dataesr/scientific-paragraphs-categorization huggingface dataset:
Robustness checking of the classifiers Robustness checking of the three optimized classifiers is carried out using five independent runs with 10-fold crossvalidation for every run. Adopting the process of Oztekin et al. [81] , seeds are randomly selected for splitting the dataset into training and testing. Five different numbers, i. e. , 221, 223, 225, 227, and 229 are assigned to seeds for five different runs, which, in turn, produces a set of 50 cross-validation accuracies (i. e. , 10-fold cross-validation accuracies per seed Â 5 seeds). Based on these values, a box plot is generated for each of the six classifiers (i. e. , ADAM-DNN, RMSProp_DNN, SGD-DNN, SVM, k-NN, and RF) (refer to Fig. 8 ). From the figure, it is unveiled that the ADNN algorithm shows the maximum accuracy values with the least range of dispersion; whereas the minimum Therefore, from the comparative study, the ADNN classifier can be deemed as the robust model.
In GD-based optimization algorithm, first, the error is calculated for the entire dataset and then the error is back propagated for weight updation. Therefore, it takes a lot of time to move even a single step closer to the optimum weight value/cost. This problem is solved in SGD, where the entire dataset is divided into several mini batches of size one. After passing through one mini batch, the parameters are updated; thereby speed up the system. These two optimization algorithms are further speeded up by incorporating momentum that defines the desired direction of the learning process so that parameters can reach to optimality with comparatively shorter time. As stated before, SGD is better than GD and hence, SGD with momentum is superior to GD with momentum. RMSProp is a GD-based optimization algorithm.
Key words related to each topic are added with other categorical predictors to form the input feature space. This input feature space is fed to the DNN for prediction. In order to achieve the improved accuracy in classification, the parameters of DNN are tuned by the three optimization algorithms, namely RMSProp, ADAM, and SGD, separately. From this study, it is evident that the proposed approach ADNN is found to be the best classifier with the highest accuracy. In support of the findings from the experiments, other algorithms, namely SGD-DNN, RMSProp-DNN, SVM, k-NN, and RF have also been applied to the incident dataset. The results reveal that the ADNN classifier outperforms others in all cases. Further, all the algorithms used in this study have been tested using Results reveal that our proposed algorithm significantly performs better than the others.
missing data handling, random forest classifier is used. For an arbitrary attribute a s ðs 2 pÞ having missing values at entries i Observed values of attribute a s are denoted as y Missing values of attribute a s are denoted as y First, find out the percentage of missing values for each a s in the dataset. • Step 2: Sort a s according to the ascending order of the percentage of missing values. • Step 3: For each a s , the random forest algorithm is trained with predictors x.
NN, and RF is somewhat similar in nature in terms of the order of best accuracies on five benchmark test datasets. Hence, from the experimental results reported in Table4, it is explored that ADNN classifier produces the highest accuracy for all the datasets. Although k-NN, RF, SGD-DNN, RMSProp-DNN, and SVM are compared with the proposed ADNN, DNN parameters are tuned using SGD, RMSProp, and ADAM optimizers to determine the best one since these optimizers are best suited for tuning deep learning model parameters rather than others.
Results of the Mann-Whitney U test for ADAM-DNN (ADNN), RMSProp-DNN, and SGD-DNN for each dataset Box plot analysis for robustness checking of different classifiers five different available benchmark datasets. In all these cases, ADNN algorithm performs the best. In order to check whether the performance of ADNN algorithm differs significantly or not from other algorithms (i. e. , SGD-DNN, and RMSProp-DNN), two statistical tests, namely Wilcoxon signed ranked test and Mann-Whitney U test have been carried out.
