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  • 1
    Language: English
    In: Applied Soft Computing Journal, 2011, Vol.11(2), pp.2334-2347
    Description: The need for solving multi-modal optimization problems in high dimensions is pervasive in many practical applications. Particle swarm optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best ( ) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. In the first approach, is updated (moved) with respect to a global estimation of the gradient of the underlying (error) surface or function and hence can avoid getting trapped into a local optimum. The second approach is based on the formation of an alternative or artificial global best particle, the so-called , which can replace the native particle for a better guidance, the decision of which is held by a fair competition between the two. For this purpose we use simultaneous perturbation stochastic approximation (SPSA) for its low cost. Since SPSA is applied only to the (not to the entire swarm), both approaches result thus in a negligible overhead cost for the entire PSO process. Both approaches are shown to significantly improve the performance of PSO over a wide range of non-linear functions, especially if SPSA parameters are well selected to fit the problem at hand. A major finding of the paper is that even if the SPSA parameters are not tuned well, results of SA-driven (SAD) PSO are still better than the best of PSO and SPSA. Since the problem of poor update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), both approaches are also integrated into MD-PSO and tested over a set of unsupervised data clustering applications. As in the basic PSO application, experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.
    Keywords: Particle Swarm Optimization ; Stochastic Approximation ; Multi-Dimensional Search ; Gradient Descent ; Computer Science
    ISSN: 1568-4946
    E-ISSN: 18729681
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  • 2
    Language: English
    In: Expert Systems With Applications, 2011, Vol.38(3), pp.2212-2223
    Description: ► Multi-dimensional particle swarm optimization. ► Fractional global-best formation. ► Optimization in dynamic environments. ► Global optimum tracking. Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the pre-mature convergence problem and improve the rate of PSO’s convergence to the global optimum, Fractional Global Best Formation (FGBF) technique is used. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle ( ) that has the potential to be a better “guide” than the PSO’s native gbest particle. To establish follow-up of local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. Finally for the multi-dimensional dynamic environments where the optimum dimension also changes in time, we utilize a recent PSO technique, the multi-dimensional (MD) PSO, which re-forms the native structure of the swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually pushes the frontier of the optimization problems in dynamic environments towards a global search in a multi-dimensional space, where there exists a multi-modal problem possibly in each dimension. We investigated both standalone and mutual applications of the proposed methods over the moving peaks benchmark (MPB), which originally simulates a dynamic environment in a unique (fixed) dimension. MPB is appropriately extended to accomplish the simulation of a multi-dimensional dynamic system, which contains dynamic environments active in several dimensions. An extensive set of experiments show that in traditional MPB application domain, FGBF technique applied with multi-swarms exhibits an impressive speed gain and tracks the global peak with the minimum error so far achieved with respect to the other competitive PSO-based methods. When applied over the extended MPB, MD PSO with FGBF can find optimum dimension and provide the (near-) optimal solution in this dimension.
    Keywords: Particle Swarm Optimization ; Multi-Dimensional Search ; Fractional Global Best Formation ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 3
    Language: English
    In: Signal Processing: Image Communication, March 2014, Vol.29(3), pp.410-423
    Description: In this paper, we propose a novel and robust modus operandi for fast and accurate shot boundary detection where the whole design philosophy is based on human perceptual rules and the well-known “Information Seeking Mantra”. By adopting a top–down approach, redundant video processing is avoided and furthermore elegant shot boundary detection accuracy is obtained under significantly low computational costs. Objects within shots are detected via local image features and used for revealing visual discontinuities among shots. The proposed method can be used for detecting all types of gradual transitions as well as abrupt changes. Another important feature is that the proposed method is fully generic, which can be applied to any video content without requiring any training or tuning in advance. Furthermore, it allows a user interaction to direct the SBD process to the user's “Region of Interest” or to stop it once satisfactory results are obtained. Experimental results demonstrate that the proposed algorithm achieves superior computational times compared to the state-of-art methods without sacrificing performance.
