This page has only limited features, please log in for full access.
Despite the fact that several studies have been conducted to study the adoption of smart-government services, little consideration has been paid to exploring the main factors that influence the adoption of smart-government services at the three main stages of smart-government services (the static, interaction, and transaction stages). Based on the results of this study, each of these three stages has different requirements in terms of system compatibility, security, information quality, awareness, perceived functional benefit, self-efficacy, perceived image, perceived uncertainty, availability of resources, and perceived trust. In addition, the results demonstrate that the requirements and perceptions of users towards the adoption and use of smart-government services in the three stages significantly differ. This study makes a unique contribution to the existing research by examining the perceptions and needs of consumers, in terms of adoption throughout the three stages.
Ahmad Althunibat; Muhammad Binsawad; Mohammed Almaiah; Omar Almomani; Adeeb Alsaaidah; Waleed Al-Rahmi; Mohamed Seliaman. Sustainable Applications of Smart-Government Services: A Model to Understand Smart-Government Adoption. Sustainability 2021, 13, 3028 .
AMA StyleAhmad Althunibat, Muhammad Binsawad, Mohammed Almaiah, Omar Almomani, Adeeb Alsaaidah, Waleed Al-Rahmi, Mohamed Seliaman. Sustainable Applications of Smart-Government Services: A Model to Understand Smart-Government Adoption. Sustainability. 2021; 13 (6):3028.
Chicago/Turabian StyleAhmad Althunibat; Muhammad Binsawad; Mohammed Almaiah; Omar Almomani; Adeeb Alsaaidah; Waleed Al-Rahmi; Mohamed Seliaman. 2021. "Sustainable Applications of Smart-Government Services: A Model to Understand Smart-Government Adoption." Sustainability 13, no. 6: 3028.
Diabetes Mellitus (DM) is one of the most common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of its medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient with only a handful of features can allow cost-effective, rapid, and widely-available screening of diabetes, thereby lessening the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic patients and compare the role of HbA1c and FPG as input features. By using five different machine learning classifiers, and using feature elimination through feature permutation and hierarchical clustering, we established good performance for accuracy, precision, recall, and F1-score of the models on the dataset implying that our data or features are not bound to specific models. In addition, the consistent performance across all the evaluation metrics indicate that there was no trade-off or penalty among the evaluation metrics. Further analysis was performed on the data to identify the risk factors and their indirect impact on diabetes classification. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing analysis of the disease using selected features, important factors specific to the Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learned from this research.
Hafiz Farooq Ahmad; Hamid Mukhtar; Hesham Alaqail; Mohamed Seliaman; Abdulaziz Alhumam. Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning. Applied Sciences 2021, 11, 1173 .
AMA StyleHafiz Farooq Ahmad, Hamid Mukhtar, Hesham Alaqail, Mohamed Seliaman, Abdulaziz Alhumam. Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning. Applied Sciences. 2021; 11 (3):1173.
Chicago/Turabian StyleHafiz Farooq Ahmad; Hamid Mukhtar; Hesham Alaqail; Mohamed Seliaman; Abdulaziz Alhumam. 2021. "Investigating Health-Related Features and Their Impact on the Prediction of Diabetes Using Machine Learning." Applied Sciences 11, no. 3: 1173.
This paper extends and generalizes former inventory models that apply algebraic methods to derive optimal supply chain inventory decisions. In particular this paper considers the problem of coordinating production-inventory decisions in an integrated n-stage supply chain system with linear and fixed backorder costs. This supply chain system assumes information symmetry which implies that all partners share their operational information. First, a mathematical model for the supply chain system total cost is formulated under the integer multipliers coordination mechanism. Then, a recursive algebraic algorithm to derive the optimal inventory replenishment decisions is developed. The applicability of the proposed algorithm is demonstrated using two different numerical examples. Results from the numerical examples indicate that adopting the integer multiplier mechanism will reduce the overall total system cost as compared to using the common cycle time mechanism.
Mohamed Seliaman; Leopoldo Cárdenas-Barrón; Sayeed Rushd. An Algebraic Decision Support Model for Inventory Coordination in the Generalized n-Stage Non-Serial Supply Chain with Fixed and Linear Backorders Costs. Symmetry 2020, 12, 1998 .
AMA StyleMohamed Seliaman, Leopoldo Cárdenas-Barrón, Sayeed Rushd. An Algebraic Decision Support Model for Inventory Coordination in the Generalized n-Stage Non-Serial Supply Chain with Fixed and Linear Backorders Costs. Symmetry. 2020; 12 (12):1998.
Chicago/Turabian StyleMohamed Seliaman; Leopoldo Cárdenas-Barrón; Sayeed Rushd. 2020. "An Algebraic Decision Support Model for Inventory Coordination in the Generalized n-Stage Non-Serial Supply Chain with Fixed and Linear Backorders Costs." Symmetry 12, no. 12: 1998.