International Journal of Computational Intelligence Systems

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....

Replication is a useful technique for distributed database systems and can be implemented in a grid computation environment to provide a high availability, fault tolerant, and enhance the performance of the system. This paper discusses a new protocol named Diagonal Data Replication in 2D Mesh structure...

In order to make Intelligent Transportation System (ITS) work effectively, a driver intention recognition method is proposed. In this research, three different recognition models were developed based on Continuous Hidden Markov Model (CHMM), and could distinguish left and right lane change intention...

In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn...

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...

Multi-attribute decision-making (MADM) has been receiving great attention in recent years due to two major issues which are basically to describe attribute values and secondly to aggregate the described information to generate a ranking of alternatives. For the first case it entails the hesitant fuzzy...

In the ongoing era of flourishing e-commerce, people prefer online purchasing products and services to save time. These online purchase decisions are mostly influenced by the reviews/opinions of others who already have experienced them. Malicious users use this experience sharing to promote or degrade...

We put forward a completely redesigned approach to soft set based decision making problems under incomplete information. An algorithmic solution is proposed and compared with previous approaches in the literature. The computational performance of our algorithm is critically analyzed by an experimental...

This paper proposes an improved intelligent vehicle localization method, which integrates the data from the odometry and the magnetic ruler(MR). The MR is used to detect the magnetic markers, which provides the absolute positioning reference. The data from odometry and the MR are used to localize the...

The new concept of a – number has been recently introduced in decision making analysis. This concept is capable of effectively dealing with uncertainty in information about a decision. As this concept is relatively new in fuzzy sets, its underlying theoretical aspects have not been established...

In this paper a comprehensive framework that maximizes advertising revenues of a company in a personalized advertisement setting is presented. The research was motivated from a real life problem faced by a company that develops a web based 3-D virtual reality social platform. The objective of the research...

This paper presents a comprehensive overview of currently known applications of computing with words (CWW) in risk assessment. It is largely grouped into the following 5 categories: (1) fuzzy number based risk assessment; (2) fuzzy rule-based risk assessment; (3) fuzzy extension of typical probabilistic...

In the text literature, many topic models were proposed to represent documents and words as topics or latent topics in order to process text effectively and accurately. In this paper, we propose LDACLM or Latent Dirichlet Allocation Category LanguageModel for text categorization and estimate parameters...

This paper proposes a novel consensus reaching model for multi-attribute group decision making (MAGDM) with information represented by means of linguistic distribution assessments. Firstly, some drawbacks of the existing distance measures for linguistic distribution assessments are analyzed by using...

Information fusion methods based on Dempster-Shafer evidence theory (DST) have been widely used in fault diagnosis. In DST-based methods, the monitoring information collected from sensors is modeled as multiple pieces of diagnosis evidence in the form of basic belief assignment (BBA), and Dempster’s...

Because of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. Artificial market mechanisms are one of the well-known negotiation multi-agent protocols in multi-agent systems. In this paper artificial...

The paper discusses the stability of workpiece-fixture system and quantitative optimization of clamping forces during precise machining. Based on the force screw theory and the minimum norm principle, a mathematical model is formulated to calculate the entire passive forces acting on the workpiece. Furthermore,...

Introducing cooperation into a wireless sensor network (WSN) has gained much attention in the recent few years mainly because of the significant effect it has on optimizing energy consumption and on enhancing the lifetime and the overall performance of the network. Cooperation can be exploited at different...

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...

The development of a computational tool for the recognition of numerical characters already segmented from Chilean license plates, located in semi-controlled scenarios, is presented. Two algorithms are highlighted: one for the Fine Segmentation of the Characters through the K-Means algorithm and a system...

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...

Under the background of economic globalization, selecting a path of low-carbon economic development and developing green supply chains are the inevitable choice of realizing the sustainable development for the enterprises. In this paper, we investigate the optimization decision problem of supplier selection...

A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster...

In this paper we study the relations among similarity measure, subsethood measure and fuzzy entropy and present several propositions that similarity measure, subsethood measure and fuzzy entropy can be transformed by each other based on their axiomatic definitions. Some new formulae to calculate similarity...

