International Journal of Computational Intelligence Systems

Nap is an effective way to reduce daily-level fatigue after several hours of work. However, no alarm clock, which intelligently manages the nap duration with good autonomic nervous recovery (ANR) from fatigue, has been reported in literature. In this work, an intelligent biological alarm clock algorithm...

The novelty of the study is to propose a hybrid IT2 decision-making approach under the hesitant fuzzy linguistic sets for evaluating the criteria and alternatives. For this purpose, the dimensions and criteria are weighted with interval type-2 fuzzy DEMATEL and the economic development plans are ranked...

Recent improvements in deep learning techniques applied to images allow the detection of people with a high success rate. However, other types of sensors, such as laser rangefinders, are still useful due to their wide field of vision and their ability to operate in different environments and lighting...

Cloud computing enables users to purchase virtual resources on demand; therefore, the service requests change over time. Dynamic resource provisioning for cloud computing has become a key challenge. To reduce the associated costs, resource utilization must be improved, and ensure the Quality of Service...

The present work is focused on multi-attribute group decision-making (MAGDM) problems with the uncertain 2-tuple linguistic information (ULI2–tuple) based on new aggregation operators which can capture interrelationships of attributes by a parameter vector P. To begin with, we present some new uncertain...

Aiming at the diversity of Nondominated Sorting Genetic Algorithm II (NSGA-II) in screening out nondominated solutions, a crowding distance elimination (CDE) method is proposed. Firstly, the crowding distance is calculated in the same level of nondominated solutions, and the solution of minimum crowding...

New products are appearing in the marketplace at an ever-increasing step. Their launching is either market driven, or technology driven. Pricing and warranty policies play a vital role in launching of a new product, consequently growth of a company. In this paper a decision model is proposed to determine...

This paper presents an efficient modified particle swarm optimization (EMPSO) algorithm for solving mixed-integer nonlinear programming problems. In the proposed algorithm, a new evolutionary strategies for the discrete variables is introduced, which can solve the problem that the evolutionary strategy...

The TODIM (an acronym in Portuguese for interactive multicriteria decision-making) method can consider the decision-makers’ (DMs’) psychological behavior. However, the classical TODIM method has been unable to address fuzzy information such as the linguistic neutrosophic number (LNN), which is an effective...

Group decision-making can effectively deal with complex decision problems in reality and takes important research status in the field of decision-making. In recent years, three-way decision has been a hot topic in the field of uncertain decision-making, so the models of three-way decision under group...

Successful blogs receive high ratings and generate marketing value. What factors contribute to the success of a blog and how to predict its success level are questions worth discussing. A hybrid swam intelligence approach is proposed in this study to predict blog success level. First, this study develops...

To solve the interval-valued intuitionistic fuzzy decision-making problems with high-dimension data, the fuzzy matrix is extended to the fuzzy tensor in this paper. Based on the constructed tensor definition, we propose the generalized interval-valued intuitionistic fuzzy weighted averaging (GIIFWA)...

Artificial Bee Colony (ABC) algorithm inspired by the intelligent source search and consumption characteristics of the real honey bees is one of the most powerful optimization techniques. Although the existing behaviours of the honey bees in standard ABC algorithm are capable of producing optimal or...

In the field of fuzzy utility mining, the characteristics of transaction time have been a widely studied topic in data mining. However, using a single-conditional threshold for all items does not suffice to reflect the true properties of items. This paper, therefore, proposes a multi-conditional minimum...

It is critical to develop secure software with long-term performance and capability to withstand and forestall the growing competition in the software development industry. To enhance the potential of Confidentiality, Integrity, and Availability (CIA), a mechanism is required to built in and secure the...

It proposes a pseudo-random number generation algorithm based on cellular neural networks. This method uses the hyper-chaos characteristics of the cellular neural networks and sets the appropriate parameters to generate the pseudo-random number. The experimental results show that, compared with other...

Group method of data handling (GMDH)-type neural network algorithms are the self-organizing algorithms for modeling complex systems. GMDH algorithms are used for different objectives; examples include regression, classification, clustering, forecasting, and so on. In this paper, we present GMDH2 package...

Different studies have shown the benefits of a cooperative activities programme for the elderly. Members of a group with similar abilities or disabilities are often encouraged by having the opportunity to share their experiences, knowledge, or opinions. Nevertheless, when caregivers try to plan specific...

