International Journal of Computational Intelligence Systems

In this paper, cellular neural networks (CNNs) with neutral type delays and time-varying leakage delays are investigated. By applying the existence of the exponential dichotomy of linear dynamic equations on time scales, a fixed point theorem and the theory of calculus on time scales, a set of sufficient...

Apple leaf disease is the foremost factor that restricts apple yield and quality. Usually, much time is taken for disease detection with the existing diagnostic techniques; therefore, farmers frequently miss the best time for preventing and treating diseases. The detection of apple leaf diseases is a...

Automatic image semantic annotation is of great importance for image retrieval, therefore, this paper aims to recommend tags for social images according to user preferences. With the rapid development of the image-sharing community, such as Flickr, the image resources of the social network with rich...

The organization maintains various information in cloud which is a loosely coupled environment. However, the nature of cloud encourages the threats in different level. Among them the data security has been a keen issue being identified and challenges the service provider. To improve the data security...

As one suitable optimization method implementing computational intelligence, ant colony optimization (ACO) can be used to solve the traveling salesman problem (TSP). However, traditional ACO has many shortcomings, including slow convergence and low efficiency. By enlarging the ants' search space...

Coreference resolution has been an active field of research in the past several decades and plays a vital role in many areas such as information extraction, document summarization, machine translation, and question answering systems. This paper presents a new coreference resolution approach by incorporating...

Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image...

Background: Melanoma is a type of threatening pigmented skin lesion, and as of now is among the most hazardous existing diseases. Suitable automated diagnosis of skin lesions and Melanoma classification can extraordinarily enhance early identification of melanomas. Methods: However, classification...

Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which can jointly and adaptively learn the spatial–temporal correlation features and long temporal interdependence...

While particle swarm optimization (PSO) shows good performance for many optimization problems, the weakness in premature convergence and easy trapping into local optimum, due to the ignorance of the diversity information, has been gradually recognized. To improve the optimization performance of PSO,...

An incomplete uncertain preference relation (UPR) is typical in group decision making (GDM) for decision makers (DMs) to express preference over alternatives because of the information interaction barrier between people and decision making environment. Completing missing values can guarantee individual...

Purpose: To examine the impact of artificial intelligence-assisted social media marketing (AISMM)on the performance of start-up businesses of small and medium enterprises (SMEs) in Saudi Arabia. Design/methodology/approach: A survey technique was employed whereby primary and secondary data was collected,...

Based on a multi-class support vector machine, an urban real estate early warning model is constructed for the Beijing real estate market. The initial indicator system is established based on the historical development of Beijing's real estate market and the selection of real estate early warning...

In this paper, the problem of measuring the degree of subsethood in the interval-valued fuzzy setting is addressed. Taking into account the widths of the intervals, two types of interval subsethood measures are proposed. Additionally, their relation and main properties are studied. These developments...

Ambiguities represent uncertainty but also a fundamental item of discussion for who is interested in the interpretation of languages and it is actually functional for communicative purposes both in human–human communication and in human–machine interaction. This paper faces the need to address ambiguity...

A single machine scheduling problem with periodic maintenance is studied in this paper. Due to many uncertainties in reality, the processing time is recognized as an uncertain variable. The aim is to minimize the makespan at a confidence level. An uncertain chance-constrained programming model is developed...

The inference rule of Modus Ponens has been extensively investigated in the framework of approximate reasoning, especially for the case of t-norms. Recently, more general kinds of conjunctors have also been considered, like semi-copulas, copulas, and conjunctive uninorms. A common feature of all these...

In this paper, we propose an efficient Least Squares Support Vector Machine (LS-SVM) training algorithm, which incorporates sparse representation and dictionary learning. First, we formalize the LS-SVM training as a sparse representation process. Second, kernel parameters are adjusted by optimizing their...

The digital transformation revolution has been crawling toward almost all aspects of our lives. One form of the digital transformation revolution appears in the transformation of our routine everyday tasks into computer executable programs in the form of web, desktop and mobile applications. The vast...

Gold price prediction has long been a crucial and challenging research topic for gold investors. In conventional models, most scholars have used the historical gold price or economic indicators to forecast gold prices. The gold prices depend mainly on confidence in the current market. To reduce the time...

