Journal of Intelligent & Fuzzy Systems - Volume 44, issue 2

Authors: Rajalakshmi, R. | Sivakumar, P. | Prathiba, T. | Chatrapathy, K.

Article Type: Research Article

Abstract: In healthcare (HC), Internet of Things (IoT) integrated cloud computing provides various features and real-time applications. However, owing to the nature of IoT architecture, their types, various modes of communication and the density of data transformed in the network, security is currently a critical issue in the IoT healthcare (IoT-HC) field. This paper proposes a deep learning (DL) model, namely Adaptive Swish-based Deep Multi-Layer Perceptron (ASDMLP) that identifies the intrusions or attacks in the IoT healthcare (IoT-HC) platform. The proposed model starts by clustering the patients’ sensor devices in the network using the Probability-based Fuzzy C-Means (PFCM) model. After clustering …the devices, the cluster heads (CHs) among the cluster members are selected based on the energy, distance and degree of the sensor devices for aggregating the data sensed by the medical sensor devices. The base station (BS) sends the patient’s data collected by the CHs to the cloud server (CS). At the cloud end, the proposed model implements an IDS by applying training of the DL model in publicly available databases. The DL approach first performs preprocessing of the data and then selects optimal features from the dataset using the Opposition and Greedy Levy mutation-based Coyotes Optimization Algorithm (OGCOA). The ASDMLP trains these optimal features for the detection of HC data intrusions. The outcomes confirm that the proposed approach works well on real-time IoT datasets for intrusion detection (ID) without compromising the energy consumption (EC) and lifespan of the network. Show more

Keywords: Smart healthcare, Internet of Things (IoT), intrusion detection system, deep learning, healthcare security

DOI: 10.3233/JIFS-223166

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2753-2768, 2023

Authors: Lovelyn Rose, S. | Ravitha Rajalakshmi, N. | Sabari Nathan, M. | Suraj Subramanian, A. | Harishkumar, R.

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Abstract: This article has been retracted.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-220705

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2769-2778, 2023

Authors: Xu, Juan | Ma, Zhen Ming | Xu, Zeshui

Article Type: Research Article

Abstract: Heronian mean (HM) operators, which can capture the interrelationship between input arguments with the same importance, have been a hot research topic as a useful aggregation technique. In this paper, we propose the generalized normalized cross weighted HM operators on the unit interval which can not only capture the interrelationships between input arguments but also aggregate them with different weights, some desirable properties are derived. Then, generalized cross weighted HM operators are extended to real number set and applied to binary classification. We list the detailed steps of binary classification with the developed aggregation operators, and give a comparison of …the proposed method with the existing ones using the Iris dataset with 5-fold cross-validation (5-f cv), the accuracy of the proposed method for the training sets and the testing sets are both 100%. Show more

Keywords: Generalized cross weighted HM operator, cross weight vector, binary classification

DOI: 10.3233/JIFS-221152

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2779-2789, 2023

Authors: Osman, Mawia | Xia, Yonghui

Article Type: Research Article

Abstract: This paper proposes a method for solving fuzzy linear and nonlinear partial q -differential equations by the fuzzy q -differential transform. Further, we implemented the fuzzy fractional q -differential transform for solving some types of fuzzy fractional q -differential equations . The technique investigated is based on gH -differentiability, fuzzy q-derivative, and fuzzy q-fractional derivative. Various concrete problems have been tested by implementing the new method, and the results show great performance. The results also reveal that the method is a very effective and quite accurate mathematical tool for solving fuzzy fractional and integer q -differential equations. Finally, we …have provided some examples illustrating our method. Show more

Keywords: Fuzzy numbers, fuzzy-valued functions, fuzzy q-derivative; fuzzy q-fractional derivative, gH-differentiability, fuzzy q-differential transform method

DOI: 10.3233/JIFS-222567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2791-2846, 2023

Authors: Wang, Biao | Wei, Hongquan | Li, Ran | Liu, Shuxin | Wang, Kai

Article Type: Research Article

Abstract: Spotting rumors from social media and intervening early has always been a daunting challenge. In recent years, Deep neural networks have begun to discover rumors by exploring the way of rumor propagation. The existing static graph models either only focus on the spatial structure information of rumor propagation or on time series propagation information but do not effectively combine them. This paper proposes the Static Spatiotemporal Model (SSM), which first extracts the textual semantic information and constructs undirected and directed propagation trees. Then obtains spatial structure information of rumor propagation through Graph Convolutional Network and extracts time series propagation information …through the Recurrent Neural Network. The extracted spatiotemporal information is enhanced using different source node information hopping. Finally, SSM uses a weighted connection ensemble to rumor classification. Experimentally validated on datasets such as Weibo and Twitter, the results show that the proposed method outperforms several state-of-the-art static graph models. To better apply SSM in early detection and characterize early concepts, this paper presents a new data collection index for early detection, which can detect events that spread faster and have more significant influence in a targeted manner. The experimental results on the new indicators further verify the superiority of SSM as it can extract sufficient information in early detection or events with fewer participants. Show more

Keywords: Rumor detection, deep learning, SSM, spatiotemporal information, early detection, data collection index

DOI: 10.3233/JIFS-220417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2847-2862, 2023

Authors: Ramkumar, N. | Sadasivam, G. Sudha | Renuka, D. Karthika

Article Type: Research Article

Abstract: Multimodal analysis focuses on the internal and external manifestations of cancer cells to provide physicians, oncologists and surgeons with timely information on personalized diagnosis and treatment for patients. Decision fusion in multimodal analysis reduces manual intervention, and improves classification accuracy facilitating doctors to make quick decisions. Genetic characteristics extracted on biopsies do not, however, provide details on adjacent cells. Images can only provide external observable details of cancer cells. While mammograms can detect breast cancer, region wise details can be obtained from ultrasound images. Hence, different types of imaging techniques are used. Features are extracted using the SelectKbest method in …the Wisconsin Breast Cancer, Clinical and gene expression datasets. The features are extracted using Gray Level Co-occurrence Matrix from Histology, Mammogram and Sonogram images. For image datasets, the Convolution Neural Network (CNN) is used as a classifier. The combined features from clinical, gene expression and image datasets are used to train an Integrated Stacking Classifier. The integrated multimodal system’s effectiveness is shown by experimental findings. Show more

