Abdalabbas Hassan Kadhim Alsaaide

The goal of this manuscript is to use deep learning based multi-modal hybrid model to forecast the value of bitcoins by analyzing the influence of social media attitudes. In order to analyze, roughly two thousand samples of on-chain, cryptocurrency market, and related social media information (Twitter/X) are gathered between the years 2014 and 2022. The attitudes shared on social media regarding bitcoin are then analyzed utilizing the Twitter-RoBERTa (robustly optimized bidirectional encoder representation from transformers) and VADER (value-aware dictionary for sentiment reasoning) algorithms after we incorporated sentiment data from Twitter into the models. The proposed model produces the least MAPE (4.37%) and least RMSE (6.55%) thereby demonstrating its ability to accurately forecast market trends with least fluctuations from the available dataset. By illustrating the promise of social networking sentiment evaluation, on-chain data collection, and the use of long short-term memory (LSTM) neural network for forecasting market patterns, this study makes an important addition to the field of economic forecasting and offers traders, agents, and investors a useful tool for making informed choices.

The goal of this manuscript is to use deep learning based multi-modal hybrid model to forecast the value of bitcoins by analyzing the influence of social media attitudes. In order to analyze, roughly two thousand samples of on-chain, cryptocurrency market, and related social media information (Twitter/X) are gathered between the years 2014 and 2022. The attitudes shared on social media regarding bitcoin are then analyzed utilizing the Twitter-RoBERTa (robustly optimized bidirectional encoder representation from transformers) and VADER (value-aware dictionary for sentiment reasoning) algorithms after we incorporated sentiment data from Twitter into the models. The proposed model produces the least MAPE (4.37%) and least RMSE (6.55%) thereby demonstrating its ability to accurately forecast market trends with least fluctuations from the available dataset. By illustrating the promise of social networking sentiment evaluation, on-chain data collection, and the use of long short-term memory (LSTM) neural network for forecasting market patterns, this study makes an important addition to the field of economic forecasting and offers traders, agents, and investors a useful tool for making informed choices.

Abstract-This paper aims to use Transition Rate Matrix for queueing theory in evaluating the work of some maintenance centers for the General Company for the Distribution of Najaf Electricity. They are three independent maintenance centers in their work one from the other, and each maintenance center is responsible for repairing of specific regions. The arrival and service data for the work of these centers has been collected and these data entered into the (Fitting Distribution) program

Abstract: Due to the recent substantial development in the digital world, transfer data over the network has become more and more critical issue in the context of cloud computing-based accounting systems.

This research aims to determine the level of integration between lean accounting and activity-based public budgeting for providing useful information to evaluate the public sector firm performance.

The purpose of this research is to identify the role of the enterprise resource planning application in supporting economic cost management within attribute-based costing technique. The process is to analyze the integrating providing quantitative information to support the organizations how to manage its costs.