious and gregarious states, chemotaxis operator, ontology matching

DOI: 10.3233/JIFS-212633

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

Authors: Zhang, Min | Yang, Haijie | Li, Pengfei | Jiang, Ming

Article Type: Research Article

Abstract: Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes …by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets. Show more

Keywords: Computer vision, human pose estimation, parallel atrous convolution, body structure constraints

DOI: 10.3233/JIFS-212061

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

Authors: Ramaraju, Satish Kumar | Kaliannan, Thenmalar | Androse Joseph, Sheela | Kumaravel, Umadevi | Albert, Johny Renoald | Natarajan, Arun Vignesh | Chellakutty, Gokul Prasad

Article Type: Research Article

Abstract: A Voltage lift performance is an excellent role to DC/DC conversion topology. The Voltage Lift Multilevel Inverter (VL-MLI) topology is suggested with minimal number of components compared to the conventional multilevel inverter (MLI). In this method, the Modified Particle Swarm Optimization (MPSO) conveys a primary task for the VL-MLI using Half Height (H-H) method, it determine the required optimum switching angles to eliminate desired value of harmonics. The simulation circuit for fifteen level output uses single switch voltage-lift inverter fed with resistive and inductive loads (R & L load). The power quality is developed by voltage-lift multilevel inverter with minimized …harmonics under the various Modulation Index (MI) while varied from 0.1 up to 1. The circuit is designed in a Field Programmable Gate Array (FPGA), which includes the MPSO rules for fast convergence to reduce the lower order harmonics and finds the best optimum switching angle values. To report this problem the H-H has implemented with MPSO to reduce minimum Total Harmonic Distortion (THD) for simulation circuit using Proteus 7.7 simulink tool. Due to the absence of multiple switches, filter and inductor element exposes for novelty of the proposed system. The comparative analysis has been carried-out with existing optimization and modulation methods. Show more

Keywords: Solar-Photovoltaic, voltage lift-multilevel inverter, particle swarm optimization algorithm, half height, field program gate array

DOI: 10.3233/JIFS-212583

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

Authors: Işik, Gürkan | Kaya, İhsan

Article Type: Research Article

Abstract: Although traditional acceptance sampling plans (ASPs) need certain mass quality characteristics, it is not easy to define them as crisp value in some real case problems. The fuzzy set theory (FST) is one of the popular techniques to model uncertainties of the process and therefore fuzzy ASPs have been offered in the literature. Fuzzy set extensions have been proposed recently for better modeling of the uncertainties having different sources and characteristics. One of these extensions named neutrosophic sets (NSs) can be used to increase the sensitiveness and flexibility of ASPs. The ASPs based on NSs can give ability to classify …the items as defective, non-defective and indeterminate. Since the operator can become indecisive for slightly defective items, these plans can provide a good representation of human evaluations under uncertainty. In this study, single and double ASPs are designed based on NSs by using binomial and poisson distributions that are also re-analyzed based on NSs. For this aim, some characteristics functions of ASPs such as probability of accepting a lot (P a ), average outgoing quality (AOQ ), average total inspection (ATI ) and average sample number (ASN ) have also been analyzed based on NSs. Numerical examples are presented to analyze the proposed plans. Show more

Keywords: Acceptance sampling plans, fuzzy sets, neutrosophic sets, neutrosophic poisson distribution

