<span>Using a camera to monitor an object or a group of objects over time is the process of object detection. It can be used for a variety of things, including security and surveillance, video communication, traffic light detection (TLD), object detection from compressed video in public places. In recent times, object tracking has become a popular topic in computer science particularly, the data science community, thanks to the usage of deep learning (DL) in artificial intelligence (AI). DL which convolutional neural network (CNN) as one of its techniques usually used two-stage detection methods in TLD. Despite all successes recorded in TLD through the use of two-stage detection methods, there is no study that has analyzed these methods in experimental research, studying the strength and witnesses by the researchers. Based on the needs this study analyses the applications of DL techniques in TLD. We implemented object detection for TLD using 5 two-stage detection methods with the traffic light dataset using a Jupyter notebook and the sklearn libraries. We present the achievements of two-stage detection methods in TLD, going by standard performance metrics used, FASTER-CNN was the best in detection accuracy, F1-score, precision and recall with 0.89, 0.93, 0.83 and 0.90 respectively.</span>

In this study, an attempt was made using machine learning techniques to discover knowledge that will assist policy makers in taking decisions that will ensure that the sustainable development goals on Health is met. Agglomerative Hierarchical clustering was used to cluster the states by personnel information (number of doctors, community health workers, nurses and midwives), this was visualized using a dendrogram. The Exploratory analysis revealed that it is only community health workers that are well distributed in all the states, the North West states have the least number of hospitals offering antenatal services. Random Forest model was used to generate a feature importance to determine the important attributes that determined the availability of maternal health delivery services in a hospital, an important discovery was the fact that the availability of doctors does not in any way determine the availability of maternal health delivery services but rather community health workers, nurses and midwives are the major determinants. Random Forest algorithm was also used to classify hospitals offering maternal health delivery services and the result compared with Logistic Regression, Bagging and Boosting. The evaluation metrics used were accuracy, precision and recall. For accuracy and precision, Random Forest performed best while for recall it performed poorly compared to all the other algorithms.