International Journal of Computational Intelligence Systems

Feature selection (FS) is a crucial data pre-processing process in classification problems. It aims to reduce the dimensionality of the problem by eliminating irrelevant or redundant features while achieve similar or even higher classification accuracy than using all the features. As a variant of particle...

Hesitant fuzzy sets, as an extension of fuzzy sets to deal with uncertainty, have attracted much attention since its introduction, in both theory and application aspects. The present work is focused on the interval-valued hesitant fuzzy sets (IVHFSs) to manage additional uncertainty. Now that distance...

The training techniques of the distributed machine learning approach replace the traditional methods with a cloud computing infrastructure and provide flexible computing services to clients. Moreover, machine learning-based classification methods are used in many diverse applications such as medical...

The probabilistic linguistic term sets (PLTSs) are powerful to deal with the hesitant linguistic situation in which each provided linguistic term has a probability. The PLTSs contain uncertainties caused by the linguistic terms and their probability information. In order to measure such uncertainties,...

Face retrieval is becoming increasingly useful and important for security maintenance operations. In actual applications, face retrieval is usually influenced by some changeable site conditions, such as various postures, expressions, camera angles, illuminations, and so on. In this paper, facial peculiar...

Accurate and timely identification of the potential churner, also known as churn prediction, is crucial to devise effective retention strategies. A number of churn prediction models have been proposed in the past, however, the existing models suffer from a number of limitations due to which these models...

Fuzzy Logic has played an important role in medical image (MI) segmentation in the last decade. Automatic blood vessel segmentation from 3D medical images is an emerging area where segmentation algorithms could be combined with evolutionary computation methods for better diagnosis and higher decision...

In this work, the use of expert systems and hyperspectral imaging in the determination of coffee rust infection was evaluated. Three classifiers were trained using spectral profiles from different stages of infection, and the classifier based on a support vector machine provided the best performance....

This paper proposes a decision support process for incomplete hesitant fuzzy preference relations (HFPRs). First, we present a revised definition of HFPRs, in which the values are not ordered for the hesitant fuzzy element. Second, we propose a method to normalize the HFPRs and estimate the missing elements...

In the paper, we express uncertain assessments information in linguistic multi-criteria decision makings (LMCDMs) as linguistic intuitionistic fuzzy sets, i.e., the decision maker provides membership and non-membership fuzzy linguistic terms to represent uncertain assessments information of alternatives...

In this paper, a direct formula is proposed for design of robust PID controller for sun tracker system using quadratic regulator approach with compensating pole (QRAWCP). The main advantage of the proposed approach is that, there is no need to use recently developed iterative soft computing techniques...

We often make decisions on the things we like, dislike, or even don’t care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that...

Multi-attribute group emergency decision making (MAGEDM) has become a valuable research topic in the last few years due to its effectiveness and reliability in dealing with real-world emergency events (EEs). Dynamic evolution and uncertain information are remarkable features of EEs. The former means...

People working in the fields of robotics, animation, computer graphics and computer vision, and biomechanics, find it difficult to conduct human motion simulations, such as lifting, walking and running. This is because it is difficult to predict all of the motion strategies in a variety of situations....

This study proposes a mathematical model of clustered multi-temperature joint distribution in fuzzy environment. In this model, a Z-shaped function is used to depict customer satisfaction. For the imprecise model, triangular fuzzy numbers are used to represent travel times. By redefining the movement...

In this paper, we introduce a logic-driven framework for modeling similarity based on interpolative Boolean algebra (IBA). It consists of two main steps: data preprocessing and similarity measuring by means of IBA similarity measure and logical aggregation. The purpose of these steps is to detect dependencies...

Scheduling of mixed-criticality systems (MCS) on a common computational platform is challenging because conventional scheduling approaches may cause inefficient utilization of shared computing resources. In this paper, we propose an approach called Clustering-based Partitioned Earliest Deadline First...

The use of prostheses is necessary to restore lost limbs to a level of functionality to enable activity of daily living. Many prostheses are now using myoelectric based control techniques to operate. However, to develop a model based controller for the system remains a challenge as accurate model is...

It is neither practical nor economic to assign a specific inventory policy for each item if there are thousands of items in one firm. This paper seeks to solve the stock problem from an integrated perspective by taking into account of both classification of items and replenishment policies for each group....