Acknowledgements The paper is original in its contents and is not under consideration for publication in any other journals/proceedings. We would like to express our special appreciation and thanks to  Ms. Anima Pramanik ,  Research Scholar  of the  Department of Industrial & Systems Engineering, IIT Kharagpur , for her assistance in this study. We deeply acknowledge ''Safety Analytics & Virtual Reality (SAVR) Laboratory '' (www. savr. iitkgp. ac. in), Department of Industrial & Systems Engineering, IIT Kharagpur , for experimental/computational and research facilities for this work. We would also like to thank the management of the plant for providing relevant data and their support and cooperation during the study. Funding There is no funding received by this work. The authors have no financial or proprietary interests in any material discussed in this article.
missing data handling, random forest classifier is used. For an arbitrary attribute a s ðs 2 pÞ having missing values at entries i Observed values of attribute a s are denoted as y Missing values of attribute a s are denoted as y First, find out the percentage of missing values for each a s in the dataset. • Step 2: Sort a s according to the ascending order of the percentage of missing values. • Step 3: For each a s , the random forest algorithm is trained with predictors x ðsÞ mis f 1; 2; :::; Q g , the dataset is separated into four parts, which are as follows: (i) ðsÞ obs. (ii) ðsÞ mis. (iii) The attributes other than a s with observations i ðsÞ obs ¼ 1; 2; :::; Q f gn i ðsÞ mis are denoted as x ðsÞ obs. (iv) The attributes other than a s with observations i ðsÞ mis are denoted as x ðsÞ mis.
e. , Div1, Div2, Div5 (0. 01%), Div6 (15. 3%), Div7 (8. 5%), Div8 Div3,. Div13 and Div14 are available in the dataset (2. 1%), Div9 (3. 7%), Div10 (1. 7%), Div11 (2. 9%), Div12 (28%), Div13 (0. 2%), and Div14 (17. 7%). Incident This attribute refers to the category of the incident occurred. The Injury (34. 9%), near miss (40. 3%), and property damage outcome (IO) attribute IO has three classes: (i) Injury (Incident 1), (ii) near (24. 8%) miss (Incident 2), and (iii) property damage (Incident 3) Incident events (PC) Condition of This attribute refers to the condition of the machine while the Working (W) (43. 8%), idle (I) (13. 6%), and not related machine (MC) incident happened. It describes the machine whether it was idle (N) (42. 6%) (MI) or working (MW), or not applicable (NA) Types of It indicates the basic or root causes of incidents.
7%) Incident type It denotes the types of the incidents; either human behavior Behavioral (Bhv) (75. 2%), and Process-related (Pro) (IT) (Bhv. ) or process type (Pro. ) (24. 8%) Standard operating procedure (SOP) This attribute refers to the event of an incident which occurred. It may be crane dashing, dashing/ collision, derailment, slip trip fall (STF), and so forth. In the dataset, this attribute has eleven classes, namely Cause 1, Cause 2,. Cause 10, and Cause 11 Cause 1 (6. 3%),Cause 2 (4. 7%),Cause 3 (1. 7%),Cause 4 (12. 1%),Cause 5 (14. 9%),Cause 6 (4. 1%), Cause 7 (18. 6%),Cause8 (4. 7%), Cause 9 (11. 2%),Cause 10 (20. 7%), and Cause 11 (0. 9%) Condition of the work (WC)The attribute refers to the status of the work while the incident took place. There are three classes of this attribute, i.
ingly, the factors can be improved or eliminated from the system to prevent the occurrences of incidents. However, like other studies, this study has also some limitations. The study suffers from the issue that demands an extensive human labor, which is necessary to sanitize the data prior to analysis. This is a time-consuming and less effective process. Further, the dataset consists of a limited number of incident records. It is noteworthy to mention that using a substantial amount of data in the analysis is necessary for achieving the model's generality. Based on the study carried out, some interesting avenues could be explored for the future research. For examples, the study could be expanded to develop an ADNN-based automated decision support system (ADSS) [82]  which not only enables us for prediction but also facilitates smart decisionmaking based on the generation of rules.
