A Mathematical Approach to Healthcare Insurance Data Analytics

The emergence of big data analytics as a way of deriving insights from data has brought excitement to mathematicians, statisticians, computer scientists and other professionals. However, the near absence of a mathematical foundation for analytics has become a real challenge amidst the flock of big data marketing activities, especially in healthcare insurance. This paper developed a mathematical model for the analytics of healthcare insurance data using set theory. A prototype for the model was implemented using Java Programming Language, MapReduce Framework, Association Rule Mining and MongoDB. Also, it was tested for accuracy using data from the National Health Insurance Scheme in Nigeria with a view to reducing delays in the processes of the Scheme. The result showed that the accuracy level was 97.14% on average, which depicts a higher performance for the model. This result implies that delays affecting the processing of data submitted by the providers and enrollees to the HMOs reduced drastically leading to the improvement in the flow of resources.

Violence Detection in Ranches Using Computer Vision and Convolution Neural Network

This study engaged the convolutional neural network in curbing losses in terms of resources that farmers spends in treating animals where injuries must have emancipated from violence among other animals and in worst case scenario could eventually lead to death of animals. Animals in a ranch was the target and the study proposed a method that detects and reports activities of violence to ranchers such that farmers are relieved of the stress of close supervision and monitoring to avoid violence among animals. The scope of the study is limited to violence detection in cattle, goat, horse and sheep. Different machine learning models were built for each animal. The models yielded good results; the horse violence detection model had an outstanding performance of 93% accuracy, 93% accuracy for the sheep model, 88% accuracy for the goat model and 84% accuracy for the cattle model.

Designing a Star Schema for Optimizing the Total Sales of Motorcycles

Motorcycle sales have increased significantly, motorcycle manufacturers are competing to produce the latest models which are then sold to consumers. As a result, motorcycle dealers are overwhelmed with more and more data, not knowing what to do with it. Motorcycle dealers also have difficulty calculating the total sales of motorcycles. We try to provide solutions to deal with data overflow. We propose designing a star schema as the basis for creating a data warehouse. To create a star schema, we propose a four-step sequence in creating an effective star schema, starting from requirements analysis and reporting, understanding business processes, connecting and matching business processes with suitable entities and determining the dimensions of the business processes. We get a star schema with 1 fact table, motorcycle_sales and 11 of dimension tables, such as brand, color, customer, customer_contract, distributor, district, motorcycle, repair_workshop, sell_location, type and time. The star schema is an optimized model that provides the best performance in presenting more complex information

Implementation of C4.5 Method and Artificial Neural Networks to Predict Sales

Technology and communication is currently growing along with the increasing needs of each individual in various fields such as: business, education, agriculture, health and technology. With the development of technology today, everyone can communicate and obtain and convey various information needed anytime and anywhere quickly, accurately and dynamically.Sales is an activity of buying and selling products or goods carried out between sellers and buyers, can interact within the same scope or online by using legal payment transactions. Building materials are materials used to design buildings such as houses, mosques, schools etc. For buildings, many natural materials are used such as: clay, sand, wood or bamboo, stone and others. In this study, the implementation of the c45 method and an artificial neural network to predict sales for the next year. The sale of building materials at the prayer shop, whose sales are not yet computerized by designing a computerized sales system. Toko Doa Mama is a building shop that sells various building materials such as: sand, paint, wood, or bamboo, saws, cement, roof tiles, gravel, nails, bricks, and others. But sales and marketing are still not computerized which results in frequent errors in calculating sales transactions, data collection of incoming goods and outgoing goods which are still in the form of archives, resulting in accumulation and lack of data security. Therefore, we need a computerized where the computer can help a job to be more effective and efficient

Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest Approach

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented random forest algorithm using Python programming language to predict the aforementioned climate parameters. The data used was gotten from the Nigerian Meteorological (NiMet) Agency, Lokoja, Kogi State between 1988 and 2018. The result shows that random forest algorithm is effective in climate prediction as the accuracy from the model based on the climatic factors considered was 94.64%. With this, farmers would be able to plan ahead to prevent the impact of the fluctuations in these climatic factors; thus, the yield of crops would be increased. This would dwarf the negative impact of food insecurity to the populace.

