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With the aim of achieving a global ranking and academic distinction, a large number of universities have decided to focus on competition and greater academic quality on a global scale. During the course of such a journey, universities have to face numerous challenges, including the enhancement of organizational efficiency. In the context of organizational efficiency, the most significant pillar supporting this drive is recognized as being digital transformation. It is widely accepted that digital transformation allows electronic systems to be used in the process of teaching and learning. These electronic systems (e-services) enhance universities’ operational efficiency. Keeping this in mind, this research paper aims to analyze the impact of digital transformation on the organizational and spending efficiency of universities, with a special focus on one particular e-service provided by the Saudi University. For this, the study examines the effort made by the government to spread the culture of rationalization and improve the efficiency of spending through a case study involving a statistical analysis of real data from an electronic system. The results of the study state that an increase in the number of subject withdrawals will weaken the spending and organizational efficiency of the University.
Hani Brdesee. A Divergent View of the Impact of Digital Transformation on Academic Organizational and Spending Efficiency: A Review and Analytical Study on a University E-Service. Sustainability 2021, 13, 7048 .
AMA StyleHani Brdesee. A Divergent View of the Impact of Digital Transformation on Academic Organizational and Spending Efficiency: A Review and Analytical Study on a University E-Service. Sustainability. 2021; 13 (13):7048.
Chicago/Turabian StyleHani Brdesee. 2021. "A Divergent View of the Impact of Digital Transformation on Academic Organizational and Spending Efficiency: A Review and Analytical Study on a University E-Service." Sustainability 13, no. 13: 7048.
Cardiotocography (CTG) is a screening tool used in daily obstetric practice to determine fetal wellbeing. Its interpretation is generally performed visually by the field experts, and this visual inspection is an error-prone and subjective process. In addition, it leads to several drawbacks, such as variability among the observers and low reproducibility rates. To tackle these drawbacks, a novel computer-aided diagnostic (CAD) model is proposed. As novel diagnostic indices, the features provided by the common spatial patterns (CSP) were considered in this study. The experiments were carried out on a publicly available CTU-UHB Intrapartum CTG database. Four different data division criteria were evaluated individually. The proposed model relied upon a combination of the conventional as well as the CSP features and machine learning models such as an artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (kNN). To validate the successes of the models, the five-fold cross-validation method was employed. The results validated that the CSP features ensured an increase in the performances of the machine learning models in the fetal hypoxia detection task. Also, the most effective results were provided by the SVM classifier with an accuracy of 94.75%, a sensitivity of 74.29% and a specificity of 99.55%. Consequently, thanks to the proposed model, a novel, consistent, and robust diagnostic model ensured for predicting fetal hypoxia.
Wafaa Alsaggaf; Zafer Cömert; Majid Nour; Kemal Polat; Hani Brdesee; Mesut Toğaçar. Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals. Applied Acoustics 2020, 167, 107429 .
AMA StyleWafaa Alsaggaf, Zafer Cömert, Majid Nour, Kemal Polat, Hani Brdesee, Mesut Toğaçar. Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals. Applied Acoustics. 2020; 167 ():107429.
Chicago/Turabian StyleWafaa Alsaggaf; Zafer Cömert; Majid Nour; Kemal Polat; Hani Brdesee; Mesut Toğaçar. 2020. "Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals." Applied Acoustics 167, no. : 107429.
This paper introduces a new electronic system for dropping courses that do not rely on the cumbersome paper system traditionally used by educational institutions. The new sub-system of the On-Demand University Services (ODUS Plus) will eliminate the hierarchical and inefficient paper system, which results in institutional delays. By using both quantitative and qualitative techniques, as well as a correlational methodology, it will be shown that, withdraw approval requests have streamlined, thus improving the system to process such requests. The following study will outline the efficacy of using ODUS Plus for course withdrawals by college and university administrators. The findings suggest that the implementation of such a system has a dual value; namely, it improves efficiency and functions as a source of knowledge regarding student academic preferences.
Hani Brdesee. A mixed method analysis of the online information course withdrawal system. Behaviour & Information Technology 2018, 37, 1037 -1054.
AMA StyleHani Brdesee. A mixed method analysis of the online information course withdrawal system. Behaviour & Information Technology. 2018; 37 (10-11):1037-1054.
Chicago/Turabian StyleHani Brdesee. 2018. "A mixed method analysis of the online information course withdrawal system." Behaviour & Information Technology 37, no. 10-11: 1037-1054.