H.3. Artificial Intelligence
Z. Sedighi; R. Boostani
Abstract
Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated ...
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Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised one. To estimate the density distribution of data, Wiebull Mixture Model (WMM) is utilized due to its high flexibility. Another contribution of this study is to propose a new hill and valley seeking algorithm to find the constraints for semi-supervise algorithm. It is assumed that each density peak stands on a cluster center; therefore, neighbor samples of each center are considered as must-link samples while the near centroid samples belonging to different clusters are considered as cannot-link ones. The proposed approach is applied to a standard image dataset (designed for clustering evaluation) along with some UCI datasets. The achieved results on both databases demonstrate the superiority of the proposed method compared to the conventional clustering methods.
H.3. Artificial Intelligence
F. Barani; H. Nezamabadi-pour
Abstract
Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent behavior of honey bees when searching for food sources. The various versions of the ABC algorithm have been widely used to solve continuous and discrete optimization problems in different ...
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Artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the intelligent behavior of honey bees when searching for food sources. The various versions of the ABC algorithm have been widely used to solve continuous and discrete optimization problems in different fields. In this paper a new binary version of the ABC algorithm inspired by quantum computing, called binary quantum-inspired artificial bee colony algorithm (BQIABC), is proposed. The BQIABC combines the main structure of ABC with the concepts and principles of quantum computing such as, quantum bit, quantum superposition state and rotation Q-gates strategy to make an algorithm with more exploration ability. The proposed algorithm due to its higher exploration ability can provide a robust tool to solve binary optimization problems. To evaluate the effectiveness of the proposed algorithm, several experiments are conducted on the 0/1 knapsack problem, Max-Ones and Royal-Road functions. The results produced by BQIABC are compared with those of ten state-of-the-art binary optimization algorithms. Comparisons show that BQIABC presents the better results than or similar to other algorithms. The proposed algorithm can be regarded as a promising algorithm to solve binary optimization problems.
H.3. Artificial Intelligence
Seyed M. H. Hasheminejad; Z. Salimi
Abstract
One of the recent strategies for increasing the customer’s loyalty in banking industry is the use of customers’ club system. In this system, customers receive scores on the basis of financial and club activities they are performing, and due to the achieved points, they get credits from the ...
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One of the recent strategies for increasing the customer’s loyalty in banking industry is the use of customers’ club system. In this system, customers receive scores on the basis of financial and club activities they are performing, and due to the achieved points, they get credits from the bank. In addition, by the advent of new technologies, fraud is growing in banking domain as well. Therefore, given the importance of financial activities in the customers’ club system, providing an efficient and applicable method for detecting fraud is highly important in these types of systems. In this paper, we propose a novel sliding time and scores window-based method, called FDiBC (Fraud Detection in Bank Club), to detect fraud in bank club. In FDiBC, firstly, based on each score obtained by customer members of bank club, 14 features are derived, then, based on all the scores of each customer member, five sliding time and scores window-based feature vectors are proposed. For generating training and test data set from the obtained scores of fraudster and common customers in the customers’ club system of a bank, a positive and a negative label are used, respectively. After generating training data set, learning is performed through two approaches: 1) clustering and binary classification with OCSVM method for positive data, i.e. fraudster customers, and 2) multi-class classification including SVM, C4.5, KNN, and Naïve Bayes methods. The results reveal that FDiBC has the ability to detect fraud with 78% accuracy and thus can be used in practice.
H.3. Artificial Intelligence
F. Fadaei Noghani; M. Moattar
Abstract
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost ...
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Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effective features, using an extended wrapper method, ensemble classification is performed. The extended feature selection approach includes a prior feature filtering and a wrapper approach using C4.5 decision tree. Ensemble classification, using cost sensitive decision trees is performed in a decision forest framework. A locally gathered fraud detection dataset is used to estimate the proposed method. The proposed method is assessed using accuracy, recall, and F-measure as evaluation metrics and compared with basic classification algorithms including ID3, J48, Naïve Bayes, Bayesian Network and NB tree. Experiments show that considering the F-measure as evaluation metric, the proposed approach yields 1.8 to 2.4 percent performance improvement compared to other classifiers.
H.3. Artificial Intelligence
V. Ghasemi; A. Pouyan; M. Sharifi
Abstract
This paper proposes a scheme for activity recognition in sensor based smart homes using Dempster-Shafer theory of evidence. In this work, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference architecture. The belief masses are calculated using ...
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This paper proposes a scheme for activity recognition in sensor based smart homes using Dempster-Shafer theory of evidence. In this work, opinion owners and their belief masses are constructed from sensors and employed in a single-layered inference architecture. The belief masses are calculated using beta probability distribution function. The frames of opinion owners are derived automatically for activities, to achieve more flexibility and extensibility. Our method is verified via two experiments. In the first experiment, it is compared to a naïve Bayes approach and three ontology based methods. In this experiment our method outperforms the naïve Bayes classifier, having 88.9% accuracy. However, it is comparable and similar to the ontology based schemes, but since no manual ontology definition is needed, our method is more flexible and extensible than the previous ones. In the second experiment, a larger dataset is used and our method is compared to three approaches which are based on naïve Bayes classifiers, hidden Markov models, and hidden semi Markov models. Three features are extracted from sensors’ data and incorporated in the benchmark methods, making nine implementations. In this experiment our method shows an accuracy of 94.2% that in most of the cases outperforms the benchmark methods, or is comparable to them.
