Information Technology or IT is a combination of technology itself and a collection of IT services that ensure the effective implementation of overall Information Technology in an organization. Like other services, Information Technology Service Management (ITSM) has become a global subject because managing IT is only possible through efficient and effective ITSM protocols. A well-implemented ITSM delivery system improves the quality of IT services, which eventually enhances the organization's overall capacity and output. Public service delivery is closely dependent on the quality IT services that can be acquired through ITSM. This research study reviews some of the established ITSM standards and frameworks like FitSM, ITIL, CobiT, and ISO/IEC 20000 and proposes a most suitable and sustainable tool and its implementation roadmap for the digital transformation of public sector governance. For this pilot study, a public sector organization (PSO) working under the Government of Punjab (GoPb) was selected. This study focuses on an important area that has not yet been adequately addressed, where ITSM delivery can produce tremendous results. This study contributes to academics and researchers since it discusses the selection and implementation of a service management system (SMS) for a PSO while focusing on its needs, requirements, and available resources. In general, the qualitative analysis methodology is used in this research study, and specifically, it can be identified as applied research. The data is collected through a questionnaire, and the results are shown and discussed in the later sections.
Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework and can handle large and complex datasets with high-dimensional features. However, it is important to note that the choice of the best algorithm for predicting diabetes may depend on the specific characteristics of the data and the research question being addressed. In addition to predicting diabetes, data analysis and predictive techniques can also be used to identify risk factors for diabetes and its complications, monitor disease progression, and evaluate the effectiveness of treatments. These techniques can provide valuable insights into the underlying mechanisms of the disease and help healthcare providers make informed decisions about patient care. Data analysis and predictive techniques have the potential to significantly improve the early detection and management of diabetes, a fast-growing chronic disease that notable health hazards. The XGBoost classifier showed the most effectiveness, with an accuracy rate of 89%.
Securing telehealth IoT infrastructure is essential to provide high-level medical care and prevent cyberattacks. A vulnerable stage in IoT telehealth is while the patient is being transported to a healthcare facility, the transporter could be an ambulance or an air ambulance. In this paper, we propose a multifactor authentication scheme to secure the system when the patient is in transit to the healthcare facility. We apply this scheme to an ambulance, using physical unclonable functions (PUFs) embedded in the ambulance to facilitate authentication and secure key exchange. We validated the security of the proposed scheme using formal and informal security analysis. The analysis supports our claim that the proposed scheme protects against many types of attacks.
Smart ambulance is a novel system where modern communication, computation, and sensing technologies are employed to revolutionize ambulance and emergency systems. We propose a smart system that aims to minimize the ambulance response time, travel time from patient's location to the hospital, and the waiting time at the hospital. We utilize the road traffic conditions and hospital loading information (collected in real-time basis) to make optimal decisions (which hospital responds to the patient's request and which ambulance it sends, which route the ambulance takes to reach the patient, which hospital the ambulance heads to after picking up the patient, and which route it should take to the selected hospital). The first two decisions are used to minimize the response time while the last two decisions are employed to minimize the doorto-needle time. We analyze the performance of the proposed algorithm; both analytically and by simulation for verification. The results showed very good consistency between simulation results and analytical results, which confirms the correctness and accuracy of the analysis. In addition, we compare the performance of our proposed smart algorithm with a previous algorithm that is reported in the literature and that minimizes the drop-off delay. The results confirmed the superiority of our smart algorithm under considered operating conditions and scenarios.
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