Search

In this article, a new family of distribution called the alpha power Marshall-Olkin-G (APMO-G) family of distributions was proposed. The new family of distribution provides a better fit for continuous distributions for lifetime processes. The proposed family of distributions extends the existing alpha power transformed family of distributions by an additional parameter. A comprehensive reliability structural properties were derived. The moments, order statistics, hazard rate, and quantile functions, were also examined. The parameters of the proposed APMO-G model were obtained by the maximum likelihood method. Monte Carlo simulation was used to access the performance of the estimators. The goodness-of-fit test statistics were examined by means of two real-life data sets to demonstrate empirical flexibility. The results show that the APMO-G density provides a better fit when compared with other existing models and can serve as a better alternative model in the literature. [ABSTRACT FROM AUTHOR]

This research focused on analyzing and classifying the survival of some breast cancer in-patients in Kwara State, Nigeria. The relationships between some variables that were measured to determine the survival of breast cancer in a cross-sectional study in Nigeria were explored. Data detailing information on gender, age, mode of diagnosis, length of stay, location of breast cancer and the outcome of the treatment on 300 patients admitted based on breast cancer issues were collected at the University of Ilorin Teaching Hospital (UITH), Ilorin, Nigeria between year 2011 and 2016. Descriptive analyses were performed; cluster analysis was performed using k-mean clustering method with the aid of Orange software. Analysis of the cluster is done to reveal the latent patterns that could lead to a better understanding on how some variables could combine to determine the extent of breast cancer survival in Nigeria The scatter plot was used for data visualization while the Silhouette plot showed validates the choice of using only three clusters. Breast cancer survival is associated with the age bracket of the patients. Also, breast cancer survival is dependent on the location of the cancer. In addition, mode of diagnosis was found to be associated with the location of the breast cancer. Furthermore, k-clustering algorithm identified three distinct clusters. The low Silhouette values of obtained in both plots showed that only few clusters are available for this type of data. Louvain clustering revealed the latent patterns that depicts the prevalence of breast cancer in Nigeria. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of death in females and survival depends on early diagnosis and treatment. This paper applied machine learning techniques in prediction of breast cancer survival (dead or alive) using age, sex, length of stay, mode of diagnosis and location of cancer as predictors (independent variables). The data was obtained from the outpatient department of the University of Ilorin Teaching Hospital, Ilorin, Nigeria. The sample size of 300 consists of 175 females and 25 males who were admitted at the hospital and treated for breast cancer. The patients were later discharged or died. Adaptive boosting (AdaBoost) performed best out of the data mining models used in the classification in all the three cases where the target class is average over classes, alive or dead. The AdaBoost performed best with the classification accuracy and area under curve (AUC) of 98.3% and 99.9% respectively. Furthermore, a probe on the prediction by AdaBoost showed that the probability of dead due to breast cancer is 0.47, which the length of stay hugely contributed to the high probability, location of breast cancer and mode of diagnosis contributed minimally while age and sex contributed insignificantly. The high probability of breast cancer mortality predicted in this paper is a call for concern as early detection of breast cancer, routine breast examination and breast cancer awareness are crucial in increasing the probability of survival. The results can be used to design a decision support system that can increase the chances of breast cancer survival. [ABSTRACT FROM AUTHOR]

Henley Passport Index (HPI) is a rank on the number of countries a passport holder of a country can travel VISA-free. Countries with high HPI ranking enjoy certain passport privileges. Little is known about the relationship between HPI and other global indices. This paper investigates the relationship between HPI and the trio of Corruption Perception Index (CPI), Global Peace Index (GPI), and World Happiness Report (WHR). The data of the country ranks of the 4 indices were obtained from the respective websites of the publishers of the indices. A final sample of 150 countries was analyzed after the cases of missing values were discarded. The result showed positive correlation between HPI and CPI (r = 0.768, p < 0.0005), GPI (r = 0.671, p < 0.005), and WHR (r = 0.775, p < 0.005), respectively. Regression analysis yielded an equation that showed that a unit increase in the country ranking of CPI, GPI, and WHR of countries increases the ranking of HPI by 0.196, 0.149, and 0.352, respectively. The coefficients of the independent variables are all significant at p value equals 0.05 and the model validation showed the absence of multicollinearity and the presence of small non-significant autocorrelation. The research concluded that countries with high passport privileges also have low corruption instances and conflicts and are perceived to be happy. The implications of this research were discussed. [ABSTRACT FROM AUTHOR]

