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Document-Level Relation Extraction with Deep Gated Graph Reasoning Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2024-03-20 Zeyu Liang
Extracting the relations of two entities on the sentence-level has drawn increasing attention in recent years but remains facing great challenges on document-level, due to the inherent difficulty in recognizing the relations of two entities across multiple sentences. Previous works show that employing the graph convolutional neural network can help the model capture unstructured dependent information
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Bio-Inspired Algorithm Based Undersampling Approach and Ensemble Learning for Twitter Spam Detection Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2024-02-20 K. Kiruthika Devi, G. A. Sathish Kumar
Currently, social media networks such as Facebook and Twitter have evolved into valuable platforms for global communication. However, due to their extensive user bases, Twitter is often misused by illegitimate users engaging in illicit activities. While there are numerous research papers available that delve into combating illegitimate users on Twitter, a common shortcoming in most of these works is
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Deep Aspect-Sentinet: Aspect Based Emotional Sentiment Analysis Using Hybrid Attention Deep Learning Assisted BILSTM Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2024-02-20 S. J. R. K. Padminivalli V., M. V. P. Chandra Sekhara Rao
Data mining and natural language processing researchers have been working on sentiment analysis for the past decade. Using deep neural networks (DNNs) for sentiment analysis has recently shown promising results. A technique of studying people’s attitudes through emotional sentiment analysis of data generated from various sources such as Twitter, social media reviews, etc. and classifying emotions based
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Endogenous Long-Term Productivity Performance in Advanced Countries: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2024-02-20 Jorge Antunes, Goodness C. Aye, Rangan Gupta, Peter Wanke, Yong Tan
Better performance at a country level will provide benefits to the whole population. This issue has been studied from various perspectives using empirical methods. However, little effort has as yet been made to address the issue of endogeneity in the interrelationships between productive performance and its determinants. We address this issue by proposing a Two-Dimensional Fuzzy-Monte Carlo Analysis
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Arithmetic Operations on Generalized Trapezoidal Hesitant Fuzzy Numbers and Their Application to Solving Generalized Trapezoidal Hesitant Fully Fuzzy Equation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2024-02-20 F. Babakordi
Algebraic operations on generalized hesitant fuzzy numbers are key tools to address the problems with decision uncertainty. In this paper, by studying the arithmetic operations on generalized trapezoidal hesitant fuzzy numbers, modified arithmetic operations are introduced for this class of numbers so that, using these arithmetic operations, the multiplication and division of two generalized trapezoidal
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Constructing Uninorms on Bounded Lattices Through Closure and Interior Operators Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2024-02-20 Gül Deniz Çaylı
Uninorms combining t-conorms and t-norms on bounded lattices have lately drawn extensive interest. In this article, we propose two ways for constructing uninorms on a bounded lattice with an identity element. They benefit from the appearance of the t-norm (resp. t-conorm) and the closure operator (resp. interior operator) on a bounded lattice. Additionally, we include some illustrative examples to
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Least Absolute Deviation Estimation for Uncertain Vector Autoregressive Model with Imprecise Data Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-07-03 Guidong Zhang, Yuxin Shi, Yuhong Sheng
The uncertain vector autoregressive model is able to model the interrelationships between different variables, which is more advantageous compared to the traditional autoregressive model, when modeling real-life objects and where the observed values are imprecise. In this paper, the parameters of the uncertain vector autoregressive model are estimated by using least absolute deviation estimation (LAD)
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On Typical Hesitant Fuzzy Languages and Automata Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-07-03 Valdigleis S. Costa, Benjamín C. Bedregal, Regivan H. N. Santiago
The idea of nondeterministic typical hesitant fuzzy automata is a generalization of the fuzzy automata presented by Costa and Bedregal. This paper, presents the sufficient and necessary conditions for a typical hesitant fuzzy language to be computed by nondeterministic typical hesitant fuzzy automata. Besides, the paper introduces a new class of typical hesitant fuzzy automata with crisp transitions
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Deep Learning based Improved Generative Adversarial Network for Addressing Class Imbalance Classification Problem in Breast Cancer Dataset Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-07-03 S. Subasree, N. K. Sakthivel, M. Shobana, Amit Kumar Tyagi
