dataesr/scientific-paragraphs-categorization huggingface dataset:
Further details pertaining to the dataset are presented in Section 3. This research work adopted a VGG16-trained model in order to accomplish successful seed identification and classification  [16]. However, the model was modified by adding new layers, which aimed to increase accuracy and reduce the error rate of the classification process. This modification in the VGG16 model leads to the optimisation of the classification process and eventually assists the common man in identifying and classifying the different type of seeds. The following points summarise the contributions of this paper: We conduct a detailed review examining the most notable work in the area of seed classification via machine learning and deep learning. We re-introduce the problem of seed classification using pre-trained VGG16 convolutional neural network architecture, where variant seeds have been classified, unlike in previously proposed models, which focused only on one kind of seed.
We propose a new model for seed identification and classification by using advanced deep learning techniques. Furthermore, dropout and data augmentation techniques have been incorporated to minimise the chances of overfitting. We fine-tune our model by applying an adjustable learning rate to make sure that our model performs equally well on the dataset and does not miss the global minimum. We develop an optimisation technique that monitors any positive change in the validation accuracy and validation error rate. In case of change, it takes the backup of an optimal model at the end of the iteration to make sure that the proposed model is at its highest level of accuracy with the least validation loss. The remainder of the paper is organised as follows. In Section 2, the previous work related to this research are reported and discussed.
The best accuracy rate was obtained from the SVM method (83. 9%), while the best accuracy from the deep learning techniques was obtained from the InceptionResNetV2 model (95. 15%)  [6]. In a study conducted on corn seed purification, a hybrid dataset preparation was reported. The authors performed a tedious procedure to extract features from corn seeds. The hybrid feature dataset was comprised of a histogram, texture features and spectral features. The classification models used in their study included random forest (RF), BayesNet (BN), LogitBoost (LB) and multilayer perceptron (MLP), along with optimised multi-feature using the (10-fold) cross-validation approach. Among these classifiers, MLP reported outstanding classification accuracy (98. 93%) on ROIs size (150 × 150)  [11]. With regards to traditional AI-based algorithms, they involve detailed steps in the feature extraction technique.
They also need assistance from experts, which has a negative impact on the efficiency of the algorithms. Deep learning (DL), unlike traditional classification learning methods, is not limited to shallow structure algorithms. It can perform complex functions with limited samples, extracting the most essential features from only a small number of training samples. A study conducted by Xinshao  [3] , based on a sample of weed seeds, used the deep learning technique principal component analysis network (PCANet). This study minimised the limitation of manual feature extraction by learning features from the dataset. DL, which mainly focuses on machine learning, has brought advancements to the producing of results within various data analysis tasks, especially in computer vision  [5]. DL is capable of representing data by automatically learning abstract deep features of a deep network.
Data Set Formation This subsection explains the formation of the dataset for this research. In this work, 14 different types of commonly known seeds have been selected for the preparation of the dataset. The chosen seeds were black-eyed pea, black pepper, chickpea, coriander, corn, cumin, fennel, fenugreek, flax, kidney beans, mustard, onion, pumpkin and sunflower, as listed in Table  1. For experimentation purposes, the weight of each seed type was around 100 gm. Table  1  and Figure  3  show the frequency distribution of the dataset. A smartphone (Samsung Galaxy Note 8) was used to capture digital images of these seeds. The resolution of the camera was set at 12-megapixels (4032 × 3024). A tripod mobile stand with a ring light was used to capture images, as illustrated in Figure  4. It is important to note that the number of captured images for each type of seed was around 200 to avoid any imbalance in classification.
Training This subsection reports the training of the proposed model based on the dataset created, as mentioned in the previous subsection. In order to train the model, the dataset was split into training and validation sets. The training of the model was accomplished in many steps, starting with the selection of the pre-trained model, followed by the modification of the model. The process started by adapting the VGG16 architecture  [16]. This architecture contains 1000 nodes in its last layer, where each one refers to one of the 1000 objects. We removed the final classification layer of the VGG16 in order to adjust it to the dataset of our research, which consisted of 14 classes. The main reason for choosing VGG16 architecture is that it uses 3 × 3 kernels, which assists in generalising the classification problems outside of the originally trained network.
This results in an updated version of VGG16 architecture with 14 classification nodes, which is better suited to investigating our problem and more convenient for transfer learning. Traditional machine learning technology has achieved great success and has been successfully applied in many practical studies. However, it still has some limitations in certain real-world scenarios  [14]. Transfer learning aims to improve the performance of target learners on target domains by transferring knowledge. In this way, constructing target learners' dependence on a large number of target domain data can be reduced and problems of insufficient training data can be solved  [14, 15]. There are two popular approaches to transfer learning. In the first approach, the initial weights of the trained model are simply discarded, and the whole of the network is trained afresh on a new dataset.