Feature selection plays a significant role in the field of data mining and machine learning to reduce the data dimension, speed up the model building process and improve algorithm performance. Tree growth algorithm (TGA) is a recent proposed population-based metaheuristic, which shows great power of...

Supply chain finance (SCF), which has the key concept of the delivery of credit, is a new type of financial service that can enhance the financial efficiency of a supply chain. Using the transaction records from the core operations (CO) of the members, financers can provide a higher level of cash flow...

The aim of this paper is to provide a systematic route of information retrieval from a knowledge-based database (or domain knowledge) through a dialog system of natural language interaction. The application is about a comprehensive building at a university, with classrooms, laboratory rooms, meeting...

This paper proposes a multi-objective approach for the minimum constraint removal (MCR) A problem. First, a multi-objective model for MCR path planning is constructed. This model takes into account factors such as the minimum constraint set, the route length, and the cost. A multi-objective particle...

Considering the low degree of abstraction of traditional shape grammar in national pattern reuse design, this paper proposes a method based on the combination of improved shape grammar and an optimization algorithm to reuse national patterns design. As an application example, we carried out research...

An autoregressive moving average (ARMA) is a time series model that is applied in everyday life for pattern recognition and forecasting. The ARMA model contains a noise which is assumed to have a specific distribution. The noise is often considered to have a Gaussian distribution. However in applications,...

To address the inter-hospital hierarchical allocation and scheduling problems, this research used the pooling resource concept to allocate medical staff among hospital branches as well as determine their monthly schedules. This study proposed a two-stage strategy. The first stage proposed three heuristic...

With the development of social networking big data, social network group decision-making (SN-GDM) has been widely applied in many fields. This paper focuses on three main components: (1) the determination of the decision makers' (DMs) weights based on different social influence; (2) the anti-deception...

Sequence labeling is a common machine-learning task which not only needs the most likely prediction of label for a local input but also seeks the most suitable annotation for the whole input sequence. So it requires the model that is able to handle both the local spatial features and temporal-dependence...

In this paper, we will present a N-Non-Intersecting-Routing (NNIR) algorithm which is used to reduce the cost of resilient routing in telecommunications problems. Resilient Routing is the connections between two locations in a graph through the use of N completely independent routes. Resilient Routing...

Multi-criteria group decision-making (MCGDM) problems are widespread in real life. However, most existing methods, such as hesitant fuzzy set (HFS), hesitant fuzzy linguistic term set (HFLTS) and inter-valued hesitant fuzzy set (IVHFS) only consider the original evaluation data provided by experts but...

This paper treats the optimization of the biped walking trajectory that can be used as a reference trajectory for control. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is derived for the specified positions of the hips and feet....

Multiple Input Multiple Output (MIMO) is a technology used to improve the channel capacity of the wireless communication systems. Rapid increase in the number of users has led to data rate demand increased in growing modern wireless communication systems. To overcome this issue, MIMO is being used with...

In this paper, the notions of m-polar picture fuzzy subalgebra (PFSA), m-polar picture fuzzy ideal (PFI) and m-polar picture fuzzy implicative ideal (PFII) of BCK algebra are introduced and some related basic results are presented. A relation between m-polar PFI and m-polar PFII is established. It is...

In this paper, multi-sine cosine algorithm (MSCA) is presented to solve nonlinear bilevel programming problems (NBLPPs); where three different populations (completely separate from one another) of sine cosine algorithm (SCA) are used. The first population is used to solve the upper level problem, while...

Randomness is a common uncertainty encountered in practical multi-objectives decision-making. But it is always a challenge for decision-makers to process randomness in multi-objective programming problems. This paper takes the decision-making objectives as fuzzy events and aims to solve numerical multi-objective...

This paper presents a new compromising approach to multiple criteria group decision-making (MCGDM) for the treatment of uncertainty which is based on Pythagorean fuzzy (PF) sets. The present work intends to propose a novel linear programming technique for multidimensional analysis of preference (LINMAP)...

The notion of inclusion is a cornerstone in set theory and therefore, its generalization in fuzzy set theory is of great interest. The degree of f-inclusion is one generalization of such a notion that differs from others existing in the literature because the degree of inclusion is considered as a mapping...

This paper investigates the issue on adaptive synchronization of delayed inertial Cohen–Grossberg neural networks (ICGNNs). By adopting the method of variable transformation, the addressed model, which includes the so-called inertial term, is transformed into first-order differential equations. On the...

