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This paper establishes a hybridized intelligent machine learning based currency exchange forecasting model using Extreme Learning Machines (ELMs) and the Jaya optimization technique. This model can very well forecast the exchange price of USD (US Dollar) to INR (Indian Rupee) and USD to EURO based on statistical measures, technical indicators and combination of both measures over a time frame varying from 1 day to 1 month ahead. The proposed ELM-Jaya model has been compared with existing optimized Neural Network and Functional Link Artificial Neural Network based predictive models. Finally, the model has been validated using various performance measures such as; MAPE, Theil's U, ARV and MAE. The comparison of different features demonstrates that the technical indicators outperform both the statistical measures and a combination of statistical measures and technical indicators in ELM-Jaya forecasting model. Keywords: Currency exchange prediction, Extreme Learning Machine (ELM), Jaya, Neural Network (NN), Functional Link Artificial Neural Network (FLANN)

For the prediction of exchange rate, this paper proposes a hybrid learning frame work model which is a joint estimation of On-Line Sequential Extreme Learning Machine (OS-ELM) along with optimized feature reduction using Krill Herd (KH). The proposed learning scheme is compared with Extreme Learning Machine (ELM) and Recurrent Back Propagation Neural Network (RBPNN), considering three factors such as; without feature reduction, with statistical based feature reduction using Principal Component Analysis (PCA) and with optimized feature reduction techniques such as KH, Bacteria Foraging Optimization (BFO) and Particle Swarm Optimization (PSO). The models are applied over USD/INR, USD/EURO, YEN/INR and SGD/INR, constructed using technical indicators and statistical measures considering 3, 5, 7, 12 and 15 as window sizes. The results of comparisons of different performance measures in testing phase and MSE in training process demonstrate that the proposed OSELM-KH exchange rate prediction model is potentiality superior compared to others.

The unique combination of electrical, mechanical and electrochemical properties offered by multi-walled carbon nanotubes (MWCNTs) has emerged as a potential element for their use in many kinds of applications including biomedical fields. In this article, the crystallographic structural and electrical properties of multi-walled carbon nanotubes (MWCNTs) have been discussed briefly. Finally the article concludes by focusing the key properties of MWCNTs which fostered their use in various biomedical applications, such as drug delivery, cancer treatment, gene therapy and diagnostics.

Captive maturation and spawning in Penaeus monodon are currently induced by unilateral eyestalk ablation, a method that removes the gonad-inhibiting hormone (GIH) secreted from the eyestalk. However, unilateral eyestalk ablation creates physiological complications in the brooder due to the unintended removal of other hormones secreted by the eyestalks. Here we studied the effect of gih silencing by long hairpin RNA on the reproductive physiology of P . monodon . We observed 3–5 fold enhanced expression of the androgenic gland hormone (AGH) transcript in males, but no effect on vitellogenin expression in females. In the destalked animals, however, positive effect on the maturation indicator transcripts was seen in both the sexes, and surpassed the efficiency of the silencing treatment. There were indications of active spermatogenesis in histological sections of both the gih silenced and destalked males, but no changes at histological level in the females. We also observed a significantly higher rate of molting and associated mortality in the gih silenced animals at the end of the experiment. Our results suggest that eyestalk ablation is still the most efficient technique to induce maturation in P . monodon . Although gih silencing could be a potential alternative to eyestalk ablation, further research is needed to enhance its efficiency over eyestalk ablation by using tissue specific and/or inducible promoter for lhRNA expression. It is also evident that females need to be fairly large in size for these interventions to succeed. Statement of relevance Induction of captive maturation has been a challenge for shrimp industry. The seed production is often hampered by the low abundance and high price of naturally collected broodstock. Collection of natural brooders also has the intrinsic problem of WSSV infection. Also, genetic improvement of this highly preferred species is difficult unless the reproductive cycle is closed. For all these reasons it is important to study the reproductive physiology and endocrinology of this species and ways to manipulate it in order to induce captive maturation. The present study utilizes the RNA interference mechanism to silence the gonad-inhibiting hormone gene to see its effect on maturation.