Keywords: Convolution neural networks, multimodal analysis, gray level co-occurrence matrix, histopathological, mammogram, sonogram and integrated stacking classifier

DOI: 10.3233/JIFS-220633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2863-2880, 2023

Authors: Xie, Ying | Zhu, Yuan | Lu, Zhenjie

Article Type: Research Article

Abstract: In view of the large-scale and high-dimensional problems of industrial data and fault-tracing problems, a fault detection and diagnosis method based on multi-block probabilistic kernel partial least squares (MBPKPLS) is proposed. First, the process variables are divided into several blocks in a decentralized manner to address the large-scale and high-dimensional problems. The probabilistic characteristics and relationship between the corresponding process variables and the quality variables of each block are analyzed using latent variables, and the PKPLS model of each block is established separately. Second, the MBPKPLS model is applied to process monitoring, statistics of each block are established in a …high-dimensional space, and the monitoring indicators in each block are used to detect faults. Third, based on fault detection, the multi-block concept is further used to locate the cause of fault, thereby solving the problem of fault tracing. Finally, a numerical example and the penicillin fermentation process (PFP) are used to test the effectiveness of the MBPKPLS method. The results demonstrate that the proposed method is suitable for processing large-scale, high-dimensional data with strong nonlinear characteristics, and the MBPKPLS process monitoring method is effective for improving the performance of fault detection and diagnosis. Show more

Keywords: Large-scale industrial process, multi-block probabilistic kernel partial least squares, fault detection, fault diagnosis

DOI: 10.3233/JIFS-220605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2881-2894, 2023

Authors: Ma, Yizhe | Yu, Long | Lin, Fangjian | Tian, Shengwei

Article Type: Research Article

Abstract:  In increasingly complex scenes, multi-scale information fusion becomes more and more critical for semantic image segmentation. Various methods are proposed to model multi-scale information, such as local to global, but this is not enough for the scene changes more and more, and the image resolution becomes larger and larger. Cross-Scale Sampling Transformer is proposed in this paper. We first propose that each scale feature is sparsely sampled at one time, and all other features are fused, which is different from all previous methods. Specifically, the Channel Information Augmentation module is first proposed to enhance query feature features, highlight part of …the response to sampling points and enhance image features. Next, the Multi-Scale Feature Enhancement module performs a one-time fusion of full-scale features, and each feature can obtain information about other scale features. In addition, the Cross-Scale Fusion module is used for cross-scale fusion of query feature and full-scale feature. Finally, the above three modules constitute our Cross-Scale Sampling Transformer(CSSFormer). We evaluate our CSSFormer on four challenging semantic segmentation benchmarks, including PASCAL Context, ADE20K, COCO-Stuff 10K, and Cityscapes, achieving 59.95%, 55.48%, 50.92%, and 84.72% mIoU, respectively, outperform the state-of-the-art. Show more

Keywords: Multi-scale fusion, Segmentation, Transformer

DOI: 10.3233/JIFS-220976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2895-2907, 2023

Authors: Xu, Huili | Li, Bing

Article Type: Research Article

Abstract: In this paper, a class of Clifford-valued neutral fuzzy neural-type networks with proportional delay and D operator and whose self feedback coefficients are also Clifford numbers are considered. By using the Banach fixed point theorem and some differential inequality techniques, we directly study the existence and global asymptotic stability of pseudo almost periodic solutions by not decomposing the considered Clifford-valued systems into real-valued systems. Finally, two examples are given to illustrate our main results. Our results of this paper are new.

Keywords: Clifford-valued neural network, fuzzy neural network, proportional delay, D operator, pseudo almost periodic solution, global asymptotic stability.

DOI: 10.3233/JIFS-221017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2909-2925, 2023

Authors: Deepa, S. | Sridhar, K.P. | Baskar, S. | Mythili, K.B. | Reethika, A. | Hariharan, P.R.

Article Type: Research Article

Abstract: A smart healthcare network can use sensors and the Internet of Things (IoT) to enhance patient care while decreasing healthcare expenditures. It has become more difficult for healthcare providers to keep track and analyze the massive amounts of data it generates. Health care data created by IoT devices and e-health systems must be handled more efficiently. A wide range of healthcare industries can benefit from machine learning (ML) algorithms in the digital world. However, each of these algorithms has to be taught to anticipate or solve a certain problem. IoT-enabled healthcare data and health monitoring-based machine learning algorithms (IoT-HDHM-MLA ) …have been proposed to solve the difficulties faced by healthcare providers. Sensors and IoT devices are vital for monitoring an individual’s health. The proposed IoT-HDHM-MLA aims to deliver healthcare services via remote monitoring with experts and machine learning algorithms. In this system, patients are monitored in real-time for various key characteristics using a collection of small wireless wearable nodes. The health care business benefits from systematic data collection and efficient data mining. Thus, the experimental findings demonstrate that IoT-HDHM-MLA enhances efficiency in patient health surveillance. Show more

Keywords: Health monitoring, machine learning algorithms, IoT, smart healthcare

DOI: 10.3233/JIFS-221274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2927-2941, 2023

Authors: Abolpour, Kh. | Zahedi, M.M. | Shamsizadeh, M.

Article Type: Research Article

Abstract:  The current study aims to investigate the L-valued tree automata theory based on t-norm/t-conorm and it further examines their algebraic and L-valued topological properties. Specifically, the concept of L-valued operators with t-norm/t-conorm is introduced and the existing relationships between them are also studied. Interestingly, we associate L-valued co-topologies/topologies for a given L-valued tree automaton, using them to characterize some algebraic concepts. Further, we introduce the concepts of Alexandroff L-graded co-topologies and Alexandroff L-graded topologies which correspond to the L-valued operators with t-norm and L-valued operators with t-conorm/implicator, respectively. In addition, we aim to specify the relationship between the L-graded co-topologies/topologies, …showing that the introduced L-graded co-topologies/topologies have some interesting consequences under homomorphism. Show more

