درخت حوزه‌های تخصصی

This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.

This paper aimed to explore IT innovations used in sustainable business models in the European single energy market. For this purpose, the academic literature and content analyses of organized marketplaces were performed. The results showed that end users are not always interested in changing the service and try to choose services/resources well known to them. At the same time, market operators see the need for radical change by introducing strategic innovations and digitizing energy markets. Sustainable e-government management models are typically sustained by IoT, blockchain, and smart grid management technologies.

Enterprise risk management represents a process of assessing exposure to risks in an institution. It is a systematic mechanism and a comprehensive tool for predicting events, including unexpected events, and their impacts. This paper is a conceptual study. It aims at designing a model for testing the mediation effect of information technology (IT) quality on the relationship between the enterprise risk management (henceforth, ERM) practice and the financial performance of financial institutions in Iraq. Based on reviewing the literature on ERM, it is found that ERM (leadership roles, risk culture and compliance) has a positive and important effect on the financial performance of financial institution in Iraq. In addition, this paper shows that the IT quality is a good medium for increasing the interaction between practices of ERM and financial performance positively. It provides a practical and theoretical contribution to practices of ERM in listed companies in Iraq. It also has major implications for professional practitioners, regulators and academics in Iraq. Finally, this paper contributes to building relevant knowledge in the field of leadership role, risk culture, compliance and IT quality.

Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of 15, 20, 45, 63, 126, and 252 days using RMSE, MAPE, and R2 as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R2 value throughout the longest forecast duration.

The aim of this study is to analyze the factors affecting the acceptance of electronic health on the basis of the theory of planned behavior. E-health is a growing field of health communication that entails using medical informatics, public health, and trades. As a result, E-health facilitates the provision of health information and services through the internet and related technologies. In this regard, this study aims to explain the acceptance of e-health by its beneficiaries such as physicians, patients, and healthcare managers. The results have shown that the most important factors affecting the acceptance of e-health are:  1. Organizational related factors of e-health services; 2. Human-related factors of acceptors; 3. Environment-related factors; 4. Factors associated with financial sources and expenditures; 5. Technical and infrastructural factors. Taking advantage of interpretive structural modeling, we demonstrated these factors and determined the level of their reciprocal relations.

Purpose: This research aimed to identify and evaluate the risks of content production in the governmental digital libraries of Tehran.Method: In terms of essence, this research is synthetic (library studies, qualitative and quantitative), and regarding purpose, it is an applied one.  In the first part, using the studies of the research literature, a set of indicators related to the risks of content production in the governmental libraries of Tehran was obtained. The second part of this research involved a fuzzy Delphi approach that was conducted at two stages among 20 experts. In this stage, a researcher-made questionnaire based on the indicators obtained from the first stage was used. The third part of the research was a survey-analysis conducted with a quantitative method using a questionnaire made out based on the results of qualitative stage with three criteria: the probability of occurrence, effect intensity, and the inability of organization to respond and was distributed among 100 managers and experts of governmental digital libraries in Tehran.Findings: In this research, the risks of content production were identified and fell into nine major categories (human force, environmental factors, infrastructure, protection and maintenance,  creators`  technical rights, integration, content evaluation of sources and authors and information security).Conclusion: The results of this evaluation show that digital libraries are not exactly at their best level when it comes to their responses to the risk of human resources, authors' rights, and integration risks.

Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of 82.74%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of 83.98%. The proposed model achieved an accuracy of 90.54%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved 94% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.

The objective of this study is to verify the importance of the capabilities of cloud computing services in managing and analyzing big data in business organizations because the rapid development in the use of information technology in general and network technology in particular, has led to the trend of many organizations to make their applications available for use via electronic platforms hosted by various Companies on their servers or so-called cloud computing that have become an excellent opportunity to provide services efficiently and at low cost, but managing big data presents a definite challenge in the cloud space beginning with the processes of extracting, processing data, storing data and analyze it. Through this study, we dealt with the concept of cloud computing and its capabilities in business organizations. We also interpreted the notion of big data and its distinct characteristics and sources. Finally, the relationship between cloud computing with big data was also explained (extraction, storage, analysis).