5). In the last section, we offer a detailed explanation of the review of LUC datasets carried out in Chap. "Land Use Cover Datasets: A Review" and Part IV (Sect. 6). The book is the fruit of two research projects which seek to provide a clearer understanding of the uncertainties associated with Land Use Cover maps and with the results of Land Use Cover Change modelling exercises (INCERTIMAPS Project: Suitability and uncertainty of land use and land cover maps for the analysis and modelling of territorial dynamics) and the promotion of Open Access software for teaching spatial science (PE117519: Herramientas para la Enseñanza de la Geomática con programas de Código Abierto). See complete information about these projects in the section Acknowledgements. 2 What is the Main Aim of This Book? The aim of this book is to provide an up-to-date state of the art on Land Use Cover (LUC) datasets and validation tools.
The book summarizes the available information and makes it accessible to any interested user, including some of the latest developments in the field. The book was conceived as a practical tool to inform readers about currently available LUC datasets at global and supra-national scales and to help them understand more about the validation of LUC data and LUCC modelling exercises, so enabling them to validate their own data and models. To this end, the book combines brief theoretical explanations with practical information and exercises. Part I of the book briefly covers the theoretical foundations of LUC mapping, LUCC modelling and the analysis and assessment of their associated uncertainties. Parts II and III were conceived as practical guides to enable any reader to use any of the tools and data.
Part II covers the visualization of LUC data and the production of reference datasets to validate LUC maps. Part III describes the use of common validation tools and the interpretation of their results. All the practical exercises are accompanied by an explanation of the basic theory behind them, so as to enable users to understand the analyses and the principles on which the techniques are based. Finally, Part IV of the book characterizes the most relevant available LUC data. It also provides all the necessary information as to how to download and use the datasets. As the book aims to reach the widest possible audience, the theory is briefly explained in simple, understandable terms. Practical exercises are implemented in QGIS, an open-source Geographical Information System, which can be downloaded for free.
For LUC data visualization and creation, readers are referred to Part II of this book. It provides an overview of the different options available for symbolizing LUC data and LUC change in GIS. It also addresses some of the problems associated with the spatial visualization of LUC information. This part of the book also includes a tutorial on the creation of a set of sample points for LUC data validation with QGIS. Users interested in the validation of LUC datasets and Land Use Cover Change (LUCC) modelling exercises should refer to Chap. "Validation of Land Use Cover Maps: A Guideline". This provides guidelines for validating different LUC products: single LUC maps, LUC map series, and outputs from LUCC modelling exercises. The different tools and methods referred to in these guidelines are then described in detail and applied in practice in the example exercises in Part III of the book.
Users interested in doing the example QGIS exercises appearing in this book should refer to Sect. 5 of this chapter, which presents all the data and the cases studied in this book. It also offers essential information about the particular version of QGIS that we use and about how to integrate R software into QGIS, a necessary step when carrying out some of the exercises set out in the book. Those interested in LUC data sources should refer to Chap. "Land Use Cover Datasets: A Review", which offers an introduction to LUC mapping at global and supra-national scales, including a review of the different datasets available. Part IV of the book offers in-depth descriptions of most of the datasets that are available for download, detailing their specific characteristics and how they can be accessed.
After installing R and the required packages, we need to install the QGIS plugin that allows us to integrate the two software packages. This is the "Processing R provider" plugin. Instructions to this end can be found in Sect. 5. 2 of this chapter. After installing the plugin, users must download the scripts we have developed to integrate the R tools and capabilities into QGIS. These scripts are listed in Table  3  and are available at https://doi. org/10. 5281/zenodo. 5418985 in the official repository for this book. Once downloaded, the script files must be pasted into the R scripts folder of QGIS. The path to this folder can be found in the "Options" menu of QGIS. To access it, go to Settings > Options…. and then select the "Processing" submenu (Fig. 7 ). In the "Providers" tab, there is a specific tab for "R".
