dataesr/scientific-paragraphs-categorization huggingface dataset:
By adding geometric augmentation such as flipping and then cropping, much higher improvement on final accuracy is obtained. This considerably enhances the classification score, by 8. 67% then 31. 31% on Resisc-45, and 13. 06% then 17. 3% on EuroSAT. These spatially deforming augmentations can be considered as stronger augmentations than color-based ones. Therefore, they are key elements to make the alignment task reliable at learning discriminative representations within such non-contrastive methods. To conclude, spatial and strong augmentations play a crucial role in the performance of self-supervised models even when applied on RS datasets. The key idea of augmentations is to create two augmented views which retain the visual features of the same pseudo-class while still being only partly faithful to the original input image.
Transfer of Pre-Trained Models to Other Datasets To explore the generalization capability of self-supervised models, we investigate their performance when the downstream task is performed on a different dataset than the one used for pre-training. The ideal scenario is to use the same dataset for pre-training since the self-supervised representations are also discriminative of the domain, as discussed in  [110]. However, where few samples and labels are available for a task, another large-size unlabeled dataset can also be used for self-supervised pre-training to help the model first learn to discriminate features and then boost the performance on downstream task. It can also democratize weight initialization from self-supervised models by pre-training only once using a large remote sensing dataset.
A popular example is the supervised pre-trained ImageNet weights which have been adopted in many downstream tasks by transfer learning. To evaluate the improvements given from SSL pre-training on another dataset, we choose MoCo-v2 (contrastive) and BYOL (non-contrastive) frameworks since they were proven to provide stable performance on the two studied datasets in Tables  3  and 4. We conduct the fine-tuning protocol by using one dataset for pre-training and another dataset for fine-tuning (and validation). We compare the results with the case that both pre-training and fine-tuning are carried out using the same dataset. Resisc-45 and EuroSAT continue to be exploited in our transfer learning experiments, in both directions. Since the images from these datasets have different sizes (256 × 256 and 64 × 64 pixels, respectively), EuroSAT images are upscaled to 256 × 256 pixels to conduct these experiments.
One could choose to downscale Resisc-45 images into 64 × 64 pixels but this strategy may cause information loss from the image. We compare the results by varying the amount of labeled samples during fine-tuning with 1%, 10%, and 100% of the available training labels, similar to the fine-tuning evaluation protocol in Section 4. The comparative results of transfer learning performance are shown in Figure  13  in the case that the target dataset (i. e. , the one used for fine-tuning and validation) is EuroSAT (Figure  13a ) and Resisc-45 (Figure  13b ), respectively. To ease the observation, blue color represents the transfer learning scenario while orange represents the ideal case of pretraining/fine-tuning on the same dataset. The first line shows the results from MoCo-v2 and the second line shows those from BYOL.
As the first remark, both SSL frameworks have a similar behavior in this experiment, perhaps with a slight difference in term of accuracy levels, but this is insignificant. Then, it is easily observed that when the number of samples in the downstream task is very low (i. e. , the setting of 1% of labels in our experiment), it is much better to have the unlabeled samples from the same dataset for pre-training since the gap between two scenarios is large. Then, when the number of labels increases, the gap between the transfer learning and the ideal scenario is significantly reduced. By having 100% of the labels, we do achieve very close performance on both scenarios. This suggests that although the domains are not identical across pre-training and fine-tuning stages, there is still a benefit in leveraging SSL pre-training on large unlabeled datasets.
These results also indicate that SSL pre-training on another dataset can be a feasible initialization strategy when ImageNet weights are not readily available, such as when dealing with multi-spectral data, as shown in  [65]   As fine-tuning from supervised models trained on other large datasets with approximately close domain is a popular strategy to improve the downstream performance over training from scratch  [13] , we also compare self-supervised against supervised pre-training in the transfer learning scenario. The setting is similar to the previous experiment except that we also examine supervised models for the pre-training stage. For a reminder, the supervised setting requires that the pre-training dataset must be labeled, whereas for the SSL pre-training only a large amount of unlabeled images is sufficient.
The performance comparison can be observed in Figure  14. The orange, blue, and green colors represent the performance using MoCo-v2, BYOL, and supervised approach for pre-training, respectively. Figure  14a  shows the results observed by transfer learning Resisc-45 → EuroSAT, while Figure  14b  shows the results from the inverse transfer. Depending on the ratio of available samples in the fine-tuning dataset, the performance gap between the supervised and self-supervised settings behaves differently. With only 1% of the labeled samples, the two self-supervised approaches perform much better on the target dataset than the supervised pre-training. In fact, by training without labels, SSL approaches provide more generalized features which are more relevant in downstream tasks with a domain shift, whereas supervised models usually learn more specific representations.