The goals of feature selection are to remove redundant and irrelevant features from high-dimensional data, extract the “optimal feature subset” of the original feature space to improve the classification accuracy, and reduce the time complexity. Traditional feature selection algorithms are based on static...

In medical imaging, vesselness diffusion is usually performed to enhance the vessel structures of interest and reduce background noises, before vessel segmentation and analysis. Numerous learning-based techniques have recently become very popular for coronary artery filtering due to their impressive...

This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous...

In this paper, we present a new integration approach for managing Information Technology variables within enterprise architecture in an integrated way. Additionially, a novel method based on fuzzy logic for cause-effect variable analysis is proposed as a useful support decision-making tool for companies...

Community detection structure is very important for understanding the organization of the complex networks. This problem is NP-hard, which is modeled as a seriously nonlinear optimization problem. Recently, different intelligence algorithm has shown promising results for this problem. The chemical reaction...

Sentiment classification is a fundamental task in NLP, and its aim to predict the sentiment polarities of the given texts. Recent researches show great interest in modeling Chinese sentiment classification. However, the complexity of Chinese grammar makes the performance of the existing Chinese sentiment...

The evaluation of the players' performance in sports teams is commonly based on the opinion of experts who do not always agree on the importance of the chosen indicators. This paper presents a novel approach based on fuzzy multi-criteria group decision-making tools for selecting those criteria that...

To improve the accuracy of automatic recognition and classification of vegetables, this paper presents a method of recognition and classification of vegetable image based on deep learning, using the open source deep learning framework of Caffe, the improved VGG network model was used to train the vegetable...

Interactive genetic algorithms have been used in a wide variety of applications and extensively developed to facilitate the personalization and customization of products for users. However, the ambiguity effect or cognitive ambiguity of users during the product customization process will affect the effects...

Multi-view learning analyzes the information from several perspectives and has largely been applied on semi-supervised contexts. It has not been extensively analyzed for inducing interpretable rule-based classifiers. We present a multi-view and grammar-based genetic programming model for inducing rules...

Nowadays, children have access to Internet on a regular basis. Just like the real world, the Internet has many unsafe locations where kids may be exposed to inappropriate content in the form of obscene, aggressive, erotic or rude comments. In this work, we address the problem of detecting erotic/sexual...

Maritime transport is one of the oldest methods of moving various types of goods, and it continues to have an important role in our modern society. More than 20 million containers are transported across the oceans daily. However, this form of transportation is constantly threatened by illegal operations,...

As an attractive generalization to hesitant fuzzy linguistic term set, double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is used to represent complex linguistic expressions by providing rich and flexible context. Previous studies on DHHFLTS show that aggregation of preference information...

The technology state is determined not by a single data, but by multiple data sources that has a certain degree of uncertainty and ambiguity, and even be contradictory which is difficult to support decision-making effectively. In this paper, an efficient intelligent decision-making method based on multi-source...

In this paper, we developed an automatic music generator with midi as the input file. This study uses long short-term memory (LSTM) and gated recurrent units (GRUs) network to build the generator and evaluator model. First, a midi file is converted into a midi matrix in midi encoding process. Then, each...

The sensory evaluation of food quality using a machine learning approach provides a means of measuring the quality of food products. Thus, this type of evaluation may assist in improving the composition of foods and encouraging the development of new food products. However, human intervention has been...

The Moore–Penrose inverse of a matrix plays a very important role in practical applications. In general, it is not easy to immediately solve the Moore–Penrose inverse of a matrix, especially for solving the Moore–Penrose inverse of a complex-valued matrix in time-varying situations. To solve this problem...

At present, most of the first-order logic theorem provers use a binary-resolution method, which can effectively solve the general first-order logic problems to a certain extent. However, the cooperative processing ability of this method for multiple clauses is insufficient, and it is easy to cause rapid...

In this paper, we study relations between fuzzy implicators and some kinds of fuzzy conjunctors, in particular, quasi-copulas and copulas. We show that there is a one-to-one correspondence between the classes of all quasi-copulas and 1-Lipschitz fuzzy implicators. A similar relation holds for copulas...

A novel clustering algorithm named local reachability density peaks clustering (LRDPC) which uses local reachability density to improve the performance of the density peaks clustering algorithm (DPC) is proposed in this paper. This algorithm enhances robustness by removing the cutoff distance dc which...

The influence of individual heterogeneity on the evolutionary game has been studied extensively in recent years. Whereas many theoretical studies have found that the heterogeneous learning ability effects cooperation rate, the individual learning ability in networks is still not well understood. It is...

In natural language processing the use of deep learning techniques is very common. In this paper, a technique to identify the subject and predicate in a sentence is introduced. To achieve this, the proposed technique completes POS tagging identifying in a later stage the subject and the predicate in...

Sepsis is a life-threatening condition that arises when the body's response to infection causes injury to its own tissues and organs. Despite the advancement of medical diagnosis and treatment technologies, the morbidity and mortality of sepsis are still relatively high. In this paper, a two-layer...

In this paper, Fuzzy Linear Programming (FLP) was used to model the production processes at a university-based bakery for optimal decisions in the daily productions of the bakery. Using the production data of five products from the bakery, a fuzzy linear programme was developed to help make decisions...

Conventional nonnegative matrix factorization and its variants cannot separate the noise data space into a clean space and learn an effective low-dimensional subspace from Salt and Pepper noise or Contiguous Occlusion. This paper proposes a weighted nonnegative matrix factorization (WNMF) to improve...

In recent years, the boom in social media sites such as Facebook and Twitter has brought people together for the sharing of opinions, sentiments, emotions, and experiences about products, events, politics, and other topics. In particular, sentiment-based applications are growing in popularity among individuals...

In data mining, data clustering is a prevalent data analysis methodology that organizes unlabeled data points into distinct clusters based on a similarity measure. In recent years, several clustering algorithms found, dependent on a predefined number of clusters and centered around the dataset with either...

Lung cancer is known as the highest mortality rate cancer, which needs biopsy to determine its subtype for further treatment. Recently, deep learning has provided powerful tools in lung cancer diagnose and therapeutic regimen making. However, it is still a challenge to identify the pathological type...

In this paper we revisit the memetic evolutionary family of metaheuristics, called Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), whose members combine Furuhashi's Bacterial Evolutionary Algorithm and various discrete local search techniques. These algorithms have proven to be efficient...

In this paper, by considering the notion of MV-modules, which is the structure that naturally correspond to lu-modules over lu-rings, we investigate some properties of a new kind of MV-modules, that we introduced in Borzooei and Saidi Goraghani, Free MV-modules, J. Intell. Fuzzy Syst. 31 (2016), 151–161...

High traffic flow is a typical characteristic of a mobilized city with a high population. Efficient traffic management is a proper solution to reduce the stress and anxiety associated with driving or traveling. The road users can have better timing for traveling as they will not experience journey delays...

Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed...

Complex q-rung orthopair fuzzy set (CQROFS), as a modified notion of complex fuzzy set (CFS), is an important tool to cope with awkward and complicated information. CQROFS contains two functions which are called truth grade and falsity grade by the form of complex numbers belonging to unit disc in a...

This paper presents the design and implementation of a smart and safe minimarket prototype for deployment in busy smart cities to mitigate the overhead of shopping experience. The prototype allows customers to remotely access and browse the available products at the minimarket using a special smart-phone...

Modern multicore architectures have an ability to allocate optimum system resources for a specific application to have improved energy and throughput balance. The system resources can be optimized automatically by using optimization algorithms. State-of-the-art using optimization algorithm in the field...

This paper proposes an advanced fuzzy load forecast method optimized by modified Jaya optimization (MJO) algorithm. MATLAB® platform is used to implement the proposed hybrid Fuzzy-MJO load forecasting algorithm and to verify the outperforming features of a Jaya technique over a fuzzy load forecast model....

Recently, a kind of heuristic optimization algorithm named gravitational search algorithm (GSA) has been rapidly developed. In GSA, there are two main parameters that control the search process, namely, the number of applied agents (Kbest) and the gravity constant (G). To balance exploration and exploitation,...

In this paper, we propose a framework for building a human-machine language dictionary. Given a concept/word, an application can extract the definition of the concept from the dictionary, and consequently “understand” its meaning. In the dictionary, a concept is defined through its relations with other...

Nowadays, drones are expected to be used in several engineering and safety applications both indoors and outdoors, e.g., exploration, rescue, sport, entertainment, and convenience. Among those applications, it is important to make a drone capable of flying autonomously to carry out an inspection patrol....

