Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.
This research examines the issue of Internet banking services and their impact on customer's perceived value to know the potential of Iraqi commercial Banks to provide Internet banking services as well as determine the customer's level of perception of the value of such services. The research aims to demonstrate the effect that Internet banking services have on the customer's perceived value and to find how online banking services can be used to add value that the customer perceive. The main findings were that research sample banks don't have a sophisticated network of electronic Windows through which banking services are provided to allow customers to manage their accounts, and perform various operational operations through the
... Show MoreReliable estimation of critical parameters such as hydrocarbon pore volume, water saturation, and recovery factor are essential for accurate reserve assessment. The inherent uncertainties associated with these parameters encompass a reasonable range of estimated recoverable volumes for single accumulations or projects. Incorporating this uncertainty range allows for a comprehensive understanding of potential outcomes and associated risks. In this study, we focus on the oil field located in the northern part of Iraq and employ a Monte Carlo based petrophysical uncertainty modeling approach. This method systematically considers various sources of error and utilizes effective interpretation techniques. Leveraging the current state of a
... Show MoreThis study investigates the influence of Strategic Consensus (SC) on Organizational Performance (OP), with Strategic Alignment (SA) serving as a mediating variable, within private academic institutions in Iraq. The research utilized scales developed to measure each variable: SC, OP, and SA, all of which are unidimensional. The sample comprised 131 senior participants, including deans and faculty council members, with questionnaires distributed across three institutions: Dijla University College (35 respondents), Baghdad College of Economic Sciences (31 respondents), and the University of Uruk (65 respondents). Data analysis employed various statistical techniques, including normality testing, confirmatory factor analysis, descriptive statis
... Show MoreThe research aims to study some of the human characteristics of the state of Singapore to know the impact of these characteristics on the strength of the state, its development and. The research included two aspects, theoretical and analytical, using the descriptive analytical method, force analysis method, as well as the historical method. The data was analyzed according to mathematical equations, including the size of the country's population, the extraction of the population growth rate and the concept of age structure, where some indicators related to this concept have been explained. The researcher reached a set of results, the most important of which were: that the population size of the state of Singapore in the period between (19
... Show MoreThis study investigates the constructs and related theories that drive social capital in energy sector from the intention perspectives. This research uses theories of 'social support' and 'planned behaviour' alongside satisfaction and perceived value to propose a research model that drives social capital for energy sectors in Malaysia. The model reveals that the Theories of Planned Behaviour (TPB) and Social Support Theory (SST) alongside satisfaction and perceived value factors promote social capital development in energy sectors. Using PLS-SEM to analyse data gathered from energy sector employees in Malaysia, this research demonstrates that social capital is present when there is trust and loyalty among the users and positively effects en
... Show MoreThe objective of this study was to assess the impact of the COVID-19 pandemic on healthcare providers (HCPs) at personal and professional levels.
This was a cross-sectional descriptive study. It was conducted using an electronic format survey through Qualtrics Survey Software in English. The target participants were HCPs working in any healthcare setting across Iraq. The survey was distributed via two professional Facebook groups between 7 April and 7 May 2020. The survey items were adopted with modifications from three previous studies of Severe Acute Respiratory Syndrome (SARS) and Avia
Water provision is sensitive to climate change, and agricultural production and food supply are sensitive to water availability. Water scarcity affects food security and agricultural economic development through changes in agricultural production and changes in the composition of produced goods. Recent droughts also led to a decrease in the volume of water allocated to agriculture, which led to a decrease in total agricultural production and exports, and this has subsequent impacts on food security and economic development. The research aimed to measure the impact of water scarcity on agricultural economic development for the period 1990-2022. The research included three behavioral equations with three endogenous variables: the cult
... Show MoreThe Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr
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