Real-Time Human Trajectory Dataset Capture Model (RT-HTDCM) using GPS and Assisted-GPS Technologies: African Perspective

Movement is one of the characteristics of human beings that allow them change their location with time in their environment to obtain essential requirements of life and also take part in other social life activities. A moving person always leave trajectory (trace) through which he/she passed. But it is often difficult for people to divulge information about their trajectories.  However, the information is often needed or required, for business, security, social, etc, reasons to monitor their trajectories and infer what led them through these trajectories. With the recent development in Telecommunications and ICT (Information & Communications Technology ) in combination with Global Positioning System (GPS) technology, traces of moving persons can be recorded digitally on real-time using GPS-enabled devices such as Smartphones, PDAs, Pads, and Cameras assigned to them. In this paper, we proposed a model for developing real-time human trajectory dataset capture software that uses GPS and Assisted-GPS Technologies on Smartphones for tracking and recording of such movement traces of individuals in African developing country, Nigeria. Some smartphones installed with the model (RT-HTDC software) were tested in geographical areas of Federal Capital Territory (FCT), Abuja, Nigeria, and samples of date/time-stamped location points of these smartphone-users were captured and recorded. These location points if connected sequentially can form trajectories (traces) of smartphone-users. Keywords: GPS, Assisted-GPS, Smartphones, location-log, Trajectory, Moving Objects

CLOSED FORM EXPRESSIONS OBTAINED FROM THE SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS OF THE PROBABILITY DENSITY FUNCTION OF THE BETA DISTRIBUTION

In this paper, some closed form expressions for selected parameters for the probability density function (PDF) of the beta distribution are obtained. The closed form expressions are recovered from the solution of the ordinary differential equations (ODEs), obtained from the differentiation of the PDF of the distribution. The paper shows that the shape of the distributions also determines the nature of the resulting ODE which has shown how distributions related to the beta distribution can be traced via the solutions of the ODEs. Numerical methods are unnecessary because the closed form expressions are the same with the values obtained from the standard statistical software

Random number datasets generated from statistical analysis of randomly sampled GSM recharge cards

In this article, a random number of datasets was generated from random samples of used GSM (Global Systems for Mobile Communications) recharge cards. Statistical analyses were performed to refine the raw data to random number datasets arranged in table. A detailed description of the method and relevant tests of randomness were also discussed

Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05

Personal name in Igbo Culture: A dataset on randomly selected personal names and their statistical analysis

This data article contains the statistical analysis of Igbo personal names and a sample of randomly selected of such names. This was presented as the following: 1). A simple random sampling of some Igbo personal names and their respective gender associated with each name. 2). The distribution of the vowels, consonants and letters of alphabets of the personal names. 3). The distribution of name length. 4). The distribution of initial and terminal letters of Igbo personal names. The significance of the data was discussed. Keywords: Igbo name, Personal name, Statistics, Distribution, Linguistics, Onomastic