Journal of Intelligent & Fuzzy Systems - Volume 43, issue 1

Authors: Duong, T.H. | Le, T.-T. | Nguyen, S.X. | Le, M.V.

Article Type: Research Article

Abstract: This study is devoted to the development of an Adaptive-Neuro-Fuzzy-Inference-System (ANFIS) model for the prediction of ultimate load of rectangular concrete-filled steel tubular structural members. The learning process of the model is performed by conducting a combination of backpropagation gradient descent and least-squares techniques. The performance of the model is examined by several quality metrics such as coefficient of determination (R2 ), Root-Mean-Squared-Error (RMSE), Mean-Absolute-Error (MAE), Index of Agreement (IA) and Slope of linear regression. Monte Carlo random sampling technique is employed to propagate input variations to the output response. Moreover, the performance of ANFIS is also compared with other …machine learning models including Artificial Neural Network (ANN), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Ensemble. Results show that the ANFIS model yields higher prediction performance than other machine learning models, for both training and testing data points and regarding all quality metrics. For instance, using training data points, the ANFIS model exhibits a RMSE of 0.0283 compared to 0.0342, 0.0588, 0.0291, and 0.0464 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding gain values are+17.3%,+51.9%,+2.8%, and+39.0%, respectively). On the other hand, using testing data points, the ANFIS model exhibits a RMSE of 0.0276 compared to 0.0393, 0.0987, 0.0403, and 0.0460 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding values of gain are+29.8%,+72.1%,+31.5%, and+40.0%, respectively). The same observation can be made for other quality metrics. It can be concluded that the ANFIS model outperforms other models for both training and testing datasets. The ANFIS model is also compared with existing works in the past, showing its improvement in prediction results. Finally, sensitivity analysis is performed to determine the degree of effect of the input parameters on the ultimate load. Show more

Keywords: Concrete-filled steel tubular members, adaptive-neuro-fuzzy-inference-system, ultimate load, sensitivity analysis, machine learning

DOI: 10.3233/JIFS-201628

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1-19, 2022

Authors: Ren, Chunhua | Sun, Linfu | Gao, Yunhui | Yu, Yang

Article Type: Research Article

Abstract: The density peaks clustering algorithm (DPC) has been widely concerned since it was proposed in 2014. There is no need to specify in advance and only one parameter required. However, some disadvantages are still witnessed in DPC: (1) Requiring repeated experiments for choosing a suitable calculation method of the local density due to the variations in the scale of the dataset, which will lead to additional time cost. (2) Difficulty in finding an optimal cutoff distance threshold, since different parameters not only impact the selection of cluster centers but also directly affect the quality of clusters. (3) Poor fault tolerance …of the allocation strategy, especially in manifold datasets or datasets with uneven density distribution. Targetting solutions to these problems, a density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy (LF-DPC) is proposed in this paper. First, to obtain a more balanced local density, two classic local density calculation methods are combined in the algorithm to calculate the local fair density through the optimization function with the smallest local density difference. Second, a robust two stage remaining points allocation strategy is designed. In the first stage, k-nearest neighbors are used to quickly and accurately allocate points from the cluster center. In the second stage, to further improve the accuracy of allocation, a fuzzy k-nearest neighbors membership method is designed to allocate the remaining points. Finally, the LF-DPC algorithm has been experimented based on several synthetic and real-world datasets. The results prove that the proposed algorithm has obvious advantages compared with the other five ones. Show more

Keywords: Density peaks clustering, local fair density, fuzzy k-nearest neighbors, membership allocation strategy

DOI: 10.3233/JIFS-202449

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 21-34, 2022

Authors: Mishra, Amit Kumar | Bhardwaj, Ramakant | Joshi, Nisheeth | Mathur, Iti

Article Type: Research Article

Abstract: This paper aims to select the appropriate node(s) to effectively destabilize the terrorist network in order to reduce the terrorist group’s effectiveness. Considerations are introduced in this literature as fuzzy soft sets. Using the weighted average combination rule and the D–S theory of evidence, we created an algorithm to determine which node(s) should be isolated from the network in order to destabilize the terrorist network. The paper may also prove that if its power and foot soldiers simultaneously decrease, terrorist groups will collapse. This paper also proposes using entropy-based centrality, vote rank centrality, and resilience centrality to neutralize the network …effectively. The terrorist network considered for this study is a network of the 26/11 Mumbai attack created by Sarita Azad. Show more

Keywords: Terrorist network mining (TNM), destabilization, centralities, fuzzy soft set, social network analysis (SNA), global network efficiency, average clustering coefficient, Dempster–Shafer theory of evidence

DOI: 10.3233/JIFS-210425

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 35-48, 2022

Authors: Oh, Ju-mok | Kim, Yong Chan

Article Type: Research Article

Abstract: Most of the research in fuzzy rough sets and fuzzy topological structures have been studied on the basis of fuzzy partially ordered sets. Instead of fuzzy partially ordered sets, the concept of distance functions in complete co-residuated lattices is introduced. Using distance functions, we define Alexandrov pretopology, Alexandrov precotopology and fuzzy interior (fuzzy closure) operators in complete co-residuated lattices, and we investigate their properties. Moreover, we prove that there exist isomorphic categories and Galois correspondence between topological categories.

