The explosive growth of unstructured data in the National Health Insurance Scheme (NHIS) in Nigeria has given rise to the lack of an appropriate data storage mechanism to house data in the Scheme. This paper x-rayed these data storage challenges with a view to implementing a storage mechanism that can handled large volume and different formats of data in the Scheme. The NHIS is currently using the paper-based, file and cabinet data storage system with some of the data stored in the form of PDF, Excel and image files on the computer system. This has led to serious challenges ranging from the loss of data, lack of appropriate data storage facilities to accommodate the data to the delay in the administration of quality care to beneficiaries of the Scheme. Also, the diversity of data with the ever-growing datasets which is also generated at very high rate has also constituted a major challenge for NHIS. This research therefore developed a computer-based data storage system using MongoDB which has full index support, replication, high availability and auto-sharding. The design was done with Enterprise Application Diagrams and implemented using Java Programming Language, MapReduce Framework and MongoDB. The study shows that there are inequities in the delivery of services within the NHIS in Nigeria due to lack of proper storage medium. This is responsible for the ineffectiveness and inefficiency of healthcare services received through the Scheme. In conclusion, this research has provided the stakeholders with access to information more easily, which will enable them to plan, evaluate, and collaborate more effectively. Keywords: Big Data, NHIS, Storage, MongoDB, Data, Analytics.
The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the historic data collected and offers suggestions to farmers on the right soil nutrients requirements that would enhance crop yield. This will enable early prediction of crop yield and prior idea to improve on the soil to increase productivity. The research used XGBoost algorithm for the crop yield prediction and the Support Vector Machine algorithm for the recommendation of appropriate improvement of soil nutrient requirements. The accuracy obtained for the prediction with XGBoost was 95.28%, while that obtained for the recommendation of fertilizer using SVM was 97.86%.
This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period from 21st January, 2017 to 30th June, 2019, the remaining data which covered from 1st July 2019 up to the 31st December, 2019 was used to test and validate the forecasting performance of the system. The Forecasting Precision (FP) value for the SVM was 96.75% and that of the RBF neural network forecasting value was 90.55%. Analysis from the results shows that the forecasting system with SVM had a greater precision in the sales of agricultural products.
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