    Keywords: Video Shot Boundary Detection ; Human Perception ; Local Image Features ; Video Content Analysis ; Engineering ; Applied Sciences ; Computer Science
    ISSN: 0923-5965
    E-ISSN: 1879-2677
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  • 4
    Language: English
    In: Neurocomputing, 08 February 2017, Vol.224, pp.142-154
    Description: There are well-known limitations and drawbacks on the performance and robustness of the feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi-Layer Perceptrons (MLPs). In this study we shall address them by Generalized Operational Perceptrons (GOPs) that consist of neurons with distinct (non-)linear operators to achieve a generalized model of the biological neurons and ultimately a superior diversity. We modified the conventional back-propagation (BP) to train GOPs and furthermore, proposed Progressive Operational Perceptrons (POPs) to achieve self-organized and depth-adaptive GOPs according to the learning problem. The most crucial property of the POPs is their ability to simultaneously search for the optimal operator set and train each layer individually. The final POP is, therefore, formed layer by layer and in this paper we shall show that this ability enables POPs with minimal network depth to attack the most challenging learning problems that cannot be learned by conventional ANNs even with a deeper and significantly complex configuration. Experimental results show that POPs can scale up very well with the problem size and can have the potential to achieve a superior generalization performance on real benchmark problems with a significant gain.
    Keywords: Artificial Neural Networks ; Multi-Layer Perceptrons ; Progressive Operational Perceptrons ; Diversity ; Scalability ; Computer Science
    ISSN: 0925-2312
    E-ISSN: 1872-8286
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  • 5
    Language: English
    In: Expert Systems With Applications, 2012, Vol.39(5), pp.4710-4717
    Description: In this paper, a robust radial basis function (RBF) network based classifier is proposed for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix elements, the H/α/A decomposition based features combined with the backscattering power (span), and the gray level co-occurrence matrix (GLCM) based texture features, which are projected onto a lower dimensional feature space using principal components analysis. For the classifier training, both conventional backpropagation (BP) and multidimensional particle swarm optimization (MD-PSO) based dynamic clustering are explored. By combining complete polarimetric covariance matrix and eigenvalue decomposition based pixel values with textural information (contrast, correlation, energy, and homogeneity) in the feature set, and employing automated evolutionary RBF classifier for the pattern recognition unit, the overall classification performance is shown to be significantly improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with the major state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.
    Keywords: Polarimetric Synthetic Aperture Radar ; Radial Basis Function Network ; Particle Swarm Optimization ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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  • 6
    Description: Outliers are samples that are generated by different mechanisms from other normal data samples. Graphs, in particular social network graphs, may contain nodes and edges that are made by scammers, malicious programs or mistakenly by normal users. Detecting outlier nodes and edges is important for data mining and graph analytics. However, previous research in the field has merely focused on detecting outlier nodes. In this article, we study the properties of edges and propose outlier edge detection algorithms using two random graph generation models. We found that the edge-ego-network, which can be defined as the induced graph that contains two end nodes of an edge, their neighboring nodes and the edges that link these nodes, contains critical information to detect outlier edges. We evaluated the proposed algorithms by injecting outlier edges into some real-world graph data. Experiment results show that the proposed algorithms can effectively detect outlier edges. In particular, the algorithm based on the Preferential Attachment Random Graph Generation model consistently gives good performance regardless of the test graph data. Further more, the proposed algorithms are not limited in the area of outlier edge detection. We demonstrate three different applications that benefit from the proposed algorithms: 1) a preprocessing tool that improves the performance of graph clustering algorithms; 2) an outlier node detection algorithm; and 3) a novel noisy data clustering algorithm. These applications show the great potential of the proposed outlier edge detection techniques. Comment: 14 pages, 5 figures, journal paper
    Keywords: Computer Science - Social And Information Networks ; Physics - Physics And Society
    Source: Cornell University
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  • 7
    Description: Due to the nonlinearity of artificial neural networks, designing topologies for deep convolutional neural networks (CNN) is a challenging task and often only heuristic approach, such as trial and error, can be applied. An evolutionary algorithm can solve optimization problems where the fitness landscape is unknown. However, evolutionary algorithms are computing resource intensive, which makes it difficult for problems when deep CNNs are involved. In this paper, we propose an evolutionary strategy to find better topologies for deep CNNs. Incorporating the concept of knowledge inheritance and knowledge learning, our evolutionary algorithm can be executed with limited computing resources. We applied the proposed algorithm in finding effective topologies of deep CNNs for the image classification task using CIFAR-10 dataset. After the evolution, we analyzed the topologies that performed well for this task. Our studies verify the techniques that have been commonly used in human designed deep CNNs. We also discovered that some of the graph properties greatly affect the system performance. We applied the guidelines learned from the evolution and designed new network topologies that outperform Residual Net with less layers on CIFAR-10, CIFAR-100, and SVHN dataset.
