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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Cisneros, Luis | Rivera, Gilberto | Florencia, Rogelio | Sánchez-Solís, J. Patricia
Article Type: Research Article
Abstract: Business analytics refers to the application of sophisticated tools to obtain valuable information from a large dataset that is generated by a company. Among these tools, fuzzy optimisation stands out because it helps decision-makers to solve optimisation problems considering the uncertainty that commonly occurs in application domains. This paper presents a bibliometric analysis following the PRISMA statement on the Dimensions database to obtain publications related to fuzzy optimisation applied to business domains. The purpose of this analysis is to gather useful information that can help researchers in this area. A total of 2,983 publications were analysed using VOSviewer to identify …the trend in the number of publications per year, relationships in terms in both the title and abstract of these publications, most influential publications, and relationships among journals, authors, and institutions. Show more
Keywords: PRISMA statement, VOSviewer, bibliometric insights, scientific landscape, fuzzy optimisation, prescriptive analytics
DOI: 10.3233/JIFS-221573
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2615-2630, 2023
Authors: Duman, Ekrem
Article Type: Research Article
Abstract: The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and …develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models. Show more
Keywords: Social media, banking, CRM, NLP, sentiment analysis
DOI: 10.3233/JIFS-221619
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2631-2642, 2023
Authors: Han, Yongguang | Zhang, Shanshan | Deng, Dexue
Article Type: Research Article
Abstract: Aiming at the multi-attribute group decision-making (MAGDM) problem with unclear index weights values, and thinking about the bounded rational behavior of decision makers (DMs), we proposed a new improved CPT-VIKOR decision method under intuitionistic fuzzy (IF-CPT-VIKOR). Due to the emergence of special cases in IFSs, a new IFS score function and distance formula are defined. Meanwhile, the use of entropy weight method to obtain the weight information of indicators enhances the objectivity of the model. Furthermore, CPT is integrated into the IFS environment, which fully reflects the psychological behavior of DMs, and take advantage of the VIKOR method to determine …the final sorting of the scheme. Finally, through the application cases of the commercial concrete supplier selection (CCSS) and the comparison with the existing authoritative methods to verify the feasibility and validity of the method. Show more
Keywords: Multiple attribute group decision making (MAGDM), cumulative prospect theory (CPT), VIKOR method, intuitionistic fuzzy sets (IFSs), Commercial concrete supplier selection (CCSS)
DOI: 10.3233/JIFS-221780
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2643-2654, 2023
Authors: Peng, Jinghong | Zhou, Jun | Liang, Guangchuan | Qin, Can | Peng, Cao | Chen, YuLin | Hu, Chengqiang
Article Type: Research Article
Abstract: Gas gathering pipeline network system is an important process facility for gas field production, which is responsible for collecting, transporting and purifying natural gas produced by wells. In this paper, an optimization model for the layout of star-tree gas gathering pipeline network in discrete space is established to find the most economical design scheme. The decision variables include valve set position, station position and pipeline connection relation. A series of equality and inequality constraints are developed, including node flow balance constraints, pipeline hydraulic constraints and pipeline structure constraints. A global optimization strategy is proposed and an improved genetic algorithm is …used to solve the model. To verify the validity of the proposed method, the optimization model is applied to a coalbed methane field gathering pipeline network in China. The results show that the global optimization scheme saves 1489.74×104 RMB (26.36%) in investment cost compared with the original scheme. In addition, the comparison between the global and hierarchical optimization scheme shows that the investment cost of the global optimization scheme is 567.22×104 RMB less than that of the hierarchical optimization scheme, which further proves the superiority of the global optimization method. Finally, the study of this paper can provide theoretical guidance for the design and planning of gas field gathering pipeline network. Show more
Keywords: Natural gas, pipeline network, layout design, global optimization, genetic algorithm
DOI: 10.3233/JIFS-222199
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2655-2672, 2023
Authors: Zhang, Qi | Su, Qian | Liu, Baosen | Pei, Yanfei | Zhang, Zongyu | Chen, De
Article Type: Research Article