Traditional quantum-inspired genetic algorithm (QGA) has drawbacks such as premature convergence, heavy computational cost, complicated coding and decoding process etc. In this paper, a novel real-coded quantum-inspired genetic algorithm is proposed based on interval division thinking. Detailed comparisons...

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...

The complex nature of the realistic decision-making process requires the use of Pythagorean fuzzy (PF) sets which have been shown to be a highly promising tool capable of solving highly vague and imprecise problems. Multiple criteria decision analysis (MCDA) methods within the PF environment are very...

In the last two decades, evolutionary computing has become the mainstream to attract the attention of the experts in both academia and industrial applications due to the advent of the fast computer with multi-core GHz processors have had a capacity of processing over 100 billion instructions per second....

Based on the general form of α-resolution principle for a lattice-valued logic with truth-values defined in a lattice-valued logical algebra structure - lattice implication algebra, the further extended α-resolution method in this lattice-valued logic is discussed in the present paper in...

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...

This paper presents a survey of some fuzzy linguistic information access systems. The review shows information retrieval systems, filtering systems, recommender systems, and web quality evaluation tools, which are based on tools of fuzzy linguistic modelling. The fuzzy linguistic modelling allows us...

The recent years have seen a rise in the number of cases of cyber-crime committed through identity theft and fraud. To address this problem, this paper uses adaptive neural-fuzzy inference system, fuzzy logic and artificial neural network to implement a multifactor authentication system through a technique...

This paper presents a new adaptive learning algorithm to automatically design a neural fuzzy model. This constructive learning algorithm attempts to identify the structure of the model based on an architectural self-organization mechanism with a data-driven approach. The proposed training algorithm self-organizes...

Emerging information system architectures will often be comprised of distributed systems and data repositories. As a result, providing efficient and secure query assurance over these emerging future information systems is a concern. This paper details the use of temporary time stamps and variable hash...

The big data term is used to describe the exponential data growth that has recently occurred and represents an immense challenge for traditional learning techniques. To deal with big data classification problems we propose the Chi-FRBCS-BigData algorithm, a linguistic fuzzy rule-based classification...

Multiple attribute decision analysis (MADA) problems often include both qualitative and quantitative attributes which may be either precise or inaccurate. The evidential reasoning (ER) approach is one of reliable and rational methods for dealing with MADA problems and can generate aggregated assessments...

We are interested in this paper in studying and developing a decision support tool for multi-skill health care tasks scheduling in the Pediatric Emergency Department. We use an evolutionary algorithm and we propose the use of fuzzy logic to formulate an adapted fitness function. We consider the potential...

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...

Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently,...

Tools and machines have an important effect on the manufacturing operations’ effectiveness and the selection process of appropriate tools and machines is a complex issue with the consideration of multiple criteria. Considering the complexity of the problem area and the difficulties in machine tools...

One of the most crucial problems in a peer-to-peer system is locating of resources that are shared by various nodes. Various techniques suggested in literature suffer from drawbacks viz. saturation of network, inability to locate multi-keyword based resource or locate resource based on semantics. We...

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 negative association between items in databases is as important and interesting as the positive one. But, it has not been studied as much. We consider negative association in a hierarchical setting, in which we are able to generate negative association rules at different hierarchy levels. It allows...

Due to the high uncertainty of business environment, the complexity and diversity of enterprise resource planning (ERP) projects and conflicting assessment criteria, appropriate ERP software selection can be viewed as a multi-criteria decision-making (MCDM) problem. Among the MCDM methods, extent analysis...

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...

The paper proposes a fuzzy logic-based group decision making (GDM) approach, which can be used for quality function deployment (QFD) in the development of product improvement strategies. Decision makers can state their preferences in various ways, including incomplete preferences which are difficult...

The purpose of this paper is to present a framework and a suitable method for selecting the best logistics supplier. In general, many quantitative and qualitative criteria should be considered simultaneously for making the decision of logistics supplier selection. The information about judging the performance...

Survival prediction of poly-trauma patients measure the quality of emergency services by comparing their predictions with the real outcomes. The aim of this paper is to tackle this problem applying C4.5 since it achieves accurate results and it provides interpretable models. Furthermore, we use sampling...

High dimensionality tends to be a challenge for most machine learning tasks, including classification. There are different classification methodologies, of which instance-based learning is one. One of the best known members of this family is the k-nearest neighbors (kNNs) algorithm. Its strategy relies...

