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During the pandemic, it has been seen that the global population follows the guidelines issued by the health organization regarding wearing face masks, but some people do not take care of this and do not use masks. The objective of the proposed system, Wollega University Face Mask Detection System (WUFMDS), is to restrict people who are not wearing a mask on the door side by identifying the face mask from the face or open the door if the incoming person is wearing the mask. This system is based on the Internet of Things (IoT) and a Deep Learning algorithm called Convolutional Neural Network (CNN). For this purpose, images with and without masks were collected as samples from the university. The CNN algorithm is used to detect the mask and classify it as with or without masks. The IoT module controls the door operation based on the classification response sent to the IoT module by the CNN algorithm. The system was tested lively with the dummy door system in order to ensure the functionality of the face mask detection system and developed software applications for the system model are working as defined objectives. Our model had 99.36% accuracy with the training dataset and 99.29% accuracy with the validation set. Hence, the proposed system could be used for the automatic identification and classification of masks on the face and to operate the door to allow the person who is wearing the mask to pass through while keeping it closed when no mask is found on the face.

Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received.