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Therefore, we are dividing our review of different machine learning and data mining approaches into four main sections depending on the main field of application: process parameters, microstructure, mechanical properties and performance.
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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 ... especially as candidate materials for the storage of natural gas or hydrogen for vehicular applications. Most recently, NU-1501 …
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Considering the growing use and potential of data mining and machine learning in fish production systems, in this review, we have consolidated and presented scientific information on the application of data mining and machine learning in fisheries and aquaculture, with insights on the various challenges and opportunities. 2.
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The applications of machine learning (ML) ... Liu et al. [112] also developed a machine state monitoring platform based on AE sensors for FDM machine states identification using unsupervised learning. ... However, with the emergence of advanced data mining techniques, ML is considered promising for modelling and uncovering the …
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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 utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, …
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Machine learning applications are increasingly becoming apparent in facets of underground mining areas such as exploration, exploitation, reclamation and associated mine processes and activities as noted by Jung and Choi (2021) in their recent systematic review of machine learning applications in the mining industry. In mineral exploitation ...
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Within machine learning frameworks, we will test whether the quantified deviation from our recently-developed normative brain atlases can detect abnormal regions and predict outcomes for individual patients as accurately as, or even more accurately, than human experts. Trial Registration Not applica …
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Enhancing the government accounting information systems using social media information: An application of text mining and machine learning. Author links open overlay panel Huijue Kelly Duan a, Miklos A. Vasarhelyi b, Mauricio Codesso c, Zamil Alzamil d. Show more. Add to Mendeley. ... (Brown-Liburd et al., 2019) in conjunction …
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Discover the transformative potential of AI and Machine Learning in Mining Safety and Risk Management. From proactive risk assessment to predictive safety incident prevention, explore how these innovative technologies are revolutionizing the mining industry and fostering a culture of safety. Embrace the future of safer mining operations …
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Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions.
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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.
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Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning.
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Deep Learning solutions have yielded outstanding results in different machine learning applications, including speech recognition -, computer vision,,, and natural language processing -. A more detailed overview of Deep Learning is presented in Section "Deep learning in data mining and machine learning".
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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 …
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With the continuous development of information technology, machine learning and data mining have gradually found widespread applications across various industries. These technologies delve deeper into uncovering intrinsic patterns through the application of computer science. This trend is especially evident in today's era of …
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The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen …
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For example, Jeihouni et al. [43] compared five data mining algorithms, that is, ordinary decision tree, RF, ... The analysis of historical data to optimize wastewater treatment systems is a practical application of machine learning. Fang et al. [83] simulated anaerobic, anoxic, and oxic conditions with an SVM and an adaptive genetic algorithm ...
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