Current Issue : January - March Volume : 2014 Issue Number : 1 Articles : 5 Articles
We considered an extension of the first-order logic (FOL) by BealerÃ¢â?¬â?¢s intensional abstraction operator. Contemporary use of the\r\nterm Ã¢â?¬Å?intensionÃ¢â?¬Â derives from the traditional logical Frege-Russell doctrine that an idea (logic formula) has both an extension and\r\nan intension. Although there is divergence in formulation, it is accepted that the Ã¢â?¬Å?extensionÃ¢â?¬Â of an idea consists of the subjects to\r\nwhich the idea applies, and the Ã¢â?¬Å?intensionÃ¢â?¬Â consists of the attributes implied by the idea. From the MontagueÃ¢â?¬â?¢s point of view, the\r\nmeaning of an idea can be considered as particular extensions in different possible worlds. In the case of standard FOL, we obtain\r\na commutative homomorphic diagram, which is valid in each given possible world of an intensional FOL: from a free algebra of\r\nthe FOL syntax, into its intensional algebra of concepts, and, successively, into an extensional relational algebra (different from\r\nCylindric algebras). Then we show that this composition corresponds to the TarskiÃ¢â?¬â?¢s interpretation of the standard extensional FOL\r\nin this possible world....
A Discrete Artificial Bee Colony (DABC) is presented for joint symbol detection at the receiver in a multidevice Space-Time Block\r\nCode (STBC) Mutli-Input Multi-Output (MIMO) communication system. Exhaustive search (maximum likelihood detection)\r\nfor finding an optimal detection has a computational complexity that increases exponentially with the number of mobile\r\ndevices, transmit antennas per mobile device, and the number of bits per symbol. ABC is a new population-based, swarm-based\r\nEvolutionary Algorithms (EA) presented for multivariable numerical functions and has shown good performance compared to\r\nother mainstream EAs for problems in continuous domain. This algorithm simulates the intelligent foraging behavior of honeybee\r\nswarms. An enhanced discrete version of the ABC algorithm is presented and applied to the joint symbol detection problem to find\r\na nearly optimal solution in real time. The results of multiple independent simulation runs indicate the effectiveness of DABC\r\nwith other well-known algorithms previously proposed for joint symbol detection such as the near-optimal sphere decoding,\r\nminimum mean square error, zero forcing, and semidefinite relaxation, along with other EAs such as genetic algorithm, estimation\r\nof distributions algorithm, and the more novel biogeography-based optimization algorithm....
Biological and medical endeavors are beginning to realize the benefits of artificial intelligence and machine learning. However,\r\nclassification, prediction, and diagnostic (CPD) errors can cause significant losses, even loss of life.Hence, end users are best served\r\nwhen they have performance information relevant to their needs, this paperÃ¢â?¬â?¢s focus. Relative class size (rCS) is commonly recognized\r\nas a confounding factor in CPD evaluation. Unfortunately, rCS-invariant measures are not easily mapped to end user conditions.\r\nWe determine a cause of rCS invariance, joint probability table (JPT) normalization. JPT normalization means that more end user\r\nefficacious measures can be used without sacrificing invariance. An important revelation is that without data normalization, the\r\nMatthews correlation coefficient (MCC) and information coefficient (IC) are not relative class size invariants; this is a potential\r\nsource of confusion, as we found not all reports usingMCC or IC normalize their data.We deriveMCC rCS-invariant expression.\r\nJPT normalization can be extended to allow JPT rCS to be set to any desired value (JPT tuning). This makes sensitivity analysis\r\nfeasible, a benefit to both applied researchers and practitioners (end users).We apply our findings to two published CPD studies to\r\nillustrate how end users benefit....
A method for solving a classification problem when there is only partial information about some features is proposed. This partial\r\ninformation comprises the mean values of features for every class and the bounds of the features. In order to maximally exploit the\r\navailable information, a set of probability distributions is constructed such that two distributions are selected from the set which\r\ndefine the minimax and minimin strategies. Random values of features are generated in accordance with the selected distributions\r\nby using the Monte Carlo technique. As a result, the classification problem is reduced to the standard model which is solved by\r\nmeans of the support vector machine. Numerical examples illustrate the proposed method....
The long-termsolution to the asthma epidemic is believed to be prevention and not treatment of the established disease.Most cases\r\nof asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life\r\ncounts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the\r\nperformance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of\r\npersistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to\r\nthe persistent asthma have been chosen.Multilayer perceptron and probabilistic neural networks topologies have been investigated\r\nin order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the\r\nasthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors\r\nto identify which of the young patients are at a high risk of asthma disease progression.Moreover, this may lead to better treatment\r\nopportunities and hopefully better disease outcomes in adulthood...
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