In the second approach, the layers of the existing model are left unchanged, but the newly added layers are trained on the new dataset. In our proposed work, we adopted a hybrid approach in which the actual layers of the model were initially frozen, and only the newly added layers were trained on our seed dataset. This process continued until the 20th iteration. After the 20th iteration, we unfroze the stalled layers in order to make slight weight adjustments to the trained layers for our dataset. The process of model training has three distinct characteristics: image augmentation, decayed learning rate and model checkpointing. In general, the trained models result in a good classification rate on the training set but fail miserably once exposed to the test set. One of the major reasons for this is overfitting.
However, it still has some limitations in certain real-world scenarios  [14]. Transfer learning aims to improve the performance of target learners on target domains by transferring knowledge. In this way, constructing target learners' dependence on a large number of target domain data can be reduced and problems of insufficient training data can be solved  [14, 15]. There are two popular approaches to transfer learning. In the first approach, the initial weights of the trained model are simply discarded, and the whole of the network is trained afresh on a new dataset. In the second approach, the layers of the existing model are left unchanged, but the newly added layers are trained on the new dataset. In our proposed work, we adopted a hybrid approach in which the actual layers of the model were initially frozen, and only the newly added layers were trained on our seed dataset.
This process continued until the 20th iteration. After the 20th iteration, we unfroze the stalled layers in order to make slight weight adjustments to the trained layers for our dataset. The process of model training has three distinct characteristics: image augmentation, decayed learning rate and model checkpointing. In general, the trained models result in a good classification rate on the training set but fail miserably once exposed to the test set. One of the major reasons for this is overfitting. To avoid overfitting and improve the generalisation capability of machine learning models, an image augmentation technique was implemented  [25]. During image augmentation, various images were artificially created through different ways of processing or a combination of multiple processing methods, such as random rotation, shifts, shear and flips.
Testing This subsection reports the testing of the model. This phase was initiated by loading the besttrained model from the previous phase for testing. To test the trained model, a test dataset was prepared. The test dataset contained 15 images of each selected seed, which were mentioned in Table  1. Unlike in the training dataset, the images were captured randomly by varying light levels and adjusting distance and angle. This test dataset helped in generalising the model for unseen data in a real-life scenario. The image size was resized to (224 × 224 × 3), similar to the training set, to make it compatible with the proposed model. After resizing, these images are fed to the proposed model to predict the test dataset. The result values obtained during the testing phase and training phase were compared.
Testing This subsection reports the testing of the model. This phase was initiated by loading the best-trained model from the previous phase for testing. To test the trained model, a test dataset was prepared. The test dataset contained 15 images of each selected seed, which were mentioned in Table  1. Unlike in the training dataset, the images were captured randomly by varying light levels and adjusting distance and angle. This test dataset helped in generalising the model for unseen data in a real-life scenario. The image size was resized to (224 × 224 × 3), similar to the training set, to make it compatible with the proposed model. After resizing, these images are fed to the proposed model to predict the test dataset. The result values obtained during the testing phase and training phase were compared.
Experimentation and Result Analysis To fairly evaluate the performance and prove the efficiency of our proposed solution to processing seed identification and classification over a broad range of seeds, several extensive experiments were designed. The proposed work is implemented in Python, a scientific programming language on the Windows 10 operating system. The system configuration includes an i7 processor with 16GB RAM. It took 67 h to train the proposed model with the specified configurations. To capture images, an android device running on android Pi with 6GB RAM and a camera resolution of 12-megapixels was used. With regards to lighting, white light was used, and seeds were placed on a white background. To assess the performance of the classification model, a range of performance metrics were employed, derived from a 2 × 2 confusion matrix.
From the 40th iteration onwards, there is a small reduction in the accuracy up to the next 60 iterations by keeping the performance steady. This indicates that the optimal model has been obtained for the training data. Figure  10b  illustrates the training loss of the model. In the beginning, the model lost value, which is normal at the initial stage of the training process, as a model has not been exposed to many examples of the data. Figure  10b  also depicts that the training loss drops with each successive epoch, which is considered to be a characteristic of a good model. From the figure, it can be seen that the model's loss oscillates between noticeable highs and lows up to the 40th iteration. However, after the 40th iteration, the model maintains its smallest loss value. The steadiness in the results shown is due to the quality of the dataset and the implementation of dense architecture with multiple layers.