Keywords: L-valued tree automaton, operator, L-valued topology, homomorphism

DOI: 10.3233/JIFS-221960

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2943-2955, 2023

Authors: Chhabra, Megha | Sharan, Bhagwati | Kumar, Manoj

Article Type: Research Article

Abstract:  The users of mobile phone are exponentially increasing. The applications are developed every day in a variety of domains to enhance the Quality of User Experience (QoUE) along with utility determinants. The design of the mobile application impacts the QoUE. QoUE in mobile applications is a measure that describes the appropriateness of the purpose of the application and the need for user retention. However, the challenge is to identify, understand, focus and interconnect the variety of determinants influencing the QoUE based on mobile application design. These determinants are based on the diversity of users and the related functional needs, user-specific …needs, and background functioning of the application. The modelling and analysis help mobile application developers to improve, increase and retain user engagement on the app based on improved QoUE. To do so, a qualitative analytical method is employed in the following steps. The first ever Fuzzy Cognitive Map (FCM) is proposed to show the causal-effect links of the interdependent determinants in mobile applications based on QoUE. In our model, the existence of relationships between determinants relies on a thorough literature review. The weight of these links is estimated by users of different ages and lines of work. This is performed by an empirical study based on a questionnaire filled by experts. The questionnaire is based on the formal utility and perceived QoUE-based topics. Finally, scenario-based analysis on formed FCM based on these inputs is performed. We show that small changes in cases using different direct determinants can be used to enhance QoUE. These changes can be studied before launching an application for the user, thereby limiting the need to rework the improvements based on QoUE and providing useful guidance for the possible increase in user base and behaviour change. Show more

Keywords: User experience, fuzzy cognitive maps, modelling, quality experience, mobile applications

DOI: 10.3233/JIFS-222111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2957-2979, 2023

Authors: Tian, Xiaoyan | Chen, Xinzhang | Feng, Linlin

Article Type: Research Article

Abstract: As the latest and hottest concept in the international arena, the metaverse concept has attracted the attention of various industries including information, economy, art, management, education and teaching for its application and technology integration research, but whether to define metaverse as a technology or a scenario has not yet reached a unified understanding in the academic and scientific communities. We believe that metaverse should be used as a key concept and emerging theory in building the future intelligent field. Therefore, we introduce the concept of metaverse in future film and animation teaching as a novel, strategic and disruptive teaching field …with great potential, and the constructed metaverse self-directed learning field will become an important part of school education resource optimization. In this study, we focus on the quality improvement path of film and animation teaching in the context of metaverse, and conduct a study on the assessment method of teaching quality after the introduction of metaverse concept. Specifically, we discuss the quality improvement measures in the future teaching of film and animation, construct a teaching field of film and animation based on the metaverse, and propose a related teaching quality assessment model and establish an index system for the quality assessment of film and animation teaching in the context of the metaverse. The index system is composed of 3 primary indicators, 9 secondary indicators and 27 tertiary indicators, and the quantitative calculation is carried out by the Analytic Hierarchy Process (AHP) in fuzzy mathematics, and the weighting results of the indicators are calculated. The research goal of combining quantitative analysis and qualitative research was achieved. What can be seen through our research is that the metaverse online classroom built with virtual reality and other technologies will have more advantages than the traditional teaching classroom. In the future, similar learning devices can be introduced in film and animation teaching, and diversified learning modules can be established. Not only can the learning efficiency of offline classroom be improved, but also more learning space can be opened for online classroom. This study bridges the gap in the theory of quality assessment of film and animation teaching after the introduction of the future metaverse concept, innovates the analysis of the new concept and the improvement of the old method, builds a new scenario of organic combination of new technology and traditional education teaching, and provides a new idea for international and domestic future education research. Show more

Keywords: Teaching quality assessment, teaching film and animation, metaverse, metaverse field architecture, fuzzy mathematical theory

DOI: 10.3233/JIFS-222779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2981-2997, 2023

Authors: Gokiladevi, M. | Santhoshkumar, Sundar

Article Type: Research Article

Abstract: Early identification of chronic kidney disease (CKD) becomes essential to reduce the severity level and mortality rate. Since medical diagnoses are equipped with latest technologies such as machine learning (ML), data mining, and artificial intelligence, they can be employed to diagnose the disease and aid decision making process. Since the accuracy of the classification model greatly depends upon the number of features involved, the feature selection (FS) approaches are developed which results in improved accuracy. With this motivation, this study designs a novel chaotic binary black hole based feature selection with classification model for CKD diagnosis, named CBHFSC-CKD technique. The …proposed CBHFSC-CKD technique encompasses the design of chaotic black hole based feature selection (CBH-FS) to choose an optimal subset of features and thereby enhances the diagnostic performance. In addition, the bacterial colony algorithm (BCA) with kernel extreme learning machine (KELM) classifier is applied for the identification of CKD. Moreover, the design of BCA to optimally adjust the parameters involved in the KELM results in improved classification performance. A comprehensive set of simulation analyses is carried out and the results are inspected interms of different aspects. The simulation outcome pointed out the supremacy of the CBHFSC-CKD technique compared to other recent techniques interms of different measures. Show more

Keywords: Chronic kidney disease, data classification, feature selection, machine learning, metaheuristics, disease diagnosis

DOI: 10.3233/JIFS-220994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2999-3010, 2023

Authors: Öztunç, Simge | İhtiyar, Sultan

Article Type: Research Article

Abstract: In this paper the concept of soft continuity is focused on for digital images by using soft sets which is defined on κ - adjacent digital images. Also the definitions of digital soft isomorphism and digital soft retraction are given. Some theorems are obtained deal with soft isomorphism and soft retraction for digital images and some numerical examples are presented in dimension 2. Finally digital soft retraction is obtained as a soft topological invariant.

Keywords: Digital image, soft set, soft continuous function, soft retraction

DOI: 10.3233/JIFS-221213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3011-3021, 2023

Authors: Zhao, Zhengwei | Yang, Genteng | Li, Zhaowen

Article Type: Research Article

Abstract: Outlier detection is a process to find out the objects that have the abnormal behavior. It can be applied in many aspects, such as public security, finance and medical care. An information system (IS) as a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. A RVIS with missing values is an incomplete real-valued information system (IRVIS). The notion of inner boundary comes from the boundary region in rough set theory (RST). This paper conducts experiments directly in an IRVIS and investigates outlier detection in an IRVIS …based on inner boundary. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relations on the object set are defined according to the distance, by the way, the tolerance classes, the λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the inner boundary under each conditional attribute in an IRVIS is presented. The more inner boundaries an object belongs to, the more likely it is to be an outlier. Finally, an outlier detection method in an IRVIS based on inner boundary is proposed, and the corresponding algorithm (DE) is designed, where DE means degree of exceptionality. Through the experiments base on UCI Machine Learning Repository data sets, the DE algorithm is compared with other five algorithms. Experimental results show that DE algorithm has the better outlier detection effect in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the DE algorithm. Show more

