In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

An optimal approach to anonymization using small data is proposed in this study. Map Reduce is a big data processing framework used across distributed applications. Prior to the development of a map reduce framework, data are distributed and clustered using a hybrid clustering algorithm. The algorithm used for grouping together similar techniques utilises the k-means clustering algorithm, along with the MFCM clustering algorithm. Clustered data is then fed into the map reduce frame work after it has been clustered. In order to guarantee privacy, the optimal k anonymization method is recommended. When using generalisation and randomization, there are two techniques that can be employed: K-anonymity, which is unique to each, depends on the type of the quasi identifier attribute. Our method replaces the standard k anonymization process by employing an optimization algorithm that dynamically determines the optimal k value. This algorithm uses the Modified Grey Wolf Optimization (MGWO) algorithm for optimization. The memory, execution time, accuracy, and error value are used to assess the recommended method’s practise. This experiment has shown that the suggested method will always finish ahead of the existing method by using the least amount of time while ensuring the greatest level of security. The current technique gets the lowest accuracy and the privacy proposed achieves the maximum accuracy while compared to the current technique. The solution is implemented in Java with Hadoop Map-Reduce, and it is tested and deployed in the cloud on Google Cloud Platform.

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This python notebook uses the libraries present to analyze the medical data and form various data diagrams that help in the better visualization of the data which helps in better visualization that leads to better decision making. This data is primarily about availability of beds and hospitals in rural and urban areas across India. It is useful tool for the government to know about the facilities and increment them based on the data during the recent pandemic time. It will come in handy in the future also during any disasters, pandemics etc. This application visualizes different data in different forms such as pie charts, bar graphs and geographical visualization with data. It can display the sorted values and can show the state or UT that is first in the provision of the medical facilities to the people. The notebook can be used to track the major diseases and there expansion among the people in the particular disease prone areas. This application may also be used to expand beyond the limitation of beds and hospitals to medicines, equipment, specialist doctors and other facilities in the hospitals.

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The paper deals with the topic of improvising human perception using Artificial Intelligence to make human beings more efficient and productive. Understanding human perception takes a lot of non-verbal cues such as facial expressions, gesture, body language and tone of voice. Recent research has been made through facial coding and neurofeedback training. To analyse the probable response of a human being at certain expression of emotion, collection of data based on facial expression, vocal utterances, brainwave frequency under challenging condition ssuch as anger, contempt, disgust, fear, sadness and surprise is required. If we can formulate a nalgorithm based on the data collected, then not only it would be possible to calculate certain human action, it can also be possible to change or reduce the chances of success of a certain action. Modern advancements has introduced faster problem solving capability but it has some restrictions, which can be coped by the utilisation of human brain which has far better capabilities.The main concern of this paper is why to use AI and how it will revolutionize the mankind.

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