The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cosine algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm...

Image segmentation plays crucial role in medical image analysis and forms the basis for clinical diagnosis and patient's treatment planning. But the large variation in organ shapes, inhomogeneous intensities, poor contrast, organic nature of textures and complex boundaries in medical images makes...

Uninorms play a prominent role both in the theory and the applications of aggregations and fuzzy logic. In this paper a class of uninorms, called group-like uninorms will be introduced and a complete structural description will be given for a large subclass of them. First, the four versions of a general...

In a context of lattice-valued functions (also called lattice-valued fuzzy sets), where the codomain is a complete lattice L, an equivalence relation defined on L by the equality of related cuts is investigated. It is known that this relation is a complete congruence on the join-semilattice reduct of...

Artificial Intelligence (AI) is part of our everyday life and has become one of the most outstanding and strategic technologies. Explainable AI (XAI) is expected to endow intelligent systems with fairness, accountability, transparency and explanation ability when interacting with humans. This paper describes...

This paper is a contribution to the study of two distinct kinds of logics for modelling uncertainty. Both approaches use logics with a two-layered modal syntax, but while one employs classical logic on both levels and infinitely-many multimodal operators, the other involves a suitable system of fuzzy...

Coreference resolution is one of the most critical issues in various applications of natural language processing, such as machine translation, sentiment analysis, summarization, etc. In the process of coreference resolution, in this paper, a fully connected neural network approach has been adopted to...

The aim of this paper is to provide an algorithm for the computation of support and confidence of the association rules on interval-valued fuzzy sets. Each element of the interval-valued fuzzy set has a membership degree defined as an interval. In other words, the membership intervals may be interpreted...

This paper presents a novel framework that automatically classifies hand grasps using Electromyogram (EMG) signals based on advanced Wavelet Transform (WT). This method is motivated by the observation that there lies a unique correlation between different samples of the signal at various frequency levels...

Fuzzy tensors encode to what extent n-ary predicates are satisfied. The disjunctive box cluster model is a regression model where sub-tensors are explanatory variables for the values in the fuzzy tensor. In this article, locally optimal patterns for that model, with high areas times squared densities,...

The concern over security in all fields has intensified over the years. The prefatory phase of providing security begins with authentication to provide access. In many scenarios, this authentication is provided by biometric systems. Moreover, the threat of pandemic has made the people to think of hygienic...

Shape recognition in images represents one of the complex and hard-solving problems in computer vision due to its nonlinear, stochastic and incomplete nature. Classical image processing techniques have been normally used to solve this problem. Alternatively, shape recognition has also been conducted...

q-rung picture fuzzy sets can handle complex fuzzy and impression information by changing a parameter q based on the different hesitation degree, and Yager operator is a useful aggregation technology that can control the uncertainty of valuating data from some experts and thus get intensive information...

Cervical cancer is one of the most leading causes of mortality among women worldwide. This deadly disease could be prevented by vaccines and easily cured if detected at an early stage. Various researchers focus on providing methods for unambiguous results of screening tests for early diagnosis of cervical...

Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, and ease of implementation. Its many applications, such as regression, binary and multiclass classification, acquired better results. However, when some attributes of the dataset...

Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques...

Sustainable supplier selection is the essential core of sustainable supply chain management, which can directly influence the manufacturer's performance and can enormously enhance the manufacturer's competitiveness in the international market. However, most of the previous studies concerning...

One of the paramount mathematical methods to compute the general performance of organizations is data envelopment analysis (DEA). Nevertheless, in some cases, the decision-making units (DMUs) have middle values. Furthermore, the conventional DEA models have been originally formulated solely for crisp...

Face detection has been well studied for many years. However, the problem of face detection in complex environments is still being studied. In complex environments, faces is often blocked and blurred. This article proposes applying YOLOv3 to face detection problems in complex environments. First, we...

Concurrent programs are being widely adopted in development of multi-core and many-core processors. However, these types of programs present some features such as concurrency, communication and synchronization which make their testing more challenging than sequential programs. Search-based techniques,...

With the intensification of the aging, the health issue of the elderly is arousing public concern increasingly. Various healthcare products for the elderly are emerging from the market, thus how to select suitable aged healthcare product is critical to the well-being of the elderly. In the literature,...