Keywords: Complete co-residuated lattice, distance spaces, Alexandrov pretopologies, Alexandrov precotopology, fuzzy interior (fuzzy closure) operators, Galois correspondence

DOI: 10.3233/JIFS-210973

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 49-65, 2022

Authors: Li, Zheng | Lei, Xuemei

Article Type: Research Article

Abstract: The occlusion in the real feedlot environment is ubiquitous, and the current research based on the cattle face recognition under occlusion conditions is almost non-existent. Thus, an attention mechanism module with high accuracy and low model complexity is designed to incorporate into MobileNet so that the cattle face under occlusion can be identify accurately, which is the RGB images captured in the ranch environment. In this paper, we also construct a Simmental cattle face image dataset for data modeling and method evaluation, which contains 10,239 images of 103 cattle. The experimental results show that when the occluder is in the …upper left and lower right corner, if the occlusion rate is less than 30%, the value of Top_1 reaches more than 90%; if it is less than 50%, the value of Top_1 is more than 80%. Even if the middle part occludes lots of important information, the occlusion rate of 40% has an accuracy of more than 80%. Furthermore, comparing the proposal model with MobileNet, the parameter and model size are equal, and the amount of calculation as a cost increase a little. Therefore, the proposal model is suitable to transplant to the embedded system in the future. Show more

Keywords: Partial occlusion, attention mechanism, cattle face recognition, convolutional neural network, image classification

DOI: 10.3233/JIFS-210975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 67-77, 2022

Authors: Badr, Marwa | Sarhan, Amany | Elbasiony, Reda

Article Type: Research Article

Abstract: Over the past decade, the computer vision community has given increased attention to the development of age estimation systems. Several approaches to more accurate and robust facial age estimation have been introduced. Apparent age datasets are typically collected from uncontrolled environments, leading to a number of challenges. In this paper, a cascade model system, which we called the ‘Integrated Classification and Regression with Landmark Ratios (ICRL), is introduced. Our system uses a classification model in order to learn the age label distribution, then uses this knowledge as an auxiliary input to a regression model. ICRL is based on context facial …information and label distribution analysis. Facial context information is introduced through the extraction of precise facial landmark ratios. Extracted landmark ratios allow the system to distinguish each age label. The ICRL system uses a classification model to train the CNN network to learn the in-between relation of age labels. ICRL sufficiently models the aging process in the form of ordered and continuous imagery. The ICRL system minimizes the number of parameters needed as well as overall computational costs whilst maintaining robust and accurate results. Despite its simplicity, our system has outperformed other state-of-the-art approaches when applied onto the MORPH II, CLAP2015, AFAD and UTKFace datasets. ICRL achieved an overall superior predictive performance, reaching 99.67% with MORPH II, 99.51% with AFAD, 96.52 with CLAP2015, and 96.28% with UTKFace. Show more

Keywords: Age estimation, ordinal regression, facial context information, age label distribution

DOI: 10.3233/JIFS-211267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 79-92, 2022

Authors: Yin, Ming | Zhu, Kuiyu | Xiao, Hongli | Zhu, Dan | Jiang, Jijiao

Article Type: Research Article

Abstract: Effectively identifying self-admitted technical debt (SATD) from project source code comments helps developers quickly find and repay these debts, thereby reducing its negative impact. Previous studies used techniques based on patterns, text mining, natural language processing, and neural networks to detect SATD. Compared with these above, Convolutional Neural Networks (CNN) have the strong feature extraction ability. Deep network ensembles are demonstrated great potential for the task of sentences classification. In order to boost the performance of CNN-based SATD detecting, we propose a deep neural network ensemble contribute to ensemble learning in a simple yet effective way. Specifically, CNN, CNN-LSTM (convolutional …neural network and long short-term memory), and DPCNN (Deep Pyramid Convolutional Neural Networks) are used as individual classifiers to diversify the deep network ensembles. In order to improve the explainability, we introduce attention to measure the contribution of feature words to SATD classification. 62,285 source code comments from 10 projects were used in our experiments. The results show that our approach can effectively reduce misjudgment and detect more SATD, especially for cross-project, so as to greatly improve the detection accuracy. Show more

Keywords: Self-admitted technical debt, ensemble learning, convolutional neural network, long short-term memory

DOI: 10.3233/JIFS-211273

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 93-105, 2022

Authors: Mahendhiran, P.D. | Subramanian, Kannimuthu

Article Type: Research Article

Abstract: The refining of information from the immense amount of unstructured data on the internet can be a critical issue in identifying public opinion. It is difficult to extract relevant concepts from huge amounts of data. Concept level semantic parsing is improved over word-based investigation as it conserves the semantical data relevant to many-word articulations. The semantic proposals offer a superior comprehension of textual data and serve to altogether precision the exactness of numerous mining operations in text assignments. The extraction of concepts from textual data is a significant step forward in content analysis at the concept stage. We present a …CLSA-CapsNet method that extracts concepts from natural language text. Then the extracted concepts are applied in Capsule networks (CapsNet). Moreover, the integration of Concept Level Sentiment Analysis (CLSA) and Capsule Networks (CapsNet) has not yet been implemented on the hotel review dataset. This is the first attempt, which we researched and embraced by the capsule network, to develop classification models for hotel reviews. The developed results demonstrated excellent performance with a prediction accuracy of 86.6% for CLSA-CapsNet models, respectively. Various similarities have also been made across our techniques and they are implemented by some other deep learning algorithms, such as rnn-lstm. Overall, the outstanding success obtained by CLSA-CapsNet in this investigation highlights its ability in the hotel review dataset. We likewise show exploratory outcomes, in which the proposed system outpaced the state-of-the-art CLSA-CapsNet model. Show more

Keywords: Capsule network, sentiment analysis, CNN, RNN, LSTM, concept level

DOI: 10.3233/JIFS-211321

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 107-123, 2022

Authors: Mehmood, Arif | Al Ghour, Samer | Imran Khan, Muhammad | Afzal, Farkhanda | Ishfaq, Muhammad | Qureshi, Humera

Article Type: Research Article

Abstract: In this article, new generliased neutrosophic soft *b open set is introduced in neutrosophic soft bi-topological spaces (NSBTS) concerning soft points of the space. This new set is produced by the combination of soft semi-open and soft pre-open sets of neutrosophic soft topological space. Different results are ushered in NSBTS. Appropriate examples are provided for verification of different results. The non-validity of some results is verified with appropriate examples.

Keywords: Neutrosophic soft set (NSS), neutrosophic soft point (NSP), neutrosophic soft bi-topological space (NSBTS), neutrosophic soft *b-open set and neutrosophic soft *b-separation axioms

DOI: 10.3233/JIFS-211492

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 125-142, 2022

Authors: Jeyabalan, Saranya Devi | Yesudhas, Nancy Jane | Harichandran, Khanna Nehemiah | Sridharan, Gayathri

Article Type: Research Article

Abstract: The development of advanced technologies in variety of domains such as health care, sensor measurements, intrusion detection, motion capture, environment monitoring have directed to the emergence of large scale time stamped data that varies over time. These data are influenced by complexities such as missing values, multivariate attributes, time-stamped features. The objective of the paper is to construct temporal classification framework using stacked Gated Recurrent Unit (S-GRU) for predicting ozone level. Ozone level prediction plays a vital role for accomplishing healthy living environment. Temporal missing value imputation and temporal classification are two functions performed by the proposed system. In temporal …missing value imputation, the temporal correlated k-nearest neighbors (TCO-KNN) approach is presented to address missing values. Using attribute dependency based KNN, the nearest significant set is identified for each missing value. The missing values are imputed using the mean values from the determined closest significant set. In temporal classification, the classification model is build using stacked gated recurrent unit (S-GRU). The performance of the proposed framework investigated using ozone multivariate temporal data sets shows improvement in classification accuracy compared to other state of art methods. Show more

Keywords: Multivariate time series data, decision making, knowledge discovery, ozone level prediction, K-nearest neighbors (KNN), stacked gated recurrent unit (S-GRU)

DOI: 10.3233/JIFS-211835

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 143-157, 2022

Authors: Khan, Rashid | Islam, M. Shujah | Kanwal, Khadija | Iqbal, Mansoor | Hossain, Md. Imran | Ye, Zhongfu

Article Type: Research Article

Abstract: Caption generation using an encoder-decoder approach has recently been extensively studied and implemented in various domains, including image captioning and code captioning. In this research article, we propose one particular approach for completing a capture generation task using an “attention”-based sequence-to-sequence framework that, when combined with a conventional encoder-decoder model, generates captions in an attention-based manner. ResNet-152 is a Convolutional Neural Network (CNN) based encoder that generates a comprehensive representation of an input image while embedding that into a fixed size length vector. To predict the next sentence, the decoder uses LSTM, a Recurrent Neural Network (RNN), and an attention-based …mechanism to concentrate attention on certain sections of an image selectively. Define a set of epochs to 69, which should be enough for training the model to generate informative descriptions, and the validation loss has reached its minimum limit and no longer decreases. We present the datasets as well as the evaluation metrics, as well as quantitative and qualitative analysis. Experiments on the MSCOCO and Flickr8k benchmark datasets illustrate the model’s efficacy in comparison to the baseline techniques. On MSCOCO, evaluation scores included BLEU-1 0.81, BLEU-2 0.61, BLEU-3 0.47, and 0.33 METEOR. For Flickr8k BLEU-1 0.68, BLEU-2 0.49, BLEU-3 0.41, METEOR 0.23, and 0.86 on SPICE. The proposed approach is comparable with several state-of-the-art methods in terms of standard evaluation metric, demonstrating that it can produce more accurate and richer captions. Show more

Keywords: Image captioning, CNN, LSTM, sequence-to-sequence, neural network

DOI: 10.3233/JIFS-211907

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 159-170, 2022

Authors: He, Yanping | Nan, TaiBen | Zhang, Haidong

Article Type: Research Article

Abstract: This paper is devoted to discussing the reverse triple I method based on the Pythagorean fuzzy set (PFS). We first propose the concepts of Pythagorean t-norm, Pythagorean t-conorm, residual Pythagorean fuzzy implication operator (RPFIO) and Pythagorean fuzzy biresiduum. The reverse triple I methods for Pythagorean fuzzy modus ponens (PFMP) and Pythagorean fuzzy modus tollens (PFMT) are also established. In addition, some interesting properties of the reverse triple I method of PFMP and PFMT inference models are analysed, including the robustness, continuity and reversibility. Finally, a practical problem is provided to illustrate the effectiveness of the reverse triple I method for …PFMP in decision-making problems. The advantages of the new method over existing methods are also expounded. Overall, compared with the existing methods, the proposed methods are based on logical reasoning rather than using aggregation operators, so the novel methods are more logical, can better deal with the uncertain problems in complex decision-making environments and can completely reflect the decision-making opinions of decision-makers. Show more

Keywords: Reverse triple I method, PFS, RPFIO, robustness, continuity

DOI: 10.3233/JIFS-211994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 171-186, 2022

Authors: Liu, Hongping | Ge, Qian | Wei, Ruiju

Article Type: Research Article

Abstract: This paper aims to further study the new kind of ordered fuzzy group named ordered L -group, which is put forward in literature [20 ]. Some algebraic properties of ordered L -groups, such as the relationship between elements, the equivalent characterizations and the products of these groups are discussed. Following that, the properties of substructures including characterization theorems, the convexity, the products of (normal) subgroups maintain the original substructure, along with the properties of ordered L -group homomorphisms are explored. The discussion of ordered fuzzy groups in this paper is from the perspective of fuzzy binary operation, which is different …from the commonly method that just discuss the fuzzification of substructures in the research of fuzzy algebra. It can better reflect the essence of fuzzy groups logically just like that of classical groups. Show more

Keywords: L-poset, ordered L-operation, ordered L-group, convex structure, subgroup

DOI: 10.3233/JIFS-212027

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 187-199, 2022

Authors: Xiao, Yuanyuan | Zhang, Xiuguo | Xu, Xuemin | Cao, Zhiying

Article Type: Research Article

Abstract: Internet of Things (IoT) services are directly deployed on resource-constrained smart devices. Smart devices are characteristic by spatial and temporal constraints and energy limitations. A single IoT service cannot meet the complex requirements of users, so multiple IoT services need to be combined to provide services to users. As more and more smart devices are deployed in IoT, how to select IoT services with lower energy consumption and better quality of service (QoS) for service composition becomes a challenging problem. Combined with the characteristics that the data information of IoT is closely related to geographical location, the GeoHash algorithm is …used to locally screen services based on geographical location, so as to narrow the range of candidate services. For smart devices with energy constraints, this paper proposes a combined optimization model. The model considers not only the transmission energy consumption and switching energy consumption, but also the execution energy consumption when the device provides services. In order to balance QoS attributes and energy consumption, the composition problem is regarded as a multi-objective optimization problem and solved using a genetic algorithm (GA). The simulation results show that service composition scheme selected by this service composition optimization model has lower energy consumption and higher service quality. Show more

