Data Augmentation Algorithm Based on Generative Antagonism Networks (GAN) Model for Optical Transmission Networks (OTN)

OTN (Optical Transmission Networks) is one of the mainstream infrastructures over the ground-transmission networks, with the characteristics of large bandwidth, low delay, and high reliability. To ensure a stable working of OTN, it is necessary to preform high-level accurate functions of data traffic analysis, alarm prediction, and fault location. However, there is a serious problem for the implementation of these functions, caused by the shortage of available data but a rather-large amount of dirty data existed in OTN. In the view of current pretreatment, the extracted amount of effective data is very less, not enough to support machine learning. To solve this problem, this paper proposes a data augmentation algorithm based on deep learning. Specifically, Data Augmentation for Optical Transmission Networks under Multi-condition constraint (MVOTNDA) is designed based on GAN Mode with the demonstration of variable-length data augmentation method. Experimental results show that MVOTNDA has better performances than the traditional data augmentation algorithms.

Application of Random Region Augmentation Algorithm in Deep Learning

In the field of computer vision, the collection and sorting of image data is the core driving force. However, the current data collection work cannot perfectly collect the image data of each actual landing scene. The purpose of the data augmentation algorithm is to increase the diversity of the data set and improve the robustness of the model. Traditional data augmentation methods include geometric augmentation and color augmentation, mainly including flipping, rotating, cropping, translation, stretching, zooming, adding noise, blurring, Dropout, Cutout, color jittering. Traditional data augmentation methods have certain limitations, and the effect is not obvious. Based on the idea of Cutout algorithm, this paper proposes the RRA augmentation algorithm, which divides four quadrant regions in the image, and randomly selects the ROI region in each region, and is different from the Cutout algorithm directly discarding the region, but randomizing the region Enhance the color, and finally do geometric augmentation processing on the overall image. Compared with the original single data augmentation operation, the algorithm improves precision by 7%, and recall improves by 7%.

IFND: a benchmark dataset for fake news detection

Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work, fake news identification poses many challenges due to the lack of a comprehensive publicly available benchmark dataset. There is no large-scale dataset that consists of Indian news only. So, this paper presents IFND (Indian fake news dataset) dataset. The dataset consists of both text and images. The majority of the content in the dataset is about events from the year 2013 to the year 2021. Dataset content is scrapped using the Parsehub tool. To increase the size of the fake news in the dataset, an intelligent augmentation algorithm is used. An intelligent augmentation algorithm generates meaningful fake news statements. The latent Dirichlet allocation (LDA) technique is employed for topic modelling to assign the categories to news statements. Various machine learning and deep-learning classifiers are implemented on text and image modality to observe the proposed IFND dataset's performance. A multi-modal approach is also proposed, which considers both textual and visual features for fake news detection. The proposed IFND dataset achieved satisfactory results. This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models.

An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.