Mahdevari et al. (2017) predicted the stability of gate roadways in longwall mining using the ANN. Katkuri et al. (2019) used a neural network-assisted analysis for longwall gate …

Applications of machine learning methods in metal–organic framework research are discussed. ... researchers have started to use various data mining techniques to obtain information from numerous existing text publications, which can be an ... In a pioneering study be He et al. [151], application of ML methods for the prediction of …

Rushikesh Battulwar, Ph.D. Dr. Rushikesh Battulwar is a data engineer at Rio Tinto. He completed his Ph.D. and M.S. degrees in mining engineering from University of Nevada Reno (UNR) with a research focus on the application of aerial photogrammetry and computer vision in discontinuity characterisation from rock mass surface models.

Advances in experimental geomechanics, combined with the growing collections of geologic rock data and insights from rock engineering, led to applications …

Penggunaan machine learning terkait dengan prediksi prestasi belajar, prediksi nilai mahasiswa (Ren et al., 2019a), prediksi kinerja mahasiswa (Wati et al., 2018), hingga memberikan rekomendasi ...

Application of machine learning in atmospheric pollution research: A state-of-art review. Author links open overlay panel Zezhi Peng a, Bin Zhang a, ... (Fang et al., 2016). However, the application of ground-based observations in large-scale monitoring of air pollution is limited by the sparse and uneven distribution of air quality monitoring ...

Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA …

The Apriori algorithm is a typical association rule-based mining algorithm, which has applications in sequence pattern mining and protein structure prediction. Many machine learning algorithms in data …

Machine learning (ML) and data mining (DM) applications came to existence in this sector nearly two decades ago to solve manufacturing problems. Intelligent systems to support effective decision-making (Cheng et al., 2018, Kujawinska et al., 2018) programs to schedule simultaneous production line (Priore, Ponte, Puente, & Gómez, …

Machine vision is the application of machine learning techniques to images and video, rather than numerical data from plant sensors. Image data presents a …

The article aims to show the ways in which data mining and machine learning methods enhance the effectiveness of digital marketing strategies. The author describes the capabilities of business analytics, such as data and application integration solutions, which...

The petroleum industry involves systems for oil field exploration, reservoir engineering, drilling and production engineering. Oil and gas is also the fuel source for other chemicals, including pharmaceutical drugs, solvents, fertilizers, pesticides, and plastics (Anderson, 2017).If prices of fossil fuels continues to rise, fossil fuel companies will need …

Bock et al. Machine Learning in Materials Mechanics identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner.

The mining industry is undergoing a transformative shift driven by the rapid advancement and adoption of automation technologies. This paper provides a comprehensive overview of the current state of automation in mining, examining the technological advancements, their applications, and the prospects of automation in this …

The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies ...

A Real-World Application of Process Mining for Data-Driven Analysis of Multi-Level Interlinked Manufacturing Processes ... manufacturing process comprises interlinked events at the superordinate business process level and at the subordinate machine level, making its analysis based on PM challenging. ... Kotzab et al. (Ed.) â€" …

The various algorithms of Artificial Intelligence app are used to build precise machines that are able to detect minor diseases inside the human body. Also, Artificial Intelligence uses the medical history and current situation of a particular human being to predict future diseases. ... In conclusion, the applications of AI are vast and ...

Yuxin Du . et al.: Preparation of Papers for IEEE Access (June 2023) VOLUME XX, 2017 3 . Table 1. Review studies related to machine vision applications in mining.

Application of Artificial Intelligence and Machine Learning in Mining. Submission status. Closed. Submission deadline is Pending. Digitalization in the mining …

Examples of the successful application of this approach are the development of a new nickel-based superalloy for high temperature application (Conduit et al., 2014) as well as the predictive and …

Several successful applications of machine learning in various stages of the drug development pipeline in pharmaceutical companies have been published. ... would otherwise be inaccessible. Recent advances in natural language processing (NLP), an ML approach applied to text mining, have enabled more effective data mining to identify …

the application of machine learning and data mining in manufact uring has carried on the overview of the system, and introduces the research status both at home and abroad, and specify the ...

The most important step in the entire KDD process is data mining, exemplifying the application of machine learning algorithms in analyzing data. A complete definition of KDD is given by Fayyad et al. [5]: KDD is the nontrivial process identifying valid, novel, potentially useful, and ultimately understandable patterns in data.

It is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Machine learning tools represent key enablers …

Application 2: Automated Drillers and Intelligent Drilling Systems. For any mining operation, drilling and blasting are two indispensable processes that take place daily. Unfortunately, they also happen to be some of the most dangerous. It typically involves an iterative process of drillers creating holes in hard surfaces.

mineral processing equipment. nodulizers. pelletizers. trams. Other types of mining machines are also available. Selecting mining machines requires an analysis of …

For example, McKinsey estimates that the use of AI will lead to between $290-$390 billion in annual savings for those mining copper, iron ore, natural gas, coal, and crude oil by 2035. With these kinds of …

For more discussions on the applications of machine scheduling theory to mining management, please refer to a comprehensive interdisciplinary literature review on mine supply management (Zeng et al., 2021) and recent agendas in mining equipment management (Liu et al., 2022a). In a word, there are still rare academic papers on …

Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that …

Applications of Machine Learning and Data Mining in SpeedWise® Drilling Analytics: A Case Study Zheren Ma, Ali Karimi Vajargah, Hanna Lee, Hamed Darabi, and David Castineira, Quantum …