Keywords: Energy consumption, QoS, service composition optimization model

DOI: 10.3233/JIFS-212033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 201-218, 2022

Authors: Wang, Pei | Qu, Liangdong | Zhang, Qinli

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212037

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 219-236, 2022

Authors: Wu, Meiqin | Wang, Xinsheng | Fan, Jianping

Article Type: Research Article

Abstract: Three-way decisions (TWDs) theory is one of the core ideas of decision-theoretic rough sets (DTRSs). Reviewing the existing research results, we find that TWDs provides us with more flexible decision choices. And the traditional fuzzy number does not take into account the absence of information (indifference) in the evaluation process. In order to construct a new model which can get flexible decision results in complex decision environment, we introduce four-branch fuzzy numbers (FBFNs) to describe the evaluation information, so that the decision-makers can express the evaluation information more comprehensively. In this paper, a novel TWDs model in four-branch fuzzy environment …is proposed to solve multiple-attribute decision-making (MADM) problem. The first challenge is to construct a TWDs model based on FBFNs and to develop a new linguistic interpretation of the loss functions. Then, we extend a method for aggregating the loss functions obtained from the attribute evaluation values. Finally, we use the nonlinear solution to solve the threshold, and apply TOPSIS method to solve the conditional probability of FBFNs. The effectiveness of this method is illustrated by an example, and the decision results are compared with a MADM method based on OWGA operator. Show more

Keywords: Three-way decisions, four-branch fuzzy numbers, multiple-attribute decision-making, loss function, nonlinear solution, TOPSIS

DOI: 10.3233/JIFS-212097

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 237-248, 2022

Authors: Javanmardi, Ehsan | Nadaffard, Ahmadreza | Karimi, Negar | Feylizadeh, Mohammad Reza | Javanmardi, Sadaf

Article Type: Research Article

Abstract: In this research, a timely diagnosis and prediction mechanism for drill failure are provided to improve the maintenance process in drilling through fuzzy inference systems. Failures and decisions are based on information and reliability as well, and that affects the quality of decision-making. We apply the potential of if-then rules and a new approach called Z-number that considers fuzzy constraints and reliability at the same time. Exerting Z-number in this research took maximum advantage of reducing uncertainty for predicting failures. Additionally, this research has a practical aspect in maintenance systems by using if-then rules that rely on Z-number. The proposed …approach can cover the expert idea during drill operation time simultaneously. This approach also helps experts encounter ambiguous situations and formulate uncertainties. Experts or drill operators can consider key factors of drilling collapse along with the reliability of these factors. The proposed approach can be applied to a real-life situation of human inference with probability for the purpose of predicting failures during drilling. Hence, this method has excellent flexibility for implementation in various maintenance systems. Show more

Keywords: Maintenance, fuzzy inference, fuzzy logic, Z-number

DOI: 10.3233/JIFS-212116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 249-263, 2022

Authors: Wu, Rong | Lin, Yidong

Article Type: Research Article

Abstract: As an important mathematical theory in intelligent learning and assessment system, knowledge space theory merely cares about items are mastered or non-mastered. Thus it needs to be further explored to achieve more precise and interpretable analysis. To this end, this paper mainly focuses on knowledge structures in corporate with Solo taxonomy. Then, fuzzy knowledge structure and fuzzy learning space are gradually developed. The corresponding knowledge base and surmise relation are explored respectively as well. In such case, the induced maximal knowledge space and its properties are further studied sufficiently. And three kinds of skill models are put forward based on …skill proficiency. Finally, a case study is presented to illustrate the advantage in learning description. Show more

Keywords: Base, fuzzy knowledge state, surmise systems, skill proficiency

DOI: 10.3233/JIFS-212176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 265-278, 2022

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

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 TEI@I 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 eRMSE , eMAE , and eMAPE , 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. 43, no. 1, pp. 279-294, 2022

Authors: Luo, Huiyin | Jiang, Feng | Lin, Hongyu | Yao, Jian | Liu, Jiaxin | Jiang, Yu | Ren, Jia

Article Type: Research Article

Abstract: Monitoring the diversity of wild animals is a core part of the research and protection of wild animals. Due to the harsh outdoor environment, researchers cannot squat in the deep forest for a long time. Therefore, designing a sensor network system for wildlife monitoring is of great value to wildlife research, protection, and management. When deploying a wildlife monitoring network in the wild environment, it is necessary to solve the problem of the effective use of energy. To this end, this paper proposes an energy-saving optimization method for node scheduling and a wake-up scheme based on a cultural genetic algorithm. …This method achieves the purpose of energy saving by making redundant nodes fall asleep and waking up sleep nodes to repair the coverage blind area caused by dead nodes. Simulation results show that this method can activate fewer sensor nodes to monitor the required sensing area, and its performance is better than other known solutions. Show more

Keywords: Cultural genetic algorithm, wild animal monitoring, wireless sensor network, coverage control, energy saving

