In this research will show a method for sound-recognition with artificial neural network backpropagation concept. The artificial neural network use sigmoid activation function to all layer. Steps to the extraction, fi...
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Artificial Immune Systems (AISs) are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the bind...
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Artificial Immune Systems (AISs) are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. As an immune response can be elicited even when the binding between an antigen and an antibody is not perfect, an approximate binding might suffice, and a Fuzzy Logic mechanism might be the most appropriate mechanism to control such process. This paper presents a novel hybrid model based on concepts of Immune and Fuzzy Systems with applications to pattern recognition problems. The preliminary results obtained here suggest the proposed model is a promising pattern recognition tool.
The Association for the Advancement of Artificial Intelligence was pleased to present the 2009 Fall Symposium Series, held Thursday through Saturday, November 5-7, at the Westin Arlington Gateway in Arlington, Virgini...
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This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany throu...
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This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://***/.
The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A...
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The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform;(ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data;(iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.
This article presents the application of Meta-Learning Evolutionary Artificial Neural Network (MLEANN) for a pharmaceutical research problem. Designing drugs is a current problem in the pharmaceutical research domain....
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This article presents the application of meta-learning evolutionary artificial neural network (MLEANN) for a pharmaceutical research problem. Designing drugs is a current problem in the pharmaceutical research domain....
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This article presents the application of meta-learning evolutionary artificial neural network (MLEANN) for a pharmaceutical research problem. Designing drugs is a current problem in the pharmaceutical research domain. By designing a drug we mean to choose some variables of drug formulation (inputs), for obtaining optimal characteristics of drug (outputs). To solve such a problem we propose an evolutionary artificial neural network and the performance is compared with a neuro-fuzzy system and an artificial neural network trained using scaled conjugate gradient algorithm. This research used the experimental data obtained from the Laboratory of Pharmaceutical Techniques of the faculty of Pharmacy in Cluj-Napoca, Romania. Bootstrap techniques were used to generate more samples of data and the number of experimental data is reduced due to the costs and time durations of experimentations. We obtain in this way a better estimation of some drug parameters. Experiment results indicate that the proposed method is efficient
Edge detection is an important topic in image processing and a main tool in pattern recognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge dete...
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Edge detection is an important topic in image processing and a main tool in pattern recognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge detectors are multiscale and include three main processing steps: smoothing, differentiation and labeling' (Ziau and Tabbone, 1997). This paper, presents a proposed method which is suitable for edge detection in images. This method is based on the use of the clustering algorithms (Self-Organizing Map (SOM), K-Means) and a gray scale edge detector (Canny, Generalized Edge Detector (GED)). It is shown that using the grayscale edge detectors may miss some parts of the edges which can be found using the proposed method.
Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance ed...
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ISBN:
(纸本)1604238216
Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance edges, followed by an edge detector to locate the edges. In this way, more detailed and relatively more unbroken edges can be found as compared to the results when an edge detector is applied alone. The algorithms compared are K-Means, SOM and SOGR for clustering, and Canny and GED for edge detection. Perceptionally, best results were obtained with the GED-SOGR algorithm. The SOGR is also considerably simpler and faster than the SOM algorithm.
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