For financial time series, the generation of error bars on the point of prediction is important in order to estimate the corresponding risk. In recent years, optimization techniques-driven artificial intelligence has been used to make time series approaches more systematic and improve forecasting performance. This paper presents a local linear radial basis functional neural network (LLRBFNN) model for classifying finance data from Yahoo Inc. The LLRBFNN model is learned by using the hybrid technique of backpropagation and recursive least square algorithm. The LLRBFNN model uses a local linear model in between the hidden layer and the output layer in contrast to the weights connected from hidden layer to output layer in typical neural network models. The obtained prediction result is compared with multilayer perceptron and radial basis functional neural network with the parameters being trained by gradient descent learning method. The proposed technique provides a lower mean squared error and thus can be considered as superior to other models. The technique is also tested on linear data, i.e., diabetic data, to confirm the validity of the result obtained from the experiment. Keywords Local linear radial basis functional neural network (LLRBFNN) Á Radial basis functional neural network (RBFNN) Á Multilayer perceptron (MLP) Á Recursive least square (RLS) Á Mean squared error (MSE)

Alkaloids U 0600Anionic [4 + 2] Cycloaddition Strategy in the Regiospecific Synthesis of Carbazoles: Formal Synthesis of Ellipticine and Murrayaquinone A. -The anionic [4 + 2] cycloaddition of furoindolones is used as method for synthesizing carbazole quinones and 1-oxygenated carbazoles. The method is regiospecific, efficient and applicable to a range of Michael acceptors. Scope and limitations of the reaction are studied. The nature of N-protection of furoindolones plays a major role in the success of annulation. As an application, the syntheses of ellipticine (XVI) and murrayaquinone A (XI) are detailed.

The main attractive feature to stock market is speedy growth of stock economic value in short yoke of time. The investor analyses the demonstration, estimated value and growth of organizations before investing money in market. The analysis may not be enough by using conventional process or some available methods suggested by different researches. In present days large number of stocks are available in market it is very difficult to study each stock by help of very few suggested foretelling methods. To know the anticipated stock value we need some advanced prediction technology for stock market. This paper introduce an advanced skillful method to plan and analyze the different organizers stock execution in market and prognosticate best suitable stock by predicting close price of stock. The projected arrangement is based on multilayer deep learning neural Network optimized by Adam optimizer. Recent 6 years (2010-2016) data of different organizations are applied to the model to demonstrate the skillfulness of the projected proficient method. From result it has been ascertained that the projected framework is best suited to all different data set of various sectors. The prediction error is very minimal as visible from outcome graph of framework

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As the human race evolves, numerous diseases have dominated humans, causing scientists to encounter myriad challenges with formulations. To rectify these issues, Nanoparticle (10-9 m) formulation approach or nanotechnology has been developed. Since nanoparticles have legion benefits, encompassing better bioavailability, target-specific, confined, accurate dose delivery, and increased surface area, are pertinent for effective treatment. Toxicity, inflammation, limited penetrating ability, accumulation, and aggregation are some of the downsides of nanoparticles. Picoscale along with nanoparticle technology in drug delivery, will enhance the permeability and myriad factors. Also, picometer (10-12 m) and futuristic femtometer (10-15 m) particles will allow scientists to deal with atomic and subatomic levels in some cases and improve the properties of nanotechnology in others. Herein, picotechnology, formulation, synthesis, and some unique properties concluding with femtotechnology have been addressed.