Wireless sensor networks (WSNs) involves a set of wireless sensor nodes located within a region of interest (ROI) to acquire and/or transmit specific information from their surroundings. A common problem in the operation of WSNs is sensor coverage, which is related to the distribution of sensor nodes...

Generative adversarial network (GANs) has significant progress in 3D model generation and reconstruction recently years. GANs can generate 3D models by sampling from uniform noise distribution. But they generate randomly and are often not easy to control. To address this problem, we add the class information...

The concept of T-normed fuzzy TM-subalgebras is introduced by applying the notion of t-norm to fuzzy TM-algebra and its properties are investigated. The ideas based on minimum t-norm are generalized to all widely accepted t-norms in a fuzzy TM-subalgebra.The characteristics of an idempotent T-normed...

The traceability is of paramount importance and considered as a prerequisite for businesses for long-term functioning in today's global supply chain. The implementation of traceability can create visibility by the systematic recall of information related to all processes and logistics movement....

Recurrent neural network (RNN) is one of the most popular architectures for addressing variable sequence text, and it shows outstanding results in many natural language processing (NLP) tasks and remarkable performance in capturing long-term dependencies. Many models have achieved excellent results based...

Now, there are a lot of multiobjective evolutionary algorithms (MOEAs) available and these MOEAs argue that they are competitive. In fact, these results are generally unfair and unfaithful. In order to make fair comparison, comprehensive performance index system is established. The weights among the...

We propose a semi-automatic method for the generation of educational-competency maps from repositories of multiple-choice question responses, using Bayesian structural learning and data-mining techniques. We tested our method on a large repository of responses to multiple-choice exam questions from an...

Product design is an important phase of the new product development process and one of the most crucial decisions in marketing. In the latest two decades, a significant number of marketing decision support systems (MDSSs) for automating new product design activities have been reported in the literature...

The Lee–Carter model is a useful dynamic stochastic model to represent the evolution of central mortality rates throughout time. This model only considers the uncertainty about the coefficient related to the mortality trend over time but not to the age-dependent coefficients. This paper proposes a fuzzy-random...

Differential evolution (DE) represents a class of population-based optimization techniques that uses differences of vectors to search for optimal solutions in the search space. However, promising solutions/regions are not adequately exploited by a traditional DE algorithm. Memetic computing has been...

An incomplete interval-valued information system (IIVIS) is an information system (IS) in which the information values are interval numbers with missing values. This article researches information structures in an IIVIS. First, information structures in an IIVIS are obtained. In addition, the dependence...

MicroRNA regulatory module (MRM) plays an important role in the study of microRNA synergism. To detect MRMs, researchers have developed a number of related methods in the preceding decades. However, some existing methods are stochastic or specific to a certain situation. In this paper, we presented a...

Fuzzy multisets represent a particularly challenging generalization of the concept of fuzzy sets. The membership degrees of fuzzy multisets are given by multisets in 0,1 rather than single values. Mathematically, they can be also seen as a generalization of the hesitant fuzzy sets. But in this general...

Electric power industry has been undergoing enormous transformations. Therefore, it is necessary to improve the security of electric power system and the decision capacity in the emergency process. As a complicated system, electric power system is affected by many factors, the reasoning of which can...

With advanced technology in medicine and biology, data sets containing information could be huge and complex that sometimes are difficult to handle. Dynamic computing is an efficient approach to solve some problems. Since multigranulation rough sets were proposed, many algorithms have been designed for...

Compressed sensing theory is widely used in image and video signal processing because of its low coding complexity, resource saving, and strong anti-interference ability. Although the compression sensing theory solves the problems brought by the traditional signal processing methods to a certain extent,...

Aiming at the decision-making problem in heterogeneous information environment and considering the influence of decision makers' psychological behavior on decision-making results, this paper proposes a multi-attribute decision-making method based on prospect theory in heterogeneous information environment....

Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this...

Roughness is a comprehensive assessment indicator of pavement performance. Prediction of pavement roughness exhibits great difficulties by using traditional methods such as mechanistic-empirical method and regression method. Considering the fact that the value of international roughness index (IRI) varies...

Dominance-based rough set approach is the extension of classical Pawlak rough set theories and methodologies, in which the information with preference-ordered relation on the domain of attribute value is fully considered. In the dominance-based information system, upper and lower approximations will...

