To keep up with the new technology modernization and the profit in shake of investors and stakeholders and importantly for the nation, and to ensure health and safety mining industry needs to approve new-age autonomous technologies and intelligent system in their field. Integration of Artificial Intelligence, Machine Learning, Internet of Things (IoT) and Automation are the keys to the 4th revolution in mining industry. This paper presents the overview of recent research upon artificial intelligence enhanced techniques and possibilities in mining operations and mining related domains. There is also a brief about the recent autonomous techniques and equipment in mining industry. Implementations and possibilities of artificial intelligence in safety and accident analysis of mining operations are sincerely detailed. Computer vision and spatial image analysis is also discussed as the recent advancement of deep learning and pattern recognition. Other mining related implementations of intelligent systems includes fragment analysis of ores, intelligent ventilation, on-site mineral processing simplification, digital twinning, mineral exploration, mineral price forecasting, mining equipment selection, post-mining land reclamation and scheduling. This paper also notes the detailed obstacles for implementing intelligent systems in mining industry.

Name of a person reflects a lot about his origin, gender and more. Naming is a fragment of language and can be an identifier as it contains patterns that could be learned in order to classify individual’s gender based on their names (first name and middle name). In this paper a multi-national, multi-ethnicity and multi-language names (first name) based classification or guessing of gender is per-formed by popular Machine Learning and Deep Learning models. Recent advancements of various Artificial Intelligence inspired algorithms offer various options for text classification. This paper investigates first name based gender identification on a dataset of 84,899 first names and middle name initials with gender labels. The algorithms tested on this task are Support Vector Classifications (linear and non-linear), Nearest Neighbors, Decision Tree, Random Forest, Multi-layer Perceptron, Long Short-Term Memory (LSTM), Bidirectional LSTM, Convolutional Neural Network and a simple embedding based deep learning model (SimpleText). Among the models the significant results out of this study is that linear model (94.62%) performs equivalent with the complex machine learning models and the simple embedding based deep learning network SimpleText outperforms (94.67%) all the models, even the other Recurrent and Convolutional networks