Time-difference method is a vitally significant algorithm for measuring natural gas flow with ultrasonic gas flowmeter. The key of this algorithm is to accurately measure the arrival time of ultrasonic signal. However, it is difficult to determine the feature points corresponding to the arrival time...

Search-based agents have shown to perform well in many game-based applications. In the context of partially-observable scenarios agent's require the state to be fully determinized. Especially in case of collectible cards games, the sheer number of decks constructed by players hinder an agent to...

Document representation is one of the foundations of natural language processing. The bag-of-words (BoW) model, as the representative of document representation models, is a method with the properties of simplicity and validity. However, the traditional BoW model has the drawbacks of sparsity and lacking...

Rapid growth and development of civil engineering in recent years inspire building enterprises to concentrate on construction contractor selection for achieving more construction quality and lower construction cost. The existing studies generally regard the process of selecting the best contractor as...

We focus on the development of a method to guide the choice of a set of users in an environment where the number of features describing the items is high and user interaction becomes laborious. Using the framework of formal concept analysis, particularly the notion of implication between attributes,...

As reserving a certain number of potential partners plays a significant role in alleviating existing partners' collaborative interruption risks, we investigate the process of discovering potential partners to improve the supply chain network's resilience. Most of the existing research confines...

A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we introduce the concept of cubic graph, which is different from the notion of cubic graph in S. Rashid, N. Yaqoob, M. Akram, M. Gulistan,...

The evidential reasoning (ER) approach has been widely applied to aggregate evaluation information in multi-expert multi-criterion decision-making (MEMCDM) problems with uncertainties. However, the comprehensive results derived by the ER approach remain uncertain. In this study, we propose a Deng-entropy-based...

This paper studies the multi-attribute group decision-making problems with unknown experts' weights under interval-valued intuitionistic fuzzy environment. First, in order to provide more flexibilities for decision-makers in actual decision-making problems, a novel cross-entropy measure with parameter...

Architecture is about evolution, there exist many types of architectural styles that depend on the geography, traditions, and culture of the particular regions. An architectural designer may have a similar preference in creating the new architectural building, which can be easily recognized from the...

As a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields. To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper....

Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT) is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation...

The most critical issue of multimodal evolutionary algorithms (EAs) is to find multiple distinct global optimal solutions in a run. EAs have been considered as suitable tools for multimodal optimization because of their population-based structure. However, EAs tend to converge toward one of the optimal...

Monarch butterfly optimization (MBO) algorithm is a newly-developed metaheuristic approach that has shown striking performance on several benchmark problems. In order to enhance the performance of MBO, many scholars proposed various strategies for benchmark evaluation and practical applications. As an...

In order to complete the task of the woodland census in Chenzhou, China, this paper carries out a remote sensing survey on the terrain of this area to produce a data set, and used deep learning methods to label the woodland. There are two main improvements in our paper: Firstly, this paper comparatively...

One of the current trends in the deep neural network technology consists in allowing a man–machine interaction and providing an explanation of network design and learning principles. In this direction, an experience with fuzzy systems is of great support. We propose our insight that is based on the particular...

We study the influence of fuzziness of trapezoidal fuzzy sets in the strong fuzzy partitions (SFPs) that constitute the database of a fuzzy rule-based classifier. To this end, we develop a particular representation of the trapezoidal fuzzy sets that is based on the concept of cuts, which are the cross-points...

Spherical fuzzy sets, originally proposed by F.K. Gündogdu, C. Kahraman, Spherical fuzzy sets and spherical fuzzy TOPSIS method, J. Intell. Fuzzy Syst. 36 (2019), 337–352, can handle the information of type: yes, no, abstain and refusal, owing to the feature of broad space of admissible triplets. This...

In this paper, game-theoretic optimization by particle swarm optimization (PSO) is used to determine the Nash equilibrium value, in order to resolve the confusion in choosing appropriate bidders in multi-round procurement. To this end, we introduce an approach that proposes (i) a game-theoretic model...

The objective of this paper is to establish the relationship between L-valued approximation spaces and L-valued transformation systems. We show that for each L-valued upper/lower fuzzy transformation system there exist an L-valued reflexive approximation space and vice versa. In between, we study the...

Neighborhood rough set is a powerful tool to deal with continuous value information systems. Graphics processing unit (GPU) computing can efficiently accelerate the calculation of the attribute reduction and approximation sets based on matrix. In this paper, we rewrite neighborhood approximation sets...

Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to. Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use...

The reliability of a dynamic system is not constant under uncertain random environments due to the interaction of internal and external factors. The existing researches have shown that some complex systems may suffer from dependent failure processes which arising from hard failure and soft failure. In...

Cancer is a fatal disease that is continuously growing in the developed countries. It is also considered as a main global human health problem. Based on several studies, which have been conducted so far, we found out that Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm has never...

This paper studies numerical solutions for fuzzy initial value problems, where the initial conditions are given by interactive fuzzy numbers. The fuzzy solution is given by a numerical method that employs the arithmetic of interactive fuzzy numbers and yields a fuzzy number at each instant of time. The...

Using the theory of monads in categories and the theory of monadic relations, the concept of general transformation operator defined by a monadic relation is introduced. It is proven that a number of standard relations used in categories of fuzzy structures are monadic relations for monads defined in...

Multi-criteria decision-making is a daily process in everyday life, in which different alternatives are evaluated over a set of conflicting criteria. Decision-making is becoming increasingly complex, and the apparition of uncertainty and vagueness is inevitable, especially when related to sustainability...

Leukemia cancer, like other types of cancer, is a deadly health condition that threatens the lives of many people around the world. Micro array data are used extensively to reveal the gene-cancer as well as gene–gene relationships of Leukemia cancer due to the fact that it allows the expression value...

Pacemaker logs are used to predict the progression of paroxysmal cardiac arrhythmia to permanent atrial fibrillation by means of different deep learning algorithms. Recurrent Neural Networks are trained on data produced by a generative model. The activations of the different nets are displayed in a graphical...

Type 2 diabetes mellitus (T2DM) has been identified as one of the most challenging chronic diseases to manage. In recent years, the incidence of T2DM has increased, which has seriously endangered people’s health and life quality. Glycosylated hemoglobin (HbA1c) is the gold standard clinical indicator...

In the big data background, the uncertainty of data is increasingly apparent. Multi-polar fuzzy feature of data has been more popularly used by the research community for the purpose of the classification of weighing cheating in dynamic truck scale characteristic and the clustering problem of multi-polar...

Accurate identification of microRNA regulatory modules can give insights to understand microRNA synergistical regulatory mechanism. However, the identification accuracy suffers from incomplete biological data. In this paper, we proposed a learning-based framework called MicroRNA regulatory module dentification...

Class imbalance is a common issue in medical diagnosis. Although standard radial basis function neural network (RBF-NN) has achieved remarkably high performance on balanced data, its ability to classify imbalanced data is still limited. So far as we know, cost-sensitive learning is an advanced imbalanced...

Information structures (i-structures) in an ordered information system (OIS) are mathematical structures of the information granules (i-granules) granulated from the data set of this OIS. This article investigates i-structures in an OIS with granular computing (GrC) view, i.e., i-structures in an OIS...

In this paper we introduce the concept of multiple bipolar fuzzy measures as a generalization of a bipolar fuzzy measure. We also propose a new definition of a group, which is based on the multidimensional bipolar fuzzy relations of its elements. Taking into account this information, we provide a novel...

Several new resampling methods for generating bootstrap samples of fuzzy numbers are proposed. To avoid undesired repetitions in the secondary samples we do not draw randomly directly observations from the primary samples but construct them allowing for some modifications in their membership functions,...

The number of function evaluations in many industrial applications of simulation-based optimization problems is strictly limited. Therefore, only little analytical information on objective and constraint functions is available. This paper presents an adaptive algorithm called the Surrogate-Based Constrained...

In practical applications, some existing multi-attribute decision-making methods based on the L∗ fuzzy set theory suffer from a lot of shortcomings, namely, incorrect choice preference orders of alternatives are obtained in some cases. In this paper, we construct a new aggregation operator based on uninorms...

Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data. In this work we propose...

The computation of link prediction is one of the most important tasks on a social network. Several methods are available in the literature to predict links in networks and RSM index is one of them. The RSM index is applicable in the fuzzy environment and it does not incorporate the notion of falsity...

Mining contrast subspace has emerged to find subspaces where a particular queried object is most similar to the target class against the non-target class in a two-class data set. It is important to discover those subspaces, which are known as contrast subspaces, in many real-life applications. Tree-Based...