Brain inspired cognitive artificial intelligence for knowledge extraction and intelligent instrumentation system

2017 ◽  
Author(s):  
Adang Suwandi Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Extraction ◽  
Intelligent Instrumentation

Defining Knowledge Constituents and Contents

2016 ◽  
Vol 5 (1) ◽  
pp. 1-7
Author(s):  
Sead Spuzic ◽  
Ramadas Narayanan ◽  
Megat Aiman Alif ◽  
Nor Aishah M.N.
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Information Technology ◽  
Knowledge Extraction ◽  
Information Environment ◽  
Electronic Records ◽  
Software Complexity ◽  
New Information ◽  
Key Terms

While it appears that a consensus is crystalising with regard to the hierarchy of concepts such as “knowledge”, “definition” and “information”, there is an increasing urgency for improving definitions of these terms. Strategies such as “knowledge extraction” or “data mining” rely on the increasing availability of digital (electronic) records addressing almost any aspect of socio-economic realm. Information processors are invaluable in the capacity of turning large amount of data into information. However, a new problem emerged on the surface in this new information environment: numerous concepts and terms are blurred by ambiguous definitions (including the concept of 'definition' itself). This triggered a need for mitigating hindrances such as homonymy and synonymy, leading further to demands on the decoding software complexity of which equals the artificial intelligence applications. Information technology presumably copes with this diversity by providing the information decoding 'tools'. This opens a never-ending opportunity for further permutations of tasks and service abilities. The solution, however, is to address the causes rather than indulge in multiplying the superficial remedies. Clearly, the multiplicity of definitions for the same concepts, false synonyms and so forth show that there is a need for introducing definitions of sufficient dimensionality. In this article, a number of examples of important concepts are presented first to point at the ambiguities associated with them, and then to propose their disambiguation. The minimum intent is to demonstrate how these key terms can be defined to avoid ambiguities such as pleonasm, homonymy, synonymy and circularity.

scholarly journals ACQUISITION AND EXTRACTION OF DYNAMIC KNOWLEDGE USING D-ROUGH SET

2011 ◽  
pp. 13-17
Author(s):  
Brojo Kishore Mishra ◽  
Susanta Kumar Das
Keyword(s):  
Artificial Intelligence ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Knowledge Extraction ◽  
Equivalence Classes ◽  
Theory Of Knowledge ◽  
Intelligence System ◽  
Special Case ◽  
Rich History

Theory of knowledge has a long and rich history. Various aspects of knowledge are widely discussed issues at present, mainly by logicians and artificial intelligence (AI) researchers. It is one of the concepts, used to build intelligence system. Many soft computing tools are available for extraction, acquisition and validation of knowledge. Rough set is one such tool, mainly used for classification and extraction of knowledge. Rough Set Theory was proposed by Pawlak in 1982 as a tool for knowledge Extraction. However, when knowledge extraction is studied, we observed that most of the knowledge is static in nature. For analyzing Knowledge having dynamic in nature, Pawlak’s Rough Set Theory must be reconsidered. Dong Ya Li (et. al.) has already proposed the concept of dynamic Rough Set in 2007. We here, further analyze this concept and try to find out some more properties of it. Dynamic Rough Set (D-rough set) is a common form of Pawlak’s Rough Set as Pawlak’s rough set can be considered as a special case of D-rough set. Drough set is based on concepts, such as elementary transfer coefficient. D-rough set and D-Approximate set can be used for studying and analyzing dynamic knowledge. Further, we study and analyze the properties mentioned by Bussee. Grzymala-Busse has established some properties of approximation of classifications. These results are irreversible by nature. Pawlak has noted that these results of Busse establish that the two concepts, approximation of sets and approximation of families of sets (or classifications) are two different issues and that the equivalence classes of approximate classifications cannot be arbitrary sets. He has further stated that if we have positive example of each category in the approximate classification then we must have also negative examples of each category. In this paper, we have mentioned these aspects of the theorems of Busse and tried to study their properties, when D-rough and D-Approximate set has been incorporated. Lastly, we had provided the physical interpretation of each one of them.

