dataesr/scientific-paragraphs-categorization huggingface dataset:
• Check that the parcel is among the least well reconstructed of its ground truth class, i. e. , 1 N plot * ∑ N plot i=1 MSE(X (plot,i) , CAE class (X (plot,i) )) > τ class. • Check that the parcel is among the best reconstructed of its new class, i. e. , 1 N plot * ∑ N plot i=1 MSE(X (plot,i) , CAE new_class (X (plot,i) )) < τ new_class. This double-check strategy ensures that relabels are performed only with high confidence. To validate the presented methodology on the dataset of Sevilla crops, we first turn to quantitative validations using the controlled disruption of labels.
Quantitative Validation Scheme: A Controlled Disturbed Environment To measure the label correction performance of our methodology, we need the ground truth of introduced label errors: i. e. , errors that we produce ourselves, from original labels in which we have a high degree of trust. For that, we need to filter out from this half of the dataset any potential mislabel and mis-split crop. Illustrated in Figure  11 , the method used to extract trustworthy crops is based on the same method as the one used to extract suspicious crops: we keep every crop with more than 75% of their time series considered as trustworthy, i. e. , where the CAE performing the best reconstruction in terms of mean-square error is of the same class as the input time series. The threshold of 75% is empirical and is also deduced from the observation that edge cases anomalies may represent up to 25% of the area of a crop.
As a result, out of the 1588 available crops, we keep 1008, for which we have high confidence in their label. After building our dataset of high-confidence labels, we now randomly introduce label errors repeatedly in different amounts. In total, we run ten experiments. We introduce errors for 1, 5, 10, 15, 20, 25, and 30% of the labels and run our methodology on these error-riddled datasets. We run these experiments ten times for statistical relevance of the retrieved performance.
Results, and Their Qualitative Validation Now that we have validated our methodology quantitatively for the relabeling, using numerical criteria, we apply FARMSAR to the other half of the dataset and extract label errors and relabels. Then, we study the suggested relabels, and the detected mis-splits using optical imagery for qualitative validation of our method. A distribution of the embedding values of the class-expert CAEs used in this analysis are provided in Appendix A, but are not used in this study, as they do not offer a clear distinction of label anomalies.
Results After applying the FARMSAR methodology to the second half of the dataset, left untouched and containing probable mislabels, we can extract suspicious crops, correct potential mislabels, and detect mis-split crops. At first, we isolate 40,000 suspicious time series, out of the approximately 600,000 that make up this half of the dataset. Then, according to Figure  13:  • FARMSAR discovers 3 mis-split crops; • our method classifies 81 crops as suspicious mislabels (around 5% out of the approx. 1600 crops of this half of the dataset). FARMSAR relabels 44 crops confidently, and 37 crops are to be inspected for potential erroneous labels. mis-split corrected uncorrected suspicious crop others We also can observe in Figure  13a  common pattern within the "suspicious" plots, corrected or not.
Acknowledgments: The authors would like to thank  Juan M. Lopez-Sanchez  for providing us with the reference data originating from the  Regional Government of Andalucía  and the  Spanish Agrarian Guarantee Fund (FEGA)  and for the preprocessed multitemporal Sentinel-1 images. The authors would also like to thank him for the thoughtful scientific discussions. We also want to thank the reviewers for their feedback and help to improve the overall quality of this work.
Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning.
Experiments were initially conducted using RGB values of pixels, and in the second series of experiments, RGB images were converted to a hue, saturation, and value (HSV) space closer to the human conceptual understanding of color. A feedforward MLPNN with backpropagation training algorithm is used. The activation function f in the hidden layer is a tangent sigmoid, and in the output layer, f is a logarithmic sigmoid function, and the neural network is implemented using MATLAB's neural network toolbox. Ref. [16]  acquired images and marked a random set of points on the analyzed images. For each point, its position (XY coordinates) was recorded, as well as the classification determined by the researcher. Based on pattern recognition methods (NN, KNN) and artificial neural network algorithms (multilayer perceptron-MLP), a multidimensional feature space was defined to achieve an automatic classification of the structures.