In this paper, we propose an extension of hesitant fuzzy sets, i.e., proportional hesitant fuzzy sets (PHFSs), with the purpose of accommodating proportional hesitant fuzzy environments. The components of PHFSs, which are referred to as proportional hesitant fuzzy elements (PHFEs), contain two aspects...

Service-based systems (SBSs) need to be reconfigured when there is evidence that the selected Web services configurations no further satisfy the specifications models and, thus the decision-related models will need to be updated accordingly. However, such updates need to be performed at the right pace....

Locally weighted regression allows to adjust the regression models to nearby data of a query example. In this paper, a locally weighted regression method for the multi-target regression problem is proposed. A novel way of weighting data based on a data gravitation-based approach is presented. The process...

This paper introduces a novel concept of web-spreadsheet, which extracts product information by crawling through related web pages and generates information like a spreadsheet where each row represents product information and each column represents product attributes. Using Decision Tree based classifier,...

The Quality of Service (QoS) offered by a network is based on criteria such as delay, jitter and message loss. QoS management is crucial to ensure that the network respects the communication constraints defined by the application requirements. The network must dynamically manage QoS according to the...

The data-intensive service provision is characterized by the large of scale of services and data and also the high-dimensions of QoS. However, most of the existing works failed to take into account the characteristics of data-intensive services and the effect of the big data sets on the whole performance...

Garment purchasing through the e-shopping platforms has become an important trend for consumers of all parts of the world. More and more e-shopping platforms have proposed recommendation functions to consumers in order to make them to obtain more easily desired products and then increase shopping sales....

In practical decision-making, we prefer to characterize the uncertain problems with the hybrid data, which consists of various types of data, e.g., categorical data, numerical dada, interval-valued data and set-valued data. The extended rough sets can deal with single type of data based on specific binary...

Recently, Neural Network Language Models have been effectively applied to many types of Natural Language Processing (NLP) tasks. One popular type of tasks is the discovery of semantic and syntactic regularities that support the researchers in building a lexicon. Word embedding representations are notably...

Due to the need of the logical foundation for uncertain information processing, development of efficient automated reasoning system based on non-classical logics is always an active research area. The present paper focuses on the resolution-based automated reasoning theory in a many-valued logic with...

An improved adaptive genetic algorithm is proposed for solving 3-SAT problems based on effective restart and greedy strategy in this paper. Several new characteristics of the algorithm are developed. According to the shortcomings of the adaptive genetic algorithm, it is easy to fall into the premature...

This study proposes implementation of Boolean consistent fuzzy inference system for credit scoring purposes. Fuzzy inference system (FIS) allows domain experts to express their knowledge in the form of fuzzy rules, which enables combination of automatic rating with human judgment. Crucial for this model...

A novel approach is proposed to complete the fault diagnosis of pumping systems automatically. Fast Discrete Curvelet Transform is firstly adopted to extract features of dynamometer cards that sampled from sucker rod pumping systems, then a sparse multi-graph regularized extreme learning machine algorithm...

The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by. The existence of different types of concept drift makes it more difficult for learning algorithms to track. This...

Personalized individual semantics (PIS) is not unusual in our daily life, and it has an important influence on the final decision results in linguistic decision making. The analytic hierarchy process (AHP) has now become a popular decision tool because of its sound mathematical design and ease of applicability...

It is necessary for automated vehicles (AVs) and advanced driver assistance systems (ADASs) to have a better understanding of the traffic environment including driving behaviors. This study aims to build a driving behavior awareness (DBA) model that can infer driving behaviors such as lane change. In...

With the arrival of the era of big data, data has become a kind of important assets. In order to get a better utilization of big data, paid or unpaid data sharing will be a trend. And as one of key techniques to maintain security of data sharing, access control will play an important role in cloud storage...

Personalized individual semantics (PIS) exist widely in our daily life, and it means that different people have different understandings regarding the same word. In decision making, decision makers are accustomed to express their preferences using a linguistic way, and it is naturally that the PIS will...

The aim of the paper is to discuss some new operations on hesitant fuzzy linguistic terms sets based on Archimedean t-norm and t-conorm. The advantage is that the operations on hesitant fuzzy linguistic terms sets are closed, by studying propositions of the operations on hesitant fuzzy linguistic terms...