Keywords: RST, IRVIS, Outlier detection, Inner boundary

DOI: 10.3233/JIFS-222777

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3023-3041, 2023

Authors: Azimifar, Maryam | Nejatian, Samad | Parvin, Hamid | Bagherifard, Karamollah | Rezaei, Vahideh

Article Type: Research Article

Abstract: We introduce a semi-supervised space adjustment framework in this paper. In the introduced framework, the dataset contains two subsets: (a) training data subset (space-one data (SOD )) and (b) testing data subset (space-two data (STD )). Our semi-supervised space adjustment framework learns under three assumptions: (I) it is assumed that all data points in the SOD are labeled, and only a minority of the data points in the STD are labeled (we call the labeled space-two data as LSTD ), (II) the size of LSTD is very small comparing to the size of SOD , and (III) …it is also assumed that the data of SOD and the data of STD have different distributions. We denote the unlabeled space-two data by ULSTD , which is equal to STD  - LSTD . The aim is to map the training data, i.e., the data from the training labeled data subset and those from LSTD (note that all labeled data are considered to be training data, i.e., SOD  ∪ LSTD ) into a shared space (ShS ). The mapped SOD , ULSTD , and LSTD into ShS are named MSOD , MULSTD , and MLSTD , respectively. The proposed method does the mentioned mapping in such a way that structures of the data points in SOD and MSOD , in STD and MSTD , in ULSTD and MULSTD , and in LSTD and MLSTD are the same. In the proposed method, the mapping is proposed to be done by a principal component analysis transformation on kernelized data. In the proposed method, it is tried to find a mapping that (a) can maintain the neighbors of data points after the mapping and (b) can take advantage of the class labels that are known in STD during transformation. After that, we represent and formulate the problem of finding the optimal mapping into a non-linear objective function. To solve it, we transform it into a semidefinite programming (SDP ) problem. We solve the optimization problem with an SDP solver. The examinations indicate the superiority of the learners trained in the data mapped by the proposed approach to the learners trained in the data mapped by the state of the art methods. Show more

Keywords: Semi-supervised domain adaptation, non-linear optimization, local-preserving domain adaptation, semidefinite programming, kernel learning, principal component analysis

DOI: 10.3233/JIFS-200224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3043-3057, 2023

Authors: Dhamodharavadhani, S. | Rathipriya, R.

Article Type: Research Article

Abstract: This paper aims to develop the methodology for enhancing the regression models using Cluster based sampling techniques (CST) to achieve high predictive accuracy and can also be used to handle large datasets. Hard clustering (KMeans Clustering) or Soft clustering (Fuzzy C-Means) to generate samples called clusters, which in turn is used to generate the Local Regression Models (LRM) for the given dataset. These LRMs are used to create a Global Regression Model. This methodology is known as Enhanced Regression Model (ERM). The performance of the proposed approach is tested with 5 different datasets. The experimental results revealed that the proposed …methodology yielded better predictive accuracy than the non-hybrid MLR model; also, fuzzy C-Means performs better than the KMeans clustering algorithm for sample selection. Thus, ERM has potential to handle data with uncertainty and complex pattern and produced a high prediction accuracy rate. Show more

Keywords: Clustering, KMeans, fuzzy c-means, multiple linear regression, regression, sampling methods

DOI: 10.3233/JIFS-211736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3059-3069, 2023

Authors: Lekha, A. | Parvathy, K.S.

Article Type: Research Article

Abstract:  Let G  = (V , μ , σ ) be a fuzzy graph on a finite set V . A fuzzy subset μ ′ of μ is called a fuzzy dominating set of G  if, μ ′ ( v ) + ∑ x ∈ V ( σ ( x , v ) ∧ μ ′ ( x ) ) ≥ μ ( v ) for every v ∈ V . Fuzzy domination number γ fz is defined accordingly. In this paper we …initiate a study of this parameter. Some properties of fuzzy dominating sets are studied and fuzzy domination number γ fz is determined for some graphs. Show more

Keywords: Fuzzy Graph, Fuzzy Dominating Sets, Fuzzy Domination Number

DOI: 10.3233/JIFS-220987

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3071-3077, 2023

Authors: Ayidzoe, Mighty Abra | Yu, Yongbin | Mensah, Patrick Kwabena | Cai, Jingye | Baagyere, Edward Yellakuor | Bawah, Faiza Umar

Article Type: Research Article

Abstract: Colorectal cancer is the third most diagnosed malignancy in the world. Polyps (either malignant or benign) are the primary cause of colorectal cancer. However, the diagnosis is susceptive to human error, less effective, and falls below recommended levels in routine clinical procedures. In this paper, a Capsule network enhanced with radon transforms for feature extraction is proposed to improve the feasibility of colorectal cancer recognition. The contribution of this paper lies in the incorporation of the radon transforms in the proposed model to improve the detection of polyps by performing efficient extraction of tomographic features. When trained and tested with …the polyp dataset, the proposed model achieved an overall average recognition accuracy of 94.02%, AUC of 97%, and an average precision of 96%. In addition, a posthoc analysis of the results exhibited superior feature extraction capabilities comparable to the state-of-the-art and can contribute to the field of explainable artificial intelligence. The proposed method has a considerable potential to be adopted in clinical trials to eliminate the problems associated with the human diagnosis of colorectal cancer. Show more

Keywords: Capsule network, colorectal polyp, convolutional neural network, explainable artificial intelligence

DOI: 10.3233/JIFS-212168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3079-3091, 2023

Authors: Li, Zepeng | Huang, Rikui | Zhang, Yufeng | Zhu, Jianghong | Hu, Bin

Article Type: Research Article

Abstract:  Knowledge Graph Embedding (KGE), which aims to embed the entities and relations of a knowledge gxraph into a low-dimensional continuous space, has been proven to be an effective method for completing a knowledge graph and improving the quality of the knowledge graph. The translation-based models represented by TransE, TransH, TransR and TransD have achieved great success in this regard. There is still potential for improvement in dealing with complex relations. In this paper, we find that the lack of flexibility in entity embedding limits the model’s ability to model complex relations. Therefore, we propose single-directional-flexible (sdf) models and multi-directional-flexible (mdf) …models to increase the flexibility and expressiveness of entity embeddings. These two methods can be applied to the TransD model and its variant models without increasing any time cost and space cost. We conduct experiments on benchmarks such as WN18 and FB15k. The experimental results show that the models significantly surpasses the classical translation models in both tasks of triplet classification and link prediction. In particular, for Hits@1 of link prediction of WN18, we get 71.7% after applying our method to TransD, which is much better than 24.1% of TransD. Show more

