The high ownership cost of mining equipment mean that downtimes are expensive and should be avoided with smart and efficient maintenance planning. Modern mines have large data sets on equipment performance and
which is of little (if any) value, and that which is highly sensitive and confidential. When data is stored—whether received, created or amended—it should always be classified at an appropriate sensitivity level. Systems may then be used to catch keywords and terms used in the classification...
Code Issues Pull requests Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas machine-learning awesome deep-learning dataset forecasting classification image-classification awesome-list multi-label-classification series-forecasting Updated Mar 13, 2023 Morvan...
Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or...
In data mining, classification and prediction are mostly applied for future planning and analysis of current trends. Data mining is a wider concept that contains different steps: Firstly data is pre-processed, where missing values are normalized, missing labels are rectified and noise will be ...
MD, USA; 2Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel Data mining is a powerful bioinformatics strategy that has been successfully applied in vitro to screen for gene-expression profiles predicting toxicological or carcinogenic response ('class predictors')....
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TS
Frontiers in Robotics and AI2.900Frontiers Media S.A.2296-9144 Systems & Control Letters2.100Elsevier0167-6911 International Journal of Reliability, Quality and Safety EngineeringWorld Scientific0218-5393 cIEEE Transactions on Big Data7.500IEEE2332-7790 ...
SER systems typically make use of classification algorithms. A classification algorithm requires an input X, an output Y, and a function that maps X to Y as in f(X)=Y. The learning algorithm approximates the mapping function, which helps predict the class of new input. The learning algorith...
2012,Library Classification Trends in the 21st Century Chapter Embedded Software in Real-Time Signal Processing Systems: Design Technologies BUse of the Classification Scheme Theclassification schemeintroduced above can be used for different purposes. First of all, it can be used to characterize a ...