Sentimental Analysis is an ongoing research field in Text Mining Arena to determine the situation of market on particular entity such as Product, Services...Etc. and it can be called as computational treatment of reviews, subjectivity and sentiment of text. Cryptocurrency can be explained as a type of digital estate and devised to mechanize as a form of trade and exchanges that uses cryptography as an encryption technique to secure the transactions and acts as decentralized controlled transaction which is opposed to centralized transactions. Cryptocurrency are a type of virtual currency, digital currency and alternative currency, On basis of categorical, there are different architecture and security protocols which are used in the cryptocurrencies to secure transactions, the different types of cryptocurrency are available in the market such as Bitcoin, Litecoin, and Namecoin…etc. This paper focuses on survey on different types of sentimental analysis methods and main contribution of this paper include sentimental analysis of social media data on different types of cryptocurrencies on basis of categorical and different terms of cryptocurrency such as Cryptocurrency, virtual currency, digital currency and discussed on trends of crypto currency in present market.
In the recent past, text-to-image translation was an active field of research. The ability of a network to know a sentence's context and to create a specific picture that represents the sentence demonstrates the model's ability to think more like humans. Common text--translation methods employ Generative Adversarial Networks to generate high-text-images, but the images produced do not always represent the meaning of the phrase provided to the model as input. Using a captioning network to caption generated images, we tackle this problem and exploit the gap between ground truth captions and generated captions to further enhance the network. We present detailed similarities between our system and the methods already in place. Text-to-Image synthesis is a difficult problem with plenty of space for progress despite the current state-of - the-art results. Synthesized images from current methods give the described image a rough sketch but do not capture the true essence of what the text describes. The re-penny achievement of Generative Adversarial Networks (GANs) demonstrates that they are a decent contender for the decision of design to move toward this issue.
In this data age tools for sophisticated generation and handling of data are at epitome of usage. Data varying in both space and time poses a breed of challenges. Challenges they possess for forecasting can be well handled by Reservoir computing based neural networks. Challenges like class imbalance, missing values, locality effect are discussed here. Additionally, popular statistical techniques for forecasting such data are discussed. Results show how Reservoir Computing based technique outper-forms traditional neural networks.
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