The breast cancer diagnosis is one of the challenging tasks of medical field. Especially, the breast cancer diagnosis among younger women (under 40 years old) is more complicated, because their breast tissue is generally denser than the older women. The Breast Cancer Wisconsin image dataset contains two classes: (i) Benign (Minority class), (ii) Malignant (Majority class). The imbalanced class distribution
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Deep Fuzzy Clustering and Deep Residual Network for Prediction of Web Pages from Weblog Data with Fractional Order Based Ranking Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-07-03 Om Prakash, P. G., K. Suresh Kumar, Balajee Maram, C. Priya
Web page recommendation system has attracted more attention in recent decades. The web page recommendation has various characteristics than the classical recommenders. It is the process of predicting the request of the next web page that users are significantly interested while searching the web. It helps the users to find relevant pages in the field of web mining. In particular, web user may spend
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Classification of Imbalanced Data Using SMOTE and AutoEncoder Based Deep Convolutional Neural Network Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-07-03 Suja A. Alex, J. Jesu Vedha Nayahi
The imbalanced data classification is a challenging issue in many domains including medical intelligent diagnosis and fraudulent transaction analysis. The performance of the conventional classifier degrades due to the imbalanced class distribution of the training data set. Recently, machine learning and deep learning techniques are used for imbalanced data classification. Data preprocessing approaches
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Can a Single Neuron Learn Predictive Uncertainty? Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-07-03 Edgardo Solano-Carrillo
Uncertainty estimation methods using deep learning approaches strive against separating how uncertain the state of the world manifests to us via measurement (objective end) from the way this gets scrambled with the model specification and training procedure used to predict such state (subjective means) — e.g., number of neurons, depth, connections, priors (if the model is bayesian), weight initialization
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Chaotic Binary Pelican Optimization Algorithm for Feature Selection Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-07-03 Rama Krishna Eluri, Nagaraju Devarakonda
This research proposes a new wrapper model based on chaos theory and nature-inspired pelican optimization algorithm (POA) for feature selection. The base algorithm is converted into a binary one and a chaotic search to augment POA’s exploration and exploitation process, denoted as chaotic binary pelican optimization algorithm (CBPOA). The main focus of chaos theory is to resolve the slow convergence
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Automated Fish Detection and Tracking System Using Pre-Trained Mask R-CNN for Ecological Biodiversity Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 Suja Cherukullapurath Mana, T. Sasipraba
Introduce a new dynamic classifying algorithm in this paper to recognize and monitor fish activity to simultaneously better comprehend their synapomorphies. The pre-trained Mask Regional Convolutional Neural Network (Mask-R-CNN) is trained using a set of test models extracted from recorded video recording. The approach suggested subsequently yields well-enhanced feature vectors. The system’s automatic
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Novel Development of Fuzzy Controller Based Multi-Agent System for Efficient Navigation of Autonomous Robots Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 G. S. R. Emil Selvan, R. Jagadeesh Kannan, Sreenivasa Chakravarthi Sangapu, S. Shalini, Raji Pandurangan, E. Fantin Irudaya Raj
This study investigates a fuzzy controller technique for autonomous robot navigation in both the static and dynamic environmental conditions and an excessive number of pathways to the destination. The design and implementation of a novel obstacle avoidance technique for autonomous robots are developed using the fuzzy controller-based multi-agent system. This method allows the Robot to identify dynamic
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Content and Location Based Point-of-Interest Recommendation System Using HITS Algorithm Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 R. Vinodha, R. Parvathi
A study of geographic information has become a significant field of concentrate in software engineering because of the expansion of much information created by electronic gadgets fit together geographic data from people, like advanced mobile phones and GPS gadgets. Area facts allow a deeper understanding of users’ options and actions by bridging the gap between the physical and digital worlds. This
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CNN — Forest Based Person Identification and Head Pose Estimation for AI Based Applications Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 D. Anitta, A. Annis Fathima
Face recognition and head posture estimation have aroused a lot of academic interest recently since the inherent information improves the performance of face-related applications such as face alignment, augmented reality, healthcare applications, and emotion detection. The proposed work explores the challenges of identifying people and determining head posture. An analysis of the features produced