In previous work we presented CoCoNAD (Continuous-time Closed Neuron Assembly Detection), a method to find significant synchronous patterns in parallel point processes with the goal to analyze parallel neural spike trains in neurobiology3,9. A drawback of CoCoNAD and its accompanying methodology of pattern...

Multi-objective evolutionary algorithms have been criticized when they are applied to classification rule mining, and, more specifically, in the optimization of more than two objectives due to their computational complexity. It is known that a multi-objective space is much richer to be explored than...

Service disruptions due to deliberate sabotage are serious threats to supply systems. To alleviate the loss of accessibility caused by such disruptions, identifying the system vulnerabilities that would be worth strengthening is a critical problem in the field of Critical Infrastructure Protection (CIP)....

With explosive growth of the astronomical data, astronomy has become a representative data-rich discipline so as to defy traditional research methodologies and paradigm to analyze data and discover new knowledge from the data. How to effectively process and analyze the astronomical data is a fundamental...

Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference...

The Sloan Digital Sky Survey (SDSS) has been one of the most successful sky surveys in the history of astronomy. To map the universe, SDSS uses their telescopes to take pictures of the sky over the whole survey area. Now the total SDSS data volume is larger than 125 TB since every night telescopes produce...

Abstract In this work we present the results for the automatic determination of the mean longitudinal magnetic field in polarized stellar spectra through the analysis of spectropolarimetric observations. In order to determine this important parameter, we first developed a synthetic database encompassing...

As a new type of heterogeneous social network, Event-Based Social Network (EBSN) has experienced rapid development after its appearance. In EBSN, the interaction data between users and events is relatively sparse because of the short life cycle of events, which brings great challenges to event recommendation....

As the rapid booming of reviews, a valid sentiment analysis model will significantly boost the review recommendation system’s capability, and present more constructive information for consumers. Topic probabilistic models have already shown many advantages for detecting potential structure of topics...

Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly. Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields,...

This paper presents a computer aided analysis system for detecting epileptic seizure from electroencephalogram (EEG) signal data. As EEG recordings contain a vast amount of data, which is heterogeneous with respect to a time-period, we intend to introduce a clustering technique to discover different...

In recent years, the relationship between companies and suppliers has changed with the continuous rise in environmental awareness and customer expectations. In order to fulfill customers’ needs, the actors in a Supply Chain (SC) network sometimes compete and sometimes cooperate with each other. In SC...

Multiple Traveling Salesman Problem (MTSP) is able to model and solve various real-life applications such as multiple scheduling, multiple vehicle routing and multiple path planning problems, etc. While Traveling Salesman Problem (TSP) focuses on searching a path of minimum traveling distance to visit...

This paper investigates the consensus decision making problem of the interval-valued fuzzy preference relation with distribution characteristics. The proposed group consensus decision making model is constructed by considering the scenarios in which the DMs are respectively equally and non-equally weighted...

E-commerce has grown exponentially in the past decade in global market. In China most E-commerce enterprises are small and medium-sized (SMEs). Compared to their large-sized counterparts, SMEs have to face many obstacles when extending their E-commerce businesses. In view of the complexity and periodicity...

Bilevel optimization are often addressed in an organizational hierarchy in which the upper level decision maker is the leader and the lower level decision maker is the follower. The leader frequently cannot obtain complete information from the follower. As a result, the leader most tends to be risk-averse,...

This study addresses the construction of Takagi-Sugeno-Kang (TSK) fuzzy models by means of clustering. A contribution-factor based fuzzy min-max neural network (CFMN) is developed based on Simpson’s well-known fuzzy min-max neural network (FMNN) for clustering. The contribution-factor (CF) is also known...

The faults occurring in different phases at multiple locations and different times are difficult to locate exact location using conventional techniques. This paper develops a fault location estimation approach using fuzzy inference system for multi location phase to ground faults and transforming phase...

In this study, an optimized extended Kalman filter (EKF), and an interval type-2 fuzzy sliding mode control (IT2FSMC) in presence of uncertainties and disturbances are presented for robotic manipulators. The main contribution is the proposal of a novel application of Biogeography-Based Optimization (BBO)...

It is crucial to evaluate the information obtained from the sensors in a fast and accurate manner in air vehicles exposed to many internal and external influences during their flights. The effectiveness and flexibility of the reasoning method comes to the forefront when the pilot or flight control system...

