Assessing factors associated with compliance to preventive measures of COVID-19 in Rwanda: a cross-sectional community survey.

Objective: To assess the level of compliance with COVID-19 preventive measures and compliance-associated factors in the Rwanda community.

Design: Cross-sectional study.

Settings: Country-wide community survey in Rwanda.

Using wavelet transform and hybrid CNN - LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection.

Early detection of plant diseases is crucial for safeguarding crop yield, especially in regions vulnerable to food insecurity, such as Sub-Saharan Africa. One of the significant contributors to maize crop yield loss is the Northern Leaf Blight (NLB), which traditionally takes 14-21 days to visually manifest on maize. This study introduces a novel approach for detecting NLB as early as 4-5 days using Internet of Things (IoT) sensors, which can identify the disease before any visual symptoms appear.

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms.

Background: COVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images.

Internet of things based visualisation of effect of air pollution on the lungs using HEPA filters air cleaner.

The impact of air quality on human health and the environment is very significant, with poor air quality being responsible for numerous deaths and environmental damage worldwide. Whereas a number of studies have been done to monitor the quality of air with help of emerging technologies, little has been done to visualize its effect on health particularly on the lungs. The study explores an approach that combines Internet of Things (IoT) technology with High Efficiency Particulate Air (HEPA) filters air cleaner to monitor and visualize the effects of air pollution on lung health, highlighting the significant damage that poor air quality causes particularly on the lungs graphically.

Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques.

Objectives: Rwanda reported a stunting rate of 33% in 2020, decreasing from 38% in 2015; however, stunting remains an issue. Globally, child deaths from malnutrition stand at 45%. The best options for the early detection and treatment of stunting should be made a community policy priority, and health services remain an issue.

Compartmental mathematical modelling of dynamic transmission of COVID-19 in Rwanda.

Objectives: Mathematical modelling is of interest to study the dynamics of coronavirus disease 2019 (COVID-19), and models such as SEIR (Susceptible-Exposed-Infected-Recovered) have been considered. This article describes the development of a compartmental transmission network model - Susceptible-Exposed-Quarantine-Infectious-Infectious, undetected-Infectious, home-based care-Hospitalized-Vaccinated-Recovered-Dead - to simulate the dynamics of COVID-19 in order to account for specific measures put into place by the Government of Rwanda to prevent further spread of the disease.

Methods: The compartments of this model are connected by parameters, some of which are known from the literature, and others are estimated from available data using the least squares method.

Tackling pandemics in smart cities using machine learning architecture.

With the recent advancement in analytic techniques and the increasing generation of healthcare data, artificial intelligence (AI) is reinventing the healthcare system for tackling pandemics securely in smart cities. AI tools continue register numerous successes in major disease areas such as cancer, neurology and now in new coronavirus SARS-CoV-2 (COVID-19) detection. COVID-19 patients often experience several symptoms which include breathlessness, fever, cough, nausea, sore throat, blocked nose, runny nose, headache, muscle aches, and joint pains.

A Data-Driven Predictive Machine Learning Model for Efficiently Storing Temperature-Sensitive Medical Products, Such as Vaccines: Case Study: Pharmacies in Rwanda.

Temperature control is the key element during medicine storage. Pharmacies sell some medical products which are kept in fridges. The opening and closing of the fridge while taking some medicine makes the outside hot air enter the fridge, which will increase the inner fridge temperature.