DOI: 10.3233/JIFS-211232

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

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

Article Type: Research Article

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

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

DOI: 10.3233/JIFS-212489

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

Authors: Jiang, Tianhua | Zhu, Huiqi | Gu, Jiuchun | Liu, Lu | Song, Haicao

Article Type: Research Article

Abstract: This paper presents a discrete animal migration optimization (DAMO) to solve the dual-resource constrained energy-saving flexible job shop scheduling problem (DRCESFJSP), with the aim of minimizing the total energy consumption in the workshop. A job-resource-based two-vector encoding method is designed to represent the scheduling solution, and an energy-saving decoding approach is given based on the left-shift rule. To ensure the quality and diversity of initial scheduling solutions, a heuristic approach is employed for the resource assignment, and some dispatching rules are applied to acquire the operation permutation. In the proposed DAMO, based on the characteristics of the DRCESFJSP problem, the …search operators of the basic AMO are discretized to adapt to the problem under study. An animal migration operator is presented based on six problem-based neighborhood structures, which dynamically changes the search scale of each animal according to its solution quality. An individual updating operator based on crossover operation is designed to obtain new individuals through the crossover operation between the current individual and the best individual or a random individual. To evaluate the performance of the proposed algorithm, the Taguchi design of experiment method is first applied to obtain the best combination of parameters. Numerical experiments are carried out based on 32 instances in the existing literature. Computational data and statistical comparisons indicate that both the left-shift decoding rule and population initialization strategy are effective in enhancing the quality of the scheduling solutions. It also demonstrate that the proposed DAMO has advantages against other compared algorithms in terms of the solving accuracy for solving the DRCESFJSP. Show more

Keywords: Dual-resource constraint, energy-saving scheduling, flexible job shop, discrete animal migration optimization

DOI: 10.3233/JIFS-211399

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

Authors: Işık, Gürkan | Kaya, İhsan

Article Type: Research Article

Abstract: Defectiveness of items is generally considered as a certain value in acceptance sampling plans (ASPs). It is clear that, it may not be certainly known in some real-case problems. Uncertainties of the inspection process such as measurement errors, inspectors’ hesitancies or vagueness of the process etc. should be taken into account to obtain more reliable results. The fuzzy set theory (FST) is one of the best methods to overcome these problems. There are some studies in the literature formulating the ASPs with the help of FST. Deciding the right membership functions of the fuzzy sets (FSs) has a vital importance …on the quality of the uncertainty modeling. Additionally, the fuzzy set extensions have been offered to model more complicated uncertainties to achieve better modeling. As one of these extensions, type-2 fuzzy sets (T2FSs) gives an ability to model uncertainty in situations where it is not possible to determine exact membership function parameters. In this study, single and double ASPs based on interval T2FSs (IT2FSs) have been designed for binomial and Poisson distributions. Thus, it becomes possible to make more flexible, sensitive and descriptive sensitivity analyzes. The main characteristic functions of ASPs have been derived and the suggested formulations have been illustrated on a comparative application from manufacturing process. Results allowing for more comprehensive analysis as against to the traditional and T1FSs based plans have been obtained. Show more

Keywords: Acceptance sampling plans, binomial distribution, fuzzy sets, interval type-2 fuzzy sets, poisson distribution

DOI: 10.3233/JIFS-211915

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

Authors: Fan, Jianping | Zhai, Shanshan | Wu, Meiqin

Article Type: Research Article

Abstract: Neutrosophic cubic set (NCS) can process complex information by combining interval neutrosophic set and single-valued neutrosophic set. It can simultaneously describe the uncertain and certain part of information. Prospect theory (PT) is based on bounded rationality and can reflect decision maker’s different risk attitudes to gains and losses. Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method can measure and rank the alternatives according to compromise solution. Considering the bounded rationality of decision makers and compromise solution of alternatives, this paper combines the PT with MARCOS method to neutrosophic cubic environment to solve multi-attribute decision-making problem. First, the …theoretical basis of NCS is introduced. Second, the PT and MARCOS method are combined. To reflect subjective views of decision makers and the objectivity of decision-making information, this paper uses geometric average method to combine subjective weights (calculated by the best-worst method) and objective weights (calculated ed by the entropy method). Then, the PT-MARCOS method is applied to a decision-making problem. Further, a sensitivity analysis is conducted to study the influence of different attenuation factor values and different expectation coefficient on the ranking; and through comparative analysis to illustrate the superiority of the PT-MARCOS method. Finally is the conclusion. Show more

Keywords: Multi-attribute decision-making, neutrosophic cubic set, prospect theory, measurement of alternatives and ranking according to compromise solution, best-worst method

DOI: 10.3233/JIFS-211189

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