Keywords: Knowledge graph embedding, translation model, complex relation, single-directional-flexible model, multi-directional-flexible model

DOI: 10.3233/JIFS-211553

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3093-3105, 2023

Authors: Wan, Chenxia | Fang, Liqun | Cao, Shaodong | Luo, Jiaji | Jiang, Yijing | Wei, Yuanxiao | Lv, Cancan | Si, Weijian

Article Type: Research Article

Abstract: The investigation on brain magnetic resonance imaging (MRI) of cerebral small vessel disease (CSVD) classification algorithm based on deep learning is particularly important in medical image analyses and has not been reported. This paper proposes an MRI classification algorithm based on convolutional neural network (MRINet), for accurately classifying CSVD and improving the classification performance. The working method includes five main stages: fabricating dataset, designing network model, configuring the training options, training model and testing performance. The actual training and testing datasets of MRI of CSVD are fabricated, the MRINet model is designed for extracting more detailedly features, a smooth categorical-cross-entropy …loss function and Adam optimization algorithm are adopted, and the appropriate training parameters are set. The network model is trained and tested in the fabricated datasets, and the classification performance of CSVD is fully investigated. Experimental results show that the loss and accuracy curves demonstrate the better classification performance in the training process. The confusion matrices confirm that the designed network model demonstrates the better classification results, especially for luminal infarction. The average classification accuracy of MRINet is up to 80.95% when classifying MRI of CSVD, which demonstrates the superior classification performance over others. This work provides a sound experimental foundation for further improving the classification accuracy and enhancing the actual application in medical image analyses. Show more

Keywords: Cerebral small vessel disease, brain magnetic resonance imaging, convolutional neural network, feature extraction, classification accuracy

DOI: 10.3233/JIFS-213212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3107-3114, 2023

Authors: Zhang, Xinyu | Yu, Long | Tian, Shengwei

Article Type: Research Article

Abstract: In today’s social media and various frequently used lifestyle applications, the phenomenon that people express their sentiment via comments or instant barrage is common. People not only show their joys and sorrows in the process of expression but also present their opinions to one thing in many aspects which include. Nowadays, aspect-based sentiment analysis has become a mature and wildly-used technology. There are many public datasets considered as a benchmark to test model performance, such as Laptop2014, Restaurant2014, Twitter, etc. In our work, we also use these public datasets as the test criteria. Current mainstream models generally use the methods …of stacking multi-RNNs layers or combining neural networks and BERT or other pre-trained models. On account of the importance displayed by the dependence between aspect words and sentiment words, we investigate a novel model (BGAT) blending bidirectional gated recurrent unit (BiGRU) and relational graph attention network (RGAT) to learn dependencies information. Extensive experiments have been conducted on five datasets, the results demonstrate the great capability of our model. Show more

Keywords: Aspect-based sentiment analysis, graph attention network, BiGRU, dependency information, natural language processing

DOI: 10.3233/JIFS-213020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3115-3126, 2023

Authors: Mythrei, S. | Singaravelan, S.

Article Type: Research Article

Abstract: In this web era, entity linking plays a major role. In the web the information’s are associated with different kinds of data and objects. Heterogeneous information networks (HIN) involved multi composed interlinked interconnected objects with various types of connections which is more prominent in this real world. Most of the research work focused towards processing homogeneous networks as well as linking entities with Wikipedia as knowledge base. In this paper we proposed a probabilistic based domain specific entity linking system that will link named entity mentions detected from unstructured web text corpus with corresponding entity in the existing domain specific …Heterogeneous information networks as knowledge base. This work is most challenging due to entity name ambiguity as well as knowledge in the network that are limited one. The proposed model framework presents a model that will link named entity from unstructured web text with domain specific Heterogeneous information network mainly focuses on to learn the weight of meta path. The experiments are done over real world dataset such as DBLP and IMDB dataset. Show more

Keywords: DBLP, IMDB dataset, homogeneous networks

DOI: 10.3233/JIFS-220331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3127-3135, 2023

Authors: Xu, Huiyan

Article Type: Research Article

Abstract: The diagnosis cycle of schizophrenia is long, there is no objective diagnostic basis. The over-energy entropy product of the speech fluency rectangular parameter is designed in the paper, the fuzzy clustering is used to double locate speech pause areas and to assist in the diagnosis of schizophrenia. The pause area of speech is located based on the low speech fluency and flat energy in schizophrenia patients, an extraction algorithm is given for speech fluency quantification parameters, support vector machine (SVM) classifier is used in the approach. The fluency acoustic features of speech are taken from 28 schizophrenia patients and 28 …normal controls, these are used to verify the effect of the method in schizophrenia recognition, there is a correct rate of over 85%. The automatic schizophrenia identification based on energy entropy product and fuzzy clustering can provide objective, effective and non-invasive auxiliary for clinical diagnosis of schizophrenia. Show more

Keywords: Schizophrenia, speech fluency rectangle parameter, fuzzy clustering, hyperenergy entropy product, speech pauses in schizophrenia

DOI: 10.3233/JIFS-220248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3137-3151, 2023

Authors: Purohit, Amit | Patheja, Pushpinder Singh

Article Type: Research Article

Abstract: Sentiment analysis is a natural language processing (NLP) technique for determining emotional tone in a body of text. Using product reviews in sentiment analysis and opinion mining various methods have been developed previously. Although, existing product review analyzing techniques could not accurately detect the product aspect and non-aspect. Hence a novel Detach Frequency Assort is proposed to detect the product aspect term using TF-ISF (Term frequency-inverse sentence frequency) with Part of Speech (POS) tags for sentence segmentation and additionally using Feedback Neural Network to combine product aspect feedback loop. Furthermore, decision-making problem occurs during classification of sentiments. Hence, to solve …this problem a novel technique named, Systemize Polarity Shift is proposed in which flow search based Support Vector Machine (SVM) with Bag of Words model classifies pre-trained review comments as positive, negative, and neutral sentiments. Moreover, the identification of specific products is not focused in sentiment analysis. Hence, a novel Revival Extraction is proposed in which a specific product is extracted based on thematic analysis method to obtain accurate data. Thus, the proposed Product Review Opinion framework gives effective optimized results in sentiment analysis with high accuracy, specificity, recall, sensitivity, F1-Score, and precision. Show more