The success and proliferation of the Semantic Web depends heavily on construction of Web ontologies. However, classical ontology construction approaches are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. Therefore, great efforts on...

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...

In this paper, hidden conflicts of belief functions are observed in the case where the sum of all multiples of the conflicting belief masses equals zero. Degrees of hidden conflicts and a degree of non-conflictness are defined and analyzed, including full non-conflictness. The notion of a hidden conflict...

This work focuses on obtaining an automatic presentation generator for a robot guide. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. This fuzzy system is used to select the most appropriate group of paragraphs for...

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)...

In this paper some axiomatic definitions about specificity for interval-valued fuzzy sets are proposed. Some examples of measures of specificity for interval-valued fuzzy sets are showed. It is also defined a extension of the notion of alpha cut for interval-valued fuzzy sets and a generalized similarity...

Fabric selection plays an important role in fashion garment design. Designers often use both physical and normalized linguistic criteria for fabric selection. Perception and preference of consumers in their specific sociocultural context, expressed by fashion themes or emotional linguistic criteria,...

Opinion surveys are widely admitted as a valuable source of information which becomes complementary to the information extracted from data by machine learning techniques. This paper focuses on a challenging and still open problem which is related to how to handle properly the inherent uncertainty of...

Hybrid sorting immune simulated annealing technique (HSISAT), a Meta - heuristic is proposed for solving the multi objective flexible job-shop scheduling problem (FJSP). The major objectives are distributing the time of machines among the set of operations and scheduling them to minimize the criterion...

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...

This paper presents an integrated Artificial Neural Network - Hybrid Meta Heuristic(ANN-HMH) method to solve the nonlinear time-cost tradeoff(TCT) problem of real life engineering projects. ANN models help to capture the existing nonlinear time-cost relationship in project activities. ANN models are...

This paper deals with the NP-hard single-machine total weighted tardiness problem with sequence dependent setup times. Incorporating fuzzy sets and genetic operators, a novel ant colony optimization algorithm is developed for the problem. In the proposed algorithm, artificial ants construct solutions...

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...

In this paper, a new method is proposed to identify the parameters of fractional order chaotic system. The parameter identification is achieved by minimizing the mean square error between the states of original fractional chaotic system and those of the estimated one, in which the parameters to be identified...

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected...

Intuitionistic fuzzy preference relation (IFPR), which express experts’ preferences from the preferred, the non-preferred and the indeterminate aspects, has turned out to be an efficient tool in describing the rough and subjective opinions of experts. This paper focuses on the consensus measures for...

Renewable energy is the energy generated from natural resources such as sunlight, wind, rain, tides and geothermal heat. Turkey has a great renewable energy potential with its natural resources such as biomass, geothermal, hydropower, solar, and wind. Selection among energy alternatives is a multicriteria...

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...

The research data in this manuscript is drawn from four main sections: First, the union and the intersection of an arbitrary family of soft sets are introduced and further results for various soft set operations are obtained. Second, by using the soft relation introduced by Babitha and Sunil, its some...

A new framework, , for multicriteria decision analysis (MCDA) of a wide range of practical problems on risk management is introduced. framework contains a library of modules that are the basis for two scalable systems: for analysis of multicriteria problems, and for multicriteria analysis of spatial...

This paper proposed a framework of segmenting multiple targets basing on multiple shape priors. The key novel idea in the proposed framework is the shape degeneration model. By breaking the balance between the competition regions adaptively, the proposed model can degenerate the region which is with...

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...

Growth phenomena are ubiquitous and pervasive not only in biology and the medical sciences, but also in economics, marketing and the computer and social sciences. We introduce a three-parameter version of the classic pure-birth process growth model when suitably instantiated, can be used to model growth...

Least Squares Support Vector Machine (LS-SVM) is powerful to solve problems such as multi-class nonlinear classification. In this study, a LS-SVM with kernel-based was applied to multi-class IC packaging type dataset classification problem. In the first stage, the greedy search algorithm in feature selection...

Multi-groups decision making (MGDM) problems, which contain multiple groups of experts acting collectively to evaluate a set of alternatives with respect to several criteria, are focused on in this study. The existing solutions for MGDM are to aggregate the evaluations three times at different levels,...