Abstract: Effectively evaluating high-embankment deformation and stability is important for heavy-haul railway safety. An improved extension model with an attribute reduction algorithm was proposed for the comprehensive evaluation method. First, a hierarchical evaluation system for high embankments in heavy-haul railways was established using the attribute reduction algorithm, which includes the principal component analysis, maximum information coefficient, coefficient of variation, and improved Dempster-Shafer evidence theory. Furthermore, the improved extension model was used to evaluate high-embankment performance in heavy-haul railways. In this improved extension model, the combination weighting method, an asymmetric proximity function, and the maximum membership principle effectiveness verification were used. Finally, …three high embankments in a Chinese heavy-haul railway were studied. The results illustrate that the main influencing factors for high-embankment performance in a heavy-haul railway are annual rainfall, annual temperature, and 21 other indicators. The performance of the three embankments is level III (ordinary), level II (fine), and level III (ordinary), respectively, indicating that these embankments have generally unfavourable performance. The three embankments’ performance matches field measurements, and the proposed method outperforms the Fuzzy-AHP method, cloud model, and gray relational analysis. This study demonstrates the feasibility of the proposed method in assessing the high-embankment performance under heavy axle loads. Show more
Keywords: Heavy-haul railway, high embankment, comprehensive evaluation, improved extension model, attribute reduction
DOI: 10.3233/JIFS-222562
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2673-2692, 2023
Authors: Cetinkaya, Suleyman | Demir, Ali
Article Type: Research Article
Abstract: The purpose of this research is to establish the solution to the time-fractional initial value problem (TFIVP) in Caputo- Fabrizio sense by implementing a new integral transform called ARA transform together with the iterative method. The existence of the ARA transform is investigated. Moreover, it is shown that the ARA integral transform of order n of a continuous function well defined. First, TFIVP is reduced into a simpler problem by utilizing the ARA transform. Secondly, the truncated solution of the reduced problem is obtained through the iterative method. Finally, the application of inverse ARA transform allows us to construct …a truncated solution of TFIVP. The novelty of this study is that the first time the ARA transform is applied to obtain the solution of TFIVP in the Caputo-Fabrizio sense. Illustrative examples with the Fokker-Planck equation present that this method works better than other methods which is one of the strong points of this research. Show more
Keywords: Caputo-Fabrizio derivative, ARA transform, iterative method, time fractional initial value problem, Fokker-Planck equation
DOI: 10.3233/JIFS-223237
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2693-2701, 2023
Authors: Dai, Qinglong | Qin, Guangjun | Li, Jianwu | Zhao, Jun | Cai, Jifan
Article Type: Research Article
Abstract: Flink is regarded as a promising distributed data processing engine for unifying bounded data and unbounded data. Unbalanced workloads upon multiple workers/task managers/servers in the Flink bring congestion, which will lead to the quality of service (QoS) decreasing. The balanced load distribution could efficiently improve QoS. Besides, existing works are lagging behind the current Flink version. To distribute workloads upon workers evenly, a resource-oriented load balancing task scheduling (RoLBTS) mechanism for Flink is proposed. The capacities of CPU, memory, and bandwidth are taken into consideration. Based on the barrel principle, the memory, and the bandwidth are respectively selected to model …the resource occupancy ratio of the physical node and that of the physical link. On the based of modeled resource occupancy ratio, the data processing of load-balancing resource usage in Flink is formulated as a quadratic programming problem. Based on the self-recursive calling, a RoLBTS algorithm for scheduling task-needed resources is presented. Trough the numerical simulation, the superiority of our work is evaluated in terms of resource score, the number of possible scheduling solutions, and resource usage ratio. Show more
Keywords: Unbounded data, bounded data, integrated stream processing, Flink, load balancing
DOI: 10.3233/JIFS-222524
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2703-2713, 2023
Authors: Lina, Ma | Hao, Ma | Yang, Zhang | Iqbal, Najaf
Article Type: Research Article