The landscapes are now made up above all of mosaics of agricultural lands, cattle pastures and human settlements. information about this dataset can be found in Chap. " General Land Use Cover datasets for Europe" of this book. A simpler version of CLC is used in the Ariège Valley (Fig. 11 ) and the Asturias Central Area (Fig. 12 ) case studies. In the latter, CLC is available in both vector and raster format. Although CLC is officially distributed in raster format at a spatial resolution of 100 m, the CLC rasters for the study areas in this book are provided at a different spatial resolution: 50 m for Asturias and 15 m for Ariège. These rasters were obtained after rasterizing the CLC vector layers. SIOSE (Sistema de Información sobre Ocupación del Suelo de España) is a Spanish dataset in vector format that provides very detailed LUC information.
It was obtained by photointerpretation of aerial imagery at 1:25,000, with a MMU of 0. 5-2 ha and a MMW of 10 m. It follows a specific data model aimed at objects, which means that all the land uses and covers in a polygon are described by a specific code. This means that instead of being assigned to a specific LUC category, each polygon is described by a code detailing its LUC composition. Some of the maps in the Asturias Central Area case study were obtained after simplification of the SIOSE database. The maps were obtained after the classification of each SIOSE polygon into a single category and after the rasterization at 50 m of the original vector dataset (Fig. 12 ). More information on how this operation was performed can be found in  García-Álvarez (2018). Extra information about the characteristics of SIOSE can be found in  Valcárcel et al.
(2008)  and  García-Álvarez and Camacho Olmedo (2017). The Marques de Comillas LUC map (Fig. 13 ) is part of a database on Land Cover and Land Cover/Land Use Changes in the State of Chiapas in Mexico. The original database covers 7. 5 million ha, of which the Marqués de Comillas map covers a small section of approximately 200,000 ha. The maps were computed via a supervised classification of 2019 Sentinel-2 imagery. They were subsequently photo-interpreted to correct errors from the supervised stage as well as to include information on agricultural land uses. The map contains eight thematic categories describing levels of forest conservation, and other land uses; the approximate scale is 1:40,000, with an MMU of one ha. More information can be found at the following link: https:// bosqueschiapasdemo.
ecosur. ourecosystem. com/. In the following tables, we list the files from the different datasets and LUC modelling exercises described above that have been used in different exercises in this book. More datasets are available online, including extra LUC maps and model drivers not considered in the exercises in this book. The tables include information about the name of the file available for download and the descriptive name used to refer to these files in the book. For each dataset, we also provide the projection of the dataset and the file describing the legend of the maps. A document listing all these characteristics for the layers only available online is also provided when downloading the data. Chapter "Land Use Cover Datasets: A Review" and Part IV of the book contain a review of the Land Use Cover datasets available at global and supra-national scales.
Due to the limited extent and scope of this book, we did not review national and regional LUC datasets, which are far too numerous for our purposes. The datasets we reviewed are classified into two groups, depending on the information they provide. The first group is made up of the datasets that provide information about the different land uses or covers without focusing on any one of them in particular, i. e. general LUC datasets. The second group consists of the LUC datasets that map a specific land use or cover in detail (e. g. vegetation, croplands, built-up areas…). These are referred to as thematic LUC datasets. Some datasets are difficult to assign to one of the two groups, as they map a wide range of LUC categories while also providing specific detail on just one of them. The authors decided which group to assign them to on a case-by-case basis.
The datasets were also classified according to their extent, differentiating between global and supra-national LUC datasets. The first group of datasets maps land uses or covers all over the Earth, while the second maps them for a specific area covering more than one country. The maps in the second group may cover a whole continent or focus on just a few countries. When making the review, we consulted the most relevant web portals and repositories of LUC data (Table  4 ). A few selected papers, reports and other relevant documents reviewing or comparing LUC datasets were also consulted  (Manakos and Braun 2014; Mora et al. 2014; Grekousis et al. 2015; Tsendbazar et al. 2015; Diogo and Koomen 2016; Klotz et al. 2016; Pérez-Hoyos et al. 2017; Fritz et al. 2019). Very old or outdated maps, which were produced according to traditional cartographic methods, are not included in this review.