    Keywords: Computer Science - Neural And Evolutionary Computing
    Source: Cornell University
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  • 8
    Language: English
    In: Multimedia Tools and Applications, 2017, Vol.76(8), p.10443(21)
    Description: To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1007/s11042-016-3431-1 Byline: Caglar Aytekin (1), Ezgi Can Ozan (1), Serkan Kiranyaz (2), Moncef Gabbouj (1) Keywords: Visual saliency; Salient object detection; Quantum mechanics; Spectral graph theory; Schrodinger's equation Abstract: In this manuscript, an unsupervised salient object extraction algorithm is proposed for RGB and RGB-Depth images. Saliency estimation is formulated as a foreground detection problem. To this end, Quantum-Cuts (QCUT), a recently proposed spectral foreground detection method is investigated and extended to formulate the saliency estimation problem more efficiently. The contributions of this work are as follows: (1) a new proof for QCUT from spectral graph theory point of view is provided, (2) a detailed analysis of QCUT and comparison to well-known graph clustering methods are conducted, (3) QCUT is utilized in a multiresolution framework, (4) a novel affinity matrix construction scheme is proposed for better encoding of saliency cues into the graph representation and (5) a multispectral analysis for a richer set of salient object proposals is investigated. With the above improvements, we propose Extended Quantum Cuts, which consistently achieves an exquisite performance over all benchmark saliency detection datasets, containing around 18 k images in total. Finally, the proposed approach also outperforms the state-of-the-art on a recently announced RGB-Depth saliency dataset. Author Affiliation: (1) Department of Signal Processing, Tampere University of Technology, P.O.Box 553, FI-33101, Tampere, Finland (2) Department of Electrical Engineering, Qatar University, P.O. Box, 2713, Doha, Qatar Article History: Registration Date: 03/03/2016 Received Date: 28/09/2015 Accepted Date: 03/03/2016 Online Date: 23/03/2016
    Keywords: Quantum Mechanics ; Signal Processing ; Algorithms ; Electrical Engineering
    ISSN: 1380-7501
    E-ISSN: 15737721
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  • 9
    Language: English
    In: Neural Computing and Applications, 2018, Vol.29(4), pp.943-957
    Description: Most existing content-based image retrieval and classification systems rely on low-level features which are automatically extracted from images. However, often these features lack the discrimination power needed for accurate description of the image content, and hence, they may lead to a poor retrieval or classification performance. We propose a novel technique to improve low-level features which uses parallel one-against-all perceptrons to synthesize new features with a higher discrimination power which in turn leads to improved classification and retrieval results. The proposed method can be applied on any database and low-level features as long as some ground-truth information is available. The main merits of the proposed technique are its simplicity and faster computation compared to existing feature synthesis methods. Extensive simulation results show a significant improvement in the features’ discrimination power.
    Keywords: Content-based image retrieval and classification ; Feature synthesis ; Multi-dimensional particle swarm optimization ; Multi-layer perceptrons
    ISSN: 0941-0643
    E-ISSN: 1433-3058
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  • 10
    Language: English
    In: Expert Systems With Applications, 15 November 2015, Vol.42(20), pp.7175-7185
    Description: Interpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm.
    Keywords: Electroencephalography ; Epileptic Seizure Classification ; Haralick ; Textural Features ; Stochastic Gradient Descent ; Chb-MIT Dataset ; Computer Science
    ISSN: 0957-4174
    E-ISSN: 1873-6793
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