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Hybrid MRK-Means + + RBM Model: An Efficient Heart Disease Predicting System Using ModifiedRoughK-Means + + Algorithm and Restricted Boltzmann Machine Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 Kamepalli S. L. Prasanna, Nagendra Panini Challa
The clinical diagnosis of heart disease in most situations is based on a difficult amalgamation of pathological and clinical information. Because of this complication, there is a significant level of curiosity among many diagnostic healthcare professionals and researchers who are keenly interested in the efficient, accurate, and early-stage forecasting of heart disease. Deep Learning Algorithms aid
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An Approach for Automated Kannada Subtitle Generation from Kannada Video Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 Santosh, L. M. Jenila Livingston
This paper presents an automated Kannada subtitle generator from Kannada video which is implemented to assist people with auditory problems for watching videos. Henceforth the subtitle generation has become an important task for supporting such special people and it integrates an audio extraction and a speech recognition module. Three phases of the proposed technique were implemented, such as extracting
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Enhancement of Kernel Clustering Based on Pigeon Optimization Algorithm Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 Mathil K. Thamer, Zakariya Yahya Algamal, Raoudha Zine
Clustering is one of the essential branches of data mining, which has numerous practical uses in real-time applications.The Kernel K-means method (KK-means) is an extended operative clustering algorithm. However, this algorithm entirely dependent on the kernel function’s hyper-parameter. Techniques that adequately explore the search spaces are needed for real optimization problems and to get optimal
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A Novel Framework for Securing ECDH Encrypted DICOM Pixel Data Stored Over Cloud Using IPFS Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 K. C. Prabu Shankar, S. Prayla Shyry
The future holds the possibility of hospitals sharing medical images obtained through non-invasive systems to patients remotely. The advent of cloud and the storage and deployment of medical healthcare images in the cloud has resulted in the increased need for application of Cryptographic techniques to protect them from unauthorized access and malicious attacks. The Digital Imaging and Communication
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Predicting Level Measurements by Supervised Learning Based on Gabor and Smote Filters: An Industrial Non-Interacting Tanks Scenario Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-05-19 B. Kalaiselvi, B. Karthik, A. Kumaravel, T. Vijayan
Fluid - Level measurement is required in recognizing the state variable of a Level processing plant for monitoring the level deviations in most the industrial plants. This issue has mostly been addressed by conventional methods using level sensors and level transmitters. However, the cost associated with these mechanisms can be reduced. It has been identified the chances with the applications machine
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On Statistically Pre-Cauchy Sequences of Complex Uncertain Variables Defined by Orlicz Functions Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-04-12 Jagannath Nath, Birojit Das, Baby Bhattacharya, Binod Chandra Tripathy
In this treatise, we define statistically pre-Cauchy sequences of complex uncertain variable for five cases of uncertainty viz., in mean, in measure, in distribution, in almost surely and in uniformly almost surely and we confine our study to statistically pre-Cauchy sequence in mean, in measure and in distribution only. Furthermore, we establish the relationship between statistically pre-Cauchy and
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Families of Value-Dependent Preference Aggregation Operators with Their Structures and Properties Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-04-12 Lesheng Jin, Radko Mesiar, Ronald Yager
In evaluation and decision making frequently we need to consider and then well model the subjective preferences and behaviors of decision makers. This study firstly defines literally three types of conformities, namely, the Bounded Conformity, Extreme Conformity and Bounded Extreme Conformity. Then, we define two types of value-dependent preference aggregation operators, s-t-A extreme operators and
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The Modularity Condition for Uni-Nullnorms Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-04-12 Bo Wen Fang
In this paper, we are mainly to solve the functional equations given by the modularity condition. Modularity condition between disjunctive (resp. conjunctive) uni-nullnorms and some most studied classes of binary aggregation operators (i.e., t-norms, t-conorms, uninorms and semi-t-operators) are discussed. Both positive and negative results of modularity condition for uni-nullnorms are obtained. Since
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The Conditional Distributivity for Uni-Nullnorms Over Uninorms Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-04-12 Xin Zhang, Feng Qin, Han-Yu Bi