DOI: 10.3233/JIFS-212187

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 295-307, 2022

Authors: Zhong, Ying | Huang, Chenze | Li, Qi

Article Type: Research Article

Abstract: With the rapid growth of data scale, the problems of collaborative filtering recommendation algorithm are more and more obvious, such as data sparsity, cold start, scalability, and the change of user interest over time. About the existing problems, we introduce the fuzzy clustering and propose a collaborative filtering algorithm based on fuzzy C-means clustering. The algorithm performs fuzzy clustering on the item attribute information to make items belonging to different categories in different membership degree, increases the data density, effectively reduces the data sparsity, and solves the issue that the inaccuracy of similarity leads to the low recommendation accuracy. Meanwhile, …the algorithm introduces the time weight function. Different evaluation times give different time weight values, and recently evaluated items are more representative of the user current interest, so we give a higher weight value, and early evaluated items have less effect on the user current interest, thus the weight value are relatively lower. The experimental results show that our algorithm can effectively alleviate the data sparsity problem and time migration of users preferences, thus achieve better performance. Show more

Keywords: Recommender systems, collaborative filtering, data sparsity, interest migration

DOI: 10.3233/JIFS-212216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 309-323, 2022

Authors: Xiao, Feng | Liu, Lu | Han, Jiayu | Guo, Degui | Wang, Shang | Cui, Hai | Peng, Tao

Article Type: Research Article

Abstract: Time series forecasting (TSF) is significant for many applications, therefore the exploration and study for this problem has been proceeding. With the advances of computing power, deep neural networks (DNNs) have shown powerful performance on many machine learning tasks when considerable amounts of data can be used. However, sufficient data may be unavailable in some scenarios, which leads to performance degradation or even not working of DNN-based models. In this paper, we focus on few-shot time series forecasting task and propose to employ meta-learning to alleviate the problems caused by insufficient training data. Therefore, we propose a meta-learning-based prediction mechanism …for few-shot time series forecasting task, which mainly consists of meta-training and meta-testing. The meta-training phase uses first-order model-agnostic meta-learning algorithm (MAML) as a core component to conduct cross-task training, and thus our method also inherits the advantages of the MAML, i.e., model-agnostic, in the sense that our method is compatible with any model trained with gradient descent. In the meta-testing phase, the DNN-based models are fine-tuned by the small number of time series data from an unseen task in the meta-training phase. We design two groups of comparison models to validate the effectiveness of our method. The first group, as the baseline models, is trained directly on specific time series dataset from target task. The second group, as comparison models, is trained by our proposed method. Also, we conduct data sensitivity study to validate the robustness of our method. The experimental results indicate the second group models outperform the first in different degrees in terms of prediction accuracy and convergence speed, and our method has strong robustness for forecast horizons and data scales. Show more

Keywords: Time series forecasting, meta-learning, few-shot learning

DOI: 10.3233/JIFS-212228

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 325-341, 2022

Authors: Liu, Shulin | Jiang, Rui

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 343-354, 2022

Authors: Gondere, Mesay Samuel | Schmidt-Thieme, Lars | Sharma, Durga Prasad | Scholz, Randolf

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 355-364, 2022

Authors: Meziane, Mohammed El-Amine

Article Type: Research Article

Abstract: The new wave of industry 4.0 has made battery-based automated guided vehicles (AGVs) an essential tool for material handling in manufacturing systems. However, many challenges related to battery management and machines and AGVs energy consumption. To handle these challenges an efficient battery management strategy is designed. The proposed approach supports multispeed operating modes for machines and AGVs, which offers a high flexibility to the manufacturing system. The aim of the proposed approach is to keep the minimal residual electric charge above the critical level, while enhancing the global performance of the manufacturing system. As a consequence, it increases the AGVs …production hours and guarantees batteries safety. The developed approach can bring economic benefits for industry 4.0, by increasing the productivity and avoiding AGVs batteries damage. Extended literature benchmark instances related to the manufacturing 4.0 are used to evaluate the efficiency of the suggested approach. Show more

Keywords: Automated guided vehicle, battery management, industry 4.0, sustainability, multi-speed operating mode

DOI: 10.3233/JIFS-212242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 365-381, 2022

Authors: Hussain, Muhammad | Alotaibi, Fouziah | Qazi, Emad-ul-Haq | AboAlSamh, Hatim A.

Article Type: Research Article

Abstract: The face is a dominant biometric for recognizing a person. However, face recognition becomes challenging when there are severe changes in lighting conditions, i.e., illumination variations, which have been shown to have a more severe effect on recognition performance than the inherent differences between individuals. Most of the existing methods for tackling the problem of illumination variation assume that illumination lies in the large-scale component of a facial image; as such, the large-scale component is discarded, and features are extracted from small-scale components. Recently, it has been shown that large-scale component is also important; in addition, small-scale component contains detrimental …noise features. Keeping this in view, we introduce a method for illumination invariant face recognition that exploits large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using ContourletDS. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model; we examined three widely employed CNN models: VGG-16, GoogLeNet, and ResNet152. To reduce the dimensions of local and global features and fuse them, we employ linear discriminant analysis (LDA). Finally, ridge regression is used for recognition. The method was evaluated on three benchmark datasets; it achieved accuracies of 99.7%, 100%, and 79.76% on Extended Yale B, AR, and M-PIE, respectively. The comparison reveals that it outperforms the state-of-the-art methods. Show more

Keywords: Face recognition, deep learning, convolutional neural network (CNN)

DOI: 10.3233/JIFS-212254

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 383-396, 2022

Authors: Zhang, Xuewu | Jing, Wenfeng

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 397-407, 2022

Authors: Wang, Jianhua | Zhu, Kai | Peng, Yongtao | Zhu, Kang

Article Type: Research Article

Abstract: Due to the fact that the real manufacturing processes are often constrained by many kinds of resources and the trend that the energy consumption of factories is regulated more and more strictly, this paper studies the energy-efficient multi-resource flexible job shop scheduling problem (EE-MRFJSP). The goal is to minimize the energy consumption and completion time for all of the jobs’ production. Firstly, a general mathematic model for EE-MRFJSP is set up, in which the unit energy consumptions of the main resource’s different states are varied, and a constraint formula to ensure no crossover working periods for any resource is included. …Then, a non-dominated sorting teaching-learning-based optimization(NSTLBO) algorithm is proposed to solving the problem, the details of NSTLBO include the real encoding method, Giffler Thompson rule for decoding, non-dominated sorting rule to rank the pareto sets and crowding distance of solution for maintaining the population’s diversity, and the traditional two evolving stages: teacher education and student mutual study. Finally, comparative experiments are made based on some new designed instances, and the results verify our proposed NSTLBO algorithm can effectively solve the EE-MMFJSP, and has obvious advantages by comparing with NSGA-II, NRGA, and MOPSO. Show more

