Fuzzy least squares twin support vector clustering

In this paper, we have formulated a fuzzy least squares version of recently proposed clustering method, namely twin support vector clustering (TWSVC). Here, a fuzzy membership value of each data pattern to different cluster is optimized and is further used for assigning each data pattern to one or other cluster. The formulation leads to finding k cluster center planes by solving modified primal problem of TWSVC, instead of the dual problem usually solved. We show that the solution of the proposed algorithm reduces to solving a series of system of linear equations as opposed to solving series of quadratic programming problems along with system of linear equations as in TWSVC. The experimental results on several publicly available datasets show that the proposed fuzzy least squares twin support vector clustering (F-LS-TWSVC) achieves comparable clustering accuracy to that of TWSVC with comparatively lesser computational time. Further, we have given an application of F-LS-TWSVC for segmentation of color images.

     

Robust Parametric Twin Support Vector Machine for Pattern Classification

In this paper, we propose a robust parametric twin support vector machine (RPTWSVM) classifier based on Parametric-\(\nu \)-Support Vector Machine (Par-\(\nu \)-SVM) and twin support vector machine. In order to capture heteroscedastic noise present in the training data, RPTWSVM finds a pair of parametric margin hyperplanes that automatically adjusts the parametric insensitive margin to incorporate the structural information of data. The proposed model of RPTWSVM is not only useful in controlling the heteroscedastic noise but also has much faster training speed when compared to Par-\(\nu \)-SVM. Experimental results on several machine learning benchmark datasets show the advantages of RPTWSVM both in terms of generalization ability and training speed over other related models.     

Stimulation of secondary metabolites by copper and gold nanoparticles in submerge adventitious root cultures of Stevia rebaudiana (Bert.)

Nanotechnology is one of the advance technologies that almost found implications in every field of science. The importance is due to the unique properties of nanoparticles. In this study, bimetallic alloys of copper (Cu) and gold (Au) were tested in submerge root cultures of Stevia rebaudiana for production of biomass and secondary metabolites. A known amount of inoculum roots were submerged in liquid Murashige and Skoog medium containing combination of naphthalene acetic acid (NAA; 0.5 mg l⁻¹) and different ratios of nanoparticles (NPs). NAA augmented medium was used as control. The addition of nanoparticles (30 µg l⁻¹) stimulated biomass accumulation (1.447 g/flask) on 27th day of log phases. The maximum total phenolics content (TPC; 16.17 mg/g‐DW) and total flavonoids content (TFC; 4.20 mg/g‐DW) were displayed using AuCu‐NPs (1:3) and NAA. The same combinations enhanced total phenolic production (TPP; 116 mg/L) and total flavonoid production (TFP; 29.5 mg/L) in submerged cultures. A strong correlation was observed between phenolics, flavonoids and dry biomass. Moreover, maximum 1, 1‐diphenyl‐2‐picrylhydrazyl (DPPH) activity of 79% was displayed by addition of AuCu (1:3) nanoparticles. In conclusion, nanoparticles application has shown a positive effect in enhancing biomass and secondary metabolites production in adventitious root cultures of Stevia rebaudiana.Inspec keywords: bimetals, copper, gold, nanoparticles, renewable materials, bioenergy conversion, toxicology, nanofabrication, nanobiotechnology, biochemistry, molecular biophysicsOther keywords: Au‐Ag, time 27 d, maximum DPPH activity, dry biomass, flavonoids, phenolics, NAA enhanced total phenolic production, total flavonoid content, maximum total phenolic content, log phases, bimetallic NPs stimulated biomass accumulation, NAA augmented medium, naphthalene acetic acid, Skoog medium, liquid Murashige, inoculum roots, culture development, seed‐derived roots, bimetallic alloys, nanotechnology, Stevia rebaudiana (Bert.), submerge adventitious root cultures, gold nanoparticles, copper nanoparticles, secondary metabolites     

Knowledge based Least Squares Twin support vector machines

We propose knowledge based versions of a relatively new family of SVM algorithms based on two non-parallel hyperplanes. Specifically, we consider prior knowledge in the form of multiple polyhedral sets and incorporate the same into the formulation of linear Twin SVM (TWSVM)/Least Squares Twin SVM (LSTWSVM) and term them as knowledge based TWSVM (KBTWSVM)/knowledge based LSTWSVM (KBLSTWSVM). Both of these formulations are capable of generating non-parallel hyperplanes based on real-world data and prior knowledge. We derive the solution of KBLSTWSVM and use it in our computational experiments for comparison against other linear knowledge based SVM formulations. Our experiments show that KBLSTWSVM is a versatile classifier whose solution is extremely simple when compared with other linear knowledge based SVM algorithms.     

Twin Support Vector Machines for pattern classification

We propose twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data     

Regularized least squares fuzzy support vector regression for financial time series forecasting

In this paper, we propose a novel approach, termed as regularized least squares fuzzy support vector regression, to handle financial time series forecasting. Two key problems in financial time series forecasting are noise and non-stationarity. Here, we assign a higher membership value to data samples that contain more relevant information, where relevance is related to recency in time. The approach requires only a single matrix inversion. For the linear case, the matrix order depends only on the dimension in which the data samples lie, and is independent of the number of samples. The efficacy of the proposed algorithm is demonstrated on financial datasets available in the public domain.     

Rapid estimation and quantification of sucrose content in fruit juices using Fourier transform infrared–attenuated total reflectance (FTIR–ATR) spectroscopy

An uncomplicated and rapid procedure has been developed for the quantitative analysis of sucrose in fruit samples (grape, pineapple, mango) through attenuated total reflectance–Fourier transform infrared absorbance measurements (ATR–FTIR). FTIR analysis takes considerably reduced time compared to the other classical methods. To calibrate the method, we used firstly, different concentrations of pure sucrose (from 1 to 5 %) and registered their IR maximal wavenumbers and peak intensity. The spectral peak of sucrose for each sample lies between 1057 and 1061 cm^?1. DNS method was used to analyse the content of sucrose by using spectrophotometry. The wave length used for analysing is 540 nm. Also high performance liquid chromatography was used to analyse the sucrose content in the fruit juices. By comparing the retention time of sucrose standards and the sample juices, sucrose concentration was identified and quantified. The results of all three experiments/techniques support each other by justifying that the mango has the high content of sucrose followed by pineapple and grape.     

Discovering mappings in hierarchical data from multiple sources using the inherent structure

Unprecedented amounts of media data are publicly accessible. However, it is increasingly difficult to integrate relevant media from multiple and diverse sources for effective applications. The functioning of a multimodal integration system requires metadata, such as ontologies, that describe media resources and media components. Such metadata are generally application-dependent and this can cause difficulties when media needs to be shared across application domains. There is a need for a mechanism that can relate the common and uncommon terms and media components. In this paper, we develop an algorithm to mine and automatically discover mappings in hierarchical media data, metadata, and ontologies, using the structural information inherent in these types of data. We evaluate the performance of this algorithm for various parameters using both synthetic and real-world data collections and show that the structure-based mining of relationships provides high degrees of precision.