Information Systems on Hesitant Fuzzy Sets

Fuzzy Systems ◽  
2017 ◽  
pp. 1425-1452
Author(s):  
Deepak D. ◽  
Sunil Jacob John
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Information Systems ◽  
Fuzzy Sets ◽  
Knowledge Extraction ◽  
The Other ◽  
Hesitant Fuzzy Sets ◽  
Power Set ◽  
Other Information ◽  
Membership Value

Knowledge extraction from information systems is one of the most significant problems in artificial intelligence. This paper attempts to study information systems in the hesitant fuzzy domain. It studies information systems which has a set of possible membership values. Illustration of a case is provided where the hesitant membership values are arrived at from attribute values whose membership values are a family of sets. The membership value here would turn out to be a subset of the power set of membership values from the usual information system. Although it does not mean that it is arrived at from usual information systems. Reduct, core, relative reduct, relative core and the corresponding indiscernibility matrices are also studied. Apart from these, paper also discusses the homomorphisms between hesitant information systems. For two homomorphic information systems the reduct and core of one information system are the corresponding images of the reduct and core of the other information system under this homomorphism.

Artificial Intelligence in Chemistry

1992 ◽  
Vol 57 (12) ◽  
pp. 2413-2451 ◽  
Author(s):  
Vladimír Jakuš
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Problem Solving ◽  
Expert Systems ◽  
Intelligent Systems ◽  
Structure Elucidation ◽  
Chromatographic Retention ◽  
Natural Languages ◽  
Synthesis Planning ◽  
Intelligent Instrumentation

The definition of artificial intelligence and the associated tasks of this branch of science are discussed. The tasks include pattern recognition, adaptation and learning, problem solving by means of expert systems or neural networks, and understanding the natural language and communication with a machine in it. The principles of problem solving are analyzed. It is demonstrated how artificial intelligence-based computer programs in which chemical expertise is encoded assist in structure elucidation, in the investigation of relations between structure and biological activity or chromatographic retention, etc.; problems emerging in the synthesis planning with a retrosynthetic analysis, or in the planning of experiments and intelligent consultations are dealt with. Several models used for structure elucidation and synthesis planning are evaluated. An overview is presented of additional expert systems which, along with artificial intelligence-based robotics, are used in intelligent instrumentation. Also discussed is the role of neural networks, which begin to be successfully employed in structure elucidation, synthesis planning, in intelligent instrumentation and in the treatment of natural languages. They are expected to be an important tool in the implementation of intelligent systems for the classification of chemical databases and prediction of properties of molecules.

Information Systems on Hesitant Fuzzy Sets

2016 ◽  
Vol 3 (1) ◽  
pp. 71-97 ◽  
Author(s):  
Deepak D. ◽  
Sunil Jacob John
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Information Systems ◽  
Fuzzy Sets ◽  
Knowledge Extraction ◽  
The Other ◽  
Hesitant Fuzzy Sets ◽  
Power Set ◽  
Other Information ◽  
Membership Value

Knowledge extraction from information systems is one of the most significant problems in artificial intelligence. This paper attempts to study information systems in the hesitant fuzzy domain. It studies information systems which has a set of possible membership values. Illustration of a case is provided where the hesitant membership values are arrived at from attribute values whose membership values are a family of sets. The membership value here would turn out to be a subset of the power set of membership values from the usual information system. Although it does not mean that it is arrived at from usual information systems. Reduct, core, relative reduct, relative core and the corresponding indiscernibility matrices are also studied. Apart from these, paper also discusses the homomorphisms between hesitant information systems. For two homomorphic information systems the reduct and core of one information system are the corresponding images of the reduct and core of the other information system under this homomorphism.

scholarly journals Intelligent instrumentation: a quality challenge

ACTA IMEKO ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 47
Author(s):  
H. J. Kohoutek
Keyword(s):  
Artificial Intelligence ◽  
Implementation Strategies ◽  
Analytical Instruments ◽  
Current Trends ◽  
Knowledge Quality ◽  
Intelligent Instrumentation ◽  
Successful Resolution ◽  
Electronic Measurement ◽  
Quality Issues ◽  
Applied Epistemology

<p>This is a reissue of a paper which appeared in ACTA IMEKO 1988, Proceedings of the 11th Triennial World Congress of the International Measurement Confederation (IMEKO), "Instrumentation for the 21st century", 16.-21.10.1988, Houston, pp. 337-345.</p><p>After a review and description of current trends in the design of electronic measurement and analytical instrumentation, changes in its application and use, and of associated quality issues, this paper deals with new quality issues emerging from the expected increase of artificial intelligence impact on system design and implementation strategies. The concept of knowledge quality in all its aspects (i.e. knowledge levels, representation, storage, and processing) is identified as the key new issue. Discussion of crucial knowledge quality attributes and associated assurance strategies suggests the need to enrich the assurance sciences and technologies by the methods and tools of applied epistemology. Described results from current research and investigation, together with first applications of artificial intelligence to particular analytical instruments, lead to conclusion that the conceptual framework of quality management is, in general, adequate for successful resolution of all quality issues associated with intelligent instrumentation.</p>

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