Background Sickle cell disease is highly prevalent in sub-Saharan Africa, where it accounts for substantial morbidity and mortality. Newborn screening is paramount for early diagnosis and enrolment of affected children into a comprehensive care programme. Up to now, this strategy has been greatly impaired in resource-poor countries, because screening methods are technologically and financially intensive; affordable, reliable, and accurate methods are needed. We aimed to test the feasibility of implementing a sickle cell disease screening programme using innovative point-of-care test devices into existing immunisation programmes in primary health-care settings.Methods Building on a routine immunisation programme and using existing facilities and staff, we did a prospective feasibility study at five primary health-care centres within Gwagwalada Area Council, Abuja, Nigeria. We systematically screened for sickle cell disease consecutive newborn babies and infants younger than 9 months who presented to immunisation clinics at these five centres, using a lateral flow immunoassay-based point-of-care test (HemoTypeSC). A subgroup of consecutive babies who presented to immunisation clinics at the primary health-care centres, whose mothers gave consent, were tested by the HemoTypeSC point-of-care test alongside a different immunoassay-based point-of-care test (SickleSCAN) and the gold standard test, high-performance liquid chromatography (HPLC).

COVID-19 pandemic expedites the development of digital technologies to tackle the spread of the virus. Several digital interventions have been deployed to reduce the catastrophic impact of the pandemic and observe preventive measures. However, the adoption and utilisation of these technologies by the affected populace has been a daunting task. Therefore, this study carried out exploratory investigation of the factors influencing the behavioural intention (BI) of people to accept COVID-19 digital tackling technologies (CDTT) using the UTAUT (Unified Theory of Acceptance and Use of Technology) framework. The study applied principal components analysis and multiple regression analysis for hypotheses testing. The study revealed that performance expectancy (PE), facilitating conditions (FC) and social influence (SI) are the best predictors of people's BI to accept CDTT. Also, organizational influence and benefit (OIB) and government expectancy and benefits (GEB) influence the people's BI. However, variables such as age, gender and voluntariness to use CDTT have no significance to influence BI because the CDTT is still nascent and not easily accessible. The results show that the decision-makers and regulators should consider inciting variables such as PE, FC, SI, OIB and GEB, that motivate the acceptance and use of CDTT. Furthermore, the populace must be sensitized to the availability and use of CDTT in all communities. Also, the path diagram and hypothesis testing results for CDTT acceptance and use, will help government and private organizations in planning and responding to the digitalization of COVID-19 protective measures and hence revise the COVID-19 health protection regulation.

Clinical methods are used for diagnosing COVID-19 infected patients, but reports posit that, several people who were initially tested positive of COVID-19, and who had some underlying diseases, turned out having negative results, after further tests. Therefore, the performance of clinical methods is not always guaranteed. Moreover, chest X-ray image data of COVID-19 infected patients are mostly used in the computational models for COVI-19 diagnosis while the use of common symptoms such as “ Fever, Cough, Fatigue, Muscle aches, Headache etc ”, in computational models is not yet reported. In this study, we employ seven classification algorithms to empirically test and verify their efficacy when applied to diagnose COVID-19 using the aforementioned symptoms. We experimented with logistic regression (LR), support vector machine (SVM), naïve Byes (NB), decision tree (DT), multilayer perceptron (MLP), fuzzy cognitive map (FCM) and Deep neural network (DNN) algorithms. The techniques were subjected to random under-sampling and over-sampling. Our results showed that with class imbalance, MLP and DNN outperform others but without class imbalance MLP, FCM and DNN outperform others with the use of random under-sampling but DNN has the best performance with the use random oversampling. This study identified MLP, FCM and DNN as better classifiers over LR, NB, DT and SVM, that healthcare software system developers can adopt to develop intelligence-based expert systems which both medical personnel and patients can use for differential diagnosis of COVID-19 based on the aforementioned symptoms however, the test of performance must not be limited to the traditional performance metrics.

The inability of a region to access a webpage, because of the ban being placed on users from that region as a result of its location policy, has led to this study. This problem is often solved by anonymizing web traffic by using The Onion Router (TOR). These tools, however, suffer from the problem of exposure of identity and also lack the ability to monitor web users. This study describes in detail a web proxy server service solution within the context of a tertiary institution in Nigeria and explains how this service improves the user experience. An identity management system using a web proxy server was developed to tackle these problems. The new system proxy was designed using a transparent proxy model with some additional translational features where no modification was done to the response or request of resources, other than the addition of its identification information or that of the server from which the message was recovered, and mediation of resources. Redeemer's University proxy was used as a case study in this research work. This system is also able to effectively monitor users' (staffs and students) operations on the web.

The use of credit cards is fast becoming the most efficient and stress-free way of purchasing goods and services; as it can be used both physically and online. Hence, it has become imperative that we find a solution to the problem of credit card information security and also a method to detect fraudulent credit card transactions. Over the years, a number of Data Mining techniques have been applied in the area of credit card fraud detection. The focus of this paper is to model a fraud detection system that would attempt to maximally detect credit card fraud by generating clusters and analyzing the clusters generated by the dataset for anomalies. The major objective of this study is to compare the performance of two hybrid approaches in terms of the detection accuracy. Review Article We employed hybrid methods using the K-means Clustering algorithm with Multilayer Perceptron (MLP) and the Hidden Markov Model (HMM) for this study. Our tests revealed that the detection accuracy of "MLP with K-means Clustering" is higher than the "HMM with K-means Clustering" for 80% percentage split but the reverse is the case when the "MLP with K-means Clustering" is compared with the "HMM with K-means Clustering" for 10 fold cross-validation but the accuracy is the same in the two hybrid methods for percentage split of 66%. More extensive testing with much larger datasets is however required to validate theses results.