SACOC: A spectral-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest–an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

A Comparative Study on the Use of Classification Algorithms in Financial Forecasting

Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial forecasting is as a classification problem, where the goal is to find a model that represents the predictive relationships between predictor attribute values and class attribute values. In this paper we present a comparative study between two bio-inspired classification algorithms, a genetic programming algorithm especially designed for financial forecasting, and an ant colony optimization one, which is designed for classification problems. In addition, we compare the above algorithms with two other state-of-the-art classification algorithms, namely C4.5 and RIPPER. Results show that the ant colony optimization classification algorithm is very successful, significantly outperforming all other algorithms in the given classification problems, which provides insights for improving the design of specific financial forecasting algorithms

MACOC: a medoid-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

NASA technology utilization program: The small business market

Technology transfer programs were studied to determine how they might be more useful to the small business community. The status, needs, and technology use patterns of small firms are reported. Small business problems and failures are considered. Innovation, capitalization, R and D, and market share problems are discussed. Pocket, captive, and new markets are summarized. Small manufacturers and technology acquisition are discussed, covering external and internal sources, and NASA technology. Small business and the technology utilization program are discussed, covering publications and industrial applications centers. Observations and recommendations include small business market development and contracting, and NASA management technology

La dirección de centros docentes

La dirección de centros docentes.<br /

Extending the SACOC algorithm through the Nystrom method for dense manifold data analysis

Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context.Using a dataset as a starting point, these techniques aim to blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are the mostly used methodologies in this area, are however sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the clustering solutions and stability, specially for dense datasets. Ant Colony Optimization (ACO) is one of these new bio-inspired methodologies. This paper presents an extension of a previous algorithm named Spectral-based ACO Clustering (SACOC). SACOC is a spectral-based clustering methodology used for manifold identification. This work is focused on improving this algorithm through the Nystrom extension. The new algorithm, named SACON, is able to deal with Dense Data problems.We have evaluated the performance of this new approach comparing it with online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several datasets

Medoid-based clustering using ant colony optimization

The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored using these techniques, such as medoid-based clustering. Medoid-based clustering methods are helpful—compared to classical centroid-based techniques—when centroids cannot be easily defined. This paper proposes two medoid-based ACO clustering algorithms, where the only information needed is the distance between data: one algorithm that uses an ACO procedure to determine an optimal medoid set (METACOC algorithm) and another algorithm that uses an automatic selection of the number of clusters (METACOC-K algorithm). The proposed algorithms are compared against classical clustering approaches using synthetic and real-world datasets

Electrochemical synthesis and characterization of self-supported polypyrrole-DBS-MWCNT electrodes

The electrochemical conditions (electrolyte potential window, monomeric oxidation) for the synthesis of polypyrrole- dodecylbenzenesulfonate-multiwalled carbon nanotube (PPy-DBS-MWCNT) composite were determined. Thick PPy-DBS-MWNT films were electrogenerated and peeled off from the working electrode. Selfsupported PPy-DBS-MWCNT electrodes were fabricated. The morphology of the film was analyzed by SEM. Self-supported electrodes were characterized by potential cycling and by consecutive square potential waves in NaClO4 aqueous solution with different cathodic potential limits. Higher reduced structures (the current never drops to zero) are obtained and analyzed fromvoltammetric responses until rising cathodic potential limits (up to−5 V). For high cathodic potentials (N−1 V) a slow hydrogen evolution coexists with the film reduction, as revealed from coulovoltammetric (charge-potential) responses, and the reduction rate decreaseswithout significant polymeric degradation. Degradation of the material electroactivity in NaClO4 is initiated by anodic overpotentials beyond 1.2 V. Both, oxidation and reduction chronoamperometric responses prove the presence of nucleation processes, most significant during oxidation. Chronocoulometric responses illustrate slower oxidation rates from deeper reduced initial states. The electrochemical responses are explained by reaction-driven conformational and structural changes that are clarified by the coulovoltammetric response

Fundamentals and Applications of Surface-Enhanced Raman Spectroscopy (SERS)

When a molecule is adsorbed on some metallic nanostructured surfaces such as silver, copper or gold, it can undergo an enormous enhancement of the Raman signal giving rise to the so called Surface-Enhanced Raman Scattering (SERS). The high sensitivity of this effect allows an accurate structural study of adsorbates at very low concentrations. The SERS effect has historically been associated with the substrate roughness on two characteristic length scales. Surface roughness on the 10 to 100 nm length scale supports localized plasmon resonances which are considered as the dominant enhancement mechanism of SERS (Electromagnetic Enhancement Mechanism: SERS-EM). It is usually accepted that these electromagnetic resonances can increase the scattered intensity by an average factor of ca. 104 to 107. A secondary mechanism often thought to require atomic scale roughness is referred to as Charge Transfer (CT) Enhancement Mechanism (SERS-CT). This mechanism involves the photoinduced transfer of an electron from the metal to the adsorbate or vice versa and involves new electronic excited CT states which result from adsorbate–substrate chemical interactions. It is also estimated that such SERS-CT mechanism can enhance the scattering cross-section by a factor of ca. 10 to 102. These two mechanisms can operate simultaneously, depending on the particular systems and experimental conditions, making difficult to recognize each one and to estimate their relative magnitude in a particular spectrum.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech