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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. Utilizing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models, nonvisual measurements of Total Volatile Organic Compounds (VOCs) and ultrasound emissions from maize plants were captured and analyzed. A controlled experiment was conducted on four maize varieties, and the data obtained were used to develop and validate a hybrid CNN-LSTM model for VOC classification and an LSTM model for ultrasound anomaly detection. The hybrid CNN-LSTM model, enhanced with wavelet data preprocessing, achieved an F1 score of 0.96 and an Area under the ROC Curve (AUC) of 1.00. In contrast, the LSTM model exhibited an impressive 99.98% accuracy in identifying anomalies in ultrasound emissions. Our findings underscore the potential of IoT sensors in early disease detection, paving the way for innovative disease prevention strategies in agriculture. Future work will focus on optimizing the models for IoT device deployment, incorporating chatbot technology, and more sensor data will be incorporated for improved accuracy and evaluation of the models in a field environment.
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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. PARTICIPANTS: 4763 participants were randomly sampled following the sampling frame used for the recent Rwanda Demographic Health Survey. Participants were aged between 22 years and 94 years. OUTCOMES: The participants' compliance with three preventive measures (wearing a face mask, washing hands and social distancing) was the main outcome. METHODS: From 14 February 2022 to 27 February 2022, a cross-sectional survey using telephone calls was conducted. Study questionnaires included different questions such as participants' demographics and compliance with COVID-19 preventives measures. Verbal consent was obtained from each participant. The compliance on three main preventive measures (wearing a mask, washing hands and social distancing) were the main outcomes. Univariate and multivariable logistic regression analyses were performed to evaluate factors associated with compliance (age, gender, level of education, socioeconomic status). RESULTS: Compliance with the three primary preventive measures (washing hands 98%, wearing a mask 97% and observing social distance 98%) was at a rate of 95%. The respondents' mean age was 46±11 SD (range 22-98) years. In addition, 69% were female and 86% had attended primary education. Bivariate and regression analyses indicated a significant association among the three primary preventive measures (p<0.05). The results showed factors associated significantly between the different models (p<0.05): proper mask use and social distancing in the hand washing model; hand washing, social distancing, avoiding handshakes and not attending gatherings in the proper mask use model; hand washing and avoiding handshakes in the social distancing model. CONCLUSION: Compliance with the three key preventive measures against COVID-19 was high in the Rwandan community and these measures were interdependent. Therefore, the importance of all three measures should be emphasised for effective disease control.
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COVID-19 , Desinfección de las Manos , Máscaras , Distanciamiento Físico , SARS-CoV-2 , Humanos , COVID-19/prevención & control , COVID-19/epidemiología , Rwanda/epidemiología , Femenino , Adulto , Masculino , Estudios Transversales , Persona de Mediana Edad , Máscaras/estadística & datos numéricos , Anciano , Adulto Joven , Anciano de 80 o más Años , Encuestas y Cuestionarios , Cooperación del Paciente/estadística & datos numéricosRESUMEN
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. To achieve this, a 3D display of the lungs is modelled using HEPA filters, which changes colour based on the air pollutant concentrations detected by IoT-based sensors. The collected air quality data is then transmitted to Thingspeak, a visualization platform for further analysis. It is observed that the colour of the 3D lung display changed to black over time as air pollutant concentrations increased which in our study is an indicator of unhealthy lung. The study presents an innovative approach to visualize the effects of air pollution on lung health using IoT and HEPA filters air cleaner, which could have significant implications for public health policies aimed at mitigating the harmful effects of air pollution, particularly on lung health.
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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. Hence, this research aimed to develop a model for predicting stunting in Rwandan children. METHODS: The Rwanda Demographic and Health Survey 2019-2020 was used as secondary data. Stratified 10-fold cross-validation was used, and different machine learning classifiers were trained to predict stunting status. The prediction models were compared using different metrics, and the best model was chosen. RESULTS: The best model was developed with the gradient boosting classifier algorithm, with a training accuracy of 80.49% based on the performance indicators of several models. Based on a confusion matrix, the test accuracy, sensitivity, specificity, and F1 were calculated, yielding the model's ability to classify stunting cases correctly at 79.33%, identify stunted children accurately at 72.51%, and categorize non-stunted children correctly at 94.49%, with an area under the curve of 0.89. The model found that the mother's height, television, the child's age, province, mother's education, birth weight, and childbirth size were the most important predictors of stunting status. CONCLUSIONS: Therefore, machine-learning techniques may be used in Rwanda to construct an accurate model that can detect the early stages of stunting and offer the best predictive attributes to help prevent and control stunting in under five Rwandan children.
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Desnutrición , Femenino , Humanos , Niño , Lactante , Rwanda/epidemiología , Factores de Riesgo , Estudios Transversales , Trastornos del Crecimiento/diagnóstico , Trastornos del Crecimiento/epidemiologíaRESUMEN
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. For the stability of the model, equilibrium points were determined and the basic reproduction number R 0 was studied; R 0 is an indicator for contagiousness. Results: The model showed that secondary infections are generated from the exposed group, the asymptomatic group, the infected (symptomatic) group, the infected (undetected) group, the infected (home-based care) group and the hospitalized group. The formulated model was reliable and fit the data. Furthermore, the estimated R 0 of 2.16 shows that COVID-19 will persist without the application of control measures. Conclusions: This article presents results regarding predicted spread of COVID-19 in Rwanda.
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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. This paper proposes an artificial intelligence (AI) algorithm that predicts the rate of likely survivals of COVID-19 suspected patients based on good immune system, exercises and age quantiles securely. Four algorithms (Naïve Bayes, Logistic Regression, Decision Tree and k-Nearest Neighbours (kNN)) were compared. We performed True Positive (TP) rate and False Positive (FP) rate analysis on both positive and negative covid patients data. The experimental results show that kNN, and Decision Tree both obtained a score of 99.30% while Naïve Bayes and Logistic Regression obtained 91.70% and 99.20%, respectively on TP rate for negative patients. For positive covid patients, Naïve Bayes outperformed other models with a score of 10.90%. On the other hand, Naïve Bayes obtained a score of 89.10% for FP rate for negative patients while Logistic Regression, kNN, and Decision Tree obtained scores of 93.90%, 93.90%, and 94.50%, respectively.
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COVID-19 , Pandemias , Inteligencia Artificial , Teorema de Bayes , Ciudades , Humanos , Aprendizaje Automático , SARS-CoV-2RESUMEN
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. When the frequency of opening and closing of the fridge is increased, the temperature may go beyond the allowed storage temperature range. In this paper, we are proposing a model with the help of machine learning that will be used in multiple chambers fridges to keep indicating the time remaining for the inner temperature to go beyond the allowed range, and if the time is short, the system will propose to the pharmacist not to open that particular room and proposes a room that has enough time slots (time to reach the upper limit temperature). By using training data got from a thermoelectric cooler-based fridge, we constructed a multiple linear regression model that can predict the required time for a given room to reach the cut-off temperature in case that room is opened. The built model was evaluated using the coefficient of determination R 2 and is found to be 77%, and then it can be used to develop a multiple room smart fridge for efficiently storing highly sensitive medical products.