We conducted three studies among European-American and Hindu Indian populations examining cultural differences in the norms underlying social support in friend relationships. Study 1 investigated the role of communal norms as compared with reciprocal exchange in real-life helping interactions among friends; Study 2 compared respondents' evaluations of contrasting modes of reciprocating help; while Study 3 experimentally tested whether reciprocation reduces readiness to respond to future need. We found that Indians give greater emphasis to communal norms in friend relationships than Americans do, with this effect unrelated to socioeconomic status; and that Americans place greater emphasis on reciprocal exchange, a relaxed form of exchange that is compatible with close interpersonal ties. Our results point to cultural variation in the strength of communal relationships and imply that reciprocal exchange assumes a more prominent role in close relationships than what has been previously observed in the communal/exchange tradition.

Forecasting future trends of the stock market using the historical data is the exigent demand in the field of academia as well as business. This work has explored the feature optimization capacity of firefly with an evolutionary framework considering the biochemical and social aspects of Firefly algorithm, along with the selection procedure of objective value in evolutionary notion. The performance of the proposed model is evaluated using four different stock market datasets, such as BSE Sensex, NSE Sensex, S&P 500 index and FTSE index. The datasets are regenerated using the proper mathematical formulation of the fundamental part belonging to technical analysis, such as technical indicators and statistical measures. The feature reduction through transformation is carried out on the enhanced dataset before employing the experimented dataset to the prediction models such as Extreme Learning Machine (ELM), Online Sequential Extreme Learning Machine (OSELM) and Recurrent Back Propagation Neural Network (RBPNN). For feature reduction, both statistical and optimized based feature reduction strategies are considered, where Principal Component Analysis (PCA) and Factor Analysis (FA) are examined for statistical based feature reduction and Firefly Optimization (FO), Genetic Algorithm (GA) and Firefly algorithm with evolutionary framework are well thought out for optimized feature reduction techniques. An empirical comparison is established among the experimented prediction models considering all the feature reduction techniques for the time horizon of 1 day, 3 days, 5 days, 7 days, 5 days and 30 days in advance, applying on all the datasets used in this study. From the simulation result, it can be clearly figured out that firefly with evolutionary framework optimized feature reduction applying to OSELM prediction model outperformed over the rest experimented models.

Classification is a vital tool for understanding the relationships of living things using which similar things can be grouped together. Classification of elements into groups makes the study relatively easy. Therefore, classification is necessary to know salient features and characteristics of living organisms as well as their inter relationship among different group of organisms, as the correct classification of a person's disease is important for proper treatment. Support vector machine (SVM) was the first proposed kernel-based method, which uses a kernel function to transfer data from input space into high dimensional feature space; it searches for a separating hyper-plane. SVM is based on simple ideas which originated in statistical learning theory; hence the aim is to solve only the problem of interest without solving a more difficult problem as an intermediate step. SVM apply a simple linear method to the data but in a high-dimensional feature space non-linearly related to the input space. Even though we can think of SVM as a linear algorithm in high dimensional space, but in practice it does not involve any computations in that high-dimensional space. As dimensionality is curse to gene expression data set, in this paper Principal Component Analysis (PCA) is used for feature reduction to breast cancer, lung cancer and cardiotography data sets, and SVM is trained by linear, polynomial and radial basis function (RBF) kernels applied on each of these data sets and the comparison among them shows that RBF is better for the three data sets.

Identification of a reference gene unaffected by the experimental conditions is obligatory for accurate measurement of gene expression through relative quantification. Most existing methods directly analyze variability in crossing point (Cp) values of reference genes and fail to account for template-independent factors that affect Cp values in their estimates. We describe the use of three simple statistical methods namely analysis of variance (ANOVA), normal quantile-quantile correlation (NQQC) and effective expression support (EES), on pooled expression ratios of reference genes in a panel to overcome this issue. The pooling of expression ratios across the genes in the panel nullify the sample specific effects uniformly affecting all genes that are falsely reflected as instability. Our methods also offer the flexibility to include sample specific PCR efficiencies in estimations, when available, for improved accuracy. Additionally, we describe a correction factor from the ANOVA method to correct the relative fold change of a target gene if no truly stable reference gene could be found in the analyzed panel. The analysis is described on a synthetic data set to simplify the explanation of the statistical treatment of data.