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Antenna designers around the globe have extensively researched upon the patch antenna and its array structures for achieving better performance for various operations. One of the novel techniques is to use nanomaterial composites over the substrate of a microstrip patch antenna to enhance its performance. Nanostructures can provide unique characteristics and have the potential to improve the performance of the patch antenna. This paper focuses on the study and design of a nanomaterial-based 1 × 2 rectangular microstrip patch antenna array for X band operation. The proposed antenna array is designed over an FR4 substrate where the individual radiating patches are realized using gold deposited Zinc Oxide (ZnO) nanorods. The comparison of performance parameters of the proposed antenna array with the conventional copper (Cu)-based patch antenna array is made. The results obtained show that there is an improvement in the return loss and bandwidth of the proposed array structure.

Forecasting currency market plays an important role basically in all aspects of international financial management. Based on the claimed poor performance and less efficiency of the popular forecasting methods, an Extreme Learning Machine (ELM)—elitism-based self-adaptive multi-population Jaya model—is designed with the possibilities of getting maximum prediction accuracy. The model has evaluated by using the exchange rate data of USD to INR and USD to EURO as in most of the countries USD is used as the base currency. The experimented model has explored its prediction ability over six-time horizon, which includes the 6 days between 1 day and 1 month in advance. For a standardized comparison, two broadly accepted prediction models such as standard ELM and Back-Propagation Neural Network (BPNN) has been recommended for this research work. Along with these two standard models, a Jaya optimized technique applying on both ELM and BPNN is also considered for experimentation. The simulated result depicted the outstanding performance of the proposed ELM-elitism-based self-adaptive multi-population Jaya model over ELM, BPNN, ELM-Jaya, and BPNN-Jaya.

Mutual fund is an ideal investment basically for those who do not know how to invest money in the security market. Several research methods have been published by the researcher for predicting mutual fund. This study has explored on three prediction models such as back propagation neural network (BPNN), recurrent back propagation neural network (RBPNN) and recurrent radial basis function neural network (RRBFNN), and the prediction models are validated using SBI Magnum Equity and UTI Equity mutual fund dataset. These two mutual funds are outperformed over market cycles since 2010. The dataset has trained and tested in 7:3 ratio and the performance is validated using MSE during the training phase. The study has explored on 1, 5 and 7 days ahead prediction, and the performance of prediction methods are evaluated by RMSE and MAPE during testing. The simulation result shows that RBPNN is outperformed over the rest two prediction methods.