Analysis Process MILAR: Mining Indirect Least Association Rule Algorithm

One of the interesting and meaningful information that is hiding in transactional database is indirect association rule. It corresponds to the property of high dependencies between two items that are rarely occurred together but indirectly emerged via another items. Since indirect association rule is nontrivial information, it can implicitly give a new perspective of relationship which cannot be directly observed from the common rule. Therefore, we proposed an algorithm for Mining Indirect Least Association Rule (MILAR) from the real and benchmarked datasets. MILAR is embedded with our scalable least measure namely Critical Relative Support (CRS). The experimental results show that MILAR can generate the desired rules in term of least and indirect least association rules. In addition, the obtained results can also be used by the domain experts to do further analysis and finally reveal more interesting findings

Alliance Rules- based Algorithm on Detecting Duplicate Entry Email

The way that email has extraordinary significance in present day business communication is certain. Consistently, a bulk of emails is sent from organizations to clients and suppliers, from representatives to their managers and starting with one colleague then onto the next. In this way there is vast of email in data warehouse. Data cleaning is an activity performed on the data sets of data warehouse to upgrade and keep up the quality and consistency of the data. This paper underlines the issues related with dirty data, detection of duplicatein email column. The paper identifies the strategy of data cleaning from adifferent point of view. It provides an algorithm to the discovery of error and duplicates entries in the data sets of existing data warehouse. The paper characterizes the alliance rules based on the concept of mathematical association rules to determine the duplicate entries in email column in data sets.

Identifying Severe Malnutrition in Children with an Expert System

Expert system is a computer program which is designed for modelling the ability of problem solving as it is an expert (human expert). The expert system method used is the forward chaining method which is the inference method that is doing logical reasoning from the problem to its solution. The aim of this research is to design and develop an expert system that is able to identify the severe malnutrition on children from the age of 0 - 5 years old. The knowledge is derived from the question askedto a nutrition expert. The data are taken from the questions asked to the user and when all of the questions has been answered, then the goal will be appeared which shows the nutrition status. This system application will enable the user to diagnose the nutrition/disease that affects children and get the solution. This system can be used by any kind of user due to the easy access. This system is also put the important information about the severe malnutrition and the recent news of children’s health so it will add more knowledge for the parents about the importance of severe malnutrition’s prevention.

Hybrid Thresholding Method in Detection and Extraction of Brain Hemorrhage on the CT-Scan Image

Brain bleeding can occur because of the outbreak of the blood vessels in the brain which culminated into hemorrhagic stroke or stroke due to bleeding. Hemorrhagic Stroke occurs when there is a burst of blood vessels result from some trigger factor. Segmentation techniques to Scanner computed tomography images (CT scan of the brain) is one of the methods used by the radiologist to detect brain bleeding or congenital abnormalities that occur in the brain. This research will determine the area of the brain bleeding on each image slice CT - scan every patient, to detect and extract brain bleeding, so it can calculate the volume of the brain bleeding. The detection and extraction bleeding area of the brain is based on the hybrid thresholding method.

Implementation Kolmogorov-Smirnov Method on Queue System Simulation

Long queue always happen as main problem in re-registration of new students who passed the New Registration Student (NRS). The long queue makes the queueing system crowded and cannot be controlled properly. This research is aimed in knowing the model, process simulation model and how to use the method of Kolmogorov-Smirnov of system for re-registration of new students. The method is suitable on how to solve long queue system with new model and simulation. The proposed method is implemented in Senior High School in Padang, West Sumatera, Indonesia. The result of this research shows that after we divided queuing post 1 into two sub-posts namely post 1a and 1b on the first day and we note that average long applicants waiting at post 1a and 1b are smaller i.e. 2.3 minutes and 2.8 seconds as previously for 15 minutes. Furthermore, we divided queuing post 2 into 2 sub-posts namely post 2a and 2b on first day note that average long applicants wait at post 1a and 1b are smaller i.e. 0.2 minutes and 0.2 minutes as previously for 32 minutes. It can be concluded that the solution to the registration posts can make an average registration time waiting to be smaller.