Thus, joint-embedding learning equipped by domain adaption may become a key self-supervised strategy in remote sensing. In addition to the above perspective works that take into account the underlying challenges, we believe that another trend of self-supervised remote sensing is the use of transformer architectures instead of the classical CNN models, thanks to their emerging development in many vision-based deep learning tasks. However, this might be slowly adopted due to the popular and familiar use of CNNs in the remote sensing community. Then, other future works may be devoted to perform a deeper analysis of specific augmentation techniques as well as to investigate the different cross-domain combinations in order to build effective SSL scene classification models. Moreover, to boost the self-supervised trend in remote sensing, more works should be contributed to create more large-scale datasets which cover different data modalities for benchmarking.
Model data ID prevalence was derived from a national, cross-sectional, multicentre study (FERRIDON study) that estimated ID risk factors in French WB donors  [6]. Four groups of donors at high risk of ID were determined based on the following independent risk factors: gender, age, donor status (new or repeat donor), mean corpuscular haemoglobin, Hb level, number of donations in the previous 12 months and interval since previous donation (Table  1 ). Model inputs from the FERRIDON study are presented in Table  2. In the FERRIDON study, ID donors (<26 ng/ml) received a letter advising them to consult their GP and to postpone their next donation for at least 6 months. Their follow-up in the EFS database provided inter-donation interval data according to donor status, with and without ID and risk group.
The dispersion was measured with the half-width used to estimate the confidence interval, which measures the error in the estimate of the true mean and is defined as the 95% confidence coefficient times the standard deviation over the square root of the sample size. A sampling rule of 1:10 donors was simulated to reflect monthly donations from high-risk donors. Sensitivity analyses were conducted to assess uncertainty relating to various data sources for the following outcomes: rates of anaemia, ferritin recovery period, time to next donation and drop-out rate. Deterministic sensitivity analyses were performed for each type of input by applying AE25% of the reference input value to all risk groups (the error margin was assumed to be similar between the risk groups).
DISCUSSION ID is a common issue in the general population. Of the 18-74-yearold French adults, 6. 9% present AIS, mostly affecting women (11. 9%) rather than men (1. 5%)  [9]. Donors at high risk of ID were investigated in France. AIS was found to be more prevalent in WB donors, with women being affected in 19. 3% of cases and men in 6. 4% of cases, this figure increasing to 45. 6% in the high-risk group of repeat female donors  [6]. This study aimed to assess the impact of a ferritin testing policy on WB donations, IDs and anaemias through patient-level simulation and data observed in the real-world setting. Our simulation showed encouraging results: between 15% and 25% of IDs could be prevented in high-risk ID groups depending on the scenario. Regardless of the scenario, approximately one third of anaemia cases were prevented during the first year (Table  3 ).
ACKNOWLEDGEMENTS The  EFS Medical Department  financially supported the simulation analysis performed by PHE: operating grant. P. R. designed the research study, analysed the results and wrote the paper. A-M. F. wrote the draft of the paper. L. M. performed the statistical analysis, contributed to the design and wrote the paper. C. L. , C. C. , G. W. , C. J. and P. M. reviewed the final draft of the paper. H. L. performed the statistical analysis. A. V. designed the research study, analysed the results and wrote the paper.
Forest fires pose a potential threat to the ecological and environmental systems and natural resources, impacting human lives. However, automated surveillance system for early forest fire detection can mitigate such calamities and protect the environment. Therefore, we propose a UAV-based forest fire fighting system with integrated artificial intelligence (AI) capabilities for continuous forest surveillance and fire detection. The major contributions of the proposed research are fourfold. Firstly, we explain the detailed working mechanism along with the key steps involved in executing the UAV-based forest fire fighting system. Besides, a robust forest fire detection system requires precise and efficient classification of forest fire imagery against nofire. Moreover, we have curated a novel dataset (DeepFire) containing diversified real-world forest imagery with and without fire to assist future research in this domain.
The DeepFire dataset consists of 1900 colored images with 950 each for fire and no-fire classes. Next, we investigate the performance of various supervised machine learning classifiers for the binary classification problem of detecting forest fire. Furthermore, we propose a VGG19-based transfer learning solution to achieve improved prediction accuracy. We assess and compare the performance of several machine learning approaches such as k-nearest neighbors, random forest, naive Bayes, support vector machine, logistic regression, and the proposed approach for accurately identifying fire and no-fire images in the DeepFire dataset. The simulation results demonstrate the efficacy of the proposed approach in terms of accurate classification, where it achieves the mean accuracy of 95% with 95. 7% precision and 94.
Artificial intelligence (AI) technology has risen for an automated surveillance system. While discussing AI technologies in surveillance systems, one cannot ignore the advancements in machine learning algorithms for detection and recognition  [5, 6] , which has developed to be the integral part of computer vision technology  [7, 8]. Researchers' interest in machine learning has recently increased in other domains as well  [9] [10] [11]  due to the development of inexpensive data storage technology and high-performing GPUs. However, there is still a major obstacle when developing better algorithms for solving the practical tasks of machine learning that require large datasets. With the recent advancements in internet technologies, UAV networks have emerged as the driving force in the information technology industry  [12, 13].
