Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

The human liver disorder is a genetic problem due to the habituality of alcohol or effect by the virus. It can lead to liver failure or liver cancer, if not been detected in initial stage. The aim of the proposed method is to detect the liver disorder in initial stage using liver function test dataset. The problem with many real-world datasets including liver disease diagnosis data is class imbalanced. The word imbalance refers to the conditions that the number of observations belongs to one class having more or less than the other class(es). Traditional K- Nearest Neighbor (KNN) or Fuzzy KNN classifier does not work well on the imbalanced dataset because they treat the neighbor equally. The weighted variant of Fuzzy KNN assign a large weight for the neighbor belongs to the minority class data and relatively small weight for the neighbor belongs to the majority class to resolve the issues with data imbalance. In this paper, Variable- Neighbor Weighted Fuzzy K Nearest Neighbor Approach (Variable-NWFKNN) is proposed, which is an improved variant of Fuzzy-NWKNN. The proposed Variable-NWFKNN method is implemented on three real-world imbalance liver function test datasets BUPA, ILPD from UCI and MPRLPD. The Variable-NWFKNN is compared with existing NWKNN and Fuzzy-NWKKNN methods and found accuracy 73.91% (BUPA Dataset), 77.59% (ILPD Dataset) and 87.01% (MPRLPD Dataset). Further, TL_RUS method is used for preprocessing and it improved the accuracy as 78.46% (BUPA Dataset), 78.46% (ILPD Dataset) and 95.79% (MPRLPD Dataset).

     

Time delays and angles of arrival estimation using known signals

Channel estimation in a multipath mobile communication system is addressed in this paper, and a novel approach based on the linear prediction in frequency domain and the singular value decomposition technique is presented for joint estimation of the angles of arrival and the time delays of multiple reflections of a known signal. Simulation results illustrating the performance of the proposed algorithm are included, and the results show that the proposed method is close in accuracy when compared to the iterative maximum-likelihood method. However, when the two methods are compared in computational complexity, it is demonstrated that the proposed method reduces the complexity to nearly half of that of the maximum-likelihood method. The Cramer–Rao bounds are computed for comparison.     

Faradaic rectification study of cadmium(II)-cadmium in different supporting electrolytes

The Faradaic rectification theory has been extended to the reaction accompanied with multiple electron charge transfer for metal ion—metal solid reactions. The theoretical formulations have been applied for determining the rate constants for each of the two steps ie Cd²⁺ +e Cd⁺ and Cd⁺ +e Cd in the Cd²⁺/Cd(s) reaction. The second step reaction is found to be thousand times slower as compared to the first step reaction and it is in control of the overall rate of reaction. On the basis that the second step of the reaction is overall in control of the reaction, the values of the rate constant determined by applying Delahay's formulations, are found to vary appreciably from those obtained from the present theoretical treatment. The values of rate constant for both the steps in different supporting electrolytes have been obtained and the results have been discussed.     

Phase Inversion Strategy to Flexible Freestanding Electrode: Critical Coupling of Binders and Electrolytes for High Performance Li–S Battery

Development of flexible and freestanding electrode is attracting great attention in lithium–sulfur (Li–S) batteries, but the severe capacity fading caused by the lithium polysulfides (PSs) shuttle effect remains challenging. Herein, a completely new polymeric binder of polyethersulfone is introduced. Not only it enables massive production of flexible/current‐free electrode by a novel concept of “phase‐inversion” approach but also the resultant polymeric networks can effectively trap the soluble polysulfides within the electrode, owing to the higher hydrophilicity and stronger affinity properties than the routine polyvinylidene fluoride. Coupling with polysulfide‐based electrolyte, the Li–S cell shows a higher capacity of 1141 mAh g^?1, a lower polarization of 192 mV, and a more stable capacity retention with 100% Coulombic efficiency over 100 cycles at 0.25C. The advantages of favored binder and electrolyte are further demonstrated in lithium‐ion sulfur full battery with lithiated graphite anode, which demonstrates much improved performance than those previously reported. This work not only introduces a novel strategy for flexible freestanding electrodes but also enlightens the importance of coupling electrodes and electrolytes to higher performances for Li–S battery.     

Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II

Parametric optimization of electric discharge machining (EDM) process is a multi-objective optimization task. In general, no single combination of input parameters can provide the best cutting speed and the best surface finish simultaneously. Genetic algorithm has been proven as one of the most popular multi-objective optimization techniques for the parametric optimization of EDM process. In this work, controlled elitist non-dominated sorting genetic algorithm has been used to optimize the process. Experiments have been carried out on die-sinking EDM by taking Inconel 718 as work piece and copper as tool electrode. Artificial neural network (ANN) with back propagation algorithm has been used to model EDM process. ANN has been trained with the experimental data set. Controlled elitist non-dominated sorting genetic algorithm has been employed in the trained network and a set of pareto-optimal solutions is obtained.

     

Genetic variants of beta-lactoglobulin gene and its association with milk composition traits in riverine buffalo

A study was carried out to determine genetic variants of beta-lactoglobulin gene and to explore associations between these and milk composition traits in riverine buffalo. Single strand conformation polymorphism was employed to detect the genetic variants of the gene. Two fragments of this gene i.e. 119 bp of exon I and 400 bp spanning exon IV and intron IV were included in the study. For 119 bp fragment, three alleles namely, A, B and C were observed in all the buffalo breeds whereas four alleles (A, B, C and D) were detected for 400 bp fragment. The frequency distribution of alleles was different in different breeds of buffaloes for both the fragments. For exon I fragment, the milk composition traits such as total SNF, protein, solid, fat and whey protein yield were found to be significantly (P<0.05) associated with genotypes in Murrah and Bhadawari buffalo whereas in Mehsana breed genotypes were significantly (P<0.05) co-related with total SNF, solid and fat yield. Genotypes of 400 bp fragment, only total fat yield in Mehsana buffalo was found to be significantly (P<0.05) associated with genotypes.     

120 keV Ar ion-induced red and blue shift of SPR Wavelength of Au nanoparticles in fullerene C60

Journal of Materials Science: Materials in Electronics - Au nanoparticles are synthesized in a fullerene C60 matrix using the thermal co-evaporation technique. Fullerene C60 is chosen due to its...     

An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease

The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease. Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases. Among many existing methods, a few have considered the class imbalance issues of liver disorder datasets. As all the samples of liver disorder datasets are not useful, they do not contribute to learning about classifiers. A few samples might be redundant, which can increase the computational cost and affect the performance of the classifier. In this paper, a model has been proposed that combines noise filter, fuzzy sets, and boosting techniques (NFFBTs) for liver disease prediction. Firstly, the noise filter (NF) eliminates the outliers from the minority class and removes the outlier and redundant pair from the majority class. Secondly, the fuzzy set concept is applied to handle uncertainty in datasets. Thirdly, the AdaBoost boosting algorithm is trained with several learners viz, random forest (RF), support vector machine (SVM), logistic regression (LR), and naive Bayes (NB). The proposed NFFBT prediction system was applied to two datasets (i.e., ILPD and MPRLPD) and found that AdaBoost with RF yielded 90.65% and 98.95% accuracy and F1 scores of 92.09% and 99.24% over ILPD and MPRLPD datasets, respectively.     

Vertically aligned nanocrystalline Cu–ZnO thin films for photoelectrochemical splitting of water

Cu-incorporated nanocrystalline ZnO thin films were deposited on glass substrate by sol–gel. To a solution of zinc acetate 2-hydrate in dimethyl formamide, calculated quantities of copper acetate were added. The clear solution, obtained after 2 h of continuous stirring, was coated on ITO plates. Pre-annealing at 250 °C was followed by sintering at 400, 500, and 600 °C. XRD analysis revealed dominant evolution of hexagonal ZnO with a possible simultaneous growth of meta-stable cubic ZnO. AFM and SEM analysis indicated preferential growth of nanocrystallites along c-axis. Optical characterization led to two prominent absorption thresholds in the UV region; one matching with the band gap of bulk ZnO and the second at slightly higher energy, suggesting quantum confinement effect in nanocrystallites. Cu incorporation influenced the two band gap energies differently. Photoelectrochemical splitting of water using 1% at. Cu–ZnO film sintered at 600 °C resulted in 141% gain in photocurrent at zero bias.