Enhanced Grey Wolf Optimization based Hyper-parameter optimized Convolution Neural Network for Kidney Image Classification

Over the last few years, Convolution Neural Networks (CNN) have shown dominant performance over real world applications due to their ability to find good solutions and deal with image data. However their performance is highly dependent on the network architecture and methods for optimizing their hyper parameters especially number and size of filters. Designing a good CNN architecture requires human expertise and domain knowledge. So, it is difficult in CNN to find sufficient number and size of filters for classification problems. The standard GWO algorithm used for any optimization purpose suffers from some issues such as slow convergence speed, trapping in local minima and unable to maintain balance between exploration and exploitation. In order to have proper balance between these phases, two modifications in GWO are introduced in this paper. A technique for finding optimum CNN architecture using methods based on Enhanced Grey Wolf Optimization (E-GWO) is proposed. The paper presents optimization of hyper parameters (numbers and size of filters in convolution layer) of CNN using E-GWO to improve the performance of the model. Kidney ultrasound images dataset collected from ultrasound centre is used to evaluate the performance of the proposed algorithm. Experimental results showed that optimization of CNN with E-GWO outperformed CNN optimized with traditional GA, PSO and GWO and conventional CNN yielding 97.01% accuracy. At last, the obtained results are statistically validated using t-test

Weibull Distribution and the multiplicity moments in pp (ppˉ)pp\,(p\bar{p}) collisions

A higher moment analysis of multiplicity distribution is performed using the Weibull description of particle production in $pp\,(p\bar{p})$ collisions at SPS and LHC energies. The calculated normalized moments and factorial moments of Weibull distribution are compared to the measured data. The calculated Weibull moments are found to be in good agreement with the measured higher moments (up to 5$^{\rm{th}}$ order) reproducing the observed breaking of KNO scaling in the data. The moments for $pp$ collisions at $\sqrt{s}$ = 13 TeV are also predicted.Comment: 5 pages, 3 figure

Price and Volatility Spillovers across North American, European and Asian Stock Markets: With Special Focus on Indian Stock Market

<div align=justify>This paper investigates interdependence of fifteen world indices including an Indian market index in terms of return and volatility spillover effect. Interdependence of Indian stock market with other fourteen world markets in terms of long run integration, short run dependence (return spillover) and volatility spillover are investigated. These markets are that of are Canada, China, France, Germany, Hong-Kong, Indonesia, Japan, Korea, Malaysia, Pakistan, Singapore, Taiwan, United Kingdom and United States. Long run and short run integration is examined through Johansen cointegration techniques and Granger causality test respectively. Vector autoregressive model (VAR 15) is used to estimate the conditional return spillover among these indices in which all fifteen indices are considered together. The effect of same day return in explaining the return spillover is also modeled using univariate models. Volatility spillover is estimated through AR-GARCH in which residuals from the index return is used as explanatory variable in GARCH equation. Return and volatility spillover between Indian and other markets are modeled through bivariate VAR and multivariate GARCH (BEKK) model respectively. It is found that there is greater regional influence among Asian markets in return and volatility than with European and US. Japanese market, which is first to open, is affected by US and European markets only and affects most of the Asian Markets. Also, high degree of correlation among European indices namely FTSE, CAC and DAX is observed. US market is influenced by both Asian and European markets. Specific to Indian context, it is found that Indian market is not cointegrated with rest of the world except Indonesia. This may provide diversification benefits for potential investors. However, strong short run interdependence is found between Indian markets and most of the other markets. Indian and other markets like US, Japan, Korea, and Canada positively affect each others conditional returns significantly. Indian market also has significant effect on Malaysia, Pakistan, and Singapore return. This study found that there is significant positive volatility spillover from other markets to Indian market, mainly from Hong Kong, Korea, Japan, and Singapore and US market. Indian market affects negatively the volatility of US and Pakistan. It is interesting to note that Chinese and Pakistan markets are less integrated with other Asian, European and US markets.</div>

The Dynamic Relationship between Price and Trading Volume:Evidence from Indian Stock Market

This study investigates the nature of relationship between price and trading volume for 50 Indian stocks. Firstly the contemporaneous and asymmetric relation between price and volume are examined. Then we examine the dynamic relation between returns and volume using VAR, Granger causality, variance decomposition (VD) and impulse response function (IRF). Mixture of Distributions Hypothesis (MDH), which tests the GARCH vs. Volume effect, is also studied between the conditional volatility and volume. The results show that there is positive and asymmetric relation between volume and price changes. Further the results of VAR and Granger causality show that there is a bi-directional relation between volume and returns. However, the results of VD imply weak dynamic relation between returns and volume which becomes more evident from the plots of IRF. On MDH, our results are mixed, neither entirely rejecting the MDH nor giving it an unconditional support.