The smart cities concept is modern and shall continue to mitigate the future demand of houses for the burgeoning flocking population from rural to urban. These complexes are prototypes of present smart cities, energy efficient green mansions nearer to large metropolis which is fulfilling prompt accesses to civic usages (like connectivity, Marketing, electricity, water, drainage and water supply) and maintains equilibrium between population, economy, resources, and environment. In contrary metropolis face mammoth challenges of space, health, quality of living,stress, pollution and hassles of city life and comforts.In this project we tried our best to plan a model of a satellite city in Jatni area. The GIS and the remote sensing method is used to conceptualize the places , commuters, dwellings etc. The 2D planning with AUTOCAD software, the 3D plan of the flats with REVIT software for Building Information Modelling architecture was used to conceptualize an ideal satellite city in and around 15 to 30km from centre of Bhubaneswar consisting of area 44300Ha accommodating 10500 inhabitants fortheir apartments, duplexes and supporting staffs

The trend of stock price prediction has always been in the focal point of analytical activity in financial domain for both the researchers and investors. Prediction with accuracy is very essential for improved investment decisions that imbibe minimum risk factors. Due to this, majority of investors depend upon that intelligent trading system which generates better forecasting results. As forecasting stock market price with high accuracy is quite a challenging task for the analysts, machine learning has been adopted as one of the popular techniques to predict future trends. Even if there are many recognized analytical time series analysis that are categorized either under soft computing or under conventional statistical techniques like fuzzy logic, artificial neural networks and genetic algorithms, researchers have been looking for more appropriate techniques which can exhibit improved results. In this paper, we developed different hybrid machine learning based prediction models and compared their efficiency. Dimension reduction techniques such as orthogonal forward selection (OFS) and kernel principal component analysis (KPCA) are used separately with support vector regression (SVR) and teaching learning based optimization (TLBO) to predict the stock price of Tata Steel. The performance of both the proposed approach is evaluated with 4143days daily transactional data of Tata steels stocks price, which was collected from Bombay Stock Exchange (BSE). We compared the results of both OFS-SVR-TLBO and KPCA-SVR-TLBO hybrid models and concludes that by incorporating KPCA is more practicable and performs better results than OFS

Cyclonic disturbances in the Bay of Bengal are natural, recurrent, and a regular devastator to the east coasts of Ceylon and India (especially to Odisha coast), Bangladesh and Myanmar. The destruction depends upon the frequency, intensity, place of formation, life span in Bay, SST, ENSO, El Nino Modoki, Indian Ocean Dipole, boreal summer atmospheric phenomena, Madden–Julian oscillation and the climatology of India’s mainland. The effective management of these vulnerable storms can reduce fatalities, degradation to environment and socio-economic consequences. The investigation to decadal trend of pre-monsoon bay disturbances for last 129 years reveals that the decadal distributions of cyclonic disturbances in BOB were irregular. From last 30 years pre-monsoon landfall data (1990 to 2019) divulges that frequencies of CS in BOB are increasing during La-Nina Modoki years than normal La-Nina years. The frequencies of SCS increase during warm, strong La-Nina years than La-Nina Modoki years and particularly during negative ONI events, La Nada, Strong ENSO, high PIOD events. Individually the events may not be conclusive regarding conceive strong pre-monsoon cyclonic storms but they become severe when taken in combination.