Inseminação artificial: tecnologias para acelerar o melhoramento genético dos rebanhos leiteiros.

2008

An artificial immune system for continuous analysis of time-varying data

M. Neal, An Artificial Immune System for Continuous Analysis of Time-Varying Data, in Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS), 2002, eds J Timmis and P J Bentley, volume 1, pages 76-85,

Meta-stable memory in an artificial immune network

Neal, M., Meta-stable memory in an artificial immune network, Proceedings of the 2nd International Conference on Artificial Immune Systems {ICARIS}, Springer, 168-180, 2003,LNCS 2787/2003

Augmenting an artificial immune network using ordering, self-recognition and histo-compatibility operators.

Timmis J Neal M J and Hunt J. Augmenting an artificial immune network using ordering, self-recognition and histo-compatibility operators. In Proceedings of IEEE international conference of systems, man and cybernetics, pages 3821-3826, San Diego, 1998. IEEE.

Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons.

Neal M J Timmis J and Hunt J. Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons. In Proceedings of IEEE international conference of systems, man and cybernetics, pages 922-927, Tokyo, 1999. IEEE.

An artificial immune system for data analysis

Timmis J and Neal M J. An artificial immune system for data analysis. In Proceedings of 3rd international workshop on information processing in cells and tissues (IPCAT), Indianapolis, U.S.A., 1999.

A resource limited artificial immune system for data analysis.

Timmis J and Neal M J. A resource limited artificial immune system for data analysis. In Proceedings of ES2000 - Research and Development of Intelligent Systems, pages 19-32, Cambrige, U.K., 2000. Springer.

Investigating the evolution and stability of a resource limited artificial immune system.

Timmis J and Neal M J. Investigating the evolution and stability of a resource limited artificial immune system. In Proceedings of GECCO - special workshop on artificial immune systems, pages 40-41. AAAI press, 2000.

The Epistemological Foundations of Artificial Agents

Lacey, N. J., Lee, M. H. (2003). The Epistemological Foundations of Artificial Agents. Minds and Machines, 13, 339-365.

The Influence of Epistemology on the Design of Artificial Agents

Lee, M. H., Lacey, N. J. (2003). The Influence of Epistemology on the Design of Artificial Agents. Minds and Machines, 13 (3), 367-395

How do we evaluate Artificial Immune Systems?

RAE2008

Endocrine Inspired Modulation of Artificial Neural Networks for Mobile Robotics

Sauze, C and Neal, M. 'Endocrine Inspired Modulation of Artificial Neural Networks for Mobile Robotics', Dynamics of Learning Behavior and Neuromodulation Workshop, European Conference on Artifical Life 2007, Lisbon, Portugal, September 10th-14th 2007.

Alignment of the genomes of brachypodium distachyon and temperate cereals and grasses using bacterial artificial chromosome landing with fluorescence in situ hybridization

Robert Hasterok, Agnieszka Marasek, Iain S. Donnison, Ian Armstead, Ann Thomas, Ian P. King, Elzbieta Wolny, Dominika Idziak, John Draper and Glyn Jenkins (2006). Alignment of the genomes of brachypodium distachyon and temperate cereals and grasses using bacterial artificial chromosome landing with fluorescence in situ hybridization.Genetics, 73 (1), 349-362. Sponsorship: Royal Society / BBSRC;BBSRC RAE2008

Nutrição Artificial nos Serviços Farmacêuticos Hospitalares

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.