Ref. [17]  collected sample image sets; used photo-editing software such as Photoshop to uniformly adjust parameters, image segmentation, and annotation of rock images; and then applied data enhancement methods such as mirror flip and random cropping to the images. Based on the deep learning system TensorFlow, a targeted Unet convolutional neural network model was designed to effectively and automatically extract the deep feature information of ore minerals and realize the intelligent recognition and classification of ore minerals under the mirror. Ref. [18]  performed data acquisition with a large amount of data and diverse features, enhanced it with data, and expanded the original dataset using both image flipping and image scale transformation. ResNet-18 was selected as the convolutional neural network and SGD was used as the optimizer to implement a deep-learning-based intelligent mineral recognition method.
Ref. [19]  selected the more important and common rock and mineral images, performed extraction of internal square slices from the raw rock images, and performed operations to expand the dataset such as image flipping and rotation. The ResNet-50 model was used as the base model and, on this basis, a python-and html5-based rock and mineral intelligent recognition tool is developed. This recognition tool uses a cloud+end service model, which is a front-end service for the user's browser and a back-end service for the cloud server, respectively. This paper attempts to introduce the research advances in this field, analyze the research methods and basic paths of identification of minerals based on artificial intelligence, show the existing specific research works, summarize these works, and provide an outlook on the research in this field in an attempt to provide a reference for scholars to carry out relevant research.
Related Work In this section, some of the work related to the mineral intelligence identification survey is presented. To present, there have been several reviews of work on mineral identification for specific dataset types. For example, Ref. [20]  summarized automatic analysis systems for minerals based on scanning electron microscopy. Ref. [21]  found that when targeting soot identification, ultrasonic and electromagnetic wave detection techniques had the lowest bureau limits and infrared imaging identification techniques had the highest bureau limits, where the working environment had the most obvious limitations on infrared imaging identification and coal rock characteristics had the most obvious limitations on reflection spectrum identification and process signal monitoring identification.
However, these works are limited to the form of scanning electron microscopy data and do not detail how the dataset is preprocessed by the automated mineral analysis system and what type of classifier is used. A further discussion of which identification methods and techniques are most appropriate for different forms of mineral datasets is presented. Ref. [22]  discusses the application of machine learning to intelligent mineral identification and does not further divide the methods based on the differences and commonalities of methods such as machine learning, nor does it address the application of other technical approaches of artificial intelligence to mineral identification. Ref. [6]  describes mineral processing challenges that may be addressed by artificial intelligence, including increased productivity, minimal ecological impact, and identification in mineral flotation.
Basic Process of Intelligent Mineral Identification Despite the variety of research methods used to carry out intelligent identification of minerals, the basic process is broadly consistent and can be summarized in four stages: (1) Acquisition of mineral datasets. Instruments are used to acquire descriptive data of minerals, including images, graphics, and physical data. Multiangle photographic images of minerals, microscopic images of thin sections, and spectral images of minerals all become important datasets for intelligent identification. (2) Preprocessing of mineral datasets. Preprocessing the dataset by partitioning SEM images, dimensionality reduction, and image noise reduction to improve the accuracy of the classifier in the next step. (3) Training the mineral identification model.
Artificial intelligence is used to train the discriminative model for mineral identification. Despite the short-term development of artificial intelligence, it has been widely used in the exploration of intelligent mineral identification and important milestones have been achieved (see Section 4). (  4 ) Validate the accuracy of the mineral identification model. The model is used to discriminate the data to be identified and obtain the category of minerals. According to the different mineral datasets, different discriminative methods are used, the current discriminative models are also diverse, and the accuracy rate varies greatly.
The technique is also very attractive when Raman spectroscopy is used for mineral identification, as there is no need to remove fluorescence, and it is shown that its presence actually improves classification performance. However, ANN implementation requires the expertise and experience of the user in order to avoid overtraining and undertraining. In addition, ANNs must be retrained for data obtained on different spectrometers (due to expected differences in noise, background/fluorescence levels, Raman peak line shape, and spectrometer resolution) or any new spectra that have been added to the database, which can be a major inconvenience. The models of artificial neural networks involved in the intelligent identification of minerals are perceptron, Autoencoder, BP neural network, Kohonen (also called SOM) network, multilayer perceptual neural network (MLP), and feedforward network structure.
(2019)  [8]  used principal component analysis to downscale the frequency bands to achieve dimensionality reduction, and then used hard and soft clustering algorithms to classify the hyperspectral data to identify minerals in the hyperspectrum. Reference  [28]  using KSOM for training, with clustering centers as input, enabling the system to identify six classes of minerals and to give the number of possible occurrences in each class. Reference  [29]  used the K-means clustering algorithm for classification via Matlab with a known number of clusters of interest and the FCC-K-means method for unsupervised mineral identification with significantly improved performance. Reference  [8]  performed unsupervised training and used PCA algorithm as a band selection technique for dimensionality reduction of HSI dimensions, and hard clustering (K-means) and soft clustering (PFCM) algorithms were used to classify the given data, and the performance of PFCM was found to be better than K-means for both original HSI images and reduced bands by DBI values.
