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Bogdan Patrut
Fiat Lux!
Fiat Lux!
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Younus Mahdi Salih Irak – A Semantic Analysis of Causative Verbs in the Holy Quran (SMART 2017)
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This is my new collection with posts about the BRAIN journal, edited by Academia EduSoft.
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The historical origin of the Artificial Intelligence is usually established in the Dartmouth Conference, of 1956. But we can find many more arcane origins. Also, we can consider, in more recent times, very great thinkers, as Janos Neumann (then, John von Neumann, arrived in USA), Norbert Wiener, Alan Mathison Turing, or Lofti Zadeh, for instance.
https://www.edusoft.ro/brain/index.php/brain/article/view/11
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Usually, the problems in Artificial Intelligence may be many times related to Philosophy of Mind, and perhaps because this reason may be in essence very disputable. So, for instance, the famous question: Can a machine think? It was proposed by Alan Turing.
https://www.edusoft.ro/brain/index.php/brain/article/view/18/127
And it may be the more decisive question, but for many people it would be a nonsense. So, two of the very fundamental and more confronted positions usually considered according this line include the
connectionism and the Computational Theory of Mind. We analyze here its content, with their past disputes, and current situation.
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This paper (https://www.edusoft.ro/brain/index.php/brain/article/view/10/126) starts from the concept of relatedness as an act of signifying: everything inside a house signifies. The metaphorical type of architecture that we have attempted to construct aims at viewing the types of objects within a house as linking knots in the web of the house, while the way in which these objects (be they ornaments or tools) are distributed in space reveals not only
a character’s profession and/or personality (objects as extensions and projections of the self), but also indicates some kind of social hierarchy.
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This paper (https://www.edusoft.ro/brain/index.php/brain/article/view/19/125) refers to the morphological analysis of words, as an important process in the domain of natural language processing. We will present the classical solution, based on the use of inflection paradigms and of an extended database, containing all roots of the every words, and then there are emphasized some of the disadvantages of this method. Then we will present an original method, which dynamically generates the roots of words, using phonological alternances in the context of inflection rules. There are also presented some optimizations of the morphologic analysis algorithm.
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The development of efficient and flexible agent-based medical diagnosis systems represents a recent research direction. Medical multiagent systems may improve the efficiency of traditionally developed medical computational systems, like the medical expert systems. In our previous researches, a novel cooperative medical diagnosis multiagent system called CMDS (Contract Net
Based Medical Diagnosis System) was proposed.

https://www.edusoft.ro/brain/index.php/brain/article/view/17/124

CMDS system can solve flexibly a large variety of medical diagnosis problems. This paper analyses the increased intelligence of the CMDS system, which motivates its use for different medical problem’s solving.
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Artificial Intelligence requires logic. But its classical version shows too many
insufficiencies. So, it is absolutely necessary to introduce more sophisticated tools, such as Fuzzy Logic, Modal Logic, Non-Monotonic Logic, and so on. Among the things that AI needs to represent are categories, objects, properties, relations between objects, situations, states, time, events, causes and effects, knowledge about knowledge, and so on. The problems in AI can be classified in two general types : Search Problems and Representation Problem. There exist different ways to reach this objective. So, we have [3] Logics, Rules, Frames, Associative Nets, Scripts and so on, that are often interconnected. Also, it will be very useful, in dealing with problems of uncertainty and causality, to introduce Bayesian Networks, and particularly, a principal tool as the Essential Graph. We attempt here to show the scope of application of such versatile methods, currently fundamental in Medicine.
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