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The health records management issues have detrimentally affected the Iraqi healthcare sector resultant from the inferior information technology integrity and the complicatedness of data. In order to resolve this problem, other methods of storage, management, and retrieval of health-related data can be offered by e-Health services. These aspects are important in tracking patients' health conditions using multiple platforms at the service provider's own convenience. However, there are numerous issues that hinder the extensive adoption of e-Health services by the health establishments in Iraq, such as issues on security and privacy, legalities connected to policies, and its implementation. The significance of the current study is its identification of the crucial aspects that will lead to the success of impacting the technical staff towards their positive acceptance and behavior with regard to the employment of e-Health information system in Iraqi hospitals. A self-administered survey was carried out on 104 technical staff from various healthcare organizations in Iraq using a simple random sampling technique. A nonparametric second-generation multivariate analysis was conducted on the compiled ordinal data by the utilization of the PLS-SEM approach. The outcomes indicated the favorable impact of several factors on the doctor's employment of e-Health in Iraqi hospitals, comprising Availability and Affordability of the hardware and software, ICT Support Service, Network Reliability, Privacy, and Security. The results are important in assisting the comprehension of e-Health systems in the management of health data, in addition to the provision of the pertinent recommendations for policymakers to provide guidance, issue advice, directives to the healthcare professionals toward the continuous consideration of using advance information and communications technology at work. [ABSTRACT FROM AUTHOR]

The rainfall-runoff process is one of the most complex hydrological phenomena. Estimating runoff in the basin is one of the main conditions for planning and optimal use of rainfall. Using machine learning models in various sciences to investigate phenomena for which statistical information is available is a helpful tool. This study investigates and compares the abilities of HEC-HMS and TOPMODEL as white box models and adaptive neural fuzzy inference system (ANFIS) and gene expression programming (GEP) as black box models in rainfall-runoff simulation using 5-year statistical data. Using the inputs of rainfall and temperature of the previous day and discharge in the steps of the previous 2 days reduced the prediction error of both models. Examining the role of different parameters in improving the accuracy of simulations showed that the temperature as an effective parameter in cold months reduces the amount of prediction error. A comparison of R 2 , RMSE, and MBE showed that black box models are more effective forecasting tools. Among the black box models, the ANFIS model with R 2 = 0.82 has performed better than the GEP model with R 2 = 0.76. For white box models, the HEC-HMS and TOPMODEL had R 2 equal to 0.3 and 0.25, respectively.