Keywords: Sentiment analysis, opinion mining, support vector machine, thematic analysis

DOI: 10.3233/JIFS-213296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3153-3169, 2023

Authors: Hernández, Sergio | López, Juan Luis | López-Cortés, Xaviera | Urrutia, Angelica

Article Type: Research Article

Abstract: Recommendations analysis of road safety requires decision-making tools that accommodate weather uncertainties. Operation and maintenance of transport infrastructure have been one of the sub-areas that require attention due to its importance in the quality of the road. Several investigations have proposed artificial neural networks and Bayesian networks to assess the risk of the road. These methods make use of historic accident records to generate useful road safety metrics; however, there is less information on how climatic factors and road surface conditions affect the models that generate recommendations for safe traffic. In this research, Bayesian Network, as a Hidden Markov Models, …and Apriori method are proposed to evaluate the open and closed state of the road. The weather and road surface conditions are explicitly written as a sequence of latent variables from observed data. Different weather variables were studied in order to evaluate both road states (open or close) and the results showed that the Hidden Markov Model provides explicit insight into the sequential nature of the road safety conditions but does not provide a directly interpretable result for human decision making. In this way, we complement the study with the Apriori algorithm using categorical variables. The experimental results show that combining the Hidden Markov Model and the Apriori algorithm provides an interpretable rule for decision making in recommendations of road safety to decide an opening or closing of the road in extreme weather conditions with a confidence higher than 90%. Show more

Keywords: Road safety analysis, hidden markov models, apriori methods

DOI: 10.3233/JIFS-211746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3171-3187, 2023

Authors: Kannan, Sridharan

Article Type: Research Article

Abstract: In today’s world, mining and learning applications play an essential role in healthcare sectors and intend to transform all the data into an understandable form. However, the healthcare sectors require an automated disease prediction system for better medical analysis and emphasize better prediction accuracy for evaluation purposes. In this paper, a new automated prediction model based on Linearly Support Vector Regression and Stacked Linear Swarm Optimization (LSVR-SLSO) has been proposed to predict heart disease accurately. Primarily, the features are analyzed in a linear and non-linear manner using LSVR feature learning approaches. The extracted features are then fed into the SLSO …model in order to extract the global optimal solutions. These global solutions will reduce the data dimensionality and computational complexity during the evaluation phase. Moreover, the optimal solution facilitates the proposed model to predict heart disease appropriately. The simulation can be carried out through the MATLAB environment by utilizing a publicly available benchmark heart disease dataset. The performance results evident that the proposed LSVR-SLSO model can efficiently predict heart disease with superior accuracy of 98%, precision of 98.76%, and recall of 99.7% when compared with conventional approaches. The better performance of the proposed model will pave the way to act as an effective clinical decision support tool for physicians during an emergency. Show more

Keywords: Heart disease prediction, feature selection, optimization, automated system, mining and learning

DOI: 10.3233/JIFS-212772

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3189-3202, 2023

Authors: Wang, Kaixiang | Yang, Ming | Yang, Wanqi | Wang, Lei

Article Type: Research Article

Abstract: Deep neural networks have been adopted in multi-label classification for their excellent performance, however, existing methods fail to comprehensively utilize the high-order correlations between instances and the high-order correlations between labels, and these methods are difficult to deal with label noise effectively. We propose a novel end-to-end deep framework named Robust Fused Hypergraph Neural Networks for Multi-Label Classification (RFHNN), which can effectively utilize the two kinds of high-order correlations and adopt them to mitigate the impact of label noise. In RFHNN, Hypergraph Neural Networks (HNNs) are adopted to mine and utilize the high-order correlations of the instances in the feature …space and the label space respectively. The high-order correlations of the instances can not only improve the accuracy of the classification and the discrimination of the proposed model, but also lay the foundation for the subsequent noise correction module. Meanwhile, a hypergraph construction method based on the Apriori algorithm is proposed to realize Hypergraph Neural Networks (HNNs), which can mine robust second-order and high-order label correlations effectively. Effective classifiers are learned based on the correlations between the labels, which will not only improve the accuracy of the model, but can also enhance the subsequent noise correction module. In addition, we have designed a noise correction module in the networks. With the help of the high-order correlations among the instances and the effective classifier, the framework can effectively correct the noise and improve the robustness of the model. Extensive experimental results on datasets demonstrate that our proposed approach is better than the state-of-the-art multi-label classification algorithms. When dealing with the multi-label training datasets with noise in the label space, our proposed method also has great performance. Show more

Keywords: Multi-label classification, fused hypergraph neural network, high-order label correlations, noise correction, robust classification framework

DOI: 10.3233/JIFS-212844

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3203-3218, 2023

Authors: Jie, Zheng | Daijun, Wei | Liming, Tang

Article Type: Research Article

Abstract: For D numbers theory, there are some drawbacks in the D numbers’ integration rule. For example, the missing information is ignored in the final decision judgment for multi-attribute decision (MADM). For this problem, some researchers have improved the D numbers’ integration rules based on optimistic criterion for overcoming the shortcoming of D numbers’ integration rule. However, optimistic and pessimistic criterion are two sides of the coin for fuzzy environment. Therefore, in this article, a new D numbers’ integration rules based on pessimistic criterion is proposed. We improve the D numbers’ integration rules to redefine the missing information distribution rules based …on pessimistic criterion. The missing information is distributed in inverse proportion to each D number according to the size of the original evidence credibility. Two examples of MADM is applied by the proposed method, the results show that the proposed method can be applied to MADM. Show more

Keywords: Uncertainty, multiple attributes decision making, D numbers, integration representation, pessimistic criterion

DOI: 10.3233/JIFS-211533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3219-3231, 2023

Authors: Yuan, Yinlong | Hua, Liang | Cheng, Yun | Li, Junhong | Sang, Xiaohu | Zhang, Lei | Wei, Wu

