Pakistan is a developing country with more than half of its population located in rural areas. These areas neither have sufficient health care facilities nor a strong infrastructure that can address the health needs of the people. The expansion of Information and Communication Technology (ICT) around the globe has set up an unprecedented opportunity for delivery of healthcare facilities and infrastructure in these rural areas of Pakistan as well as in other developing countries. Mobile Health (mHealth)-the provision of health care services through mobile telephony-will revolutionize the way health care is delivered. From messaging campaigns to remote monitoring, mobile technology will impact every aspect of health systems. This paper highlights the growth of ICT sector and status of health care facilities in the developing countries, and explores prospects of mHealth as a transformer for health systems and service delivery especially in the remote rural areas.
Soft computing is a combination of methods that complement each other when dealing with ambiguous real life decision systems. Rough Set Theory (RST) is a technique used in soft computing that enhances the idea of classical sets to deal with incomplete knowledge and provides a mechanism for concept approximation. It uses reducts to isolate key attributes affecting outcomes in decision systems. The paper summarizes two algorithms for reduct calculation. Moreover, to automate the application of RST, different software packages are available. The paper provides a survey of packages that are most frequently used to perform data analysis based on Rough Sets. For benefit of researchers, a comparison of based on functionalities of those software is also provided.
Revolution in information and communication technologies has transformed the information management structure in almost all the organizations. It has had a great impact on healthcare organizations by improving health services and management by integrating technology with the knowledge management infrastructure. The healthcare industry is a knowledge-based community and is connected to hospitals, physicians, patients, laboratories, pharmaceuticals, clinics, pharmacies, and customers for sharing knowledge. A knowledge-based healthcare industry can improve the quality of care and service given to its people and also reduces the administrative cost. The objective of this research is to present and describe the knowledge management capabilities, the technical infrastructure, and the decision support architecture for such a healthcare management system. It envisions a healthcare knowledge management system (HKMS) that would help to integrate important components, disseminate knowledge to the respective users and to store historical data in a database. This will immensely help the managers and developers to identify their IT needs and to plan for and develop the technical infrastructure of the health care management system for their organizations.
This paper aims to establish a relative study between a relational Microsoft SQL Server database and a non-relational MongoDB database within the unstructured representation of data in JSON format. There is a great amount of work done regarding comparison of multiple database management applications on the basis of their performances, security etc., but we have limited information available where these databases are assessed on the basis of provided data. This study will mainly focus on looking at all the possibilities that both these database types offer us when handling data in JSON. We will accomplish this by implementing a series of experiments while taking into consideration that the subjected data does not require to be normalized; and therefore, evaluate the outcome to conclude the result.
Google Maps and other such maps in GIS have a lot of significance in every one's life for in modern world due to technological development as well as contemporary needs in travelling, business planning, agriculture, e-marketing supply chain management, census and planning and excessive use of mobile phones. Being a revolutionary technology, it attracts the users from its inception. It has been revolutionary in having an impact on one's daily life by helping one explore geographical locations virtually anywhere on the whole planet.
This study examine twenty-nine parametric mortality models and assess their suitability for graduating mortality rates of urban and rural areas in Pakistan. Grouped age specific mortality rates of rural and urban populations for the year 2019 are used. The data is collected from the website of National Institute of Population Studies which conduct Maternal Mortality Survey in Pakistan on regular basis. The parametric mortality models were applied to rural and urban mortality data. We used R software to estimate the model’s parameters and assess their suitability for urban and rural populations. The suitability of these models was assessed by using 3 different loss functions. Our analyses found that the fourth type of Heligman-Polard’s model with loss function 3 provides reliable results for graduating the mortality of rural population while second type of Carriere model with loss function 3 produce best results for graduating the urban mortality of Pakistan. Based on two models, mortality rates of urban and rural population have been graduated over age range 0-85. We suggest the use the graduated mortality rates of urban and rural areas for pricing life insurance products in rural and urban areas respectively. In addition, graduated mortality rates are also suggested for use in calculation of life insurance liabilities.
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