Keywords: Scheduling, energy-efficient, multi-resource constraint, flexible job shop, NSTLBO

DOI: 10.3233/JIFS-212258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 409-423, 2022

Authors: Li, Mingxia | Chen, Kebing | Liu, Baoxiang

Article Type: Research Article

Abstract: The substitutability between products or the intensity of market competition is the key parameter affecting the supplier’s pricing decision. However, the parameter cannot be accurately measured in real life. This paper provides a method based on prior information to solve this issue. First, compared to classical concept lattice theory, the interval concept lattice theory can deal with uncertain information more accurately. It is used to extract the objects within the interval parameters [α , β ], and then interval concepts and lattice structure are built. Second, based on the interval concepts and lattice structure, the association rule mining algorithm is …designed to further extract the association rules under different interval parameters. Third, to obtain the effective association degree between two objects, the rule optimization algorithm is put forward by comparing the update of rules. Finally, the association degree can indirectly reflect the substitutability between products. Then the price of a new product can be determined. Our paper provides some implication on pricing for suppliers in competitive supply chain. Show more

Keywords: Pricing decision, formal context, interval concept lattice structure, optimization and mining of association rule

DOI: 10.3233/JIFS-212265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 425-435, 2022

Authors: Srifi, Mehdi | Oussous, Ahmed | Ait Lahcen, Ayoub | Mouline, Salma

Article Type: Research Article

Abstract: In the era of big data, recommender systems (RSs) have become growing essential tools. They represent important machine learning solutions that mainly contribute to keeping users engaged with personalized content in e-platforms. Several RSs have been proposed in the literature, and most of them have focused on English content. However, for content in other languages like Arabic, very restricted works have been done to develop RSs. In recent times, the Arabic content on the Web has increased significantly because of the growing number of Arabic web users. This highlights the need for building RSs for Arabic content. To better handle …this challenge, we decided to provide the research community with a novel deep learning (DL)-based RS devoted to Arabic content. The main goal of the proposed RS is to predict user preferences from textual reviews written in the Arabic language. This is achieved by combining two independent DL techniques into one system: a convolutional neural network (CNN)-text processor for representing users and items; and a neural network, in particular, a multi-layer perceptron (MLP) to estimate interactions between user-item pairs. Extensive experiments on four large-scale Arabic datasets demonstrate that our proposed system can achieve better prediction accuracy than other state-of-the-art alternatives. Notably, it improves the MSE between 0.84% and 16.96%, and the MAE between 0.14% and 13.71%. This work is the first attempt designed to deal with a large volume of data in the Arabic context, opening up new research possibilities for future developments of Arabic RSs. Show more

Keywords: Arabic, recommender systems, user reviews, natural language processing, deep learning

DOI: 10.3233/JIFS-212274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 437-449, 2022

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. 43, no. 1, pp. 451-461, 2022

Authors: Shuang, Chen | Tao, Ren | Yuntai, Ding

Article Type: Research Article

Abstract: Automatic Text Summarization(ATS) is distinctly beneficial due to a vast amount of textual data and time-consuming manual summarization. In order to enhance ATS for single document in huge datasets, a new extractive graph framework - text extractive SUMmarization framework based on EDge information with COreference resolution EDCOSUM is proposed in this paper that relies on coreference resolution, adding edge information in word-level graph and a sentence-ranking strategy. EDCOSUM combines the graph-based and statistical-based extractive summarization methods. It is a general method for any document (not limited to a specific domain). Moreover, two ranking strategies(sentence and LSA ranking strategy) are proposed …for sentence selection. A set of extensive experiments on CNN/Daily Mail and NEWSROOM are conducted for investigating the proposed method. The widely used automatic evaluation tool: Recall-Oriented Understudy for Gisting Evaluation(ROUGE) is utilized to evaluate EDCOSUM. Compared to the state-of-the-art ATS methods, EDCOSUM achieves a competitive result by improvements of over the highest scores in the literature for metrics ROUGE-1, ROUGE-2 and ROUGE-L respectively. Show more

Keywords: Text extractive summarization, Graph theory, Coreference resolution, Word-level graph, Ranking strategy

DOI: 10.3233/JIFS-212289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 463-475, 2022

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. 43, no. 1, pp. 477-494, 2022

Authors: Wang, Ya-na | Zhou, Guo-hua

Article Type: Research Article

Abstract:  The aim of this paper is to investigate pricing and production decisions of a monopoly firm that operates a co-product technology with two grades. A novel mathematical model that embeds a utility-maximizing customer choice model is developed to solve this problem. The closed-form expressions for the optimal solutions are derived and the results suggest that the distribution of customer valuations, yield rate and demand uncertainties have a vital influence on the firm’s optimal prices and profits. We then extend our study by allowing stockout-based substitution where a customer may be willing to purchase a substitute if his most preferred product …is not available but the substitute provides him with non-negative utility. The results indicate that disregarding stockout-based substitution (i) results in severe supply-demand mismatches for the product line in two directions; (ii) leads to higher or lower profit margins for both products; (iii) may not cause profit loss when the prices of both products are exogenous; however, this result does not hold when the prices are endogenous. Show more

Keywords: Random yield, utility-maximizing customers, customer substitution, product line

DOI: 10.3233/JIFS-212317

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 495-507, 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. 43, no. 1, pp. 509-523, 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. 43, no. 1, pp. 525-544, 2022

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. 43, no. 1, pp. 545-559, 2022

Authors: Anoop, P.S. | Sugumaran, V.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212336

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 561-573, 2022

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. 43, no. 1, pp. 575-585, 2022

