In order to reduce their exposure to the erratic fluctuations of the financial markets, traders are nowadays increasingly dealing with options and other derivative securities instead of directly trading in the underlying assets. This paradigm shift has attracted the attention of many researchers, and there has been a tremendous increase in the awareness and activities of derivative securities. In particular there is a need to devise new techniques to address the limitations of traditional parametric pricing methods, which rely on assumptions and approximations to capture the complex dynamics of pricing processes. This paper proposes a novel non-parametric method using an ad-hoc recurrent neural network for estimating the future prices of war commodities such as gold and crude oil as well as currencies, which are increasingly gaining importance in the financial markets. The price predictions from the network, shown to be accurate and computationally efficient, are used in a hedging system to avoid unnecessary risks. Experiments with actual gold and currency trading data show that our system using the proposed network and strategy can construct portfolios yielding a return on investment of about 4.76%.
The agricultural segment is a major supporter of the Indian economy as it represents 18% of India's GDP, and it gives work to half of the nation's work power. The farming segment is required to satisfy the expanding need for food because of the increasing populace. Therefore, to cater to the ever-increasing needs of people of the nation, yield prediction is done prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crops prior to cultivating. There are multiple parameters that affect the yield of crops like rainfall, temperature, fertilizers, pH level, and other atmospheric circumstances. Thus, considering these factors, the yield of a crop is thus hard to predict and becomes a challenging task. In this chapter, the dataset of different states producing different crops in different seasons is considered; further, after preprocessing the data, the authors applied machine learning algorithms, and their results are compared.
Today's era is the era of technologies. Technologies have widely been employed in each and every field. The field of agriculture is not untouched with the technologies, and in several segments of agriculture; it has been employed at large. Deep learning techniques and its variants like convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial network (AGN), and their various subcategories like AlexNet, ImageNet, visual geometry group (VGG), etc. have widely been employed in many sectors of agriculture in order to increase the quality and quantity of production. In this chapter, some applications of deep learning have been explored.
A pyramid is a polyhedron with any base and three or more triangular faces that meet at a point called an apex or a pyramid is a three-dimensional shape with triangular sides. The pyramidal structure is a storehouse of energy. The pyramid is said to have the power to heal your chakras and align your mind. There are three main examples of a pyramid in our real life, are perfume bottles, tents in military camps and the roof of a house. Pyramid meditation is designed to increase vision and for other many benefits such as strengthening eye muscles, improving hearing, lowering blood pressure, relieving arthritis symptoms and treating insomnia. It can also help improve memory and cognitive function, as well as healthy skin and hormonal balance. Now a day, anxiety and stress are common problem among the youth. This study talks about the effect of Pyramid meditation on the variables of stress and anxiety among adults. There are 200 participants participated in this survey out of which 100 were meditation practitioners and other 100 participants were non-practitioners. To assess the level of anxiety, Beck Anxiety Inventory and for stress, perceived stress scale by Cohen were administered. The data was processed using statistical computations like mean, median, mode and standard deviation. The results reflected a significant difference between the scores of meditation practitioners and non-practitioner youths. It was found that those who practice pyramid meditation had considerably low level of anxiety and stress than that of those who do not meditate.
In this paper, a denoising technique is proposed based on a nonlinear anisotropic diffusion model with two diffusivities parameters, i.e., Charbonnier and total variation. These diffusivities are dependent on the diffusivity function for balancing. Furthermore, proof of the existence and uniqueness theorem of the model are presented. The convergent iterative scheme is proposed for the diffusion model. To discretize the diffusion model, the finite difference method with forward-backward diffusivities is used. The numerical results are given in terms of peak signal-to-noise ratio (PSNR) as a metric.
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