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Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively. [ABSTRACT FROM AUTHOR]

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper. [ABSTRACT FROM AUTHOR]

Unlike previous studies where models/methods used in determining the carbon emission are presented, in this paper, a detailed analysis of the causes, trends, and solutions to carbon emission in Africa is presented. Economic development plays a crucial role in the well-being of a country/continent, thereby, affecting energy consumption. The impact of economic development on Africa's carbon (CO 2) emissions trend is first investigated. After which, three neural network models are developed to predict the future trend of total CO2 emission in the continent. Then, the use of renewable energy (RE) sources for power generation is analyzed/proposed as a viable solution for CO2 emission reduction in Africa. Finally, the impact of battery electric vehicles (BEVs) integration and hydrogen production in maximizing RE production in Africa's largest economy is analyzed. Secondary data of the economic indicators for twenty-five different African countries have been used to justify the effect of economic development on their carbon emission. From the results of the analyses, gross national income and carbon emissions in all sectors were found to be significantly positively correlated. That is, as national wealth across Africa increases, carbon emissions in the continent increase. Also, the predicted total annual CO2 emission showed that most countries will witness an increase in total CO2 emission by 2022 in comparison to 2018. The proposed RE-based method for power generation showed that the CO2 emission from the power industry can be reduced to zero for an African country. Nevertheless, the use of BEVs and the production of hydrogen will be integral in achieving this. • Integration of EVs and hydrogen production to maximize RE integration. • The impact of GNI and other economic indicators on CO2 emission. • Development of three neural network models for CO2 emission prediction. • The sole use of RE for electricity generation in Africa. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present to the scientific community a new dataset derived from existing literature on soil respiration in Africa. The data has thus been obtained by searching for records in peer review papers and grey literature. The main search engines used are: Scientific Citation Index (SCI) database, ISI Science web and Google scholar. This data description paper has greatly advanced the number of data points on soil respiration in Africa from 4 in 2010 to 62 in 2014. The new data points are culled from 47 peer review publications and grey literature reports. The data lends its self to a lot of possible analytical methods such as correlation analysis, multiple linear regressions, artificial neural network analysis and process base modeling. The overall conclusion that can be drawn here is that this paper has greatly advanced the availability of soil respiration data in Africa by presenting all the available records that before now were only reported in different studies. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration (ET0) is essential for the computation of crop water requirements, irrigation scheduling, and water resources management. In this context, having a battery of alternative local calibrated ET0 estimation methods is of great interest for any irrigation advisory service. The development of irrigation advisory services will be a major breakthrough for West African agriculture. In the case of many West African countries, the high number of meteorological inputs required by the Penman-Monteith equation has been indicated as constraining. The present paper investigates for the first time in Ghana, the estimation ability of artificial intelligence-based models (Artificial Neural Networks (ANNs) and Gene Expression Programing (GEPs)), and ancillary/external approaches for modeling reference evapotranspiration (ET0) using limited weather data. According to the results of this study, GEPs have emerged as a very interesting alternative for ET0 estimation at all the locations of Ghana which have been evaluated in this study under different scenarios of meteorological data availability. The adoption of ancillary/external approaches has been also successful, moreover in the southern locations. The interesting results obtained in this study using GEPs and some ancillary approaches could be a reference for future studies about ET0 estimation in West Africa. [ABSTRACT FROM AUTHOR]

The accurate representation of the ionospheric electron density in 3-dimensions is a challenging problem because of the nature of horizontal and vertical structures on both small and large scales. This paper presents the development of a global three-dimensional (3-D) electron density reconstruction based on radio occultation data during 2006–2019 and neural networks. We demonstrate that the developed model based on COSMIC dataset only is capable of reproducing different ionospheric features when compared to independent datasets from ionosondes and incoherent scatter radars (ISR) in low, middle and high latitude regions. Following some existing modelling efforts based on similar or related datasets and technique we divided the problem into fine resolution grid cells of 5 ∘ × 1 5 ∘ (geographic latitudes/longitudes) followed by development of the neural network subroutine per cell and later combining all the 864 sub-models to compile one global model. This approach has been demonstrated to be appropriate in enabling neural networks to learn, reproduce and generalise local and global behaviour of the ionospheric electron density. Based on ISR data, the 3D model improves maximum electron density of the F2 layer (NmF2) prediction by 10%–20% compared to IRI 2016 model during quiet conditions. For estimation of ionosonde ordinary critical frequency of the F2 layer (foF2) in 2009 at 1200 UT (universal time), the developed 3-D model gives average root mean square error (RMSE) values of 0.83 MHz, 1.06 MHz and 1.16 MHz compared to the IRI 2016 values of 0.92 MHz, 1.09 MHz and 1.01 MHz over the Africa–European, American and Asian sectors respectively making their performances statistically comparable. Compared to ionosonde data, the IRI 2016 model consistently shows a better performance for the hmF2 modelling results in almost all sectors during the investigated periods. • A 3D electron density reconstruction model is developed based only on radio occultation data and artificial neural networks. • When benchmarked on the ISR, the developed model improves NmF2 prediction by 10%–20% compared to IRI 2016 model during quiet conditions. • We suggest that the new model is applicable for future assimilation efforts especially over areas which are not sufficiently covered by ground-based instrumentation such as ionosondes. [ABSTRACT FROM AUTHOR]

In this paper, laser-induced breakdown spectroscopy (LIBS) combined with artificial neural network (ANN) was investigated to classify four species of wood samples (Africa rosewood, Brazil bubinga, Myanmar padauk, and Pterocarpus erinaceus). The wood samples were ablated by laser pulses to generate plasma emission, which was measured by a spectrometer and transmitted into a computer for further data analysis. The feature spectral data were selected out based on loadings of principal component analysis (PCA) and normalized using the sum of all feature spectra data. The ANN model was built based on the feature spectral data to classify the wood species. The relationship between correct classification rate (CCR) and settings of ANN was discussed. The CCR of ANN model for test set data achieved 100% with multilayer perceptron network and Broyden–Fletcher–Goldfarb–Shanno iterative algorithm. This result was also compared with the CCRs of PLS-DA, KNN, and SIMCA model for test set (82.5%, 95.83%, and 51.67%, respectively). Using the ratio between feature variables to recognize the species of wood was also discussed. The experimental results demonstrated that LIBS integrated with ANN could be applied for analyzing and recognizing wood species. [ABSTRACT FROM AUTHOR]