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Recent advancements in deep learning architecture have increased its utility in real-life applications. Deep learning models require a large amount of data to train the model. In many application domains, there is a limited set of data available for training neural networks as collecting new data is either not feasible or requires more resources such as in marketing, computer vision, and medical science. These models require a large amount of data to avoid the problem of overfitting. One of the data space solutions to the problem of limited data is data augmentation. The purpose of this study focuses on various data augmentation techniques that can be used to further improve the accuracy of a neural network. This saves the cost and time consumption required to collect new data for the training of deep neural networks by augmenting available data. This also regularizes the model and improves its capability of generalization. The need for large datasets in different fields such as computer vision, natural language processing, security and healthcare is also covered in this survey paper. The goal of this paper is to provide a comprehensive survey of recent advancements in data augmentation techniques and their application in various domains.

Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy, with the brim-full horizon yet to unfold. In the absence of effective antiviral and limited medical resources, many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources. Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals. Regardless of discourse on medical resources and diversities in masks, all countries are mandating coverings over nose and mouth in public areas. Towards contribution of public health, the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask. The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy. We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps. In addition, we also propose a bounding box transformation to improve localization performance during mask detection. The experiments are conducted with three popular baseline models namely ResNet50, AlexNet and MobileNet. We explored the possibility of these models to plug-in with the proposed model, so that highly accurate results can be achieved in less inference time. It is observed that the proposed technique can achieve high accuracy (98.2%) when implemented with ResNet50. Besides, the proposed model can generate 11.07% and 6.44% higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.

Past few decades have witnessed an informat ion big bang in the form of World Wide Web leading to gigantic repository of heterogeneous data. A humble journey that started with the network connection between few co mputers at ARPANET p roject has reached to a level wherein almost all the co mputers and other communication devices of the world have joined together to form a huge global in formation network that makes availab le most of the information related to every possible heterogeneous domain. Not only the managing and indexing of th is repository is a big concern but to provide a quick answer to the user's query is also of critical importance. A mazingly, rather miraculously, the task is being done quite efficiently by the current web search engines. This miracle has been possible due to a series of mathematical and technological innovations continuously being carried out in the area of search techniques. This paper takes an overview of search engine evolution from primitive to the present.

Abstract-The conventional clustering algorithms have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. Fuzzy clustering methods have the potential to manage such situations efficiently. Fuzzy clustering method is offered to construct clusters with uncertain boundaries and allows that one object belongs to one or more clusters with some membership degree. In this paper, an algorithm and experimental results are presented for fuzzy clustering of web documents using equivalence relations and fuzzy hierarchical clustering.