Authors: Hao, Bing | Zhang, Tianwei

Article Type: Research Article

Abstract: Exponential Euler differences for semi-linear differential equations of first order have got rapid development in the past few years and a variety of exponential Euler difference methods have become very significant researching topics. In allusion to fuzzy genetic regulatory networks of fractional order, this paper firstly establishes a novel difference method called Mittag-Leffler Euler difference, which includes the exponential Euler difference. In the second place, the existence of a unique global bounded solution and equilibrium point, global exponential stability and synchronization of the derived difference models are investigated. Compared with the classical fractional Euler differences, fuzzy Mittag-Leffler discrete-time genetic regulatory …networks can better depict and retain the dynamic characteristics of the corresponding continuous-time models. What’s more important is that it starts a new avenue for studying discrete-time fractional-order systems and a set of theories and methods is constructed in studying Mittag-Leffler discrete models. Show more

Keywords: Genetic regulatory, Caputo, Mittag-Leffler Euler difference, exponential stability, exponential synchronization

DOI: 10.3233/JIFS-212361

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 587-613, 2022

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. 43, no. 1, pp. 615-626, 2022

Authors: Kaliappan, Manikandan | Manimegalai Govindan, Sumithra | Kuppusamy, Mohana Sundaram

Article Type: Research Article

Abstract: Cardio vascular disease threatens human life with higher mortality rate. Therefore it is quite important to monitor. An arrhythmia is an abnormal heart beat and rhythm which causes the disease. The best tool to find the heart rhythm of heart is Electro Cardiogram (ECG) which provides information about the different types of arrhythmias. This paper aims at proposing an automatic framework by employing multi-domain features to classify ECG signals. Proposed work uses optimum method of feature selection to improvise the efficiency of the classification process. A hybrid optimization algorithm is used for feature selection and proposed to optimize the parameters …of the existing Support Vector Machine (SVM) classifier. Proposed hybrid optimization algorithm was developed using Particle Swarm Optimization (PSO) and Migration Modified Biogeography Based Optimization (MMBBO) algorithm. Algorithm provides an improved solution to the optimizing the parameters of ECG signals. Results are evaluated by implementing in MATLAB software and the performance is justified with comparative analysis. The proposed framework enhances the process of automatic prediction of various arrhythmias or rhythm abnormalities which performs in gaining better accuracy. For data sets, the average classification accuracy of this method is 97.89%. This result is an improvement of 4–5% over the comparison of other methods. Show more

Keywords: Heart disease, arrhythmia, feature selection, hybrid optimization algorithm, classification, particle swarm optimization

DOI: 10.3233/JIFS-212373

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 627-642, 2022

Authors: Li, Jun | Wei, Lixin | Wen, Yintang | Liu, Xiaoguang | Wang, Hongrui

Article Type: Research Article

Abstract: With the continuous development of sensor and computer technology, human-computer interaction technology is also improving. Gesture recognition has become a research hotspot in human-computer interaction, sign language recognition, rehabilitation training, and sports medicine. This paper proposed a method of hand gestures recognition which extracts the time domain and frequency domain features from surface electromyography (sEMG) by using an improved multi-channels convolutional neural network (IMC-CNN). The 10 most commonly used hand gestures are recognized by using the spectral features of sEMG signals which is the input of the IMC-CNN model. Firstly, the third-order Butterworth low-pass filter and high-pass filter are used …to denoise the sEMG signal. Secondly, effective sEMG signal segment from denoised signal is applied. Thirdly, the spectrogram features of different channels’ sEMG signals are merged into a comprehensive improved spectrogram feature which is used as the input of IMC-CNN to classify the hand gestures. Finally, the recognition accuracy of IMC-CNN model, three single channel CNN of IMC-CNN model, SVM, LDA, LCNN and EMGNET are compared. The experiment was carried out on the same dataset and the same computer. The experimental results showed that the recognition accuracy, sensitivity and accuracy of the proposed model reached 97.5%, 97.25% and 96.25% respectively. The proposed method not only has high average recognition accuracy on MYO collected dataset, but also has high average recognition accuracy on NinaPro DB5 dataset. Overall, the proposed model has more advantages in accuracy and efficiency than that of the comparison models. Show more

Keywords: Hand gesture recognition, sEMG, spectrogram feature, multi-channels, convolutional neural network

DOI: 10.3233/JIFS-212390

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 643-656, 2022

Authors: Qendraj, Daniela Halidini | Xhafaj, Evgjeni | Thanasi, Teuta

Article Type: Research Article

Abstract: Learning Management Systems is a challenge of implementing information technology (IT) in the higher educational field. This paper introduces a framework for assessing an LMS by integrating partial last squares-structural equation modeling (PLS-SEM) and fuzzy analytic hierarchic process with Z-numbers (Fuzzy Z-AHP). The objective is to propose the combination of the two approaches via results of PLS-SEM for the construction of the decision matrix for Fuzzy Z-AHP. The PLS-SEM method was used firstly to evaluate the conceptual model Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and extracting the significant connections between the independent constructs and the behavioral …intention to use an LMS. Secondly is adapted the Fuzzy Z-AHP method to rank the independent significant constructs initializing from the PLS-SEM results. Using a questionnaire survey, the study sampled 530 users of LMS in 4 Albanian universities as respondents. To the best of our knowledge this paper is among the first that combines PLS-SEM with Fuzzy Z-AHP for the UTAUT2 model while using an LMS. This combination showed that the most important construct of UTAUT2 affecting behavioral intention to use an LMS was habit. This study assist the decision makers and policy makers to provide the means to obtain better managerial conclusions for the improvement and progress of an LMS. Show more

Keywords: Google classroom, UTAUT2, PLS-SEM, Fuzzy Z-AHP, behavioral intention

DOI: 10.3233/JIFS-212396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 657-669, 2022