Article Type: Research Article

Abstract: Reward signal reinforcement learning algorithms can be used to solve sequential learning problems. However, in practice, they still suffer from the problem of reward imbalance, which limits their use in many contexts. To solve this unbalanced reward problem, in this paper, we propose a novel model-based reinforcement learning algorithm called the expected n-step value iteration (EnVI). Unlike traditional model-based reinforcement learning algorithms, the proposed method uses a new return function that changes the discount of future rewards while reducing the influence of the current reward. We evaluated the performance of the proposed algorithm on a Treasure-Hunting game and a …Hill-Walking game. The results demonstrate that the proposed algorithm can reduce the negative impact of unbalanced rewards and greatly improve the performance of traditional reinforcement learning algorithms. Show more

Keywords: Reinforcement learning, Model-based learning, Unbalanced reward, Multi-step methods

DOI: 10.3233/JIFS-210956

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3233-3243, 2023

Authors: Song, Xudong | Chen, Yilin | Liang, Pan | Wan, Xiaohui | Cui, Yunxian

Article Type: Research Article

Abstract: In recent years, imbalanced data learning has attracted a lot of attention from academia and industry as a new challenge. In order to solve the problems such as imbalances between and within classes, this paper proposes an adaptive boundary weighted synthetic minority oversampling algorithm (ABWSMO) for unbalanced datasets. ABWSMO calculates the sample space clustering density based on the distribution of the underlying data and the K-Means clustering algorithm, incorporates local weighting strategies and global weighting strategies to improve the SMOTE algorithm to generate data mechanisms that enhance the learning of important samples at the boundary of unbalanced data sets and …avoid the traditional oversampling algorithm generate unnecessary noise. The effectiveness of this sampling algorithm in improving data imbalance is verified by experimentally comparing five traditional oversampling algorithms on 16 unbalanced ratio datasets and 3 classifiers in the UCI database. Show more

Keywords: Imbalanced data, oversampling, classifier, boundary weighted, within and between class imbalance

DOI: 10.3233/JIFS-220937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3245-3259, 2023

Authors: Nallasivan, G. | Akshaya, V.S. | Padmavathy, C.

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-221667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3261-3269, 2023

Authors: Liu, Hui

Article Type: Research Article

Abstract: Since 2010, China’s traditional industry has entered a critical stage of development and enterprise reform and development is imminent. Product homogenization is serious in this market, so that the competition among enterprises is fierce. At the same time, international brands continue to enter the Chinese consumption market, which intensifies the competition and seriously squeezes the market share of Chinese local brands. However, the popularization and development of the Internet and the change of people’s consumption concept and level make the market put forward higher requirements for the development of business operation and many traditional family enterprises have embarked on the …road of transformation. It is of great significance and value to clarify the influence of internal factors of family enterprises on strategic transformation. The performance evaluation of family business strategic transition is really a multiple attribute group decision making (MAGDM) problems. In this paper, the 2-tuple linguistic neutrosophic number grey relational analysis (2TLNN-GRA) method is proposed along with on the traditional grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNNSs). Firstly, the 2TLNNSs is introduced. Then, combine the traditional fuzzy GRA model with 2TLNNSs information, the 2TLNN-GRA method is established and the computing steps for MAGDM are built. Finally, a numerical example for performance evaluation of family business strategic transition has been given and some comparisons is used to illustrate advantages of 2TLNN-GRA method. Show more

Keywords: Multiple attribute group decision making (MAGDM) problems, 2-tuple linguistic neutrosophic sets (2TLNSs), GRA method, family business strategic transition

DOI: 10.3233/JIFS-221514

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3271-3283, 2023

Authors: Ni, Ting | Wang, Bo | Jiang, Jiaxin | Wang, Meng | Lei, Qing | Deng, Xinman | Feng, Cuiying

Article Type: Research Article

Abstract: The issue of how to fully utilize natural daylighting of public buildings is one of the greatest practical objectives for lighting savings. The rapid and accurate prediction of the daylighting coefficient at the early design stage can provide a quantitative basis for energy-saving optimization. However, it is not comprehensive to determine the design parameters according to experience. The key problem that is still facing designers is the interoperability between building modeling and energy simulation tools. In this paper, an integrated approach using a dataset created by building information modeling and artificial neural network technology is developed for the fast optimal …daylight factor prediction of large public spaces at the early design stage. According to this approach, the value of daylight factors is calculated for different windowsill heights, window heights and widths by Autodesk® Revit and Ecotect Analysis to form a dataset. With this dataset, an artificial neural network model is established using the backpropagation algorithm to predict the relevant design parameters. With their large interior spaces, the reading areas of the aboveground five floors in Chengdu University of Technology Library are selected to carry out the daylight factor experiment and rapid prediction. A total of 495 groups of experimental data are randomly divided into training and testing sets. The root mean squared errors are below 0.1, which indicates a high regression model fitting. A total of 225,369 groups of prepared data are used in the prediction model to obtain the optimal windowsill height (1.0 m), window height (2.4 m) and window width (2.1 m) for five floors in the case of the maximum daylighting coefficient. Finally, a smartphone app is designed to facilitate daylight factor prediction without any experience in modeling and simulation tools, which is simple and available to realize prediction visualization and historical result analysis. Show more

Keywords: Daylight factor, rapid prediction, building information modelling, artificial neural network, library, app

DOI: 10.3233/JIFS-220930

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3285-3297, 2023

Authors: Namala, Vasu | Karuppusamy, S. Anbu

Article Type: Research Article

Abstract: The amount of audio visual content kept in networked repositories has increased dramatically in recent years. Many video hosting websites exist, such as YouTube, Metacafe, and Google Video. Currently, indexing and categorising these videos is a time-consuming task. The system either asks the user to provide tags for the videos they submit, or manual labelling is used. The aim of this research is to develop a classifier that can accurately identify videos. Every video has content that is either visual, audio, or text. Researchers categorised the videos based on any of these three variables. With the Pattern Change with Size …Invariance (PCSI) algorithm, this study provides a hybrid model that takes into account all three components of the video: audio, visual, and textual content. This study tries to classify videos into broad categories such as education, sports, movies, and amateur videos. Key feature extraction and pattern matching would be used to accomplish this. A fuzzy logic and ranking system would be used to assign the tag to the video. The proposed system is tested only on a virtual device in addition a legitimate distributed cluster for the aim of reviewing real-time performance, especially once the amount and duration of films are considerable. The efficiency of video retrieval is measured with metrics like accuracy, precision, and recall is over 99% success. Show more

