Multistrategy Self-Organizing Map Learning for Classification Problems

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test

Biostabilised icosahedral gold nanoparticles: synthesis, cyclic voltammetric studies and catalytic activity towards 4-nitrophenol reduction

A green and cost-effective biosynthetic approach for the preparation of icosahedral gold nanoparticles (AuNPs) using an aqueous leaf extract of Polygonum minus as reducing and stabilising factor is described. The reduction of Au3+ ions to elemental Au rapidly occurred and is completed within 20 minutes at room temperature. The size of the nanoparticles is highly sensitive to the AuCl4 −/leaf extract concentration ratio and pH. Transmission electron microscopy and X-ray diffraction data indicated that the nanoparticles were in a crystalline shape (face-centred cubic), mostly icosahedral and nearly monodispersed with an average size of 23 nm. Cyclic voltammetric studies suggested that flavonoids, such as quercetin and myricetin present in the leaf extract had a tendency to donate electrons to Au3+ ions and the formation of elemental Au was possible due to the transfer of electrons from these flavonoids to Au3+ ions. Infrared absorption of the AuNPs and the leaf extract revealed that the oxidised (quinone) form of quercetin and myricetin were presumably the main stabilising agents in the formation of stable nanoparticles. The present biosynthesis of AuNPs was simple, rapid, cost-effective and environmentally friendly. The newly prepared biostabilised icosahedral AuNPs show good catalytic activity in the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP)

Embedded Scale United Moment Invariant for Identification of Handwriting Individuality

Past few years, a lot of research on moment functions have been explored in pattern recognition. Several new techniques have been investigated to improve conventional regular moment by proposing the scaling factor of geometrical function. In this paper, integrated scaling formulations of Aspect Invariant Moment and Higher Order Scaling Invariant with United Moment Invariant are presented in Writer Identification to seek the invarianceness of authorship or individuality of handwriting perseverance. Mathematical proving and results of computer simulations are included to verify the validity of the proposed technique in identifying eccentricity of the author in Writer Identification

DISCRETIZATION OF INTEGRATED MOMENT INVARIANTS FOR WRITER IDENTIFICATION

Conservative regular moments have been proven to exhibit some shortcomings in the original formulations of moment functions in terms of scaling factor. Hence, an incorporated scaling factor of geometric functions into United Moment Invariant function is proposed for mining the feature of unconstrained words. Subsequently, the discrete proposed features undertake discretization procedure prior to classification for better feature representation and splendid classification accuracy. Collectively, discrete values are finite intervals in a continuous spectrum of values and well known to play important roles in data mining and knowledge discovery. Many induction algorithms found in the literature requires that training data contains only discrete features and some works better on discretized data; in particular rule based approaches like rough sets. Hence, in this study, an integrated scaling formulation of Aspect Scaling Invariant is presented in Writer Identification to hunt for the individuality perseverance. Successive exploration is executed to investigate for the suitability of discretization techniques in probing the issues of writer authorship. Mathematical proving and results of computer simulations are embraced to attest the feasibility of the proposed technique in Writer Identification. The results disclose that the proposed discretized invariants reveal 99% accuracy of classification by using 3520 training data and 880 testing data

Embedded Scale United Moment Invariant for Identification of Handwriting Individuality

Past few years, a lot of research on moment functions have been explored in pattern recognition. Several new techniques have been investigated to improve conventional regular moment by proposing the scaling factor of geometrical function. In this paper, integrated scaling formulations of Aspect Invariant Moment and Higher Order Scaling Invariant with United Moment Invariant are presented in Writer Identification to seek the invarianceness of authorship or individuality of handwriting perseverance. Mathematical proving and results of computer simulations are included to verify the validity of the proposed technique in identifying eccentricity of the author in Writer Identification

Fast upsetting of circular cylinders of aluminium metal matrix composites: experimental results and numerical analysis

Cylindrical specimens of Al/Cu and Al/Li metal matrix composite (MMC) were subjected to dynamic compression at room temperature using an experimental drop hammer. Force-time and displacement-time traces were recorded. The experimental results are compared with theoretical results obtained using finite-difference analysis proposed in a previous paper by the authors [1]. The computational results obtained for the force-time histories agree reasonably with the experimental observation. Effect of strain rate and thermal softening on the mechanical behaviour of Al/Cu MMC and Al/Li MMC were examined

GPU-based multiple back propagation for big data problems

The big data era has become known for its abundance in rapidly generated data of varying formats and sizes. With this awareness, interest in data analytics and more specifically predictive analytics has received increased attention lately. However, the massive sample sizes and high dimensionality peculiar with these datasets has challenged the overall performance of one of the most important components of predictive analytics of our present time, Machine Learning. Given that dimensionality reduction has been heavily applied to the problems of high dimensionality, this work presents an improved scheme of GPU based Multiple Back Propagation (MBP) with feature selection for big high dimensional data problems. Elastic Net was used for automatic feature selection of high dimensional biomedical datasets before classification with GPU based MBP and experimental results show an improved performance over the previous scheme with MBP

In-Vitro Study of Low Viscosity, and High Viscosity Direct Compression and conventional Grade Hypromellose for Modified Release Gliclazide tablets

Six different low to high viscosity hypromellose were used with lower soluble Gliclazide, alone to investigate the dissolution study and flow property. Dissolution behavior of formulated tablets was tested to identify the better efficacy. Dose dumping, pH dependency also was examined. Anti-diabetic Gliclazide tablets were prepared by direct compression method and the results of dissolution was found good in Methocel K100M DC for 73.25%. Tablets showed uniform weight, thickness, and lower percent ( 0.5%) friability. Result of Carrs index and Hausner ratio indicated good flow properties of powder granules. The percent release of the Gliclazide was analyzed by kinetic models. Release of the drug was higher using the higher viscosity grade. Gliclazide tablets were determined with the goodness of fit test of kinetic models. The release showed linearity in Higuchi Model with correlation coefficient value of R 2 = 0.973. In-vitro study demonstrated improved release profile using DC grade than CR grade alone

Biofertilizer And Bioenhancer Concepts For Sustainable Oil Palm Seedling Production.

In oil palm production, nitrogen fertilizer is the most expensive nutrient input required