Algorithm Pros Cons Principal component analysis  [29]  Greater ease of use of datasets. Reducing the computational overhead of the algorithm. Removing noise. Making the results easier to understand. Complete absence of parameter restrictions. If the user has some a priori knowledge of the observed object and has mastered some features of the data, but is unable to intervene in the processing process through methods such as parameterization, the expected results may not be obtained and the efficiency may not be high. The decomposition of eigenvalues has certain limitations. In the case of non-Gaussian distribution, the resulting principal elements may not be optimal. Partial least squares regression  [30] [31] [32]  The regression of multiple dependent variables on multiple independent variables can be performed simultaneously, which is also applicable when the sample is small, and the exact regression equation can be obtained.
The degree of influence of independent variables on dependent variables can be quantified when the number of variables is suitable. It is possible to control and predict more effectively. The regression coefficients are difficult to interpret. Not applicable when the number of independent variables is small. Decision Tree  [33]  Easy to understand and simple to explain the mechanism. Can be used for small datasets. Less time complexity. Can handle numbers and classes of data. Can handle multiple output problems. Insensitive to missing values. Can handle uncorrelated feature data. High efficiency, requiring only one construction and repeated use, with the maximum number of calculations per prediction not exceeding the depth of the decision tree. More difficult to predict for continuous fields.
Features that take more divided values tend to have a greater impact, which affects the effectiveness of the fitted model. PCA: Principal component analysis is often used to reduce the dimensionality of a dataset while maintaining the features of the dataset that contribute the most to variance. Reference  [29]  grouped mineral regions into different classes based on the K-means clustering method of hue saturation value (HSV) principal component analysis (PCA). PLS: PLS overcomes both the undesirable effects of multiple correlation of variables in system modeling and considers the correlation between inputs and outputs. PLSDA is a commonly used chemometric technique for statistical regression of high-dimensional data. Remus et al. (2012)   [49]  used this method to identify obsidian provenance with over 90% accuracy in the Coso Volcanic Belt, Bodie Massif, and other obsidian-producing areas of north-central California, USA.
In order to extract values for mineral phase presence/abundance, PCA and PLS use systematic latent variables that must be classified and/or calibrated. When performing classification of high-dimensional data such as spectra, for example, PCA and PLS have the unique advantage of not requiring data downscaling techniques prior to classification  [31]. Decision Tree: A decision tree is a predictive model that represents a mapping relationship between object attributes and object values. The data are identified by classification from top to bottom based on the distinguishability of the attributes, with leaf nodes representing specific categories and the paths experienced from the root node to that leaf node forming specific classification rules. It is easy to understand and interpret, can handle both numerical and categorical data, and is highly robust in large or noisy datasets.
Reference  [33]  used decision trees to extend applications in the optical identification of common opaque minerals. Random Forest: A random forest (RF) is a classifier containing multiple decision trees, where a random subset of candidate features is selected during the learning process to train the decision trees, and the output class is determined by the plurality of the output classes of the multiple decision trees. It can handle a large number of input variables and produce highly accurate classifiers, as well as reduce the classification error of categoryimbalanced datasets. Xuefeng Liu et al. (2022)  [50] used a random forest model to train a classifier for SEM grayscale anomaly image segmentation. Ref. [10]  used linear discriminant analysis (LDA) in an attempt to project the dataset into a space with fewer dimensions and maximum separability between classes by maximizing the relationship between intraclass variance and interclass variance.
Ref. [35]  selected the Inception-v3 model as a pretraining model for rock mineral image identification. ResNet: Guo et al. (2020)  [18]  successfully trained a more accurate mineral identification model based on five mineral images-quartz, hornblende, black mica, garnet, and olivine-using the ResNet-18 neural network model as the basis, and achieved an accuracy of 89%, realizing the intelligent mineral identification based on deep learning. Ren et al. (2021)   [48]  reached the highest accuracy when using the ResNet-50 model as the base model for intelligent identification of rock mineral image samples. Reference  [38]  achieved a validation accuracy of 90. 5% using a 47-layer ResNet-2 architecture. Transfer learning: Zhang et al. (2021)  [52]  proposed a multiproduct coal image classification method combining convolutional neural network and Transfer learning, constructed a deep learning model based on the convolutional neural network Inception-v3 of Tensorflow, Keras framework, and applied the Transfer learning method to train and test different coal product image datasets until the loss values and accuracy of the training process converged.