New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously...

Computer vision-based tracking and fault detection methods are increasingly growing method for use on railway systems. These methods make detection of components of the railways and fault detection and condition monitoring process can be performed using data obtained by means of computers. In this study,...

As one of research and practice hotspots in the field of intelligent manufacturing, the machine learning approach is applied to diagnose and predict equipment fault for running state data. Despite deep learning approach overcomes the problem that the traditional machine learning approaches for fault...

Considering the rapid growth, complications and treatment side-effects of MI, so using data mining techniques seems necessary. On the other hand, in real-world MI cases are much less compared to healthy cases. The traditional algorithms for imbalanced problems lead to very low Sensitivity, thus, we propose...

Logarithm Least-Squares Support Vector Regression (LLS-SVR) has been applied in addressing forecasting problems in various fields, including bioinformatics, financial time series, electronics, plastic injection moulding, Chemistry and cost estimations. Cautious Random Particle Swarm Optimization (CRPSO)...

The evolution of wireless multimedia sensor networks (WMSN) has opened the door to a wide range of applications such as telemedicine, surveillance, and intrusion detection. However, the delivery of multimedia content over wireless sensor networks requires maintaining the quality of service demands of...

Delays often occur during some activities in software development projects. Without handling of project delays effectively, many software development projects fail to meet their deadlines. If extra employees with same or similar skills and domain knowledge can be rescheduled for the remaining activities...

The theory of Pythagorean fuzzy sets possesses significant advantages in handling vagueness and complex uncertainty. Additionally, Pythagorean fuzzy information is useful to simulate the ambiguous nature of subjective judgments and measure the fuzziness and imprecision more flexibly. The aim of this...

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling...

In this paper, a novel fusion method based on Total Error Rate (TER) and multiple hidden layer probabilistic extreme learning machine is proposed. At first, the study transfers the matching scores into TER based on corresponding False Reject Rates (FRR) and False Accept Rates (FAR) aims at avoiding to...

Recent deep convolutional neural network-based object detectors have shown promising performance when detecting large objects, but they are still limited in detecting small or partially occluded ones—in part because such objects convey limited information due to the small areas they occupy in images....

In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights...

This paper aims at developing a voltage-based impedance model-reference controller using fuzzy uncertainty estimator for the robust control of electrically driven robot manipulators. The proposed control scheme not only utilizes a desired impedance as a reference model, but also provides the integrated...

A new methodology for processing non-normal fuzzy sets is proposed. To break the predominant constraint on normality of fuzzy numbers the concept of fuzzy semi-numbers is introduced Then it is shown how to generalize operations defined on fuzzy numbers onto a family of fuzzy semi-numbers with possibly...

Generalized fuzzy graphs are perfect to represent any system like networks, images, scheduling, etc. compared to fuzzy graphs. This study introduces the concept of a generalized fuzzy neighbourhood of a vertex and generalized fuzzy graphs. Also, an associated graph, called minimal graphs of competition...

In the field of financial investment, investing in stocks is relatively easy compared to other investment commodities, since making a profit through buying a stock at a low price and selling it at a higher price is intuitive. However, it is really challenging work for an investor to choose stocks which...

This paper comprises a new iterative method for multi-person decision making based on multiplicative consistency with incomplete reciprocal preference relations (IRPRs). Additionally, multiplicative transitivity property of reciprocal preference relation (RPR) is used at the first level to estimate the...

Cloud computing is defined as an on-demand large-scale distributed network to provide and realize computational resources. This flexibility expectation of cloud products forces cloud service providers (CSPs) to tailor their products to the needs of their customers. The framework in this paper proposes...

Convolutional Neural Network (CNN) has become an increasingly important research field in machine learning and computer vision. Deep image features can be learned and subsequently used for detection, classification and retrieval tasks in an end-to-end model. In this paper, a supervised feature embedded...

Restricted communication in unmanned aerial vehicle (UAV) swarms means that configuration needs to vary dynamically with changing tasks. We propose a mission planning model that uses a motif, a grouping of related functions, as the basic task unit. The planning model automatically generates a mission...

This study determined the aspects required to professionalize volleyball, by analyzing the literature and interviewing 11 experts and scholars. The criteria of assessments were set, and the strategies of promotion were delivered. We discovered the points of view on the professionalization of volleyball...