The conditional distributivity of aggregation operators has always been the focus of research because it is crucial for various areas including integration theory, utility theory and so on. Therefore, this article is mainly devoted to dealing with the conditional distributivity of a uni-nullnorm over a uninorm with the continuous underlying operators and also gives the full characterization of each
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Popularity Prediction Model With Context, Time and User Sentiment Information: An Optimization Assisted Deep Learning Technique Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-04-12 Kasiprasad Mannepalli, Suryabhan Pratap Singh, Chandra Sekhar Kolli, Sundeep Raj, Giridhar Reddy Bojja, B. R. Rajakumar, D. Binu
In social media, the data-sharing activities have turned out to be more pervasive; individuals and companies have comprehended the significance of promoting info by social media network. However, these individuals and companies face more challenges with the issue of “how to obtain the full benefit that the platforms provide”. Therefore, social media policies to improve the online promotion are turning
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A Genetic Algorithm for Solving Nonlinear Optimization Problem with Max-Archimedean Bipolar Fuzzy Relation Equations Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-04-12 Vijay Lakshmi Tiwari, Antika Thapar, Richa Bansal
This paper discusses a nonlinear optimization problem with the system of max-Archimedean bipolar fuzzy relation equations as constraints. Some results related to the structure of the solution set of max-Archimedean bipolar fuzzy relation equations are proved. Using these results, a genetic algorithm is proposed to solve the problem for obtaining optimal or converging solutions. The effectiveness of
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Fuzzy Fractional Order Proportional Integral Derivative Controller Design for Higherorder Time Delay Processes Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-04-12 R. Anuja, T. S. Sivarani, M. Germin Nisha
Process control is the interested domain of interest as the industrial high-order applications require an effective control mechanism with higher robustness. Since the conventional method of proportional integral derivative (PID) controller remains inadequate for the higher-order processes, this research concentrates on the fuzzy fractional-order controllers, that more and more attention nowadays in
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Sparse FCM-Based Map-Reduce Framework for Distributed Parallel Data Clustering in E-Khool Learning Platform Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-02-27 A. Suki Antely, P. Jegatheeswari, M. Bibin Prasad, V. Vinolin, S. Vinusha, B. R. Rajakumar, D. Binu
Parallel clustering serves as a platform for handling big data. The literature displays a number of clustering algorithms using a map-reduce framework, but they did not assure the effective clusters such that knowledge extraction becomes tough. With the aim to render a better and effective data clustering method to analyze the big data arriving from distributed systems, this paper uses a new clustering
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Abstracting Instance Information and Inter-Label Relations for Sparse Multi-Label Classification Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-02-27 Si-Ming Lian, Jian-Wei Liu
In this paper, for sparse multi-label data, based on inter-instance relations and inter-label correlation, a Sparse Multi-Label Kernel Gaussian Neural Network (SMLKGNN) framework is proposed. Double insurance for the sparse multi-label datasets is constructed with bidirectional relations such as inter-instance and inter-label. When instance features or label sets are too sparse to be extracted effectively
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On the Polytope of 3-Tolerant Fuzzy Measures Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-02-27 P. García-Segador, P. Miranda
In this paper we study some geometrical properties of the polytope of 3-tolerant fuzzy measures. To achieve this task, we profit that this polytope is an order polytope and hence many properties can be extracted from the subjacent poset. The main result in the paper is a straightforward procedure for obtaining a random 3-tolerant fuzzy measure. We also compute the volume and obtain some other properties
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Distributivity Conditions of Idempotent Uninorms and Two Special Kinds of Aggregation Functions Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-02-27 Ting-Hai Zhang, Feng Qin, Jie Wan, Wen-Huang Li
Recently, some authors studied the distributive equations for continuous t-norms and some families of usual classes of uninorms over overlap functions in Refs. 25 and 32, but lacked a complete characterization of the distributivity on idempotent uninorms and overlap or grouping functions widely used in image processing. As a supplement to the previous results, in this article we fully characterize
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Determination of the Smallest-Greatest Uni-Nullnorms and Null-Uninorms on an Arbitrary Bounded Lattice L Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-02-27 Mehmet Akif İnce, Funda Karaçal
In this paper, we study uni-nullnorms and null-uninorms on bounded lattices, which are generalizations of uninorms, nullnorms, t-norms and t-conorms. We construct two uni-nullnorms and two null-uninorms on a bounded lattice L. We determine the smallest-greatest uni-nullnorms with 2-neutral element {e,1}a and the smallest-greatest null-uninorms with 2-neutral element {0,e}a by using these construction