Authors: Periasamy, Madhumathi | Kaliannan, Thenmalar

Article Type: Research Article

Abstract: Distributed Generating (DG) units, Energy Storage Systems (ESS), Distributed Reactive Sources (DRS), and resilient loads make up the microgrid (MG), which can operate in both connected and isolated modes. Because the amount of power generated by Renewable Energy Sources (RES) such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES) is unpredictable, it becomes difficult for MGs planners to make judgments. In this article, the uncertainties caused by RES are resolved through the successful application of a hybrid optimization approach and the integration of hybrid DGs. The Teaching Learning Algorithm (TLA) is used in this study to determine the …best site for DGs and reconfiguration, and heuristic fuzzy has been merged with TLA to handle multi-objectives such as total generation and emission cost minimization, and bus voltage deviation. In addition, the impact of replacing RES with hybrid DGs on RES performance is investigated. The ideal structures are determined by solving four different scenarios with the suggested approach, allowing DSO to make decisions with greater flexibility. The proposed technique is validated using a benchmark IEEE 33 bus system that has been converted into a microgrid. WES, PVES, and hybrid DGs are validated using a 24-hour daily load pattern with 24-hour load dispatching characteristic behaviors. Show more

Keywords: Renewable energy sources, radial distribution system, wind energy systems, photovoltaic energy systems, teaching learning algorithm

DOI: 10.3233/JIFS-212397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 671-686, 2022

Authors: Ajilisa, O.A. | Jagathy Raj, V.P. | 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 U-Net architecture. Hybrid models which use minimum residual connections provide efficient segmentation frameworks similar to Res-UNet architecture with a minimum computational requirement. The experimental results indicate that all the segmentation models based on residual learning and U-Net 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. 43, no. 1, pp. 687-705, 2022

Authors: Alagu, Matheswaran | Selladurai, Ravindran | Chelladurai, Chinnadurrai

Article Type: Research Article

Abstract: The electric vehicle market has surged the consideration of charging station requirements in the commercial and residential areas of the urban regions. The addition of charging stations at the existing power network introduces a greater challenge on voltage stability and losses. The effect of the charging station can be addressed through the optimal integration of Distributed Generation (DG) units into the network. The improper placement of DG units can jeopardize the network stability. These issues are addressed by optimal placement of DG units and charging stations in the network to improve voltage, reduce transmission loss and maximize the charging station …capacity. Here the objectives are considered as a multi-objective problem and solved using an enhanced Ant-lion optimization algorithm. The proposed method is implemented and tested over IEEE – 33, 69 and 94 radial bus system in MATLAB R2020a version. In IEEE – 33 bus system, the total loss reduction of 67.63% and the minimum voltage of 0.981 is attained with 2909.2 kW of DG and 1770.7 kW of charging station. The voltage stability index is improved to 0.92. The efficacy of the proposed method is evaluated through comparison with existing methods such as Genetic Algorithm (GA) with VRP method, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO). It is evident that the proposed method gives improved performance than other methods. Show more

Keywords: Charging stations, distributed power generation, optimization, renewable energy sources, smart grids

DOI: 10.3233/JIFS-212401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 707-719, 2022

Authors: Borza, Mojtaba | Rambely, Azmin Sham

Article Type: Research Article

Abstract: Finding efficient solutions for the multi-objective linear fractional programming problem (MOLFPP) is a challenging issue in optimization because more than one target has to be taken into account. For the problem, we face the concept of efficient solutions which is an infinite set especially when the objectives are in conflict. Since a classical method generally comes out with only one efficient solution, thus introducing new efficient approaches is helpful and beneficial for the decision makers to make their decisions according to more possibilities. In this paper, we aim to consider the MOLFPP with fuzzy coefficients (FMOLFPP) where the concept of …α - cuts is utilized so as to transform the fuzzy numbers into closed intervals and rank the fuzzy numbers as well. Consequently, the fuzzy problem is changed into an interval valued multi-objective linear fractional programming problem (IV-MOLFPP). Subsequently, the IV-MOLFPP is further changed into linear programming problems (LPPs) using a parametric approach, weighted sum and max-min methods. It is demonstrated that the solution obtained is at least a weakly ɛ - efficient solution, where the value of ɛ helps a decision maker (DM) to make his decision appropriately i.e. DMs chose more likely the solutions with the lowest value of ɛ. Numerical examples are solved to illustrate the method and comparison are made to show the accuracy, and the reliability of the proposed solutions. Show more

Keywords: Efficient solution, weighted sum approach, parametric approach, fuzzy numbers, interval arithmetic

DOI: 10.3233/JIFS-212403

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 721-734, 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. 43, no. 1, pp. 735-744, 2022

Authors: Al-Sharqi, Faisal | Ahmad, Abd Ghafur | Al-Quran, Ashraf

Article Type: Research Article

Abstract: Interval complex neutrosophic soft sets (I-CNSSs) refers to interval neutrosophic soft sets (I-NSSs) featuring three two-dimensional independent membership functions accordingly (falsity, indeterminacy, as well as uncertainty interval). A relation is a tool that helps in describing consistency and agreement between objects. Throughout this paper, we insert and discuss the interval complex neutrosophic soft relation (simply denoted by I-CNSR), a novel soft computing technique used to examine the interaction degree among corresponding models known as I-CNSSs. We present the definition of the Cartesian product of I-CNSSs followed by the definition of I-CNSR. Furthermore, the definitions and some theorems and properties related …to the composition, inverse, and complement of I-CNSR are provided. The notions of symmetric, reflexive, transitive, and equivalent of I-CNSRs are proposed, and the algebraic properties of these concepts are verified. Furthermore, we demonstrate the relevance of our notion to real-world situations by offering a suggested method for solving a decision-making issue in the field of economics. Ultimately, an analysis is made between the current relationships and the proposed model to determine the model’s significance. Show more

Keywords: Complex neutrosophic set, complex neutrosophic relation, decision-making, interval neutrosophic set, interval complex neutrosophic soft set

DOI: 10.3233/JIFS-212422

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 745-771, 2022