Abstract: In the context of the strategic target of carbon emission peaking and carbon neutrality, industrial green technology innovation (GTI) has become the focus of discussion in academia these days. Based on the panel data of 30 provinces in China from 2011 to 2019, we construct Spatial Durbin Models to explore the spatial effects of capital enrichment (CE) on GTI by using the geographical distance matrix, the economic distance matrix and the adjacency matrix. The results reveal that: (1) The regional differences in the development of GTI are prominent, showing a higher level in the east and lower in the west. …(2) GTI exhibits the spatial characteristic of polarization. Its spatiotemporal evolutionary pattern reveals a phased feature of first strengthened and then weakened. (3) The CE has a significant inhibitory effect on GTI, which may be caused by the “rebound effect”, dominated by short-term economic interests and the ineffective capital allocation. This effect is more prominent in regions with unbalanced economies. (4) The spatial spillover effect of CE is significantly negative, indicating a “siphon effect”. Based on these findings, the suggestions for promoting GTI are put forward. Show more
Keywords: Capital enrichment, green technology innovation, spatio-temporal evolution, spatial spillover effect, low-carbon economy
DOI: 10.3233/JIFS-213565
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2715-2727, 2023
Authors: Qiu, Yutan | Zhou, Qing
Article Type: Research Article
Abstract: Role-oriented network embedding aims to preserve the structural similarity of nodes so that nodes with the same role stay close to each other in the embedding space. Role-oriented network embeddings have wide applications such as electronic business and scientific discovery. Anonymous walk (AW) has a powerful ability to capture structural information of nodes, but at present, there are few role-oriented network embedding methods based on AW. Our main contribution is the proposal of a new framework named REAW, which can generate the role-oriented embeddings of nodes based on anonymous walks. We first partition a number of anonymous walks starting from …a node into the representative set and the non-representative set. Then, we leverage contrastive learning techniques to learn AW embeddings. We integrate the learned AW embeddings with AW’s empirical distribution to obtain the structural feature of the node, and finally we generate the node’s embedding through message passing operations. Extensive experiments on real network datasets demonstrate the effectiveness of our framework in capturing the role of nodes. Show more
Keywords: Network embedding, network structure, role-oriented, anonymous walk
DOI: 10.3233/JIFS-222712
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2729-2739, 2023
Authors: Song, Xudong | Wan, Xiaohui | Yi, Weiguo | Cui, Yunxian | Li, Changxian
Article Type: Research Article
Abstract: In recent years, the lack of thermal images and the difficulty of thermal feature extraction have led to low accuracy and efficiency in the fault diagnosis of circuit boards using thermal images. To address the problem, this paper presents a simple and efficient intelligent fault diagnosis method combined with computer vision, namely the bag-of-SURF-features support vector machine (BOSF-SVM). Firstly, an improved BOF feature extraction based on SURF is proposed. The preliminary fault features of the abnormally hot components are extracted by the speeded-up robust features algorithm (SURF). In order to extract the ultimate fault features, the preliminary fault features are …clustered into K clusters by K-means and substituted into the bag-of-features model (BOF) to generate a bag-of-SURF-feature vector (BOSF) for each image. Then, all of the BOSF vectors are fed into SVM to train the fault classification model. Finally, extensive experiments are conducted on two homemade thermal image datasets of circuit board faults. Experimental results show that the proposed method is effective in extracting the thermal fault features of components and reducing misdiagnosis and underdiagnosis. Also, it is economical and fast, facilitating savings in labour costs and computing resources in industrial production. Show more
Keywords: Thermal images, circuit boards, fault diagnosis, bag-of-features, support vector machine
DOI: 10.3233/JIFS-223093
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2741-2752, 2023
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: Recently computer vision and NLP based techniques have been employed for document layout analysis where different types of elements in the document and their relative position are identified. This process is trickier as there are blocks which are structurally similar but semantically different such as title, text etc. This works attempts to use region-based CNN architecture (F-RCNN) for determining five different sections in the scientific articles. To improve the performance of detection algorithm, reading order is used as an additional feature and this model is known as MF-RCNN. First, an algorithm is formulated to find the reading order in documents …which adopts Manhattan-layout using a color-coding scheme. Secondly, this information is fused with the input image without changing its shape. Experimental results show that MF-RCNN which uses the reading order performs better when compared with F-RCNN when tested on Publaynet dataset. Show more
Keywords: FRCNN, reading order, XY tree, multiple channels, manhattan layout
