Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.     

CNFET Based Ultra-Low-Power Schmitt Trigger SRAM for Internet of Things (IoT) Applications

Wireless Personal Communications - This paper presents Carbon Nanotube FET (CNFET) based ultra-low-power Schmitt trigger SRAM designs which can operate at voltage levels as low as 200 mV, with high...     

Outage Performance of Uplink (UL) NOMA Network

Wireless Personal Communications - This paper proposes and analyses the power allocation coefficient normalization for successive interference cancellation in power-domain non-orthogonal multiple...     

A Comparative Investigation of Optical and Structural Properties of Cu-Doped CdO-Derived Nanostructures

In this report, we studied various structural and optical properties of pure and copper-doped cadmium oxide (CdO) thin films. Nanostructured Cu-doped CdO films were deposited using sol–gel spin-coating technique. The structural and morphological changes have been observed by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and atomic force microscopy (AFM) studies. The optical and electrical properties of the pure and Cu-doped CdO thin films were studied by UV–vis spectroscopy and four-point probe method, respectively. The XRD peaks show the formation of nanocrystalline CdO with cubic face-centered crystal structure. The band gaps of the as deposited films were found in the range of 2.32–2.73 eV, while after doping, it decreases due to structural deformation. The electrical resitivity was found to decrease approximately ～10 in Cu-doped CdO thin films.     

Green synthesis,characterisation and bioactivity of plant‐mediated silver nanoparticles using Decalepis hamiltonii root extract

Consistent search of plants for green synthesis of silver nanoparticles (SNPs) is an important arena in Nanomedicine. This study focuses on synthesis of SNPs using bioreduction of silver nitrate (AgNO₃) by aqueous root extract of Decalepis hamiltonii. The biosynthesis of SNPs was monitored by UV–vis analysis at absorbance maxima 432 nm. The fluorescence emission spectra of SNPs illustrated the broad emission peak 450–483 nm at different excitation wavelengths. The surface characteristics were studied by scanning electron microscope and atomic force microscopy, showed spherical shape of SNPs and dynamic light scattering analysis confirmed the average particle size 32.5 nm and the presence of metallic silver was confirmed by energy dispersive X‐ray. Face centred cubic structure with crystal size 33.3 nm was revealed by powder X‐ray diffraction. Fourier transform infrared spectroscopy indicated the biomolecules involved in the reduction mainly polyols and phenols present in root extracts were found to be responsible for the synthesis of SNPs. The stability and charge on SNPs were revealed by zeta potential analysis. In addition, on therapeutic forum, the synthesised SNPs elicit antioxidant and antimicrobial activity against Bacillus cereus, Bacillus licheniformis, Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus.Inspec keywords: silver, nanoparticles, nanomedicine, antibacterial activity, biomedical materials, nanofabrication, particle size, microorganisms, ultraviolet spectra, visible spectra, fluorescence, scanning electron microscopy, atomic force microscopy, light scattering, X‐ray diffraction, X‐ray chemical analysis, Fourier transform infrared spectra, molecular biophysics, electrokinetic effectsOther keywords: phenols, zeta potential analysis, therapeutic forum, antioxidant activity, antimicrobial activity, Bacillus cereus, Bacillus licheniformis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Ag, polyols, biomolecules, Fourier transform infrared spectroscopy, powder X‐ray diffraction, crystal size, face centred cubic structure, energy dispersive X‐ray analysis, metallic silver, particle size, dynamic light scattering analysis, spherical shape, atomic force microscopy, scanning electron microscopy, surface characteristics, excitation wavelengths, fluorescence emission spectra, UV‐visible analysis, biosynthesis, silver nitrate bioreduction, nanomedicine, Decalepis hamiltonii aqueous root extract, bioactivity, plant‐mediated silver nanoparticles, green synthesis     

Indium selenide microrod films: chemical bath deposition from acidic bath

Films of indium selenide was deposited onto glass and indium tin oxide coated glass substrates in acidic medium using chemical bath deposition at room temperature. Indium sulphate and sodium selenosulphate were used as precursors of In³⁺ and Se^2?, respectively. The structural, surface morphological and optical properties of the deposited films were studied. Diffractograms in structural study revealed the deposited material is In₂Se₃ films. Controlled bath conditions resulted in the evolution of the In₂Se₃ microrod-like morphology. The optical band gap of the film was found to be 1.7 eV.