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Algebraic Structure Based Clustering Method from Granular Computing Prospective Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-02-27 Linshu Chen, Fuhui Shen, Yufei Tang, Xiaoliang Wang, Jiangyang Wang
Clustering, as one of the main tasks of machine learning, is also the core work of granular computing, namely granulation. Most of the recent granular computing based clustering algorithms only utilize the plain granule features without taking the granule structure into account, especially in information area with widespread application of algebraic structure. This paper aims at proposing an algebraic
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Fuzzy Neighbors and Deep Learning-Assisted Spark Model for Imbalanced Classification of Big Data Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-02-27 G. Nalinipriya, M. Geetha, D. Sudha, T. Daniya
Big data is important in knowledge manipulation, assessment, and prediction. However, extracting and analyzing knowledge through big database are complex because of imbalance data distribution that leads to wrong decisions and biased classification outputs. Hence, an effective and optimal big data classification approach is designed using the proposed Bird Swarm Deer Hunting Optimization-Deep Belief
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An Integrated Framework for COVID-19 Classification Based on Ensembles of Deep Features and Entropy Coded GLEO Feature Selection Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-02-27 Abdul Muiz Fayyaz, Mudassar Raza, Muhammad Sharif, Jamal Hussain Shah, Seifedine Kadry, Oscar Sanjuán Martínez
COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The
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Generating Deep Learning Model-Specific Explanations at the End User’s Side Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-01-27 R. Haffar, N. Jebreel, D. Sánchez, J. Domingo-Ferrer
End users who cannot afford to collect and label big data to train accurate deep learning (DL) models resort to Machine Learning as a Service (MLaaS) providers, who provide paid access to accurate DL models. However, the lack of transparency in how the providers’ models make predictions causes a problem of trust. A way to increase trust (and also to align with ethical regulations) is for predictions
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A New Fuzzy Propagation Model for Influence Maximization in Social Networks Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-01-27 Laya Aliahmadipour, Ezat Valipour
In this paper we introduce a fuzzy propagation model to deal with the influence maximization (IM) problem. The IM problem for the most of existing propagation model is NP-hard. Here, we model social networks as fuzzy directed graphs to propose an application-oriented propagation process. To this aim, we investigate an interesting relationship between zero forcing set concept in graphs and IM problem
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A Study on How Food Colour May Determine the Categorization of a Dish: Predicting Meal Appeal from Colour Combinations Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2023-01-27 V. Casales-Garcia, Z. Falomir, Ll. Museros, I. Sanz, L. Gonzalez-Abril
A person’s preference to select or reject certain meals is influenced by several aspects, including colour. In this paper, we study the relevance of food colour for such preferences. To this end, a set of images of meals is processed by an automatic method that associates mood adjectives that capture such meal preferences. These adjectives are obtained by analyzing the colour palettes in the image
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ℐ𝒢,𝒩-Implications Induced from Quasi-Grouping Functions and Negations on Bounded Lattices Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-12-28 Junsheng Qiao, Bin Zhao
Grouping functions, as one new case of not necessarily associative particular binary aggregation functions, have been proposed in the literature for their vast applications in fuzzy community detection problems, image processing and decision making. On the other hand, due to the wide applications in fuzzy reasoning, fuzzy control and approximate reasoning, the investigations of fuzzy implications derived
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Construction of Uninorms on Bounded Lattices with Incomparable Elements that are Neither Conjunctive Nor Disjunctive Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-12-28 Gül Deniz Çaylı
This paper investigates uninorms that are neither conjunctive nor disjunctive on bounded lattices. New methods are introduced for construction of such uninorms, where some restrictions on the identity and the annihilator are considered. In particular, new types of idempotent uninorms on bounded lattices are obtained. Furthermore, some specific examples are provided to illustrate that these constructions
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On Statistical Convergence of Uncertain Sequence of Fuzzy Numbers Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-12-28 P. Baliarsingh, S. Nanda, L. Nayak
In this paper, we extend the various ideas of convergence such as convergence in distribution, in mean, and in measure along with the convergence uniformly almost surely of the uncertain fuzzy sequence of real numbers in statistical context. We also establish some interesting results among these convergence.