Nor are other old maps that combine LUC information with other data about climate or biogeographic variables, such as the maps produced by  Matthews (1983)  and  Olson et al. (1983). Traditional maps obtained through photointerpretation of aerial imagery and field survey, which offer information about certain specific land covers such as vegetation and agricultural areas, are not included in the review either. Although they may be interesting sources for historical LUC change analysis, they are usually only available for national or more detailed areas and normally have not been digitalized. There are plenty of other spatial datasets that provide important information for studying specific land covers. For vegetation covers, maps of live biomass are a good example  (Kindermann et al. 2008; Thurner et al.
2014). These datasets were not included in our review because they are not specific sources of LUC information focusing exclusively on land cover. However, there is an enormous amount of data like this that may be useful for the study and characterization of LUC. This data comes in many different forms and from a range of different sources. Part IV of the book characterizes in detail all the reviewed LUC datasets that are currently available for download and may be relevant for a wide community of users. Datasets produced at very coarse scales or which are already very outdated are not described in Part IV, as they are of limited utility for most members of the LUC community. LUC datasets currently unavailable for download are not characterized in Part IV either. We tried to obtain, either online or by contacting the authors, all the global or supra-national datasets to which we found references.
Some of them, however, are no longer available. These datasets have not been reviewed. The LUC datasets described in Part IV were characterized according to the following elements: information about the project or context within which they were produced; information about their method of production; description of the data available for download; and practical information for using the dataset in an effective way. For each dataset we also provide all the technical references in which it is described as well as other references of interest in which it is used or analysed. A table summarizing the main characteristics of the dataset (extent, temporal availability, spatial resolution, updates, accuracy…) is also provided.
Researchers have been proposing new methods and techniques for producing LUC maps. This has increased the number of LUC datasets available at global, continental, regional and local scales. This has also led to an increase in the number of users who decide to make their own LUC maps. The validation of LUC data has also been the subject of specific research and new methods, strategies and techniques have been proposed for validating and analysing LUC maps. Despite all these advances, many users are still unaware of the wide range of datasets available, while others lack a clear understanding of the methods or techniques that can be used to validate LUC data. Thus, in addition to producing more LUC datasets, more information is required. Users must be able to find out more about the most appropriate datasets for their field of study, and the general uncertainties and limitations of each one.
Sometimes the land use can be deduced from contextual information in the image, but, in most cases, additional information is required. This makes map production more difficult and expensive. As a result, most maps only provide information about land covers. In other cases, they focus on the land use of certain specific covers, such as artificial or agricultural areas, so providing both Land Use and Land Cover (LUC) data. This is why in LUC science, we generally talk about Land Use and Land Cover information, as the two aspects tend to be combined within the same datasets. 3 Land Use and Land Cover Mapping: A History Some information on Land Use and Land Cover was available prior to the advent of remote sensing instruments  (Campbell 1983 ). However, it was the appearance of aerial and, above all, satellite images that promoted the production of systematic LUC maps at regional, continental and global scales  (Loveland 2016).
(2007) ), Africa (AFRICOVER; see Di  Gregorio and Latham (2003) ;  Fritz et al. (2015) ) and China  (Zhang et al. 2014). As LUC mapping became easier, cheaper and quicker, many institutions, scientists and other users began producing LUC datasets at all the different scales  (Grekousis et al. 2015; Loveland 2016). Initial efforts were mainly focused on regional and national scales  (Loveland 2016). However, the appearance of the first satellites with sensors providing free imagery covering the whole Earth at coarse resolutions allowed the first global LUC datasets to be developed  (Congalton et al. 2014; Mora et al. 2014; Grekousis et al. 2015). The AVHRR sensor on board the NOAA weather satellites launched in 1978  (Campbell and Wynne 2011) , and the VEGETATION sensor, installed in the SPOT satellite in 1998  (Gutman et al.