This paper identifies a new paradigm of prediction, of ongoing temporal sequences, which achieves an acceptable accuracy just by the historical subsequences as short as possible and as close to the predicted time point as possible. To address agile prediction, a new concept, (DRS for short), is first...

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...

The evaluation of solutions is an important phase in power distribution system planning (PDSP) which allows issues such as quality of supply, cost, social service and environmental implications to be considered and usually involves the judgments of a group of experts. The planning problem is thus suitable...

Indeed, interesting properties of artificial neural networks approach made this non-parametric model a powerful tool in solving various complicated mathematical problems. The current research attempts to produce an approximate polynomial solution for special type of fractional order Volterra integro-differential...

Chinese named entity recognition (NER) has important effect on natural language processing (NLP) applications. This recognition task is complicated in its strong dependent-relation, missing delimiters in the text and insufficient feature representation in a single model. This paper thus proposes a dual...

This paper proposes a comparative model for the day-ahead electricity price forecasting that could be realized using multi-layer neural network (MLNN) with levenberg-marquardt (LM) algorithm, generalized regression neural network (GRNN) and cascade-forward neural network (CFNN). In this work applications...

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...

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...

This paper aims to study the lightpaths provisioning problem in optical WDM networks with scarce available wavelengths under the static (off-line) traffic demands such that network operator’s (NO’s) revenue is maximized. To achieve this goal, a NO has to be addressed with the issue how to solve the call...

Feature extraction and selection are the most important techniques for ultrasonic flaw signal classification. In this study, empirical mode decomposition (EMD) is used to obtain the intrinsic mode functions (IMFs) of original signal, and their corresponding traditional time and frequency domain based...

Universal gravitation is a natural phenomenon. Inspired by Newton's universal gravitation model and based on binary differences strategy, we propose an algorithm for global optimization problems, which is called the binary difference gravitational evolution (BDGE) algorithm. BDGE is a population-based...

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...

Performance appraisal is a process used by companies in order to evaluate their employees' efficiency and productivity for planning their Human Resource policies. Traditionally this process has just been carried out by the executive staff but recently it has started to be based on opinions of different...

In this paper, the problem of grouping machines and parts into cells (machine-part cell formation problem) is considered with the objective of minimizing grouping efficacy. An ant colony optimization algorithm is developed to solve such problem. In the proposed algorithm, solutions are constructed in...

A mass of information grows explosively in socially networked industries, as extensive data, such as images and texts, is captured by vast sensors. Pedestrians are the main initiators of various activities in socially networked industries, hence, it is very important to quickly obtain relevant information...

Risk assessment is a very important issue for an effective institution, since the lack of accurate risk assessment method or the improper risk management might cause problems to achieve institutions’ strategic objectives. There are a finite number of risks which have to be ranked considering many...

There are usually some leftovers (usable pieces of raw material) and scraps (unusable pieces of raw material).generated after the completion of a manufacturing process. These leftovers consist of many different types, materials, styles and sizes so the use of such materials is difficult to manage, resulting...

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...

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...

Incremental Attribute Learning (IAL) is a feasible approach for solving high-dimensional pattern recognition problems. It gradually trains features one by one. Previous research indicated that supervised machine learning with input attribute ordering can improve classification results. Moreover, input...

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...

In pervasive computing systems, service selection and fault tolerance are two primary factors, which determines reliability and efficient of service selection system. In this paper, according on ANN-based (Artificial Neural Network) service selection model, we extend the definition and formal description...

In medical field visualization of the organs is very imperative for accurate diagnosis and treatment of any disease. Brain tumor diagnosis and surgery also required impressive 3D visualization of the brain to the radiologist. Detection and 3D reconstruction of brain tumors from MRI is a computationally...

Though cloud computing has become an attractive technology due to its openness and services, it brings several security hazards towards cloud storage. Since the distributed nature of clouds is achieved through internetworking technologies, clouds suffer from all the vulnerabilities by which networking...

In social network group decision-making, adjusting the opinions of decision makers is often used to promote consensus. But decision makers are not always willing to accept the feedback mechanism, and decision-makers' trust relationships are not constant during the consensus process. This paper constructs...