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An Adaptive Self-Reduction Type-2 Fuzzy Clustering Algorithm for Pattern Recognition Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-12-28 Mukerrem Bahar Baskir
Decisions in real-life can be adversely affected by various uncertainty-sources such as perception-diversity, data-structure and analytical tools. Fuzzy clustering can successfully handle the uncertainties while recognizing patterns in any given data. Nevertheless, type-1 fuzzy clustering techniques has uncertainties on account of precise-nature of primary memberships. Type-2 fuzzy clustering are preferred
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Feature Selection Using Games with Imperfect Information (FSGIN) Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-12-28 Nasrin Banu Nazar, Radha Senthilkumar
Game Theory (GT) is the study of strategic decision making. By virtue of its importance, several GT based methodologies for Feature Selection (FS) are proposed in recent times. FS problem can be abstracted as a game by considering each feature as a player and their values as their strategies. Additionally, overall goal of the game is set to classify a data instance appropriately. Most of the existing
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Time Series Forecasting Using Range Regression Automata Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-12-28 Sagarkumar S. Badhiye, Prashant N. Chatur, Mukesh M. Raghuwanshi
Time Series (TS) models are well-known techniques that help to predict the weather in a certain time period. The traditional TS prediction models take more prediction time, overfitting and under-fitting of training data. In addition, state-of-art method like regression automata technique’s computational complexity is high due to the learning process based on heuristic method. In this study, learning
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Ant Lion Optimized Lexicographic Model for Shortest Path Identification Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-12-28 Sunita Kumawat, Chanchal Dudeja, Pawan Kumar
Associated path detection is considered as the major concern of the traditional shortest path issue. The associated path is generally represented by the shortest distance among the source and destination. In the transportation network, distance or cost detection may identify this associated path. Specifically, it is very important to discover the shortest distance that has a minimum number of nodes
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Interval Methods in Knowledge Representation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-12-28 Vladik Kreinovich
Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA…
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Multiple Criteria Decision Analysis Based on Ill-Known Pairwise Comparison Data Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-24 Masahiro Inuiguchi, Shigeaki Innan
The analytic hierarchy process (AHP) provides a systematic approach to the evaluation of alternatives based on pairwise comparison matrices (PCMs) under multiple criteria. As human evaluation is not always accurate and precise, each component of a PCM showing relative importance has been expressed by an interval or a fuzzy number. In this paper, we treat a PCM whose components are represented by twofold
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Multiple Criteria Decision Analysis Based on Ill-Known Pairwise Comparison Data Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-24 Masahiro Inuiguchi, Shigeaki Innan
The analytic hierarchy process (AHP) provides a systematic approach to the evaluation of alternatives based on pairwise comparison matrices (PCMs) under multiple criteria. As human evaluation is not always accurate and precise, each component of a PCM showing relative importance has been expressed by an interval or a fuzzy number. In this paper, we treat a PCM whose components are represented by twofold
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Multi-Objective Particle Swarm Optimization Based Preprocessing of Multi-Class Extremely Imbalanced Datasets Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18 R. Devi Priya, R. Sivaraj, Ajith Abraham, T. Pravin, P. Sivasankar, N. Anitha
Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more difficult when there are many class labels for the target class attribute and hence many researchers have introduced methods to select features for
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MIGR: A Categorical Data Clustering Algorithm Based on Information Gain in Rough Set Theory Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18 Saddam Raheem, Shadi Al Shehabi, Amaal Mohi Nassief
Clustering techniques are used to split data into clusters where each cluster contains elements that look more similar to elements in the same cluster than elements in other clusters. Some of these techniques are capable of handling clustering process uncertainty, while other techniques may have stability issues. In this paper, a novel method, called Minimum Information Gain Roughness (MIGR), is proposed
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Detection of COVID-19 Cases from Chest X-Rays using Deep Learning Feature Extractor and Multilevel Voting Classifier Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18 G. Suganya, M. Premalatha, S. Geetha, G. Jignesh Chowdary, Seifedine Kadry
Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19. Methods: In this paper, an ensemble-based
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Almost λ-Statistical Convergence of Complex Uncertain Sequences Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18 Kuldip Raj, Sonali Sharma, Mohammad Mursaleen
In the present article, we introduced almost λ-statistical convergence of complex uncertain sequences in all five aspects of uncertainty viz., almost surely, mean, measure, distribution and uniformly almost surely. Further, with the aid of interesting examples and diagram we investigated some interrelationships among these uncertain complex sequences.
