Political Optimizer Based Feedforward Neural Network for Classification and Function Approximation

Neural Processing Letters - Political optimizer (PO) is a recently proposed human-behavior inspired meta-heuristic, which has shown tremendous performance on complex multimodal functions as well as...     

Three-qubit Grover’s algorithm using superconducting quantum interference devices in cavity-QED

We present a scheme for the implementation of three-qubit Grover’s algorithm using four-level superconducting quantum interference devices (SQUIDs) coupled to a superconducting resonator. The scheme is based on resonant, off-resonant interaction of the cavity field with SQUIDs and application of classical microwave pulses. We show that adjustment of SQUID level spacings during the gate operations, adiabatic passage, and second-order detuning are not required that leads to faster implementation. We also show that the marked state can be searched with high fidelity even in the presence of unwanted off-resonant interactions, level decay, and cavity dissipation.     

A kinetic flux-vector splitting method for the shallow water magnetohydrodynamics

A kinetic flux-vector splitting (KFVS) scheme for the shallow water magnetohydrodynamic (SWMHD) equations in one- and two-space dimensions is formulated and applied. These equations model the dynamics of a thin layer of nearly incompressible and electrically conducting fluids for which the evolution is nearly two-dimensional with magnetic equilibrium in the third direction. The proposed numerical scheme is based on the direct splitting of macroscopic flux functions of the SWMHD equations. In two-space dimensions the scheme is derived in a usual dimensionally split manner; that is, the formulae for the fluxes can be used along each coordinate direction. The high-order resolution of the scheme is achieved by using a MUSCL-type initial reconstruction and Runge-Kutta time stepping method. Both one- and two-dimensional test computations are presented. For validation, the results of KFVS scheme are compared with those obtained from the space-time conservation element and solution element (CE/SE) method. The accuracy, efficiency and simplicity of the KFVS scheme demonstrate its potential in modeling SWMHD equations.     

Senti‐CS: Building a lexical resource for sentiment analysis using subjective feature selection and normalized Chi‐Square‐based feature weight generation

Sentiment analysis involves the detection of sentiment content of text using natural language processing. Natural language processing is a very challenging task due to syntactic ambiguities, named entity recognition, use of slangs, jargons, sarcasm, abbreviations and contextual sensitivity. Sentiment analysis can be performed using supervised as well as unsupervised approaches. As the amount of data grows, unsupervised approaches become vital as they cut down on the learning time and the requirements for availability of a labelled dataset. Sentiment lexicons provide an easy application of unsupervised algorithms for text classification. SentiWordNet is a lexical resource widely employed by many researchers for sentiment analysis and polarity classification. However, the reported performance levels need improvement. The proposed research is focused on raising the performance of SentiWordNet3.0 by using it as a labelled corpus to build another sentiment lexicon, named Senti‐CS. The part of speech information, usage based ranks and sentiment scores are used to calculate Chi‐Square‐based feature weight for each unique subjective term/part‐of‐speech pair extracted from SentiWordNet3.0. This weight is then normalized in a range of ?1 to +1 using min–max normalization. Senti‐CS based sentiment analysis framework is presented and applied on a large dataset of 50000 movie reviews. These results are then compared with baseline SentiWordNet, Mutual Information and Information Gain techniques. State of the art comparison is performed for the Cornell movie review dataset. The analyses of results indicate that the proposed approach outperforms state‐of‐the‐art classifiers.     

A rule-based expert system for earthquake prediction

Earthquake is a natural disaster which causes extensive damage as well as the death of thousands of people. Earthquake professionals for many decades have recognized the benefits to society from reliable earthquake predictions. Techniques like: mathematical modelling, hydrology analysis, ionosphere analysis and even animal responses have been used to forecast a quake. Most of these techniques rely on certain precursors like, stress or seismic activity. Data mining techniques can also be used for prediction of this natural hazard. Data mining consists of evolving set of techniques such as association rule mining that can be used to extract valuable information and knowledge from massive volumes of data. The aim of this study is to predict a subsequent earthquake from the data of the previous earthquake. This is achieved by applying association rule mining on earthquake data from 1979 to 2012. These associations are polished using predicate-logic techniques to draw stimulating production-rules to be used with a rule-based expert system. Prediction process is done by an expert system, which takes only current earthquake attributes to predict a subsequent earthquake. The rules generated for predicting the earthquake are mathematically validated as well as tested on real life earthquake data. Results from our study show that the proposed rule-based expert system is able to detect 100 % of earthquakes which actually occurred within 15 hours at-most within a defined range, depth and location. This work solely relies on previous earthquake data for predicting the next.     

Synthesis of highly active nanocrystalline WO3 and its application in laser-induced photocatalytic removal of a dye from water

Tungsten oxide nanoparticles were synthesized using the sol–gel process and applied for heterogeneous photocatalytic removal of a dye using a 355 nm laser radiation generated from Nd:YAG for the first time. Effect of various parameters, such as calcination temperature, calcination time, catalyst concentration and laser energy on the photocatalytic removal of dye has been investigated. The study showed that almost complete degradation of dye can be achieved within very short time of reaction (within few minutes) in presence of nanocrystalline WO₃ under laser irradiation. The removal process obeys first-order kinetics with an appreciable rate constant 0.146 min⁻¹. The main reason of high efficiency is the nanostructure nature of WO₃ and the laser as an excitation source as compared with the conventional setups using lamps and conventional microstructure catalysts.     

Monte Carlo simulation of extrusion die life

Due to the high cost of metal forming tools (especially in hot extrusion), one of the major goals in tool design is a longer service life. Estimation and prediction of tool life thus becomes critically important for performance evaluation of the tools. The two most dominant failure mechanisms for extrusion dies (solid, hollow, and semi-hollow dies all taken together) are fracture and wear. In the first part of the paper, a fracture mechanics based fatigue life prediction model is described. A similar treatment is then presented for wear-related failures. Fracture and wear usually coexist as failure modes, and final die breakdown occurs due to the mechanism that becomes dominant. Therefore, a competing fracture–wear model has been later developed to represent the complete die failure situation. Attempt has been made to correlate the stochastic nature of various fatigue and wear related die parameters to die life. Monte Carlo simulation has been used to predict the life distribution of a die for a given set of manufacturing conditions and mechanical properties. In comparison with actual life data from the industry, the simulated life yields very realistic predictions.