They concluded that the test accuracy and validation accuracy of the deep learning model reach more than 90%. Ref. [53]  used a combination of Transfer learning and Siamese neural networks to improve the ability to extract multielement geochemical anomalies and tried using multiscale geochemical data to improve the model performance. The accuracy of the model using both Transfer learning and Siamese neural networks reached 85%, indicating that the improved deep learning approach can greatly improve the ability of the model to identify anomalies. Reference  [36]  used a Transfer learning technique that uses pretrained parameters trained on a larger ImageNet dataset as initialization of the network to achieve high accuracy and low computational cost. Reference  [11]  explored the use of CNN as a tool to accelerate and automate microphase classification using Transfer learning based on a robust and reliable CNN model trained on a large number of nongeological images.
Algorithm Pros Cons Transfer learning  [5, 11, 35, 36]  Requires less training data and can make more efficient use of existing data. Better generalization of the model by migration learning. The training process is more stable and easier to debug, increasing the robustness of the model. Makes deep learning easier. Enables customization. Although it can be quantified, it has an upper limit and is not suitable for solving all problems. Convolutional Neural Network  [5, 9, 12, [37] [38] [39] [40] [41] [42] [43] [44] [45]  Shared convolutional kernel, which can handle high-dimensional data. No manual feature selection and good feature classification. Need to normalize the dataset; difficult to train with different sizes mixed together. No memory function. Physical meaning is not clear enough.
Data and Visualization Tools We searched the academic literature through Scopus, selected the search condition "TITLE-ABS-KEY" in the advanced search box, and searched all the relevant literature on intelligent mineral identification up to 19 June 2022, using the search formula ("mineral" OR "ore") AND ("identification" OR "recognition") AND ("learning"). By filtering articles including "Article", "Conference Paper", "Conference Review", and "Review", and manually filtering the content of the literature to eliminate irrelevant literature, we finally obtained 201 literature records. The obtained literature records were published between 1975-2022, with 142 literature sources (Journals, Conferences), including 693 authors from 28 countries and regions. VOSviewer  [54]  is a scientific knowledge visualization software developed by the VAN ECK and WALTMAN team at Leiden University in the Netherlands, which is commonly used for large-scale data analysis and has a strong visualization mapping capability.
Bibliometrix  [55]  is a scientific bibliometric software based on R language developed by Italian MASSIMOA, which can perform complete scientometric and visual analysis of the literature exported from the Scopus database, including statistical analysis of relevant scientific literature indices, construction of data matrices, cocitation, coupling, coauthorship analysis and coword analysis, etc. , with excellent visualization performance. This paper focuses on the visual analysis of the retrieved literature records using a combination of VOSviewer and Bibliometrix in order to explore the development path, hot spots, and trends in the field of intelligent mineral identification and to further analyze the development of taxonomy-based mineral identification approaches.
At this stage, mineral types such as calcite and feldspar  [64]  were given major attention because their samples are easy to collect and their datasets are rich and reliable for further research. From 2017 to the present, intelligent mineral identification based on deep learning has become a major direction in academic research, and different ways of mineral identification such as remote sensing and scanning electron microscopy  [65]  have been actively explored, while more ore types are targeted for mineral identification. In order to better explore the hot spots of intelligent mineral identification research in recent years, we detected the emergence of field keywords and plotted them as shown in Figure  3. As we can see from the Figure  3 , image analysis and image classification of mineral data have been studied for years.
Conclusions Mineral identification is a fundamental task in geology, mining engineering, and other related fields. Intelligent identification of minerals reflects the frontier needs of scientific research and industrial demands. The combination of computer science and earth science is the current trend, and the application of artificial intelligence related to the background of digital earth science in deep time has gained widespread attention. It is important to pay close attention to the powerful potential demonstrated by the method in the intelligent identification of minerals, since it represents an important direction for future development. While there is still a huge gulf between the subjects of geology and artificial intelligence, artificial intelligence identification processes and criteria are often difficult to interpret directly and there is a lack of recognized, unified, benchmark mineral datasets, pending progressive foundation building.