International Journal of Computational Intelligence Systems

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...

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...

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...

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 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...

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...

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...

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...

Distributed Permutation Flowshop Scheduling Problem (DPFSP) is a newly proposed scheduling problem, which is a generalization of classical permutation flow shop scheduling problem. The DPFSP is NP-hard in general. It is in the early stages of studies on algorithms for solving this problem. In this paper,...

The fuzzy set theory addresses the development of concepts and techniques for dealing with uncertainty or impression conditions. If the collected data from a process include vagueness due to human subjectively or measurement system, fuzzy control charts are available tools for monitoring and evaluating...

No-wait flowshop scheduling problem (NW-FSSP) with the objective to minimize the makespan is an important sequencing problem in the production plans and applications of no-wait flowshops can be found in several industries. In a NW-FSSP, jobs are not allowed to wait between two successive machines. The...

This paper proposes an enhanced Multi-objective Go with the Winners (MOGWW) algorithm to solve multi-objective combinatorial optimization problems. The original MOGWW algorithm is equipped with the well known Pareto Local Search (PLS) procedure. In order to assess the performance of the hybridization,...

The variability in travel times is a very important feature in determining the arrival of the passenger at the destination within a given travel time threshold and can be noted as one of the most important characteristic for travel time reliability that is an increasing concern of travelers, because...

Ant colony optimization (ACO) takes inspiration from the foraging behaviour of real ant species. This ACO exploits a similar mechanism for solving optimization problems for the various engineering field of study. Many successful implementations using ACO are now available in many applications. This paper...

SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis is a commonly used and an important technique for analyzing internal and external environments in order to provide a systematic approach and support for a decision making. SWOT is criticized mostly for considering only qualitative examination...

For secure biometric verification, most existing methods embed biometric information directly into the cover image, but content correlation analysis between the biometric image and the cover image is often ignored. In this paper, we propose a novel biometric image hiding approach based on the content...

Among the various discriminant analysis (DA) methods, researchers have investigated several directions in this area: statistics, econometrics, computer data mining technologies and mathematical programming. Recently, as a nonparametric mathematical programming approach, Data envelopment analysis has...

Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance between features and decision. However, it is difficult to directly calculate relevance between continuous or fuzzy features using mutual information....

A new hybrid adaptive control algorithm is proposed for the nonlinear system controller design of underwater robot. Compared with the previous works in the controller design of underwater robot, the main advantages of this work are: (1) A new disturbance prediction and compensation model is proposed;...

This paper investigates a novel approach to the design of two-dimensional recursive digital filters using differential evolution (DE) algorithm. The design task is reformulated as a constrained minimization problem and is solved by an Self-adaptive Mutation DE algorithm (SAMDE), which adopts an adaptive...

Feature selection is an important technique for dimension reduction in machine learning and pattern recognition communities. Feature evaluation functions play essential roles in constructing feature selection algorithms. This paper introduces a new notion of knowledge granularity, called conditional...

Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture...

In this work we predict time series of air pollution data taken in Mexico City and the Valley of Mexico, by using the Gamma Classifier which is a novel intelligent associative mathematical model, coupled with an emergent coding technique. Historical and current data about the concentration of specific...

For the fuzzy systems with the kernel-shaped fuzzy sets of if part, we estimate the rates of the uniform approximation for continuous functions. Results are given associatively with the rates of convergence of the sequence (logk/k)a.

This paper proposes the concept of intervention rule which tries to reveal the interventional relationship between elements in a system in the following three aspects. (1) Casual relationship. Intervention rule shows which element is the cause and which element is the consequence. (2) Quantitative relationship:...

This paper analyzes the collective behaviors of swarm robots that play role in the aggregation scenario. Honeybee aggregation is an inspired behavior of young honeybees which tend to aggregate around an optimal zone. This aggregation is implemented based on variation of parameters values. In the second...