These advantages of UAVs make them useful for forest fire detection. As fire seriously impacts human lives, various researches have been conducted towards early detection of fire, including urban and rural environments  [17]. The scope of these works covers all the settings from wildfires to forests and the urban. Conversely, the databases for forest fire are still progressing, as currently, there are limited datasets for this task. However, this research direction is recently gaining much attention, and some notable contributions have been proposed in the literature. The research varies from sensor networks to computer vision-based forest fire detection mechanisms. The main objective of this work is to propose a novel solution of UAV-based forest fire fighting system to further help in the development of forest fire detection and monitoring systems.
At the core of this research, we presume that intelligent systems for automatic fire detection can help fire brigades and disaster relief teams to respond quickly and reduce the impact of forest fires on the environment, society, and economy. The paper is structured as follows: Section 2 has the proposed UAV-based forest fire fighting system, Section 3 details the newly created DeepFire dataset, Section 4 has the overview of benchmarking methods for forest fire classification, Section 5 explains the DeepFire dataset benchmarking and performance evaluation, and Section 6 concludes this research paper.
DeepFire Dataset Acquisition We collected and arranged the forest fire and no-fire images for the classification problem to facilitate the researchers in proposing new methodologies in the domain of forest fire detection. We collected images from various online sources using keywords like forest fire, mountain fire, forest, and mountains. We have equally divided the DeepFire dataset into two classes of fire and no-fire, where the fire class contains images of forest and mountains with visible fire flames or fire flames with smoke clouds. On the contrary, the nofire class contains images of forest and green mountains with different angles. 3. 2. Preprocessing the Data. The DeepFire dataset is created by downloading the images from various search engines using different keywords, as mentioned earlier.
In addition to forest and mountain landscapes, many images contained undesirable objects, including persons and extinguishing machinery. Refining the dataset is essential for optimal model training and achieving better results. Therefore, we performed the necessary preprocessing steps on our Deep-Fire dataset, where we scanned through all the images to crop only the relevant region of interest (forest part). We purposely omitted the irrelevant objects in the images. After cropping, we resized all the images to a common resolution of 250 × 250. These preprocessing steps enabled the algorithm to easily and quickly learn the features for the classification problem. Figure  4  shows some images of both classes in the DeepFire dataset. 3. 3. Dataset Splitting. Our DeepFire dataset consists of total 1900 images, the fire class has 950 images, and the rest 950 are of no-fire class.
Transfer learning  [44]  is a deep learning method that has benefits twofold: saving the time and computational resources to train the model from scratch. Secondly, neural networks require a large dataset for training. To overcome  8 Mobile Information Systems a value between 0 and 1. In our proposed method, we used the prediction threshold of 0. 5. If the probability is less than 0. 5, the output is 0; otherwise, the output is 1. R x ð Þ = max 0, x ð Þ, σ x ð Þ = 1 1 + e -x ð Þ : ð1Þ 4. 2. 2. Hyperparameter Selection. In our proposed approach, we used Stochastic Gradient Descent (SGD) and binary crossentropy as optimization and loss functions, respectively. SGD is a first-order optimization and the widely used optimization method for training neural networks. For hyperparameter selection, we tested ten different learning rate values from 0.
Dataset Benchmarking and Performance Evaluation The DeepFire dataset is classified in our research, and results are analyzed on different machine learning algorithms such as KNN, GNB, SVM, RF, and LR. Moreover, the proposed VGG19-based transfer learning approach is evaluated on the DeepFire dataset to prove the effectiveness of CNNbased solution over the machine learning algorithms. The simulation environment is Anaconda Python 3. 7 with Keras/TensorFlow libraries with the system configurations of Dell i5-1135G7, 12 GB DDR4, Intel Iris Xe 6 GB. Subsequent subsections present the detailed performance evaluation of the proposed approach. 5. 1. Simulation Parameter Selection. The input for the proposed method was set to 128 × 128 image size. We also performed k-fold cross-validation for different values of k varied from 1 to 10 to ensure the best performance results.
Feature Extraction. Machine learning algorithms require prior feature extraction before training on these algorithms. The method for feature extraction we used in our research is the pixel features. The image size is the product of its rows, columns, and channels. The number of pixels in an image is the same as the image size; therefore, the number of features in every image of our dataset is 128 × 128 × 3, equal to 49,152 pixels or features. The algorithm identifies TN and TP as true negatives (correctly classified as no-fire) and true positives (correctly classified as fire). False positive FP is that no-fire image labeled as fire, and false negative FN is that fire image classified as no-fire. ER is the error rate calculated by the false predicted images to the total testing images. Precision is the ratio of correct positive outcomes to positive outcomes predicted by the method where recall is the ratio of correct positive outcomes to all samples that should be predicted positive.
