Nrusingha Tripathy

Language processing and linguistics researchers are interested in gender identification through audio, as human voices have many distinctive features. Although several gender identification algorithms have been developed, the accuracy and efficiency of the system can still be improved. Despite extensive studies on the topic in various languages, there aren’t many studies on gender identification in the KUI language. Using a variety of machine learning (ML) and deep learning (DL) classifiers, including decision tree (DT), multilayer perceptron (MLP), gradient boosting (GB), linear discriminant analysis (LDA), recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), and transformer, the goal of this study is to assess the accuracy of gender identification among diverse KUI language speakers. To verify the effectiveness of the suggested model, several prediction evaluation metrics were calculated, such as the area under the receiver operating characteristic curve (AUC), F1-score, precision, accuracy, and recall. While the findings are compared to other learning models, the gradient-boosting strategy yielded better results with an accuracy rate of 97.0%.

A digital currency known as a cryptocurrency uses blockchain technology to record transactions electronically, guaranteeing security and transparency. Cryptocurrencies, in contrast to conventional hard currency, are virtual or soft currencies; that do not exist in the actual world like coins or banknotes. Since all transactions occur digitally, cryptocurrencies are decentralized and frequently stand-alone from conventional financial institutions. Peer-to-peer transfers, increased anonymity, and often quicker transaction processing without middlemen are made possible by this. In this study, two machine learning models; autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), and two deep learning models; long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM) were compared. By employing past Bitcoin data from 2012 to 2020, we evaluated the models' mean absolute error (MAE) and root mean squared error (RMSE). Compared to other models, the Bi-LSTM model yields minimal RMSE scores of 67.18 and MAE scores of 24.73. This aids in capturing all temporal correlations, which are important for forecasting the price of Bitcoin.

The world of cryptocurrencies has expanded rapidly over the last ten years, with the most recent advancements being witnessed. According to Twitter statistics, there are roughly 1,500 tweets on Bitcoin alone per hour, which lends credence to this claim. In this work the weekly dataset for Bitcoin (BTC) and average weekly sentiment data for Bitcoin (BTC) are considered. This work employs four distinct models: Naive Bayes, valence aware dictionary and Sentiment Reasoner (VADER), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). The LSTM model performs well and has a greater accuracy of 0.952 than other models. Text sentiment is frequently impacted by intricate, non-linear word-phrase interactions. These complex patterns are captured by LSTMs, resulting in more precise sentiment analysis. By monitoring real-time tweets, we can predict the sentiment of the Bitcoin market. The LSTM model gains the ability to forecast price changes based on the mood conveyed in tweets by training it on past tweet data and matching fluctuations in the price of Bitcoin.

Blockchain technology is the foundation of cryptocurrencies, which are virtual currencies. The decentralized nature of cryptocurrencies has resulted in a significant reduction of central authority over them, which has implications for global trade and relations. The need for an effective model to anticipate the price of cryptocurrencies is essential due to their wide variations in value. Due to the shortcomings of conventional production forecasting, in this work, four distinct models were used. The deep learning models are the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), and both the Facebook-Prophet and Silverkite support the exponential smoothing technique. Silverkite is designed to handle a wide range of time series forecasting tasks. Considering past bitcoin information from January 2012 to March 2021, a period of nine years, we looked at the models. The Bi-LSTM model yields a 7.073 mean absolute error (MAE) and a 3.639 root mean squared error (RMSE). The Bi-LSTM model identifies the deviations that might draw attention and avert any problems.

The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.