Stock markets serve as a platform where individuals and institutional investors can come together to buy and sell shares in a public venue and ultimately impact the economy. With the advent of digital technology these markets or exchanges exist as electronic marketplaces. These markets are very volatile thus making the stock market prediction a highly challenging problem. These predictions of stock value offer great profits which serve as a huge motivation for extensive research in this area. Identifying and predicting a stock value beforehand by even a fraction of a second can result in very high profits. Similarly, a near to precise prediction can be extremely profitable in the amortized case. This attractiveness of finding a solution has motivated researchers in both industry and academia to devise techniques despite the complications due to volatility, seasonality and time dependency, economy, and other such factors. Lately, AI/ML techniques like Fuzzy Logic and Support Vector Machines (SVMs) have been used to arrive at different solutions. In this paper, we explore and develop an ensemble predictive system to forecast the market prices using deep learning algorithms. Here we consider the fractional change in stock value and the intra-day high and low values of the stock to train and employ a neural network for obtaining the trading strategy that leads to relatively superior market returns. The focus here is on the use of regression and LSTM based deep learning strategies used to predict stock values. Factors considered are open, close, low, high, and volume.

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The recent advancements and introduction of computer technology in the rural areas of countries like India have narrowed down the digital divide between urban and rural immensely. The rural sector has been important for the society as it has always been the primary reason for growth and development of the civilizations to sustain and achieve human goals for better existence. The modern computing systems comprise of numerous elements involving technicians, networks, and hardware. Electricity has been one of the major prerequisites for powering these systems. Due to the limitation of this and other such resources in the rural areas, with the rising costs of energy and increased concerns of environmental impacts of energy generation and consumption, there has been an immediate need for research and development in the solutions for low power wastage and low power consuming green systems and strategies. Major studies have shown that the main advantage of implementation of green computing would be to prolong the respective equipment’s lifespan, further reducing the ecological footprint and facilitating sustainable development. In this paper, we analyze and explore the more energy efficient (green) systems that consume relatively less power, such as solid-state hard disk drives, ethernet wired connection, and virtualization of machine resources for the computing and implementation in rural areas.

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