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Authors:
Bogdan
Patrut
Abstract:
TABLE OF CONTENTS Sections BRAINStorming and BRAINovations 1. Evolving Spiking Neural Networks for Control of Artificial Creatures 5Arash Ahmadi 2. Artificial Neuron Modelling Based on Wave Shape 20Kieran Greer 3. Brain-Like Artificial Intelligence for Automation — Foundations, Concepts andImplementation Examples 26Rosemarie Velik 4. Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation 55Tushar H Jaware and Dr. K B Khanchandani 5. High Performance Data mining by Genetic Neural Network 60Dadmehr Rahbari 6. Isomorphism Between Estes' Stimulus Fluctuation Model and a Physical-Chemical System 71Makoto Yamaguchi 7. Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing 74Nima Farajian, Kamran Zamanifar 8. An Enhancement Over Texture Feature Based Multiclass Image Classification UnderUnknown Noise 84Ajay Kumar Singh, V P Shukla, Shamik Tiwari and S R Biradar 9. Suicide: Neurochemical Approaches 97Ritabrata Banerjee, Anup K. Ghosh, Balaram Ghosh, Somnath Bhattacharya and Amal C. Mondal 10. L1 Transfer in Post-Verbal Preposition: An Inter-level Comparison 125Samira Mollaei, Ali Jahangard and Hemaseh Bagheri Section BRAINotes 11. Looking for Oriental fundamentals Fuzzy Logic 141Ãngel Garrido and Piedad Yuste Instructions for authors 146
Articles
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Authors:
Arash
Ahmadi
Abstract:
To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods and
approaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN) of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed of
randomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has the
capability to find or synthesis artificial creatures which can survive in the environment successfully.
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Authors:
Kieran
Greer
Abstract:
This paper describes a new model for an artificial neural network processing unit or neuron. It is slightly different to a traditional feedforward network by the fact that it favours a mechanism of trying to match the wave-like ‘shape’ of the input with the shape of the output against specific value error corrections. The expectation is then that a best fit shape can be transposed into the desired output values more easily. This allows for notions of reinforcement through resonance and also the construction of synapses.
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Authors:
Rosemarie
Velik
Abstract:
Over the last decades, automation technology has made serious progress and can today automate a wide range of tasks having before needed human physical and mental abilities. Nevertheless, a number of important problem domains remain that cannot yet be handled by our current machines and computers. A few prominent examples are applications involving “realworld” perception, situation assessment, and decision-making tasks. Recently, researchers have suggested to use concepts of “Brain-Like Artificial Intelligence”, i.e. concepts inspired by the functioning principles of the human or animal brain, to further advance in these problem domains. This article discusses the potential of Brain-Like Artificial Intelligence for innovative automation solutions and reviews a number of approaches developed together with the ICT cognitive automation group of the Vienna University of Technology targeting the topics “real-world” perception, situation assessment, and decision-making for applications in building automation environments and autonomous agents. Additionally, it is demonstrated by a concrete example how
such developments can also contribute to an advancement of the state of the art in the field of brain sciences.
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Authors:
Tushar
H Jaware
, K
B Khanchandani
Abstract:
Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clustering
methods is presented.
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Authors:
Dadmehr
Rahbari
Abstract:
Data mining in computer science is the process of discovering interesting and useful patterns and relationships in large volumes of data. Most methods for mining problems is based on artificial intelligence algorithms. Neural network optimization based on three basic parameters topology, weights and the learning rate is a powerful method. We introduce optimal method for solving this problem. In this paper genetic algorithm with mutation and crossover operators change the network structure and optimized that. Dataset used for our work is stroke disease with twenty features that optimized number of that achieved by new hybrid algorithm. Result of this work is very well in
comparison with other similar method. Low present of error show that our method is our new approach to efficient, high-performance data mining problems is introduced.
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Authors:
Makoto
Yamaguchi
Abstract:
Although Estes' Stimulus Sampling Theory has almost completely lost its influence, its theoretical framework has not been disproved. Particularly, one theory in that framework, Stimulus Fluctuation Model, is still important because it explains spontaneous recovery. In this short note, the process of the theory is shown to be isomorphic to the diffusion of solution between compartments. Envisioning the theory as diffusion will make it appear less artificial and suggest natural extensions.
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Authors:
Nima
Farajian
, Kamran
Zamanifar
Abstract:
Market-oriented approach is an effective method for resource management because of its regulation of supply and demand and is suitable for cloud environment where the computing resources, either software or hardware, are virtualized and allocated as services from providers to users. In this paper a continuous double auction method for efficient cloud service allocation is presented in which i) enables consumers to order various resources (services) for workflows and coallocation, ii) consumers and providers make bid and request prices based on deadline and workload time and in addition providers can tradeoff between utilization time and price of bids, iii) auctioneers can intelligently find optimum matching by sharing and merging resources which result more trades. Experimental results show that proposed method is efficient in terms of successful allocation rate and resource utilization.