Real world problems for prediction usually try to predict rare occurrences. Application of standard classification algorithm is biased toward against these rare events, due to this data imbalance. Typical approaches to solve this data imbalance involve oversampling these “rare events” or under sampling...

The purpose of a brain–computer interface (BCI) is the recording of brain signals to translate them into commands. This work proposes new naturalistic and intuitive motor imagery (MI) BCI task for aircraft Pilots for a BCI System, and explore if they take advantage of their previous motor experience...

Effective channel order determination is an important problem in convolutive blind channel identification. The classical techniques are based on information theoretic criteria, which show a great potentiality to estimate the effective channel order. However, these methods are just effective for the overdetermined...

Ever since the introduction of Pythagorean fuzzy (PF) sets, many scholars have focused on solving multicriteria decision-making (MCDM) problems with PF information. The technique for order preference by similarity to ideal solutions (TOPSIS) is a well-known and effective method for MCDM problems. The...

We describe an applied methodology to build fuzzy models of geographical expressions, which are meant to be used for natural language generation purposes. Our approach encompasses a language grounding task within the development of an actual data-to-text system for the generation of textual descriptions...

Tremendous effort has been exerted over the past few decades to construct multi-attribute decision functions with the capacity to model heterogeneous interrelationships among attributes. In this paper, we report an empirical study aiming to test whether or not considering interrelationships among attributes...

To obtain reliable results in a qualitative multi-attribute group decision-making (MAGDM) problem, how to retain the evaluation information as much as possible and how to determine the reasonable weights of experts and attributes are two important issues. Proportional hesitant fuzzy linguistic term set...

Due to the seriousness of the network security situation, as a low-cost, high-efficiency email attack method, it is increasingly favored by attackers. Most of these attack vectors were embedded in email attachments and exploit vulnerabilities in Adobe and Office software. Among these attack samples,...

In recent years, hesitant multiplicative preference relation (HMPR) has been a powerful means to represent the evaluation information of decision makers during the pairwise comparison concerning alternatives. As the important parts of group decision making (GDM) issues with HMPRs, the consistency improving...

This paper proposes a novel hybrid forecasting model combining autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) with incorporating moving average and the annual seasonal index for Thailand's cassava export (i.e., native starch, modified starch, and sago). The...

The key factors required to establish a precise soft sensor model for industrial processes include selection of variables affecting vital indicators from a large number of online measurement variables and elimination of the effects of unrelated disturbance variables. How to compress redundant information...

We present three (main one and two auxiliary) fuzzy algorithms to stratify observations in homogenous classes. These algorithms modify, upgrade and fuzzify crisp algorithms from our earlier works on a medical case study to select the most appropriate surgical treatment for patients with ischemic heart...

The choice of green suppliers involves a large amount of inaccurate, incomplete, and inconsistent information, and the single-valued triangular Neutrosophic number that is an extension of the single-valued Neutrosophic number can effectively handle such problems. Considering the advantages of the single-valued...

In order to deal with the decision making problem including some linguistic values uncertainty information, we propose an approach for decision making with linguistic weighted and unavoidable incomparable ranking based on Linguistic-valued lattice implication algebra (LV-LIA). The properties of binary...

The plan and usage of a smart power management system for household and buildings that control numerous electrical appliances in real time have been reported in this work. The system is based on using artificial intelligence with low-cost single board computer in order to design a smart power management...

Multi-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for...

Ship detection is a canonical problem in computer vision. Motivated by the observation that the major bottleneck of ship detection lies on the different scales of ship instances in images, we focus on improving the detection rate, especially for the small-sized ships which are relatively far from the...

In this paper, a novel algorithm called opposition-based coral reefs optimization (OCRO) is introduced. The algorithm is built as an improvement for coral reefs optimization (CRO) using opposition-based learning (OBL). For efficient modeling as the main part of this work, a novel time series forecasting...

Fuzzy systems have become widely accepted and applied in a host of domains such as control, electronics or mechanics. The software for construction of these systems has traditionally been exploited from tools, platforms and languages run on-premise computing infrastructure. On the other hand, rise and...

Energy security (ES) has great impact on power grids. Therefore it is important to have power security service (PSS). The PSS should be designed to handle interference and interruption attack in the grid. The interference and interruption attack in the grid is handled by incursion-detection system (IDS)....