Malaria is a leading cause of death in Nigeria and remains a public health concern because of the increasing resistance of the disease to antimalarial drugs. Pregnant women and children under five years of age are the most vulnerable. Efforts to eradicate malaria is often frustrated due to some various sociodemographic factors and medical factors. One of the vital therapeutic factors is misdiagnosis. Hence, the paper applied different data mining models to diagnose malaria using fifteen symptoms of patients that attended a hospital in Nigeria. The data were obtained from a peer reviewed data article that comprises 337 subjects at Federal Polytechnic Ilaro Medical Centre, Ogun State. The independent variables are 15 symptoms, age, and sex, while the target or dependent is the outcome. The outcome is the result of the diagnosis, which is positive for negative for malaria. Eight machine learning tools were applied to the data on the Orange Software platform. Weak non-significant correlations were obtained between the 15 symptoms and the outcome, and hence no pattern was observed. However, the application of data mining tools revealed a hidden pattern that correctly predicted the outcome using the subjects' symptoms, age, and sex. 6 out of the 8 machine learning models were adjudged to perform well using different performance metrics. The Adaptive boosting model gave a percent 100% precision in the classification, and logistic regression was the least. Furthermore, a percent performance of Adaboost implies that the model correctly predicted all the 221 true negatives and 116 true positives with a misclassification (misdiagnosis) of zero. Classify using only the 15 symptoms reduced the predictive accuracy of the 6 models. Nevertheless, Adaboost performance was the best with a classification accuracy of 98.2%, precision of 96.6%, and an error rate of just 1.8%. Again, logistic regression performance was the least. The present work has presented a strong relationship between age and sex and the outcome. Adaboost model can be used to design decision support systems or rapid diagnostic tools that utilise the internet or mobile devices as platforms in the diagnosis of malaria. The application of the present work as potentials in reduction of misdiagnosis incidences, reducing the mortality due to malaria and improving the overall public health of people residing in malaria endemic areas. [ABSTRACT FROM AUTHOR]

The Lomax distribution was extended in this paper using the Topp–Leone family of distributions. Some of its specific structural properties were established and the model parameters were estimated using maximum likelihood estimation method. The usefulness of the Topp–Leone Lomax distribution was demonstrated using an airbone communication transceiver dataset and comparison were made with respect to the Topp–Leone Burr XII, Topp–Leone Flexible Weibull and Lomax distributions. [ABSTRACT FROM AUTHOR]

Priority rule-based scheduling technique is a scheduling method for constructing minimum feasible schedules when solving project scheduling problems. This approach is made up of two parts: a priority rule for determining the activity list and a schedule generation scheme which constructs the feasible schedule of the constructed activity list. Quite a number of priority rules are available, selecting the best one for a particular input problem is extremely difficult. Hence, we present a new priority rule which assembles a set of priority rules and uses machine learning to form a hybrid strategy out of the assembled strategies. The hybrid strategy operates by choosing the strategy with the best performance at every point in time to construct an activity list of a project. The one with better performance is used most frequently. This removes the problem of manually searching for the best priority rule amongst the dozens of rules that are available. Experimentally, we solved a fictitious single-mode resource-constrained project scheduling problem (single-mode RCPSP) which was solved with 13 different priority rules in Pm Knowledge Center. Our result showed that the total completion time of the project obtained with our approach competes favorably with the completion times gotten with the 13 priority rules. Additionally, we computed initial population for Genetic Algorithm in solving some multi-mode RCPSP. We compared our results with the initial solutions the authors started with, and our results competes favorably with their initial solutions, making our algorithm a good entry point for metaheuristic procedures. [ABSTRACT FROM AUTHOR]

Motion planning problem though widely studied in robotics is a difficult problem. It finds a feasible path from an initial position to a final position in an environment with obstacles. Recent researches do not just aim to find feasible paths but to find paths that are optimal in respect to time, distance and safety of the robots. Optimization based techniques have been proposed to solve this problem but some of them used techniques that may converge to local minimum and they seldom consider the speed of the technique. Hence this paper presents a fast and global motion planning algorithm for a mobile robot in a known environment with static obstacles. It uses particle swarm optimization (PSO) technique for convergence to global minimum and a customized algorithm which generates the coordinates of the search space. The coordinate values when generated by the customized algorithm are passed to the PSO algorithm which uses them to determine the shortest path between the two given end positions. We perform our experiments using four different environments with population sizes 100, 50, 20 and 10 in a 10 × 10 grid and our results are favorable. [ABSTRACT FROM AUTHOR]