Keywords: Video indexing, video retrieval, key feature extraction, pattern change with size invariance (PCSI) algorithm

DOI: 10.3233/JIFS-221905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3299-3313, 2023

Authors: Ghasemi, Mohsen | Bagherifard, Karamollah | Parvin, Hamid | Nejatian, Samad

Article Type: Research Article

Abstract:  Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the non-repetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of …same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works’ results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published. Show more

Keywords: Next release problem, multi-objective evolutionary algorithm, search-based software engineering, teaching-learning based optimization, non-repetitive population

DOI: 10.3233/JIFS-200223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3315-3339, 2023

Authors: Kazi, Samreen | Rahim, Maria | Khoja, Shakeel

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-211275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3341-3351, 2023

Authors: Lawrance, N.A. | Shiny Angel, T.S.

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-213573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3353-3367, 2023

Authors: Hu, Kekun | Zhu, Zheng | Xu, Yukun | Jiang, Chao | Dai, Chen

Article Type: Research Article

Abstract: Maintaining accurate topology of the low-voltage distribution grid (LVDG) are critical to the operations and maintenance of power distribution systems. However, this goal is hard to achieve due to the fast-changing LVDG topology. To this end, we focus on the abnormal customer-transformer relationships identification in the LVDG and propose an identification method based on an A daptive D ual-channel G raph W avelet Neural N etwork (ADGWN) consisting of two identical GWNs connected with the attention mechanism. In the proposed ADGWN, two GWNs learn customer embedding simultaneously from the LVDG topology graph and the feature graph that is …constructed from customer electricity consumption data with the k -Nearest Neighbor algorithm. The topology identification results of these two GNNs are then adaptively fused to form the ultimate identification result with the attention mechanism by dynamically balancing the aforementioned two types of information. To validate the performance of our proposed method, we further build a real benchmarking dataset from customer electricity consumption data collected from a certain substation in Shanghai, China. Experimental results show that the proposed ADGWN achieves 100.0% LVDG topology identification accuracy and significantly outperforms the state-of-the-art. Our proposed method can help operators of power distribution systems maintain the accurate topology in a timely and economic manner. Show more

Keywords: Low-voltage distribution grid, topology identification, dual-channel, graph wavelet transform, attention

DOI: 10.3233/JIFS-220653

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3369-3380, 2023

Authors: Jagtap, Vinayak | Kulkarni, Parag | Joshi, Pallavi

Article Type: Research Article

Abstract: A dynamic world has different uncertainties. These uncertainties always impact adversely while making decisions. Existing systems sometimes fail as they are trained without considering uncertainty inclusion due to the dynamic nature of the problem. This is quite observed in gaming, which is most dynamic and contributes adversely while deciding for the next move. Strategic games have fewer uncertainties rather than ground sports. Many types of factors add uncertainty to the system. There is a need of handling the required uncertainty which will help in making the decision. Also while finding similarities between games or matches, player and playing style results …don’t depict exact similarities between them. There is a need to measure uncertainty-based similarities as it helps in deciding the situation of the game or player. Here Uncertainty based decision support system is proposed which takes uncertainty as input rather than only considering patterns of input. Patterns always help if the system is more static while considering a dynamic system where we need to consider patterns and uncertainties in the scenarios. Results are shown on limited types of moves in game data and how uncertainty-based similarity and next move selection are improved. Show more

Keywords: Uncertainty based decision support, decision support, uncertainty, gaming

DOI: 10.3233/JIFS-221611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3381-3397, 2023

Authors: Liu, Shengyan | Zhou, Yao | Wang, Xiao

Article Type: Research Article

Abstract: With the steady development of China’s economy, under the new economic normal, the creative cultural industry has been continuously optimized and developed in terms of structure, scale and quality, and the connotation of the creative cultural industry has been continuously enriched, forming a three-dimensional and diversified pattern. With the help of high-tech, culture, multimedia and other means, the current creative cultural industry is continuously absorbing and integrating it on a large scale, promoting the optimization, upgrading and innovative development of the industry. The consumer competitiveness evaluation in creative and cultural industries is a classical MAGDM problems. In this paper, WDBA …method is designed for solving the probabilistic linguistic MAGDM(PL-MAGDM) with the completely unknown weights. In the end, an empirical application for consumer competitiveness evaluation in creative and cultural industries is used to demonstrate the use of the developed method. The proposed method can also contribute to the selection of suitable alternative successfully in other selection problems. Show more

Keywords: Multiple attribute group decision making (MAGDM), probabilistic linguistic term sets (PLTSs), information entropy, WDBA method, consumer competitiveness evaluation in creative and cultural industries

DOI: 10.3233/JIFS-221799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3399-3409, 2023

Authors: Andavan, Mohanaprakash Thottipalayam | Vairaperumal, Nirmalrani

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-220206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3411-3425, 2023

Authors: Subha Darathy, C. | Agees Kumar, C.

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-222872

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3427-3440, 2023

Authors: Joseph Robinson, M. | Veeramani, C. | Vasanthi, S.

Article Type: Research Article

Abstract: Neutrosophic Set (NS) allows us to handle uncertainty and indeterminacy of the data. Several researchers have investigated the Transportation Problems (TP) with various forms of input data. This paper emphasizes a dynamic optimal solution framework for TPs in a neutrosophic setting. This paper investigates a Neutrosophic Transportation Problem (NTP) in which supply, demand, and transportation cost are considered as Single-Valued Neutrosophic Trapezoidal Numbers (SVNTrNs). The weighted possibilistic mean value of their truth, indeterminacy, and facility membership function are calculated. Then, NTP is modelled as a parametric Linear Programming Problem (LPP) and solved. Further, the drawbacks of the existing approaches and …advantages of the developed method are discussed. Finally, the real-time problem and numerical illustrations are presented and compared to existing solutions. This study helps the Decision-Makers (DMs) in budgeting their transportation expenses through strategic distribution. Show more

Keywords: Single valued neutrosophic trapezoidal number, transportation problem, linear programming problem, weighted possibilistic mean

DOI: 10.3233/JIFS-221802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3441-3458, 2023