In recent times, to improve the interpretability and accuracy of computing with words processes, a rich linguistic representation model has been developed and referred to as Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT). This model extends the definition of the comparative...

The paper proposes and analyses performance of a fuzzy-based mediator with showcase examples in robot navigation. The mediator receives outputs from two controllers and uses estimated collision probability for adapting the signal proportions in the final output. The approach was implemented and tested...

The goal of biomimicry is to resolve problems by studying and mimicking the characteristics of organisms or design elements in nature. Although wireless sensor networks are used in various fields, they have limited network lifespan. Research has thus focused on observing and modeling the behavioral principles...

Deep neural network-based end-to-end visuomotor control for robotic manipulation is becoming a hot issue of robotics field recently. One-hot vector is often used for multi-task situation in this framework. However, it is inflexible using one-hot vector to describe multiple tasks and transmit intentions...

Semantic face inpainting from corrupted images is a challenging problem in computer vision and has many practical applications. Different from well-studied nature image inpainting, the face inpainting task often needs to fill pixels semantically into a missing region based on the available visual data....

Most of the advanced first-order logic automated theorem proving (ATP) systems adopt binary resolution methods as the core inference mechanism, where only two clauses are involved and a complementary pair of literals are eliminated during each deduction step. Recently, a novel multi-clause inference...

Presently, a conventional coalition structure can no more cover all the types of cooperative structures in practice, external cooperation between the coalitions also affects the payoff allocation between the participants. We propose a solution to solve the bargaining problem with level structure by defining...

Epilepsy can well be stated as a disorder of the central nervous systems (CNS) that brought about recurring seizures owing to chronic abnormal blasts of electrical discharge on the brain. Knowing if an individual is having a seizure and diagnosing the seizure type or epilepsy syndrome could be hard....

The optimization problems and algorithms are the basics subfield in artificial intelligence, which is booming in the almost any industrial field. However, the computational cost is always the issue which hinders its applicability. This paper proposes a novel hybrid optimization algorithm for solving...

The current study highlights the utilization of a non-linear model to analyze an important decision-making process in the study of corporate finance where managers are deciding on the capital structure of a firm. This study compares the results from based on the unbalanced panel data multiple regression...

Objective To determine the impact of artificial intelligence (AI) on the selected economies in the Asia-Pacific region. Methods This secondary research collected data from macroeconomic and AI-specific data sets. The sources of data from which insights were gained included digital technology sectors...

To assess the potential of China's industrial restructuring on energy conservation and greenhouse gas (GHG) emission reduction in 2020, this study proposes an input–output multi-choice goal programming model and method. In this model, the goals include the maximization of gross domestic product...

A novel accurate and robust metric called II-Learn for measuring the increase of intelligence of a system after a learning process is proposed. We define evolving learning systems, as systems that are able to make at least one measurable evolutionary step by learning. To prove the effectiveness of the...

Consistency and priority weights of preference relations are two important phases of decision-making process since the decision-making solutions are determined by them. Therefore, it is meaningful to investigate consistency and priority weights for preference relations. In this paper, consistency and...

In proof-of-work (PoW)-based blockchain network, the blockchain miners publish blocks by contributing computing power to solve crypto-puzzles. Due to the weak computing power of single miner, miners tend to join a mining pool and share the profits from the mining pool according to the contribution proportions...

In order to adapt to the development of the new times, enterprises should not only care for the economic benefits, but also properly cope with environmental and social problems to achieve the integration of environmental, economic and social performance of sustainable development, so as to maximize the...

The reasoning ability of the belief rule-based system is easy to be weakened by the quality of training instances, the inconsistency of rules and the values of parameters. This paper proposes an ensemble approach for extended belief rule-based systems to address this issue. The approach is based on the...

Recommender systems have played a relevant role in e-commerce for supporting online users to obtain suggestions about products that best fit their preferences and needs in overloaded search spaces. In such a context, several authors have proposed methods focused on removing the users' inconsistencies...

As a powerful extension to hesitant fuzzy sets (HFSs), dual hesitant fuzzy sets (DHFSs) have been closely watched by many scholars. The DHFSs can reflect the disagreement and hesitancy of decision-makers (DMs) flexibly and conveniently. However, all the evaluation values under the same membership degree...