It is evident from the literature that, during the training of the model, the performance can be made desirable. However, this process can be misleading and may fail on the validation dataset. This is because the model has only seen supervised data and is not well-learned. To consider a model as the best fit, it should not only perform well on the training dataset, but also the validation dataset. Figure  11  elucidates the validation accuracy and loss of the proposed model. From Figure  11a , it can be seen that the model initially performs poorly with an accuracy rate of around 0. 22. This is because the model is not trained enough. The performance of the model stays more or less the same for the first 20 iterations. From the 20th to the 30th iteration, there is a gradual improvement in the performance of the model.
It reaches its peak value at the 40th iteration. From there onwards, the performance of the model remains steady. This is due to the fact that we implemented dense architecture with multiple layers. Figure  11b  shows the validation loss of the model. It can be seen that the model had a very high validation loss at the initial stage during the validation process, which was not even close to the acceptable range. The figure also shows that the loss oscillates between highs and lows for the first 40 iterations. From the 40th iteration onwards, the loss starts to reduce monotonously and eventually reaches the minimum value in the 55th iteration, remaining unchanged from there. is because the model has only seen supervised data and is not well-learned. To consider a model as the best fit, it should not only perform well on the training dataset, but also the validation dataset.
The figure also shows that the loss oscillates between highs and lows for the first 40 iterations. From the 40th iteration onwards, the loss starts to reduce monotonously and eventually reaches the minimum value in the 55th iteration, remaining unchanged from there. The model showed stability in its performance during the training and validation processes, which was due to the pre-processing techniques employed in the model. This started with the collection of data, within which an effort was made to have a balanced distribution of data across all of the classes. In addition, the image augmentation technique also helped to make sure that the system was exposed to the most variations of the dataset. The dropout technique played a major role in the model's validation performance by making sure that the model did not deviate much from its training performance.
This started with the collection of data, within which an effort was made to have a balanced distribution of data across all of the classes. In addition, the image augmentation technique also helped to make sure that the system was exposed to the most variations of the dataset. The dropout technique played a major role in the model's validation performance by making sure that the model did not deviate much from its training performance. Table  2  outlines the performance of each seed class mentioned in the proposed model in terms of precision, recall, F1-score and support. Support represents the number of instances of each class The model showed stability in its performance during the training and validation processes, which was due to the pre-processing techniques employed in the model. This started with the collection of data, within which an effort was made to have a balanced distribution of data across all of the classes.
In addition, the image augmentation technique also helped to make sure that the system was exposed to the most variations of the dataset. The dropout technique played a major role in the model's validation performance by making sure that the model did not deviate much from its training performance. Table  2  outlines the performance of each seed class mentioned in the proposed model in terms of precision, recall, F1-score and support. Support represents the number of instances of each class that was performed during model training. It is important to note that, with the help of image augmentation for each class, around 1700 to 1800 random instances were synthetically created. From the table, it can be noticed that the model showed the maximum possible values for each seed class except the chickpea and pumpkin seed classes.
Conclusions and Future Work This paper proposed an efficient model for seed identification and classification. The model is trained on a dataset containing images of 14 variants of seed. The image augmentation technique was used to produce more training images artificially by using an inbuilt API ImageDataGenerator in Keras' library. The augmented images were rescaled to 224 × 224 × 3. The proposed model adopted existing VGG16 architecture for classification, in which the last layer was replaced with five newly implemented layers: the average pooling layer, the flatten layer, the dense layer, the dropout layer and the softmax layer. To improve classification performance, the model was fine-tuned. To increase the accuracy rate and reduce the error rate, the model was optimised and validated.
There is increasing interest in the use of nature-based approaches for mitigation of storm surges along coasts, deltas, and estuaries. However, very few studies have quantified the effectiveness of storm surge height reduction by a real-existing, estuarine-scale, nature-based, and engineered flood defense system, under specific storm surge conditions. Here, we present data and modelling results from a specific storm surge in the Scheldt estuary (Belgium), where a hybrid flood defense system is implemented, consisting of flood control areas, of which some are restored into tidal marsh ecosystems, by use of culvert constructions that allow daily reduced tidal in-and outflow. We present a hindcast simulation of the storm surge of 6 December 2013, using a TELEMAC-3D model of the Scheldt estuary, and model scenarios showing that the hybrid flood defense system resulted in a storm surge height reduction of up to half a meter in the estuary.
