Rough‐Neural Computing, Techniques for Computing with Words20053Sankar Kumar Pal, Lech Polkowski and Andrzej Skowron (Eds). Rough‐Neural Computing, Techniques for Computing with Words. Berlin: Springer 2004. xxv +734 pp., ISBN: 3‐540‐43059‐8, hardback £61.50, ISSN: Cognitive Technologies Series, 1611-2482 Modelling with Words: Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework Jonathan Lawry, Jimi Shanahan and Anca Ralescu (Eds) Springer Berlin 2003 ISBN 3‐540‐20487‐3, paperback xi +228 pp. £27.50 Lecture Notes in Artificial Intelligence Series no. 2873 ISSN 0302‐9743

Kybernetes ◽  
2005 ◽  
Vol 34 (5) ◽  
pp. 744-745
Author(s):  
Alex M. Andrew
Keyword(s):  
Artificial Intelligence ◽  
Computing With Words ◽  
Neural Computing ◽  
Linguistic Representation ◽  
Lecture Notes

A FORMALISM FOR REPRESENTING AND REASONING WITH LINGUISTIC INFORMATION

2002 ◽  
Vol 10 (03) ◽  
pp. 281-307 ◽  
Author(s):  
TRU H. CAO
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Natural Language ◽  
Functional Relation ◽  
Computing With Words ◽  
Conceptual Graphs ◽  
Graph Language ◽  
Conceptual Graph ◽  
Knowledge Representation Language ◽  
Representation Language

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

Mathematical Modeling of Artificial Neural Networks

2011 ◽  
pp. 1056-1063
Author(s):  
Radu Mutihac
Keyword(s):  
Mathematical Modeling ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Neuroscience ◽  
Distributed Processing ◽  
Neural Modeling ◽  
Cognitive Systems ◽  
Neural Computing ◽  
Alternative Means

Models and algorithms have been designed to mimic information processing and knowledge acquisition of the human brain generically called artificial or formal neural networks (ANNs), parallel distributed processing (PDP), neuromorphic or connectionist models. The term network is common today: computer networks exist, communications are referred to as networking, corporations and markets are structured in networks. The concept of ANN was initially coined as a hopeful vision of anticipating artificial intelligence (AI) synthesis by emulating the biological brain. ANNs are alternative means to symbol programming aiming to implement neural-inspired concepts in AI environments (neural computing) (Hertz, Krogh, & Palmer, 1991), whereas cognitive systems attempt to mimic the actual biological nervous systems (computational neuroscience). All conceivable neuromorphic models lie in between and supposed to be a simplified but meaningful representation of some reality. In order to establish a unifying theory of neural computing and computational neuroscience, mathematical theories should be developed along with specific methods of analysis (Amari, 1989) (Amit, 1990). The following outlines a tentatively mathematical-closed framework in neural modeling.

scholarly journals Using Artificial Intelligence Approaches to Categorise Lecture Notes

2018 ◽  
Vol 9 (8) ◽  
Author(s):  
Naushine Bibi Baijoo ◽  
Khusboo Bharossa ◽  
Somveer Kishnah ◽  
Sameerchand Pudaruth
Keyword(s):  
Artificial Intelligence ◽  
Lecture Notes

scholarly journals Application of Artificial Intelligence for Optimization of Organic Solar Cells Production Process

2020 ◽  
Vol 12 (2) ◽  
pp. 34
Author(s):  
Grazia Lo Sciuto
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Solar Cells ◽  
Computer Science ◽  
Organic Solar Cells ◽  
Full Potential ◽  
Production Chain ◽  
Lecture Notes ◽  
Occurrence Matrix ◽  
Conversion Efficiencies

The study of organic solar cells (OSCs) has been rapidly developed in recent years. Organic solar cell technology is sought after mainly due to the ease of manufacture and their exclusive properties such as mechanical flexibility, light-weight, and transparency. These properties of OSCs are well-suited for unconventional applications with power conversion efficiencies more high than 10%. The flexibility of the used substrates and the thinness of the devices make OSCs ideal for roll-to-roll production. However the organic solar cells still have very low conversion efficiencies due to degradation and stability of the technology. In order to extract their full potential, OSCs have to be optimized. On the other hand the production chain of the organic solar cells (OSC) can take advantage of the use of artificial intelligence (AI). In fact the integration into the production workflow makes solar cells more competitive and efficient. This paper presents some applications of the AI for optimization of OSCs production processes Full Text: PDF ReferencesLo Sciuto, G., Capizzi, G., Coco, S., Shikler, R., "Geometric shape optimization of organic solar cells for efficiency enhancement by neural networks." (2017) Lecture Notes in Mechanical Engineering, pp. 789-796. CrossRef Barnea, S.N., Lo Sciuto, G., Hai, N., Shikler, R., Capizzi, G., Wozniak, M., Polap, D., "Photo-electro characterization and modeling of organic light-emitting diodes by using a radial basis neural network." (2017) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, pp. 378-389. CrossRef Ye, L.; Hu, H.; Ghasemi, M.; Wang, T.; Collins, B.A.; Kim, J.H.; Jiang, K.; Carpenter, J.H.; Li, H.; Li, Z.; et al. "Quantitative relations between interaction parameter, miscibility and function in organic solar cells." Nat. Mater. 2018, 17, 253-260. CrossRef Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610-621 (1973) CrossRef Capizzi, G., Sciuto, G.L., Napoli, C., Tramontana, E., Wozniak, M.: Automatic classification of fruit defects based on co-occurrence matrix and neural networks. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 861-867, September 2015. CrossRef

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