Imbalance learning is a challenging task for most standard machine learning algorithms. The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing approach for handling imbalanced datasets, where the minority class is oversampled by producing synthetic examples in feature vector...

Growing use of social media such as Twitter, Instagram, Facebook, etc., by consumers leads to the vast repository of consumer generated data. Collecting and exploiting these data has been a great challenge for clothing industry. This paper aims to study the impact of Twitter on garment sales. In this...

During last two decades, power adaptation and beamforming solutions have been proposed for multiple input multiple output (MIMO) Ad Hoc networks. Game theory based methods such as cooperative and non-cooperative joint beamforming and power control for the MIMO ad hoc systems consider the interference...

In order to solve the shortcomings of the existing mathematical expressions of the user's personalized recommendation search and to increase the accuracy of the user's personalized information recommendation, in this study, the collaborative filtering algorithm, the related theory of fuzzy...

In this paper, we evaluate the development level of internet finance by focusing on three major virtual economies, internet “Baby” fund, internet financial credit and Shanghai Composite Index for the first time. We examine the disequilibrium relations between the development of both internet finance...

The notions of complex q-rung orthopair fuzzy sets (Cq-ROFSs) and linguistic sets (LSs) are two different concepts to deal with uncertain information in multi-attribute group decision-making (MAGDM) problems. The Heronain mean (HM) and geometric Heronain mean (GHM) operators are an effective tool used...

There are many challenges in accurately measuring cigarette tar constituents. These include the need for standardized smoke generation methods related to unstable mixtures. In this research were developed algorithms using fusion of artificial intelligence methods to predict tar concentration. Outputs...

Accurate daily precipitation prediction is crucially important. However, it is difficult to predict the precipitation accurately due to inherently complex meteorological factors and dynamic behavior of weather. Recently, considerable attention has been devoted in soft computing-based prediction approaches....

The challenges faced in underwater communication systems are limited bandwidth, Energy consumption rate, larger propagation delay time, End-End Delay (E-ED), 3D topology, media access control, routing, resource utilization and power constraints. These issues and challenges are solved by means of deploying...

In the fashion market, the lack of historical sales data for new products imposes the use of methods based on Stock Keeping Unit (SKU) attributes. Recent works suggest the use of functional data analysis to assign the most accurate sales profiles to each item. An application of siamese neural networks...

In this essay, we investigate the probabilistic linguistic multiple attribute group decision making (PL-MAGDM) with incomplete weight information. In this method, the linguistic representation developed recently is converted into probabilistic linguistic information. For deriving the weight information...

The aim of this paper is to provide a multiple attribute group decision making (MAGDM) method based on the 2-dimension linguistic weight Bonferroni mean aggregation (2DLWBMA) operator. Firstly, the new operations of 2-dimension linguistic variables are defined. Then, the 2-dimension linguistic Bonferroni...

Teacher engagement is a newly-emerged concept in the field of Indonesian teacher education. To support this concept, we designed an artificial neural network (ANN) using backpropagation, stochastic learning, and steepest gradient descent algorithms to determine teacher engagement based on the Indonesian...

This study has aimed to examine the empirically relationship between strategic management of organizational knowledge and awareness about artificial intelligence (AI) through mediating effect of learning climate in service sector of Saudi Arabia. For better understanding, a structural questionnaire was...

In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence...

To date, the subgroup discovery (SD) task has been considered in problems where a target variable is unequivocally described by a set of features, also known as instance. Nowadays, however, with the increasing interest in data storage, new data structures are being provided such as the multiple instance...

This article proposes a character-level neural language model (NLM) that is based on quantum theory. The input of the model is the character-level coding represented by the quantum semantic space model. Our model integrates a convolutional neural network (CNN) that is based on network-in-network (NIN)....

Music has a great role and importance in human life since it has the ability to trigger or convey feelings. As recognizing music emotions is the subject of many studies conducted in many disciplines like science, psychology, musicology and art, it has attracted the attention of researchers as an up-to-date...

Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications. Finding the best network and optimizing its hyperparameters for a specific application...

Water-energy-food (W-E-F) nexus is an essential element for human survival, as well as the basis for the sustainable development of regional economy and ecological environment. Water resource is an important input element of energy production and food production, which makes the W-E-F nexus become closer...