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Sectors of the mining industry where AI, machine learning, and autonomous technologies implementation is currently in progress
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The implementation of artificial intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with autonomous trucks. Artificial intelligence, machine learning, and autonomous technologies provide many economic benefits for the mining industry through cost reduction, efficiency, and improving pr...
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Two different tire configurations consisting of a dual tire and a super single wide tire having different range and distribution of contact pressures have been analyzed. Along with the effect of speed on development of pavement damage at speeds of 5, 50 and 80 km h ⁻¹ under zero and uniform wander modes. Results show that at super slow speeds of 5...
Citations
This study investigated the application of machine learning to optimise the pumping load shift of a complex dewatering system in a deep-level mine, aiming to reduce energy costs associated with the dewatering process, which consumes an average of 14% of the mine’s electricity. Traditional practices, reliant on human control and simulations, often lead to inconsistent savings and occasional losses. The study employed multivariate linear regression (MLR) and extreme gradient boosting (XGBoost) on a mine dewatering system, to identify important parameters influencing the pumping load shift performance. Critical parameters significantly impacting the energy consumption of the dewatering system were identified by the best-performing model, XGBoost. Implementing a pumping schedule based on XGBoost insights resulted in consistent load shifting and enhanced energy cost savings. These findings highlight the potential of machine learning in comprehending and optimising complex systems in deep-level mines, with the case study approach proving effective in quantifying and validating real-world impacts. This approach could offer substantial energy savings through data-driven decision-making.
A phenomenal surge in data generation, accessibility, storage, and processing hardware capacitates the artificial intelligence (AI)-based learning algorithms to solve the nature-inspired complex problems in day-to-day life. Fine-tuned intelligent models can outperform human involvement in a repertoire of domains, significantly impacting performance and productivity metrics. AI technologies are being utilized nowadays in several fields of application in healthcare, such as predictive modeling for neurological disorders, bioinformatics, surgical procedures, physical rehabilitation, medical robots, and management of clinical data in hospitals. With the ever-evolving nature of state-of-the-art learning models, the influence of AI in healthcare is constantly changing. This necessitates the need for a comprehensive review of the current scenario of AI applications in healthcare. In this work, a systematic review of AI technologies in the different levels of healthcare, ranging from benign research to full-fledged clinical setups, has been carried out for the last 10 years. Primarily, the integration of AI tools in three broad categories of healthcare, i.e., clinical settings, biomedical computations, and pharmaceutical industries, is introduced. Thereafter, this work provides an overview of fundamental and advanced forms of different AI techniques with recent developments. A PRISMA statement is provided to show the inclusion and exclusion criteria of the articles reviewed while conducting the systematic review. Based on articles selected and reviewed, the application of AI in different clinical settings is presented in a comprehensive yet systematic manner. Furthermore, the ethical and legal issues about AI implementation in healthcare domains are reviewed. The challenges and possible opportunities of deploying AI in real-life settings are discussed. Finally, the conclusion of the current work is presented. This review will engage the researchers to understand the merits and demerits of applying AI for healthcare applications.
