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Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as tools for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Aim: In this work, a Natural Language Processing framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Two million tweets with 18 features were collected from Twitter containing public and personal tweets of the three top contestants – Atiku Abubakar, Peter Obi and Bola Tinubu – in the forthcoming 2023 Presidential election. Sentiment analysis was performed on the preprocessed dataset using three machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. This study spanned ten weeks starting from the candidates’ declaration of intent to run for Presidency. Results: The sentiment models gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2%, 87.6% and 82.9% respectively for LSTM; 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively for BERT and 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively for LSVC. Result also showed that Peter Obi has the highest total impressions the highest positive sentiments, Tinubu has the highest network of active friends while Atiku has the highest number of followers. Conclusion: Sentiment analysis and other Natural Language Understanding tasks can aid in the understanding of the social media space in terms of public opinion mining. We conclude that opinion mining from Twitter can form a general basis for generating insights for election as well as modeling election outcomes.

Coronavirus Disease 2019 (COVID-19) spreads rapidly and is easily contracted by individuals who come near infected persons. With this nature and rapid spread of the contagion, different types of research have been conducted to investigate how non-pharmaceutical interventions can be employed to contain and prevent COVID-19. In this review, we analyzed the key elements of digital contact tracing strategies developed for the prevention and containment of the dreaded epidemic since its outbreak. We carried out a scoping review through relevant studies indexed in three databases, namely Google Scholar, PubMed, and ACM Digital Library. Using some carefully defined search terms, a total of 768 articles were identified. The review shows that 86.32% (n = 101) of the works focusing on contact tracing were published in 2020, suggesting there was an increased awareness that year, increased research efforts, and the fact that the pandemic was given a very high priority by most journals. We observed that many (47.86%, n = 56) of the studies were focused on design and implementation issues in the development of COVID-19 contact tracing systems. In addition, has been established that most of the studies were conducted in 41 countries and that contract tracing app development are characterized by some sensitive issues, including privacy-preserving and case-based referral characteristics.

Background: This study is part of the Stroke Investigative Research and Educational Network (SIREN), the largest study of stroke patients in Africa to date, with computed tomography (CT) or magnetic resonance (MR) imaging data for each patient to confirm stroke. Prior imaging studies performed using high-field MR (≥1.5T) have shown that white matter hyperintensities (WMH), signs of microangiopathy in the subcortical brain, are correlated with many stroke risk factors as well as poor stroke outcomes. The aim of this study was the evaluation of MR images (0.3T–1.5T) from the SIREN study to determine associations between WMH volumes in West African patients and both stroke outcomes and stroke risk factors identified in the SIREN study. Materials and methods: Brain MR images of 130 Western African stroke patients (age ​= ​57.87 ​± ​14.22) were processed through Lesion Segmentation Toolbox of the Statistical Parametric Mapping software to extract all areas of hyperintensity in the brain. WMH was separated from stroke lesion hyperintensity and WMH volume was computed and summed. A stepwise linear regression and multivariate analysis was performed between patients’ WMH volume and sociodemographic and clinical indices. Results: Multivariate analysis showed that high WMH volume was statistically significantly positively correlated with age (β ​= ​0.44, p ​= ​0.001), waist/hip ratio (β ​= ​0.22, p ​= ​0.03), and platelet count (β ​= ​0.19, p ​= ​0.04) after controlling for head size in a Western African stroke population. Conclusion: Associations between WMH and age and waist/hip ratio previously identified in Western countries were demonstrated for the first time in a resource-limited, homogeneous black African community using low-field MR scanners.

Background and Objectives: The recent progress in medical image analysis has given birth to image-guided therapy, virtual reality and augmented reality. These among other innovations have greatly improved healthcare delivery, improved quality of life and also saved lives.. The advances in Internet and network technology have produced web technologies which make it possible to offer platform or software as a service via the web, making it possible for end users to access computer resources or specialized computer tools remotely. However, the high cost of image acquisition, the limited availability of medical image analysts, and the limited collaborative efforts between medical experts and scientists are major challenges for medical image analysis in the developing world. The aim of this project was to develop a medical image e-infrastructure called Medical Image Processor and Analysis (MIPAR) to contain a repository of medical images acquired from Africa and a platform for processing medical images. Methods: The backend of MIPAR which is resident on a High-Performance Computing infrastructure was built using FutureGateway, a framework for building science gateway. The image upload and download module was built upon the framework of Open Access Repository with the front end developed using HTML, CSS and BootStrap. JavaScript and JQuery were used for scripting. User's access to the server is controlled with HTTP response and a Client-Server Architecture, while the image processing tools on the server side communicate with PHP using Representational State Architecture (REST) API. Results: MIPAR was tested using brain MRI images. Images were submitted remotely via MIPAR's web interface and kept in the repository. Image processing facility of MIPAR was tested using the brain extraction module, a 3D image of approximately 43MB was successfully brain extracted within 60 s. Conclusion: MIPAR allows users to donate, download and process medical images at no cost. It is our hope that such useful and unique tool will encourage collaboration, improve diagnosis, improve patient management, and promote open science in Africa. Keywords: Medical image, Image analysis, Repository, E-infrastructure, Open source

Background and objectives: The recent progress in medical image analysis has resulted in image-guided therapy, virtual reality, and augmented reality, other innovations that have greatly improved healthcare delivery, improved the quality of life, and saved lives. The advances in Internet and network technology have produced web technologies which make it possible to offer platform or software as a service via the web, making it possible for end users to access computer resources or specialized computer tools remotely. However, the high cost of image acquisition, the limited availability of medical image analysts, and the limited collaborative efforts between medical experts and scientists are major challenges for medical image analysis in the developing world. The aim of this project was to devise a medical image e-infrastructure called Medical Image Processor and Analysis (MIPAR) to contain a repository of medical images acquired from Africa and a platform for processing medical images. Methods: The backend of MIPAR which is resident on a High-Performance Computing infrastructure was built using FutureGateway, a framework for building science gateway. The image upload and download module was built upon the framework of Open Access Repository with the front end developed using HTML, CSS and BootStrap. JavaScript and JQuery were used for scripting. User's access to the server is controlled with HTTP response and a Client-Server Architecture, while the image processing tools on the server side communicate with PHP using Representational State Architecture (REST) API. Results: MIPAR was tested using brain MRI images. Images were submitted remotely via MIPAR's web interface and kept in the repository. A 3D image of approximately 43MB was uploaded within 43 seconds. Downloading of the same image took approximately 30 seconds. To test the image processing facility, our request for brain extraction of the same image was successfully completed within 60 seconds. Conclusion: MIPAR allows users to donate, download and process medical images at no cost. It is our hope that such useful and unique tools will encourage collaboration, improve diagnosis, improve patient management, and promote open science in Africa. Keywords: Medical image, Image analysis, Repository, E-infrastructure, Open source