H.3. Artificial Intelligence
T. Zare; M. T. Sadeghi; H. R. Abutalebi; J. Kittler
Abstract
Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared ...
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Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the topic of metric learning, especially using kernel functions, which map data to feature spaces with enhanced class separability, and implicitly define a new metric in the original feature space. The formulation of the problem of metric learning depends on the supervisory information available for the task. In this paper, we focus on semi-supervised kernel based distance metric learning where the training data set is unlabelled, with the exception of a small subset of pairs of points labelled as belonging to the same class (cluster) or different classes (clusters). The proposed method involves creating a pool of kernel functions. The corresponding kernels matrices are first clustered to remove redundancy in representation. A composite kernel constructed from the kernel clustering result is then expanded into an orthogonal set of basis functions. The mixing parameters of this expansion are then optimised using point similarity and dissimilarity information conveyed by the labels. The proposed method is evaluated on synthetic and real data sets. The results show the merit of using similarity and dissimilarity information jointly as compared to using just the similarity information, and the superiority of the proposed method over all the recently introduced metric learning approaches.
H.3. Artificial Intelligence
M. Heidarian; H. Jalalifar; F. Rafati
Abstract
Uniaxial compressive strength (UCS) and internal friction coefficient (µ) are the most important strength parameters of rock. They could be determined either by laboratory tests or from empirical correlations. The laboratory analysis sometimes is not possible for many reasons. On the other hand, ...
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Uniaxial compressive strength (UCS) and internal friction coefficient (µ) are the most important strength parameters of rock. They could be determined either by laboratory tests or from empirical correlations. The laboratory analysis sometimes is not possible for many reasons. On the other hand, Due to changes in rock compositions and properties, none of the correlations could be applied as an exact universal correlation. In such conditions, the artificial intelligence could be an appropriate candidate method for estimation of the strength parameters. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) which is one of the artificial intelligence techniques was used as dominant tool to predict the strength parameters in one of the Iranian southwest oil fields. A total of 655 data sets (including depth, compressional wave velocity and density data) were used. 436 and 219 data sets were randomly selected among the data for constructing and verification of the intelligent model, respectively. To evaluate the performance of the model, root mean square error (RMSE) and correlation coefficient (R2) between the reported values from the drilling site and estimated values was computed. A comparison between the RMSE of the proposed model and recently intelligent models shows that the proposed model is more accurate than others. Acceptable accuracy and using conventional well logging data are the highlight advantages of the proposed intelligent model.
H.3. Artificial Intelligence
M. Vahedi; M. Hadad Zarif; A. Akbarzadeh Kalat
Abstract
This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. The contribution ...
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This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. The contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of induction motors. The online training of the neuro-fuzzy systems is based on the Lyapunov stability analysis and the reconstruction errors of the neuro-fuzzy systems are compensated in order to guarantee the asymptotic convergence of the speed tracking error. Moreover, to improve the control system performance and reduce the chattering, a PI structure is used to produce the input of the neuro-fuzzy systems. Finally, simulation results verify high performance characteristics and robustness of the proposed control system against plant parameter variation, external load and input voltage disturbance.
H.3. Artificial Intelligence
H. Motameni
Abstract
This paper proposes a method to solve multi-objective problems using improved Particle Swarm Optimization. We propose leader particles which guide other particles inside the problem domain. Two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. The ...
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This paper proposes a method to solve multi-objective problems using improved Particle Swarm Optimization. We propose leader particles which guide other particles inside the problem domain. Two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. The first one is based on the mean of the m optimal particles and the second one is based on appointing a leader particle for any n founded particles. We used an intensity criterion to delete the particles in both techniques. The proposed techniques were evaluated based on three standard tests in multi-objective evolutionary optimization problems. The evaluation criterion in this paper is the number of particles in the optimal-Pareto set, error, and uniformity. The results show that the proposed method searches more number of optimal particles with higher intensity and less error in comparison with basic MOPSO and SIGMA and CMPSO and NSGA-II and microGA and PAES and can be used as proper techniques to solve multi-objective optimization problems.
H.3. Artificial Intelligence
Y. Vaghei; A. Farshidianfar
Abstract
In recent years, underactuated nonlinear dynamic systems trajectory tracking, such as space robots and manipulators with structural flexibility, has become a major field of interest due to the complexity and high computational load of these systems. Hierarchical sliding mode control has been investigated ...
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In recent years, underactuated nonlinear dynamic systems trajectory tracking, such as space robots and manipulators with structural flexibility, has become a major field of interest due to the complexity and high computational load of these systems. Hierarchical sliding mode control has been investigated recently for these systems; however, the instability phenomena will possibly occur, especially for long-term operations. In this paper, a new design approach of an adaptive fuzzy hierarchical terminal sliding-mode controller (AFHTSMC) is proposed. The sliding surfaces of the subsystems construct the hierarchical structure of the proposed method; in which the top layer includes all of the subsystems’ sliding surfaces. Moreover, terminal sliding mode has been implemented in each layer to ensure the error convergence to zero in finite time besides chattering reduction. In addition, online fuzzy models are employed to approximate the two nonlinear dynamic system’s functions. Finally, a simulation example of an inverted pendulum is proposed to confirm the effectiveness and robustness of the proposed controller.
