We have reported the electronic, magnetic and optical properties of carbon doped bilayer hexagonal boron nitride (h-BN) using thedensity functional theory. A single Cdoping at B/N sites gives the large band gap similar to dilute magnetic semiconducting behaviour with a finite net magnetic moment of 1.001 and 0.998μ B , respectively.For double doping at B/N sites the net magnetic moment increases to 1.998 and 1.824μ B , respectively. Upon C-doping at N-site, we obtained transition from nonmagnetic semiconductor (pristine) ! magnetic semiconductor (1C) ! half-metal ferromagnetic (2C) ! metal (3C). In case of the B site, we observed metallic behaviour for 2C-doping. As 1,2 C-doping at the B site reduces the energy band gap from 1.8 eV to 0.81 eV, falls in the visible range and offers an opportunity to utilized as a photocatalyst material. C-doped systems show a magnetic semiconducting behavior crucial for spintronic applications.

There are many classifiers that are used for diagnosis of diabetes but the result of this paper shows that how logistic regression having best accuracy among the other classifiers. Logistic regression removes the disadvantages of linear regression. There are different classifiers that are used for prediction. In the worldwide millions of peoples are suffering from diabetes according to WHO report. In the medical region, many researches have done with the help of data mining. The aim of this paper is to diagnosis of diabetes by using the best classifiers and providing best parameter tuning. The study helps to find whether a patient is enduring from diabetes or not using classification methods and it further investigate and evaluates the functioning of different classification in relations of precision, accuracy, recall & roc

Grey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. It has been used in numerous fields such as numerical optimization, engineering problems, and machine learning. The different variants of GWO have been developed in the last 5 years for solving optimization problems in diverse fields. Like other metaheuristic algorithms, GWO also suffers from local optima and slow convergence problems, resulted in degraded performance. An adequate equilibrium among exploration and exploitation is a key factor to the success of meta-heuristic algorithms especially for optimization task. In this paper, a new variant of GWO, called inertia motivated GWO (IMGWO) is proposed. The aim of IMGWO is to establish better balance between exploration and exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values and in turn, attains poor convergence. The metaheuristic approaches are better alternative instead of BP. The proposed IMGWO is used to train the ANN to prove its competency in terms of prediction. The proposed IMGWO-ANN is used for medical diagnosis task. Some benchmark medical datasets including heart disease, breast cancer, hepatitis, and parkinson's diseases are used for assessing the performance of IMGWO-ANN. The performance measures are described in terms of mean squared errors (MSEs), classification accuracies, sensitivities, specificities, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. It is found that IMGWO outperforms than three popular metaheuristic approaches including GWO, genetic algorithm (GA), and particle swarm optimization (PSO). Results confirmed the potency of IMGWO as a viable learning technique for an ANN.

Heart disease is the most dangerous disease among all the non-communicable diseases. Annually 17900 thousand of peoples die due to heart problems. Cardiovascular disease (CVD) is the general term used for most of the heart diseases. There are two types of methods for diagnosing a CVD: (i) Invasive Methods (ii) Non-Invasive Methods. Coronary angiography is an invasive method for diagnosing a CVD which is a costly, painful and complicated process. A variety of Non-Invasive (NI) methods are available for diagnosing a CVD. NI methods generate a lot of data which is mainly of 3 kinds :(i) data based on clinical parameters, lab tests and symptoms (ii)data based on raw heart signals (ECG and PCG) (iii)data based on heart images. Majorly, three different machine learning (ML) frameworks may be developed based on the 3 types of data. First framework is simple and main concern is feature selection and classification. Second and third framework is complicated and requires a lot of techniques (preprocessing, segmentation and feature extraction) prior to classification of heart signals and images respectively. In this paper a comprehensive review is presented that summarizes some recent and prevalent machine learning methodologies in all the frameworks. Most of the papers reviewed in this study are from IEEE Explorer, Science Direct, PubMed, Springer, Hindawi, ACM digital library and MDPI libraries. It is found that Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are superseding in most of the studies in all the frameworks. Deep neural network is comparatively newer machine learning methodology which is giving prominent results in classifying heart sound signals and cardiovascular images. The present study will help to automate diagnosis process of heart disease by providing guidelines and avenues to new researchers in domain of machine learning.

SUMMARY In order to develop polymer‐clay nanocomposites with reduced flammability by incorporation of char‐forming conventional nitrogen and phosphorus based flame retardants at low loading levels, the polypropylene‐clay nanocomposites were prepared by melt blending method with Cloisite 15A (15A), organic phosphinate (OP) and ammonium polyphosphate (AP) additives. Thermal analysis shows that addition of 5% 15A along with 15% (w/w) OP in polypropylene (PP)/PPgMA increases the thermal stability of PP/PPgMA/OP/15A composite by 82 °C showing synergistic effect, and the PP/PPgMA/AP/15A sample with same loading becomes thermally stable by 70 °C. Cone calorimeter analysis of the PP/PPgMA/OP/15A and PP/PPgMA/AP/15A composites measures the reduction in peak heat release rate values by 66% and 58%, respectively. Addition of 20% OP to PP/PPgMA enhances the limiting oxygen index (LOI) value and gives V‐2 rating of UL‐94 test. Further, on replacing 5% OP with 5% 15A for PP/PPgMA/OP/15A sample without changing the total 20% loading, the LOI value increases further slightly but give no UL‐94 rating. Also, PP/PPgMA/AP/15A sample with same loading similar to that of PP/PPgMA/OP/15A sample shows an enhancement in LOI value and gives no rating in UL‐94 test. No relation was observed between LOI values and UL‐94 test rating in the present study. Copyright © 2012 John Wiley & Sons, Ltd.