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
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 Paper Deals With Image Retrieval Process Of Liver Computer Tomography (Ct) Scan Images Using Orthogonal Moment Features And Content Based Image Retrieval. Medical Images Are Useful Diagnostic Evidence As It Can Provide Vital Information In Anatomical Pathology. The Objective Is To Efficiently Retrieve Medical Images From The Database Using Orthogonal Moments And Content Based Image Retrieval Methods. The Orthogonal Moment Viz Discrete Racah Polynomial, Continuous Legendre Moments And Zernike Moments Are Computed For The Study. The Region Of Interest Based Segmentation And Watershed Segmentation Is Applied To The Preprocessed Input Images And Features Are Extracted Using Orthogonal Moments And …Shape And Texture Features Are Extracted Using Content Based Image Retrieval (Cbir). The Performances Of Each Moment In Terms Of Accuracy And Error Rate Are Compared With Cbir. Show more
Keywords: Orthogonal moment, Cbir, accuracy, Mse, Psnr
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: The study examines various studies on Named Entity Recognition (NER) and Part of Speech (POS) tagging for the Urdu language conducted by academics and researchers. POS and NER tagging for Urdu still faces obstacles in terms of increasing accuracy while lowering false-positive rates and labelling unknown terms, despite the efforts of numerous researchers. In addition, ambiguity exists when tagging terms with different contextual meanings within a sentence. Due to the fact that Urdu is an inflectional, derivational, morphologically rich, and context-sensitive language, the existing models, such as Linguistic rule application, N-gram Markov model, Tree Tagger, random forest (RF) tagger, etc., …were unable to produce accurate experimental results on Urdu language data. The significance of this study is that it fills a gap in the literature concerning the lack of POS and NER tagging for the Urdu language. For Urdu POS and NER tagging, we propose a deep learning model with a well-balanced set of language-independent features as well as a survey of important Urdu POS/NER techniques. In addition, this is the first study to use residual biDirectional residual Long short-term memory (residual biLSTM) architecture trained on the Urmono dataset in conjunction with the randomly initialised word2vec, fastText and mBERT embeddings are utilised to generate word or character vectors.For each experiment, the paper also employs the evaluation methods of Macro-F1, precision, precision, and recall. The proposed method with mbert embedding as word vectors provides best results of F1 score for POS and NER at 91.11% and 99.11% respectively. Also, the accuracy, precision and recall for POS are reported at 94.85%, 91.79% and 90.77%. Similarly, the accuracy, precision and recall for NER of the proposed model are reported at 99.77%, 98.78% and 99.45% respectively, which are higher than baseline models. Show more
Keywords: POS, NER, Urdu language, tagger, natural language, linguistic, deep learning, machine learning
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: The technique of integrating images from two or more sensors that were taken from the same place or the same object is known as image fusion. The goal is to get more spectral and spatial information from the combined image as a whole than from the individual images. It is required to fuse the images in order to improve the spatial and spectral quality of both panchromatic and multispectral images. This study introduces a novel method for fusing remote sensing images that combines L0 smoothing, NSCT (Non-subsampled Contourlet Transform), SR (Sparse Representation), and MAR (Max absolute rule). The multispectral and …panchromatic images are initially divided into lower and higher frequency components using the L0 smoothing filter as the method of fusion. The fusion process is then carried out, utilising a technique that combines NSCT and SR to fuse low-frequency components. Similar to this, the Max-absolute fusion rule is used to fuse high-frequency components. In conclusion, the disintegration of fused low-frequency and high-frequency data yields the final image. Our method yields an enhanced outcome in terms of the correlation coefficient, Entropy, spatial frequency, and fusion of mutual information for both the term of picture quality enhancement and visual evaluation. This suggested approach produces superior outcomes after execution. This study makes use of the Landsat-7ETM+, IKONOS, and Quick Bird datasets. Different satellites are used to take each image. There have been two examples of each image used. In comparison to previous Traditional Methods, the proposed image fusion techniques’ output has a quality that is more than 20% higher. Show more
Keywords: Remote sensing, multispectral image, pan chromatic image, L0 smoothening filter, NSCT, SR
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
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