In this paper, we present a hydrodynamic modelling study (using TELEMAC-MASCARET software), aiming to assess the effectiveness of storm surge height reduction that is caused by the presence of constructed FCAs with and without CRT during the 2013 'Sinterklaas' storm in the Scheldt estuary  (Belgium). In order to achieve this goal, the modelling study consists of the following steps. This paper starts from the development and implementation of equations in TELEMAC, which allow to model flow through culverts of FCAs with CRT in the Scheldt estuary  [36]. The presented modelling approach is validated by comparing measured and modeled discharges and water levels in a detailed 3D model of one FCA with CRT. Next, discharges measured through a physical scale model of a CRT culvert are compared with modeled discharges.
TELEMAC-3D Model: SCALDIS SCALDIS is a 3D hydrodynamic model of the Scheldt estuary developed at Flanders Hydraulics Research for various applications. It uses the software TELEMAC-3D, which is one of the finite element software modules of the open TELEMAC-MASCARET suite. In TELEMAC-3D, the threedimensional Reynolds averaged Navier-Stokes equations are solved hydrostatically or nonhydrostatically  [42]. The model domain includes the entire Scheldt estuary with tributaries as far as the tidal influence reaches, as well as the Belgian coastal zone, parts of the adjacent French and Dutch coastal zones, and the adjacent Eastern Scheldt estuary (see Figure  1 ). The domain has eight upstream boundaries, where boundary conditions are defined by daily averaged measured discharges. Downstream, water-level time series, extracted from a continental shelf model, are imposed on the seaward boundary.
TELEMAC-3D Model: SCALDIS SCALDIS is a 3D hydrodynamic model of the Scheldt estuary developed at Flanders Hydraulics Research for various applications. It uses the software TELEMAC-3D, which is one of the finite element software modules of the open TELEMAC-MASCARET suite. In TELEMAC-3D, the three-dimensional Reynolds averaged Navier-Stokes equations are solved hydrostatically or non-hydrostatically  [42]. The model domain includes the entire Scheldt estuary with tributaries as far as the tidal influence reaches, as well as the Belgian coastal zone, parts of the adjacent French and Dutch coastal zones, and the adjacent Eastern Scheldt estuary (see Figure  1 ). The domain has eight upstream boundaries, where boundary conditions are defined by daily averaged measured discharges. Downstream, water-level time series, extracted from a continental shelf model, are imposed on the seaward boundary.
f. This subroutine is used by the 2D and the 3D module. The difference is that in TELEMAC-3D, the water is actually added or taken from the water column (model layer) at the specific height corresponding with the culvert bottom level. All the geometric parameters and head loss coefficients for each culvert are listed in a text file that is read by a subroutine called lecbus. f. Examples are given in the following sections.
An overview of the culvert parameter values after calibration is given in Appendix B Table  A1. This 3D model of FCA Bergenmeersen is also available in the TELEMAC-MASCARET software package as a validation case. In this section, the choices for the culvert parameter values are described. The culvert entrance head loss coefficient C1 was chosen equal to 0. 5 according to  [48]  when a large contraction is assumed going from the river to the culvert. The correction factor for the entrance head loss coefficient for flow type 5 was chosen equal to 6 after calibration and given the interval proposed by Bodhaine  [46]. The inflow culverts have squared pillars in the middle and Cp is calculated according to Equation  (3)  where Lp equals 0. 35 m; b equals 1. 35 m; and β equals 2. 42 for rectangular pillars.
These measurements are used to calibrate the input parameters for the culverts of FCA Bergenmeersen. A detailed description of all model parameters needed to model the discharge through culverts is given in Appendix A. An overview of the culvert parameter values after calibration is given in Appendix B Table  A1. This 3D model of FCA Bergenmeersen is also available in the TELEMAC-MASCARET software package as a validation case. In this section, the choices for the culvert parameter values are described. The culvert entrance head loss coefficient C 1 was chosen equal to 0. 5 according to  [48]  when a large contraction is assumed going from the river to the culvert. The correction factor for the entrance head loss coefficient for flow type 5 was chosen equal to 6 after calibration and given the interval proposed by Bodhaine  [46].
Culvert Flow Implementation in TELEMAC The implementation of the equations to calculate discharges through culverts for different flow types was done using a number of head loss coefficients that have a physical basis. This has the advantage that for culverts, where no discharge measurements are available, a good estimation of the head loss coefficients can still be made and thus a good estimation of the discharge through the culverts can be achieved. The implementation of the code in TELEMAC has a few limitations. One of these limitations is that the discharge is calculated per time step. Within this time step, water is extracted from the inflow side of the culvert in the model and is added at the outflow side of the culvert in the model. For longer culverts and smaller time steps, this immediate exchange of water is an approximation.