24 Computer Artificial Consciousness: Utopia or Real Possibility? S ince the beginnings of computer technology, researchers have speculated about the possibility of building smart machines that could compete with human intelligence. Given the current pace of advances in artificial intelligence and neural computing, such an evolution seems to be a more concrete possibility. Many people now belie...

It is assumed in the standard DEA model that the aggregate output (input) is a pure linear function of each output (input). This means, for example, that if DMU j1 generates twice as much of an output as does another DMU j2, then the former is credited with having created twice as much value. In many situations, however, linear pricing ðlryrjÞ may not adequately reflect differences in value cre...

One of the weak points of DEA (Data Envelopment Analysis) models indicated in literature [1,2] is their sensitivity to variable measurement errors. The occurrence of data interference, which is the basis of the productivity analysis, may distort the classification of the units and may cause misjudgement of their effectiveness. In the article the results of simulation concerning the DEA models s...

This paper proposes two-stage model for prioritizating decision making units (DMU) where each unit has multiple inputs and outputs. The rst stage model is a noncooperative game where each optimal input-output weight set of DMU is de ned by the Data Envelopment Analysis (DEA) and each DMU evaluates other DMUs by its own weights. The equilibrium state obtained from the rst stage motivates none of...

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic techniqu...

This paper presents a novel method for learning from a labeled dataset to accurately classify unknown data. The recursive algorithm, termed Recursive Hyperspheric Classification, or RHC, can accurately learn the classes of a labeled, n-dimensional dataset via a training method that recursively spawns a set of hyperspheres, endeavoring to separate and divide the feature space into partitions. Th...

Handwritten recognition is a popular problem which requires special Artificial Intelligent techniques for solving it. In this paper we use Genetic Programming (GP) for addressing the off-line variant of the handwritten digit recognition. We propose a new type of input representation for GP: histograms. This kind of representation is very simple and can be adapted very easily for the GP requirem...

In-memory computing (IMC) aims to solve the performance gap between CPU and memories introduced by memory wall. However, it does not address energy wall problem caused data transfer over hierarchies. This paper proposes data-locality management unit (DMU) efficiently from a DRAM computational SRAM (C-SRAM) allowing IMC operations. The DMU is tightly coupled within C-SRAM allows one align struct...

DEA (Deta Envelopment Analysis) is a non-parametric technique for measuring the efficiency of DMUs (Decision Making Units)with common inputs and outputs [2,5]. viewpoint for each DMU because of taking a maximum ratio. During recent years, the issue of sensitivity and stability of data envelopment analysis results has been extensively studied. The first DEA sensitivity analysis paper by Charnes ...

1 3 8.4324 -0.8306 6.8220 0.7043 2 2 17.1755 1.6999 8.3402 0.8578 3 2 0.8521 0.0844 5.6139 0.5757 4 1 1.9013 0.1884 8.4324 0.8306 5 1 0.4427 0.0423 17.1755 1.6999 6 4 0.0000 0.0003 5.3750 0.5881 7 3 0.6757 0.0591 12.4109 1.2886 8 3 33.9552 3.3631 13.6421 1.4070 9 1 8.3402 0.8578 0.8521 0.0844 10 2 5.6139 0.5757 3.0959 0.3061 11 2 3.0959 0.3061 2.0167 0.1990 12 1 2.0167 0.1990 1.9013 0.1884 13 4...