Performance of Machine Learning Algorithms. The training performance of the machine learning algorithms on the DeepFire dataset in terms of accuracy, recall, precision, error rate, F1 score, and prediction accuracy is presented in Tables  4  and 5. From Table  5 , it can be observed that all machine learning algorithms performed well for classification problems to discriminate fire and no-fire images in the dataset. From the results, it can be noticed that NB performs worst in classification for our newly created forest fire dataset compared to the other machine learning algorithms. It might be because our dataset has some close covariance in the features of images such as sunset on the mountain, sunlight on trees, and yellow leaves, which the classifier took as fire images instead of no-fire.
The same happened with fire images labeled as belonging to the fire class. At the same time, the best performing algorithm is SVM with 0. 9131 prediction accuracy, 0. 9025 precision, and 0. 9263 recall. Figure  5  shows the confusion matrices of machine learning algorithms for comparative study. 5. 5. Performance of the Proposed Approach. The proposed VGG19-based transfer learning approach has predicted the images based on the presence or absence of fire instances around the trees or mountainous areas. Moreover, the results confirm that the CNN-based approach has superiority over the machine learning algorithms in classifying the DeepFire dataset. Figure  6  illustrates the confusion matrix of the proposed approach. It can be noted that the CNNbased approach has resulted in 179 TP and 182 TN while the falsely classified images are 11 and 8 belonging to the FN and FP, respectively.
The reason for misclassifications of images without fire (FP) might be due to the color of leaves that is too close or similar to that of fire, and the classifier misinterpreted them as fire images. It can be because of the lack or limited representation of such images with different angles in the training dataset. Observing the falsely classified fire images (FN), there could be multiple possibilities of fire being misclassified as no-fire images. The fire on top of the mountain gave a similar effect to sunlight at dawn on top of the mountain. Moreover, the fire color looked similar to autumn leaves. This might be due to two reasons: (1) the image quality can   2 ) the lack of representation of similar images with different angles in the training dataset. We curated the DeepFire dataset by retrieving images from online resources where the spatial resolution of some images was very low.
Further resizing the images to a common size also deteriorated the image quality. 5. 6. Comparative Evaluation. The machine learning algorithms show promising results in the classification of the forest fire problem. While SVM has the best prediction accuracy of 0. 9131 among the other considered machine learning algorithms, the LR shows the best precision of 0. 9090. The proposed VGG19-based transfer learning approach achieves higher performance than the machine learning algorithms with 0. 95 prediction accuracy, 0. 05 error rate, 0. 94 precision, and 0. 96 recall. The comparative evaluation of the proposed approach with SVM and LR is presented in Table  6. We have also compare the performance of our proposed approach on the DeepFire dataset with the previous works in the literature for forest fire classification.
Conclusion In this research work, we explored AI-based surveillance for forest fire detection. Forest fire poses a significant threat to the ecological and environmental system and the natural resources, impacting human lives. In this research, we proposed a UAV-based forest fire fighting system where UAVs with AI capabilities do continuous forest surveillance to detect fire. Further, we explained the detailed working mechanism and the key steps involved in executing the UAVbased forest fire fighting system. Besides, a robust forest fire detection system requires precise and efficient classification of forest fire imagery against no-fire. Moreover, we curated a new dataset (DeepFire) for the forest fire binary problem to help the prospective researchers in this domain. The DeepFire dataset consists of 1900 diverse colored images with 950 each for fire and no-fire classes.
Further, we explored computer vision technology for forest fire classification based on machine learning algorithms and proposed the VGG19-based transfer learning approach. For DeepFire dataset classification, we evaluated and compared the performance of machine learning algorithms such as k-nearest neighbors (KNN), naïve Bayes (NB), random forest (RF), support vector machine (SVM), and logistic regression (LR) and the proposed VGG19-based transfer learning approach. The simulation results demonstrated that the proposed approach performed better than machine learning algorithms, achieving 95% prediction accuracy with 94. 2% recall and 95. 7% precision. Overall, the performance of the proposed approach on the DeepFire dataset showed promising outcomes for the forest fire classification. In the future work, we will improve the images in the DeepFire dataset in terms of spatial resolution and decrease the false alarms by proposing a CNN-based model for forest fire detection problem.
3. 1. 1 Dataset Classification. We have considered the binary classification problem, so the images in the DeepFire dataset belong to two classes. The DeepFire dataset has images of multiple angles of view and diversified range of scenery to better train the model in terms of discriminating the images with and without the fire. The newly created DeepFire dataset has a total of 1900 images, where 950 images are of the fire instance, and the remaining 950 images belong to no-fire.