Interval-valued Pythagorean fuzzy sets (PFSs), as an extension of PFSs, have strong potential in the management of complex uncertainty in real-world applications. This study aims to develop several interval-valued Pythagorean fuzzy Frank power (IVPFFP) aggregation operators with an adjustable parameter...

The stability and robustness analysis is a vital issue of fuzzy soft inference. In this paper, λ–Triple I inference methods based on the fuzzy soft modus ponens (FSMP) and fuzzy soft modus tollens (FSMT) are presented. The related computational formulas for inference conclusions with respect to the residual...

In alternative generation, reusing past experience is a potential methodology and case retrieval is a primary step. In order to improve the performance of case retrieval process, many applications have used different similarity measurements and the selection method for the most suitable historical case...

This paper proposes using engine’s sensors data flow and exhaust emissions information to diagnose engine’s faults, enhancing the accuracy of fault diagnosis. Engine fault diagnosis model is built using both this information and the mature BP neural network and genetic algorithms. In order to verify...

Nowadays, security of the computer systems has become a major concern of security experts. In spite of many antivirus and malware detection systems, the number of malware incidents are increasing day by day. Many static and dynamic techniques have been proposed to detect the malware and classify them...

Software products quality assessment is a highly complex process, given the variety of criteria to consider. For a better understanding, they are organized in so-called software quality models. An important aspect of these models is their structural complexity, forming a hierarchical structure. At present,...

Granular computing is a essential mathematical tool in artificial intelligence. An incomplete information system is an important model and its basic structures are information structures. This paper investigates information structures in an incomplete information system from granular computing viewpoint,...

The explosion of the Internet has deeply affected the labour market. Identifying most rewarded and demanded items in job offers is key for recruiters and candidates. This work analyses 4, 000 job offers from a Spanish IT recruitment portal. We conclude that (1) experience is more rewarded than education,...

In the highly dynamic complex product design process, task allocations recovered by reactive allocating decisions are usually subject to design changes. In this paper, a robust predictive–reactive allocating approach considering possible disruption times is proposed, so that it can absorb the disruption...

Software Fault Prediction (SFP) research has made enormous endeavor to accurately predict fault proneness of software modules, thus maximize precious software test resources, reduce maintenance cost and contributes to produce quality software products. In this regard, Machine Learning (ML) has been successfully...

The aim of this research is to automate an analysis of the EGFR gene as a whole, and especially an analysis of those exons with clinically identified microdeletion mutations which are recorded with non-mutated nucleotides in a long chains of a, c, t, g nucleotides, and “-“ (microdeletion) in the NCBI...

After analyzing the enterprise’s production strategy under the constraints of carbon quota, this paper proposes new mathematical models aiming for the optimal choice of enterprise’s production strategy under the monopoly and competitive environments respectively. Combining the neural network optimization...

Brain-Computer Interface systems based on motor imagery are able to identify an individual’s intent to initiate control through the classification of encephalography patterns. Correctly classifying such patterns is instrumental and strongly depends in a robust machine learning block that is able to properly...

Curve and surface fitting are important and attractive problems in many applied domains, from CAD techniques to geological prospections. Different methodologies have been developed to find a curve or a surface that best describes some 2D or 3D data, or just to approximate some function of one or several...

This paper discusses the so-called missing data problem, i.e. the problem of imputing missing values in information systems. A new algorithm, called the ARSI algorithm, is proposed to address the imputation problem of missing values on categorical databases using the framework of rough set theory. This...

Cluster Validity Indices (CVI) evaluate the efficiency of a clustering algorithm and Data Envelopment Analysis (DEA) evaluate the efficiency of Decision-Making Units (DMUs) using a number of inputs data and outputs data. Combination of the CVI and DEA inspired the development of a new automatic clustering...

A measure of knowledge may be viewed as a dual measure of entropy in a fuzzy system; thus, it appears that the less entropy may always accompany the greater amount of knowledge. In this paper, we propose a novel measure of knowledge for an intuitionistic fuzzy set (IFS) through an axiomatic approach....

Due to the difficult problem of avoiding obstacles to achieve the complete coverage path planning (CCPP) for special missions, this paper introduces a novel integrated algorithm of CCPP for autonomous mobile robot under an obstacles-included environment. The algorithm combines cellular decomposition...