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Authors:
Ajay
Kumar Singh
, V
P Shukla
, Shamik
Tiwari
, S
R Biradar
Abstract:
In this paper we deal with classification of multiclass images using statistical texture
features with two approaches. One with statistical texture feature extraction of the whole image, another with feature extraction of image blocks. This paper presents an experimental assessment of classifier in terms of classification accuracy under different constraints of images. This paper examined classification accuracy of multiclass images without noise, with some unknown noise and after filtering of noise using feed forward neural network. Results shows that blocking of image improves the performance of classifier.
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Authors:
Ritabrata
Banerjee
, Anup
K. Ghosh
, Balaram
Ghosh
, Somnath
Bhattacharya
, Amal
C. Mondal
Abstract:
Despite the devastating effect of suicide on numerous lives, there is still a dearth
of knowledge concerning its neurochemical aspects. There is increasing evidence that brain-derived neurotrophic factor (BDNF) and Nerve growth factor (NGF) are involved in the pathophysiology and treatment of depression through binding and activating their cognate receptors trk B and trk A respectively. The present study was performed to examine whether the expression profiles of BDNF and/or trk B as well as NGF and/or trk A were altered in postmortem brain in subjects who commit
suicide and whether these alterations were associated with specific psychopathologic conditions. These studies were performed in hippocampus obtained 21 suicide subjects and 19 non-psychiatric control subjects. The protein and mRNA levels of BDNF, trk B and NGF, trk A were determined with Sandwich ELISA, Western Blot and RT PCR respectively. Given the importance of BDNF
and NGF along with their cognate receptors in mediating physiological functions, including cell survival and synaptic plasticity, our findings of reduced expression of BDNF, Trk B and NGF, Trk A in both protein and mRNA levels of postmortem brain in suicide subjects suggest that these molecules may play an important role in the pathophysiological aspects of suicidal behavior.
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Authors:
Samira
Mollaei
, Ali
Jahangard
, Hemaseh
Bagheri
Abstract:
The study intended to investigate the well-known issue of L1 transfer in L2
acquisition. The primary aim of this research was to compare the extent to which L1 transfer may take place in different developmental stages in L2 learning procedure. Persian learners of English have been observed to misuse a number of the prepositions with some of the verbs. Having scrutinized more than a hundred pieces of students’ writing assignments, the authors came up with a pattern of errors in this area. It was observed that the majority of these errors could be attributed to Persian: the learners’ choice of preposition mirror the corresponding case in their L1, Persian. Moreover, the pattern of mistakes was put to test to check whether these mistakes increase or decrease according to the level of proficiency of the learners. To this end, two groups of students, one in elementary and the other in intermediate level, were tested on their use of proper prepositions with different verbs and the results of these tests were compared to see whether any significant difference exists between the two groups of students. The results showed no significant difference between the students of the two proficiency levels.
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Authors:
Angel
Garrido
, Piedad
Yuste
Abstract:
For quite some time we have been trying to trace the river of Non-Classical
Logics, and especially, Fuzzy Logic, trying to find the sources of this today flowing quite mighty river. Following from Lotfi A. Zadeh, we have traced his inspiring, the Polish logician Jan Lukasiewicz, who in turn was inspired by Aristotle's Peri Hermeneias (De Interpretatione). Also, Lukasiewicz occupies a central position in the Lvov-Warsaw School, who founded Kazimierz Twardowski, a student of Franz Brentano, and this in turn disciple of Bernard Bolzano. The connection with Leibniz and Bolzano come through medieval scholastic thinkers, especially John Duns Scotus and William of Ockham and the problem of future contingents, they had collected from the Aristotelian tradition. But there was to trace the “eastern (oriental) track, which leads to the ancient Chinese and Indian philosophy. Here we will treat it as a first and necessary approach.
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Authors:
Sara
Atef-Vahid
, Keivan
Zahedi
Abstract:
In accordance with Lakovian cognitive linguistics in metaphoric analysis, this paper aims to explore the human cognitive capacity of metaphoric conceptualization of body parts. A contrastive analysis on the cognitive features of metaphorical expressions utilizing various body parts pertaining to the "˜head' domain, e.g., face and tongue in the English and Farsi languages is carried
out. After a cross-linguistic comparison of metaphors in both languages, five main linguistic categories emerge. Similarities and differences of metaphor construction, mappings and mechanisms in both languages used to convey common concepts are highlighted using these categories. While corroborating Lakoff's approach whereby metaphors constitute an inherent part of
language itself, it is shown that there is a universal cognitive grid from which different languages externalize the world differently through semiosis. Therefore, the main aim is to show how language invariance and variation may be explained within a cognitive framework. These universals are due to cognitive constraints, whereas languages owe their variation to the options
they have out of the cognitively available pool. They are limited to their selections which are restrained by cultural and perhaps religious factors of semiotic mechanisms which are cognitively accessible to them.
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