Determinants of utilisation of traditional birth attendant services by pregnant women in Ogbomoso, Nigeria

Background: This study was designed to assess the determinants of utilization of Traditional Birth Attendants (TBAs) services by pregnant women in different communities in Ogbomoso, Nigeria.Methods: This was a community- based cross-sectional study. Fisher's formula was used to calculate the sample size and a total of 270 eligible pregnant women were enrolled for the study using multistage sampling technique. Data was collected using pretested structured interviewer-administered questionnaire. Data analysis was done using SPSS version 20 and results were presented in frequencies and percentages.Results: Factors found to have a significant influence on the utilization of TBA services in this study include: low educational status (p <0.001), lower socioeconomic status (p <0.001), and compassionate care given by the TBAs (p=0.004). Other factors include service proximity and lower cost of TBA services.Conclusions: The impact of TBAs and their services cannot be overemphasized in the present state of maternal and child health in Nigeria.  Lower educational status among others has been found to be a strong predictor of utilization of TBA services. There is, therefore, the need to improve the educational and socioeconomic status of women in order to allow them to access quality health care services that will safeguard their well-being. Inculcating compassionate care into orthodox healthcare delivery will go a long way to improve patronage and discourage TBA utilization

Social Meaning and Consequences of Infertility in Ogbomoso, Nigeria

Background: This study examined the meaning of infertility from layman's perspective, and experiences of women suffering from infertility among reproductive age women seeking care at the gynaecology unit of the Bowen University Teaching Hospital, Ogbomoso, Nigeria.Materials and Methods: It was a cross-sectional study. Quantitative and qualitative data collection methods were employed. Quantitative data collection was by the aid of a structured interviewer-administered questionnaire among 200 women seeking care for infertility at the hospital. Qualitative data collection was by Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs).Result: Approximately 40% and 60% of the respondents seeking care for infertility were suffering from primary and secondary infertility respectively. Perceived meaning and   etiologies of childlessness were multidimensional, but 33% of the respondents not sure of the causal factor. Seventy-nine percent   were under pressure to become pregnant. The high premium placed on fertility within marriage has placed   a larger proportion of them under pressure from their husbands (25%), their mother-in-laws (40%), and the community (14%).Conclusion: This study concluded that women regard infertility to be caused by multiplicity of factors. Most of these etiologies were unscientific and unverifiable. Fruitful expectations also put enormous burden on those women suffering from infertility including adverse psychosexual effects. The unceasing pressure due to infertility   in this group of patients calls for urgent intervention as most of these women become susceptible to high risk sexual behavior, depression and other severe consequences

PHYSICO-CHEMICAL AND GEOCHEMICAL PROPERTIES OF SOILS UNDER SAWAH SYSTEM OF INLAND VALLEYS IN NIGERIA

This study investigated the physico-chemical and geochemical properties of soils under sawah in Nigeria. It was found that soils under sawah were majorly sandy loam to sandy clay loam having acidic reactions, low exchangeable Ca, Mg, K and Na. These soils were deficient in available P, SiO2, S, Total Nitrogen and Total Carbon while SiO2, Al2O3 and Fe2O3 dominated total elemental composition, accounting for a cumulative average of 96.16%. Except total elemental TiO2 and K2O which showed average values &gt;1%, MnO, MgO, CaO, Na2O and P2O5 showed average values &lt;1%. Soils under sawah exhibited intermediated to extreme weathering degree with majority of the soil sampled falling into the category of extreme weathering. With extreme degree of weathering, rapid loss of mobile species such as basic cations from soil is imminent which may account for the results observed in this study. Thus, combination of conservative agricultural practices is recommended. &nbsp; &nbsp

Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System

In the process of clarifying whether a patient or patients is suffering from a disease or not, diagnosis plays a significant role. The procedure is quite slow and cumbersome, and some patients may not be able to pursue the final test results and diagnosis. The method in this paper comprises many fact-finding and data-mining methods. Artificial Intelligence techniques such as Neural Networks and Fuzzy Logic were fussed together in emerging the Coactive Neuro-Fuzzy Expert System diagnostic tool. The authors conducted oral interviews with the medical practitioners whose knowledge were captured into the knowledge based of the Fuzzy Expert System. Neuro-Fuzzy expert system diagnostic software was implemented with Microsoft Visual C# (C Sharp) programming language and Microsoft SQL Server 2012 to manage the database. Questionnaires were administered to the patients and filled by the medical practitioners on behalf of the patients to capture the prevailing symptoms. The study demonstrated the practical application of neuro-fuzzy method in diagnosis of malaria. The hybrid learning rule has greatly enhanced the proposed system performance when compared with existing systems where only the back-propagation learning rule were used for implementation. It was concluded that the diagnostic expert system developed is as accurate as that of the medical experts in decision making. DIAGMAL is hereby recommended to medical practitioners as a diagnostic tool for malaria

ECTOPIC CHORIOCARCINOMA IN A PRETEEN IN OGBOMOSO, SOUTH-WEST NIGERIA. A CASE REPORT

Ectopic Choriocarcinoma is an extremely rare occurrence. The case of ectopic choriocarcinoma discussed here involved a 12 year old girl who presented with lower abdominal pain and dizziness following a short period of amenorrhea after attaining menarche. Aprovisional diagnosis of ruptured ectopic gestation was made based on the clinical evaluation and patient had emergency laparotomy. Histopathology report revealed a choriocarcinoma of the ovary. Patient defaulted on subsequent follow up care. This case is presented as an eye opener on the need to also focus on the reproductive health challenges, early sex education in preteen and rare occurrence of the disease amongst the Pre-teenage groups. It is also important to deal with the possibility of a non gestational choriocarcinoma of the ovary which has a worse prognosis

Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended

Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended