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Authors:
Bogdan
Patrut
Abstract:
October 2012
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Authors:
Abstract:
BRAIN vol. 3, issue 3, first pages
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Authors:
Editors
Editorial Board
Abstract:
INSTRUCTIONS FOR AUTHORS
Author Guidelines
Authors must submit their papers via email to brain@edusoft.ro (please!) or they can create an account and submit their papers online, at www.brain.edusoft.ro. Submited papers must be written in DOC format (Microsoft Word document), in as clear and as simple as possible English. Preferred maximum paper length for the papers is 20 pages, including figures.
The template for the paper is at this address:
http://www.edusoft.ro/Template_for_BRAIN.docRAIN vol. 3, issue 3, Instructions for authors
BRAINovations
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Authors:
Vishwambhar
Pathak
, Praveen
Dhyani
, Prabhat
Mahanti
Abstract:
Recent efforts in modeling of dynamics of the natural immune cells leading to artificial immune systems (AIS) have ignited contemporary research interest in finding out its analogies to real world problems. The AIS models have been vastly exploited to develop dependable robust
solutions to clustering. Most of the traditional clustering methods bear limitations in their capability to detect clusters of arbitrary shapes in a fully unsupervised manner. In this paper the recognition and communication dynamics of T Cell Receptors, the recognizing elements in innate immune
system, has been modeled with a kernel density estimation method. The model has been shown to successfully discover non spherical clusters in spatial patterns. Modeling the cohesion of the antibodies and pathogens with ‘local influence’ measure inducts comprehensive extension of the
antibody representation ball (ARB), which in turn corresponds to controlled expansion of clusters and prevents overfitting.
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Authors:
Utku
Köse
Abstract:
This paper introduce a software system including widely-used Swarm Intelligence algorithms or approaches to be used for the related scientific research studies associated with the subject area. The programmatic infrastructure of the system allows working on a fast, easy-to-use,
interactive platform to perform Swarm Intelligence based studies in a more effective, efficient and accurate way. In this sense, the system employs all of the necessary controls for the algorithms and it ensures an interactive platform on which computer users can perform studies on a wide spectrum
of solution approaches associated with simple and also more advanced problems.
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Authors:
Mojgan
Askarizade
, Mohammad
Ali Nematbakhsh
, Enseih
Davoodi Jam
Abstract:
With the growing amount of published RDF datasets on similar domains, data conflict between similar entities (same-as) is becoming a common problem for Web of Data applications. In this paper we propose an algorithm to detect conflict of same properties values of similar entities and select the most accurate value. The proposed algorithm contains two major steps. The first step filters out low ranked datasets using a link analysis technique. The second step calculates and evaluates the focus level of a dataset in a specific domain. Finally, the value of the top ranked dataset is considered. The proposed algorithm is implemented by Java Platform and is evaluated by geographical datasets containing "country" entities.
BRAINStorming
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Authors:
Rosemarie
Velik
Abstract:
The general objective of Artificial Intelligence (AI) is to make machines – particularly computers – do things that require intelligence when done by humans. In the last 60 years, AI has significantly progressed and today forms an important part of industry and technology. However, despite the many successes, fundamental questions concerning the creation of human-level intelligence in machines still remain open and will probably not be answerable when continuing on the current, mainly mathematic-algorithmically-guided path of AI. With the novel discipline of
Brain-Like Artificial Intelligence, one potential way out of this dilemma has been suggested. Brain-Like AI aims at analyzing and deciphering the working mechanisms of the brain and translating this knowledge into implementable AI architectures with the objective to develop in this way more efficient, flexible, and capable technical systems This article aims at giving a review about this young and still heterogeneous and dynamic research field.
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Authors:
Hassan
Talebi
, Behzad
Poursoleyman
Abstract:
In this paper, a wavelet-based logo watermarking scheme is presented. The logo watermark is embedded into all sub-blocks of the LLn sub-band of the transformed host image, using quantization technique. Extracted logos from all sub-blocks are mixed to make the extracted watermark from distorted watermarked image. Knowing the quantization step-size, dimensions of logo and the level of wavelet transform, the watermark is extracted, without any need to have access to the original image. Robustness of the proposed algorithm was tested against the following attacks: JPEG2000 and old JPEG compression, adding salt and pepper noise, median filtering, rotating, cropping and scaling. The promising experimental results are reported and discussed.
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Authors:
Enseih
Davoodi Jam
, Mohammadali
Nematbakhsh
, Mojgan
Askarizade
Abstract:
Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on numerical attributes. Recently, there have been several proposals to develop clustering methods that support mixed attributes. There are
three basic groups of clustering methods: partitional methods, hierarchical methods and densitybased methods. This paper proposes a hybrid clustering algorithm that combines the advantages of hierarchical clustering and fuzzy clustering techniques and considers mixed attributes. The proposed algorithms improve the fuzzy algorithm by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, and it can determine the number of clusters based on the complexity of cluster structure. Our approach is organized in two phases: first, the division of data in two clusters; then the determination of the worst cluster and splitting. The number of clusters is unknown, but our algorithms can find this parameter based on the complexity of cluster structure. We demonstrate the effectiveness of the clustering approach by evaluating datasets of linked data. We applied the proposed algorithms on three different datasets. Experimental results the proposed algorithm is suitable for link discovery between datasets of linked data. Clustering can decrease the number of comparisons before link discovery.
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