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2016 – Up now, an overall of 1567 citations among 74 works (including 3 books) on GOOGLE SCHOLAR (https://scholar.google.com/citations?user=gSyQ-g8AAAAJ&hl=en) [with an Hirsh h-index=19, and an average of 160.2 citations each for any work on my top five] + 900 citations among 57 works on the new RESEARCH GATE site (https://www.researchgate.net/profile/Vitorino_Ramos).
Refs.: Science, Artificial Intelligence, Swarm Intelligence, Data-Mining, Big-Data, Evolutionary Computation, Complex Systems, Image Analysis, Pattern Recognition, Data Analysis.
Video lecture by Ken Long (at the Statistics Problem Solvers blog) on Nassim Taleb‘s 4th Quadrant problems [1,2], i.e. a region where statistics not only don’t work but in which statistics are downright dangerous, because they lead you to make predictions as well as control systems that are unprepared for the kinds of systems shocks awaiting you.
“Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the “logic of science”; it is the instrument of risk-taking; it is the applied tools of epistemology; you can’t be a modern intellectual and not think probabilistically—but… let’s not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let’s face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).”, Nassim Taleb, in .
 Nassim Nicholas Taleb, “The Fourth Quadrant: A Map of the Limits of Statistics“, An Edge Original Essay, Set., 2008. (link)
 Nassim Nicholas Taleb,”Convexity, Robustness, and Model Error inside the Fourth Quadrant“, Oxford Lecture (Draft version), Oxford, July 2010. [PDF paper]
 Crina Grosan, Ajith Abraham, Sang Yong Han, Vitorino Ramos, Stock Market Prediction using Multi Expression Programming, in ALEA´05, Workshop on Artificial Life and Evolutionary Algorithms at EPIA´05 – Proc. of the 12th Portuguese Conference on Artificial Intelligence, C. Bento, A. Cardoso and G. Dias (Eds.), IEEE Press, pp. 73-78, 2005.
The use of intelligent systems for stock market predictions has been widely established. In this paper we introduce a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artifcial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model, a difference boosting neural network. We considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index as test data.
(to obtain the respective PDF file follow link above or visit chemoton.org)