<Emphasis Type="SmallCaps">SILVERBACK+</Emphasis>: scalable association mining via fast list intersection for columnar social data

We present Silverback+, a scalable probabilistic framework for accurate association rule and frequent item-set mining of large-scale social behavioral data. Silverback+ tackles the problem of efficient storage utilization and management via: (1) probabilistic columnar infrastructure and (2) using Bloom filters and sampling techniques. In addition, probabilistic pruning techniques based on Apriori method are developed, for accelerating the mining of frequent item-sets. The proposed target-driven techniques yield a significant reduction of the size of the frequent item-set candidates, as well as the required number of repetitive membership checks through a novel list intersection algorithm. Extensive experimental evaluations demonstrate the benefits of this context-aware consideration and incorporation of the infrastructure limitations when utilizing the corresponding research techniques. When compared to the traditional Hadoop-based approach for improving scalability by straightforwardly adding more hosts, Silverback+ exhibits a much better runtime performance, with negligible loss of accuracy.     

OWA Operator‐Based Hybrid Framework for Outlier Reduction in Web Mining

Web mining is the process of extracting useful information from Web resources. Handling outliers is one of the primary challenges of present Web mining techniques. The complex nature of the Web, by virtue of both data and users, makes it very difficult to mine the information and convert to knowledge base with little outlier values. In this paper, a framework for reducing outliers in regression analysis with the help of ordered weighted operators (OWA) as a multicriteria decision‐making problem is being formulated. First, a regression problem with a real‐time Web data set will be formulated followed by solving the same with the help of a variant of OWA operators. Results, thus obtained are able to prove that outliers can be reduced significantly with the help of proposed approach.     

A second generation current conveyor based PID controller optimized using a crossover improved genetic algorithm

This article presents a second generation current conveyor (CCII) based PID controller that has been optimally designed using a crossover improved genetic algorithm (CIGA). PID controller is widely adopted in practical industrial applications and the current conveyors are being favored because of their enhanced characteristics. Thus, designing a PID controller using the current conveyors would augment its performance. Another unique aspect of the reported research work is the utilization of a crossover improved genetic algorithm for finding the controller’s transfer function. The controller has been optimally designed taking into consideration the unit step response characteristics. Subsequently, AD844 IC was used for practical implementation of the controller and the experimental observations are reported, which are found to be in good agreement with simulation as well as theoretical results.

     

Wide Dynamic Range in Tunable Electrochromic Bragg Stacks from Doped Semiconductor Nanocrystals

Electrochromic properties can be enhanced by constructing photonic architectures, in which the reflectance contributes to optical modulation along with the intrinsic dynamic absorptivity of the material. However, optimization of reflectance is challenging without a rational design approach. Here, electrochemically tunable Bragg reflectors are demonstrated that are tuned to be highly transparent in the “off” state, achieving synergistic dynamic optical modulation of absorption and reflection in the visible and near‐infrared range. These Bragg stacks are composed of alternating doped semiconductor nanocrystal (NC) layers of 5 nm sized oxygen vacancy‐doped WO_3?x and 15 nm sized 0.4 at% Sn:In₂O₃ NCs. Combining judicious NC selection and processing optimization with guidance from optical simulations, optimized Bragg stacks are implemented for electrochromic window applications. NCs with high absorption coefficients are essential for strong transmission modulation, though this characteristic limits the dynamic range of the Bragg reflectance. Optimal reflectance modulation including a highly transparent “off” state is confirmed with in situ reflectance and transmittance measurement. More broadly, ligand‐stripped NCs can enable fabrication of complex device architectures on low‐cost flexible substrates. These results guide the design rules for accessing different types of doped semiconductor NC‐based tunable Bragg stacks, an exemplary photonic structure, over a broad wavelength range.     

Performance improvement of wireless secret key generation with colored noise for IoT

Achieving security in the Internet of things (IoT) networks by generating symmetric keys from the wireless channel parameters like received signal strength (RSS) is a promising approach. Despite the easy acquisition of the RSS signal, RSS‐based security is less explored for IoT. In this work, we analyze the performance of RSS‐based wireless physical layer key generation with correlated colored noise components and proposed a low complexity filtering approach to improve the performance for the IoT network. We started with providing a survey of various recent researches related to RSS‐based key generation and also discussed correlated colored noise components with a few of the recent works considering them. Further, we analyze various colored noise components in the time domain by the Allan variance and Ljung‐Box test. Furthermore, we develop a key generation model and proposed a moving window averaging‐based filtering followed by Lloyd max quantization to improve the BDR performance, degraded due to the presence of correlated colored noise components. The simulation results show that the proposed preprocessing technique has a considerable improvement in the BDR performance, and the keys generated have sufficient randomness, which is verified by NIST test.     

Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network

The visual sleep stages scoring by human experts is the current gold standard for sleep analysis. However, this method is tedious, time-consuming, prone to human errors, and unable to detect microstructure of sleep such as cyclic alternating pattern (CAP) which is an important diagnostic factor for the detection of sleep disorders such as insomnia and obstructive sleep apnea (OSA). The CAP is only observed as subtle changes in the electroencephalogram (EEG) signals during non-rapid eye movement (NREM) sleep, making it very difficult for human experts to discern. Hence, it is important to have an automated system developed using artificial intelligence for accurate and robust detection of CAP and sleep stages classification. In this study, a deep learning model based on 1-dimensional convolutional neural network (1D-CNN) is proposed for CAP detection and homogenous 3-class sleep stages classification, namely wakefulness (W), rapid eye movement (REM) and NREM sleep. The proposed model is developed using standardized EEG recordings. Our developed CNN network achieved good model performance for 3-class sleep stages classification with a classification accuracy of 90.46%. Our proposed model also yielded a classification accuracy of 73.64% using balanced CAP dataset, and sensitivity of 92.06% with unbalanced CAP dataset. Our proposed model correctly identified majority of A-phases which comprised of only 12.6% in the unbalanced dataset. The performance of the developed prototype is ready to be tested with more data before clinical application.

     

An energy-mix model for round-the-clock power supply in a decarbonized electricity generation scenario: case study of South India

Clean Technologies and Environmental Policy - There is increasing urgency towards integration of renewable sources into electricity generation so as to minimize greenhouse gas (GHG) emissions....     

Facile preparation of agaraldehyde chitosan-based composite beads as effectual adsorbent especially towards amido black

Agaraldehyde and chitosan (CHI) functional beads as dye adsorbent were prepared under aqueous medium under ambient temperature and used for removing seven dyes from aqueous solutions. The resulting porous CHI-A_ald (R-A_ald-CHI) bio-beads were characterized by X-ray photoelectron spectroscopy, Fourier transform infrared spectroscopy, and scanning electron microscope. The dye removal efficiency was studied using seven dyes (amido black [AB], bromophenol blue, crystal violet, bismarck brown, cotton blue, methyl orange, methylene blue), but detail study was done on AB. AB adsorption was followed the Langmuir-isotherm and pseudo-second order kinetics. The effects of adsorption parameters were investigated, and maximum AB 1506 mg/g was adsorbed under the optimum conditions. The linear and nonlinear forms of Langmuir and Freundlich isotherms and two kinetic models pseudo-first-order and pseudo-second-order were studied. The results show that adsorption follow both isotherm model while for kinetics of adsorption follow pseudo-second-order. The results of this work showed that R-A_ald-CHI was encouraging adsorbent to remove dyes from wastewater, especially AB.     

S. I: hybridization of neural computing with nature-inspired algorithms

Neural Computing and Applications -