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Enhanced CRNN-Based Optimal Web Page Classification and Improved Tunicate Swarm Algorithm-Based Re-Ranking Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18 Syed Ahmed Yasin, P. V. R. D. Prasada Rao
The main intention of this paper is to develop a new intelligent framework for web page classification and re-ranking. The two main phases of the proposed model are (a) classification, and (b) re-ranking-based retrieval. In the classification phase, pre-processing is initially performed, which follows the steps like HTML (Hyper Text Markup Language) tag removal, punctuation marks removal, stop words
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A Novel Defuzzification Approach of Ranking Parametric Fuzzy Numbers Based on the Value and Ambiguity Calculated at Decision Levels Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18 Rituparna Chutia
Generally, in every decision-making process under the fuzzy domain, ranking of fuzzy numbers is indispensable. Although such approaches are abundant, yet a universally accepted approach is not apparent. Hence, newer methodologies have been developed since its inception. In many instances, defuzzification techniques are being criticized as these methodologies are based on intuition and the geometry
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A Fractional Programming Model for Improving Multiplicative Consistency of Intuitionistic Fuzzy Preference Relations Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18 Hyonil Oh, Jungchol Cho
In this paper, we propose a method that improves multiplicative consistency based on a fractional programming model to derive the normalized intuitionistic fuzzy priority weight vector from an intuitionistic fuzzy preference relation. To do so, a new definition is formulated that captures previous definitions for multiplicative consistency of intuitionistic fuzzy preference relations. A transformation
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Solving a Mathematical Model for Small Vegetable Sellers in India by a Stochastic Knapsack Problem: An Advanced Genetic Algorithm Based Approach Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18 Chiranjit Changdar, Pravash Kumar Giri, Rajat Kumar Pal, Alok Haldar, Samiran Acharyya, Debasis Dhal, Moumita Khowas, Sudip Kumar Sahana
In this paper, we have proposed a stochastic Knapsack Problem (KP) based mathematical model for small-scale vegetable sellers in India and solved it by an advanced Genetic Algorithm. The knapsack problem considered here is a bounded one, where vegetables are the objects. In this model, we have assumed that different available vegetables (objects) have different weights (that are available), purchase
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Interval Methods in Knowledge Representation Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-18
Please send your abstracts (or copies of papers that you want to see reviewed here) to [email protected], or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA…
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An Extended Necessity Measure Maximisation Incorporating the Trade-Off between Robustness and Satisfaction in Fuzzy LP Problems Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.5) Pub Date : 2022-11-21 Zhenzhong Gao, Masahiro Inuiguchi
When some coefficients of the constraints are uncertain with only their possible ranges being given, a conventional linear programming (LP) problem can be generalised to the one with set-inclusive constraints. We consider the case where the possible ranges are given by fuzzy sets in this paper. The set-inclusive constraints with fuzzy coefficients have been treated by a necessity measure. However,