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Bayesian Spatial-temporal Modelling and Mapping for Crime Data in Nairobi County
George Ngogoyo Chege,
Samuel Musili Mwalili,
Anthony Wanjoya
Issue:
Volume 5, Issue 6, December 2019
Pages:
111-116
Received:
8 October 2019
Accepted:
29 October 2019
Published:
4 November 2019
Abstract: Nairobi is a county in Kenya that is more prone to crime occurrence. This has made many researchers, for the past years, to study about crime occurrence in its suburbs and which factors promote crime. The theories around crime are always coupled with an attempt to predict their occurrence, for better crime analysis, and management, in case they happen, the associated covariates and their changes are analyzed. At the sub-county level, the crime occurrence is highly studied and understood. In this study, using Bayesian theory, this study builds spatial-temporal Bayesian model approach to crime to analyze its spatial-temporal patterns and determine any developing trends using data regarding robberies that occurred in Nairobi County in Kenya from January 1, 2011 to December 31, 2018. Of the diverse socio-economic variables associated with crime rate, including unemployment rate, poverty, weak law enforcement, Alcohol and drug abuse, and illiteracy, this study finds that robbery crime rate is significantly correlated with the poverty index and the unemployment rate. This finding provides a statistical reference for County safety protection. For further work, we recommend that further study can be done to determine factors associated with the dynamics and the distribution of crime in Nairobi County while accounting for measurement error that might be present in the covariates.
Abstract: Nairobi is a county in Kenya that is more prone to crime occurrence. This has made many researchers, for the past years, to study about crime occurrence in its suburbs and which factors promote crime. The theories around crime are always coupled with an attempt to predict their occurrence, for better crime analysis, and management, in case they hap...
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The Modeling of a Stochastic SIR Model for HIV/AIDS Epidemic Using Gillespie's Algorithm
Kavyu Mary Kamina,
Samuel Mwalili,
Anthony Wanjoya
Issue:
Volume 5, Issue 6, December 2019
Pages:
117-122
Received:
8 October 2019
Accepted:
28 October 2019
Published:
4 November 2019
Abstract: Mathematical modeling of disease has been an indispensable tool in accounting for disease transmission dynamics as well as disease spread. Epidemiological disease models have been used to explain the dynamics of HIV/AIDS in the population from the early 1900s. The models developed however faced considerable challenges ranging from inaccurate representation of natural data for deterministic models, to methods of forecasting such as statistical extrapolation which assumes that current conditions will prevail which is not always the case. Despite the spread of HIV/AIDS having been explored widely, not much literature is available on the Gillespie Algorithm based SIR model. This algorithm is able to give a statistically correct of the course of a disease with initial conditions to begin with and propensity functions to update the system. The purpose of this paper is to build on the concept of Gillespie's Algorithm based SIR models by developing a stochastic SIR model to simulate disease evolution in the population setting. The values produced through simulation by the model developed in this paper using a tau value as the time step of the model were compared to HIV/AIDS data from 1985 to 2018, given by NACC. We conclude that the simulated model reflects reality.
Abstract: Mathematical modeling of disease has been an indispensable tool in accounting for disease transmission dynamics as well as disease spread. Epidemiological disease models have been used to explain the dynamics of HIV/AIDS in the population from the early 1900s. The models developed however faced considerable challenges ranging from inaccurate repres...
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Issues of Class Imbalance in Classification of Binary Data: A Review
Samuel Adewale Aderoju,
Emmanuel Teju Jolayemi
Issue:
Volume 5, Issue 6, December 2019
Pages:
123-127
Received:
25 September 2019
Accepted:
8 November 2019
Published:
17 November 2019
Abstract: Handling classification issues of class imbalance data has gained attentions of researchers in the last few years. Class imbalance problem evolves when one of two classes has more sample than the other class. The class with more sample is called major class while the other one is referred to as minor class. The most classification or predicting models are more focusing on classifying or predicting the major class correctly, ignoring the minor class. In this paper, various data pre-processing approaches to improve accuracy of the models were reviewed with application to terminated pregnancy data. The data were extracted from the 2013 Nigeria Demographic and Health Survey (NDHS). The response variable is “terminated pregnancy” (asking women of reproductive age whether they have ever experienced terminated pregnancy or not), which has two possible classes (“YES” or “NO”) that exhibited class imbalanced. The major class (“NO”) is 86.82% (of the sample) representing Nigerian women of age 15 – 49 years who had never experience terminated pregnancy while the other category (minor class) is 13.18%. Hence, different resampling techniques were exploited to handle the problem and to improve the model performance. Synthetic Minority Oversampling Technique (SMOTE) improved the model best among the resampling techniques considered. The following socio-demographic factors: age, age at first birth, residential area, region, education level of women were significantly associated with having terminated pregnancy in Nigeria.
Abstract: Handling classification issues of class imbalance data has gained attentions of researchers in the last few years. Class imbalance problem evolves when one of two classes has more sample than the other class. The class with more sample is called major class while the other one is referred to as minor class. The most classification or predicting mod...
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A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses
Adewale Opeoluwa Ogunde,
Emmanuel Ajibade
Issue:
Volume 5, Issue 6, December 2019
Pages:
128-135
Received:
15 October 2019
Accepted:
14 November 2019
Published:
21 November 2019
Abstract: The task of selecting a few elective courses from a variety of available elective courses has been a difficult one for many students over the years. In many higher institutions, guidance and counsellors or level advisers are usually employed to assist the students in picking the right choice of courses. In reality, these counsellors and advisers are most times overloaded with too many students to attend to, and sometimes they do not have enough time for the students. Most times, the academic strength of the student based on past results are not considered in the new choice of electives. Recommender systems implement advanced data analysis techniques to help users find the items of their interest by producing a predicted likeliness score or a list of top recommended items for a given active user. Therefore, in this work, a collaborative filtering-based recommender system that will dynamically recommend elective courses to undergraduate students based on their past grades in related courses was developed. This approach employed the use of the k-nearest Neighbour algorithm to discover hidden relationships between the related courses passed by students in the past and the currently available elective courses. Real-life students’ results dataset was used to build and test the recommendation model. The new model was found to outperform existing results in the literature. The developed system will not only improve the academic performance of students; it will also help reduce the workload on the level advisers and school counsellors.
