Evolving an optimal decision template for combining classifiers.

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets like those having imbalanced data. Moreover, point estimation by computing the average value on the outputs of base classifiers in the Decision Template method is sometimes not a good representation, especially for skewed datasets. Here we propose to search for an optimal decision template in the combining algorithm for a heterogeneous ensemble. To do this, we first generate the base classifier by training the pre-selected learning algorithms on the given training set. The meta-data of the training set is then generated via cross validation. Using the Artificial Bee Colony algorithm, we search for the optimal template that minimizes the empirical 0–1 loss function on the training set. The class label is assigned to the unlabeled sample based on the maximum of the similarity between the optimal decision template and the sample’s meta-data. Experiments conducted on the UCI datasets demonstrated the superiority of the proposed method over several benchmark algorithms

Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes.

Understanding molecular recognition is of fundamental importance in applications such as therapeutics, chemical catalysis and sensor design. The most common recognition motifs involve biological macromolecules such as antibodies and aptamers. The key to biorecognition consists of a unique three-dimensional structure formed by a folded and constrained bioheteropolymer that creates a binding pocket, or an interface, able to recognize a specific molecule. Here, we show that synthetic heteropolymers, once constrained onto a single-walled carbon nanotube by chemical adsorption, also form a new corona phase that exhibits highly selective recognition for specific molecules. To prove the generality of this phenomenon, we report three examples of heteropolymer-nanotube recognition complexes for riboflavin, L-thyroxine and oestradiol. In each case, the recognition was predicted using a two-dimensional thermodynamic model of surface interactions in which the dissociation constants can be tuned by perturbing the chemical structure of the heteropolymer. Moreover, these complexes can be used as new types of spatiotemporal sensors based on modulation of the carbon nanotube photoemission in the near-infrared, as we show by tracking riboflavin diffusion in murine macrophages

Synthesis of benzylidenemalononitrile by Knoevenagel condensation through monodisperse carbon nanotube-based NiCu nanohybrids

Monodisperse nickel/copper nanohybrids (NiCu@MWCNT) based on multi-walled carbon nanotubes (MWCNT) were prepared for the Knoevenagel condensation of aryl and aliphatic aldehydes. The synthesis of these nanohybrids was carried out by the ultrasonic hydroxide assisted reduction method. NiCu@MWCNT nanohybrids were characterized by analytical techniques such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HR-TEM), and Raman spectroscopy. According to characterization results, NiCu@MWCNT showed that these nanohybrids form highly uniform, crystalline, monodisperse, colloidally stable NiCu@MWCNT nanohybrids were successfully synthesized. Thereafter, a model reaction was carried out to obtain benzylidenemalononitrile derivatives using NiCu@MWCNT as a catalyst, and showed high catalytic performance under mild conditions over 10-180 min.Dumlupinar UniversityDumlupinar University [2014-05, 2015-35, 2015-50]; Duzce UniversityDuzce University [2015.26.04.371]The authors would like to thank Dumlupinar University (2014-05, 2015-35, and 2015-50) and Duzce University (grant no. 2015.26.04.371) for funding.WOS:0005563883000122-s2.0-85088705103PubMed: 3272817