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

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