Abstract: The task of selecting a few elective courses from a variety of available elective courses has been a difficult one for many students over the years. In many higher institutions, guidance and counsellors or level advisers are usually employed to assist the students in picking the right choice of courses. In reality, these counsellors and advisers ar...
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Determination of an Optimal Smoothing Technique for Maternal Health Care Statistics (A Case Study of Nakuru County 2012-2016)
Lena Anyango Onyango,
Thomas Mageto,
Caroline Mugo
Issue:
Volume 5, Issue 6, December 2019
Pages:
136-142
Received:
16 November 2019
Accepted:
28 November 2019
Published:
10 December 2019
Abstract: One of the Big four agenda is Universal health. This study focused on maternal health. The main aim of maternal health is usually to reduce maternal deaths. One way in aiding to reduce maternal deaths is to forecast maternal deaths using various statistical smoothing techniques. This would enable better future planning for example increase in health facilities. Shapiro-Wilk Normality Test confirmed that there was clear observable difference between the normal distribution and the data. The study hence focused on non-parametric regression methods which include Kernel and Cubic spline smoothing techniques which were applied on maternal health care data. The technique that best dealt with this type of data was identified and used to focus maternal deaths. Selecting an appropriate technique was important to achieve a good forecasting performance. The performance of the two smoothing technique was compared using MSE, MAE and RMSE and the best model identified. In both methods we have smoothing parameters. Selecting smoothing parameter goal is usually to base it on the data. According to the results obtained in the study, it is concluded that Cubic spline smoothing technique which has a lower MSE, MAE and RMSE is better than Kernel based smoothing technique. The statistical software that was used for the analysis was R. The study used maternal health care statistics data for Nakuru County.
Abstract: One of the Big four agenda is Universal health. This study focused on maternal health. The main aim of maternal health is usually to reduce maternal deaths. One way in aiding to reduce maternal deaths is to forecast maternal deaths using various statistical smoothing techniques. This would enable better future planning for example increase in healt...
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Modelling Cases of Spontaneous Abortion Using Logistic Regression
Edwin Kung’u Kagereki,
Anthony Wanjoya,
Thomas Mageto
Issue:
Volume 5, Issue 6, December 2019
Pages:
143-147
Received:
22 November 2019
Accepted:
16 December 2019
Published:
25 December 2019
Abstract: Spontaneous abortion is the expulsion of a foetus before the 28th week of gestation. Studies approximate that 10-25% of pregnancies are lost due to miscarriages. This phenomenon's aetiology remains a mystery hence uncertainty of detecting its cause. Furthermore, most pregnant women realize they have conceived later in the gestation period and some start antenatal care late during the pregnancy.In Kenya, total fertility rate has decreased for the last three decades from 8.1 to 3.9. However, with the decrease of total fertility rate, prevalence of maternal mortality and morbidity factors has greatly impacted on the pregnancy. Among them is spontaneous abortion. This study used secondary data from Kenyatta national hospital and employed logistic regression to model miscarriage's risk factors, investigate socio demographic and lifestyle factors, to investigate interactions among identified risk factors and fit a predictive model. Significant socio demographic factors identified were age and recurrent miscarriage. A woman who had experienced prior miscarriage had a 7.5-fold risk. Lifestyle factors identified were body mass index, diabetes mellitus and HIV. Underweight women had a 13.2-fold risk. There were significant interactions between gravidity and previous miscarriage; diabetes and body mass index. A predictive model was fit. The model has a good measure of separability, 80% classification accuracy and it is significant.
Abstract: Spontaneous abortion is the expulsion of a foetus before the 28th week of gestation. Studies approximate that 10-25% of pregnancies are lost due to miscarriages. This phenomenon's aetiology remains a mystery hence uncertainty of detecting its cause. Furthermore, most pregnant women realize they have conceived later in the gestation period and some ...
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A Novel Ontology Construction and Reasoning Approach Based on the Case Investigation
Han Zhong,
Hongzhou Zhang,
Jianqian Zhang,
Ziyang Yuan
Issue:
Volume 5, Issue 6, December 2019
Pages:
148-158
Received:
8 November 2019
Accepted:
11 December 2019
Published:
31 December 2019
Abstract: The big data has become a key component for intelligent systems and it is very important about data mining and cognitive reasoning in the field of criminal data analysis. Modeling of investigation knowledge is very important to realize the semantic retrieval, knowledge discovery, information push and classification for case data. Ontology modeling combined with the characteristics of the case in the investigation process, a method of ontology construction based on investigation knowledge is proposed in this paper. It builds an organization system of the investigation process at the first, which is described in stages by collecting terminology. Then the ontology of investigation knowledge is constructed. In addition, an instance is added for verification to describe the investigation process in detail. The method has a good advantage of describing the detection process quickly and integrate knowledge according to different investigation stages, formulating a standardized organization mode and providing standardized knowledge assistance in the investigation process.
Abstract: The big data has become a key component for intelligent systems and it is very important about data mining and cognitive reasoning in the field of criminal data analysis. Modeling of investigation knowledge is very important to realize the semantic retrieval, knowledge discovery, information push and classification for case data. Ontology modeling ...
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