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Over the decades World Wide Web has become abundance source of distributed web content repository hyper-linked with diverse information domains. Performance of search engines in locating the information is exemplary but still there is inadequacy in search engines for focused crawling of web content. Web Page Classification being pivotal for information retrieval and management task plays imperative role for natural language processing in creating classified web document repositories and building indexed web directories. The conventional machine learning approaches extract the desired features from web pages in order to classify them whereas deep leaning algorithms learns the covet features as the network goes deeper and deeper. Transfer learning based Pre-trained models such as BERT attains impressive performance for text classification. In this study, we evaluate the effectiveness of adopting pre-trained model BERT for the task of classifying web pages into different categories. In this paper, we proposed an ensemble approach for web page classification by learning contextual representation using pre-trained bidirectional BERT and then applying deep Inception modelling with Residual connections for fine-tunes the target task by utilizing parallel multi-scale semantics. Experimental evaluation exhibit that proposed ensemble model outperforms benchmark baselines and achieve better performance in contrast to other transfer learning approaches evaluated on the web page classification task for different classification datasets.

This paper proposes a novel algorithm based on reinforcement learning-entitled asynchronous advantage actor-critic (A3C). Overflow queries are optimized to crawl the ranked deep web. A3C assigns the reward and penalty to the various queries. Queries are derived from the domain-based taxonomy that helps to fill the search forms. Overflow queries are the collection of queries that match with more than k number of results and only top k matched results are retrieved. Low ranked documents beyond k results are not accessible and lead to low coverage. Overflow queries are optimized to convert into non-overflow queries based on the proposed technique and lead to more coverage. As of yet, no research work has been explored by using A3C with taxonomy in the domain of ranked deep web. The experimental results show that the proposed technique outperforms the three other techniques (i.e., document frequency, random query, and high frequency) in terms of average improvement metric by 26%, 69%, and 92%, respectively.

The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of scientific literature, scholarly knowledge graphs have become critical to various applications where semantics can impart meanings to concepts. The objective of study is to report a literature review regarding knowledge graph construction, refinement and utilization in scholarly domain. Based on scholarly literature, the study presents a complete assessment of current state-of-the-art techniques. We presented an analytical methodology to investigate the existing status of scholarly knowledge graphs (SKG) by structuring scholarly communication. This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG. It further presents the review of knowledge graph utilization and refinement to provide a view of current research efforts. In addition, we offer existing applications and challenges across the board in construction, refinement and utilization collectively. This research will help to identify frontier trends of SKG which will motivate future researchers to carry forward their work.

Recognition of an object from an image or image sequences is an important task in computer vision. It is an important low-level image processing operation and plays a crucial role in many real-world applications. The challenges involved in object recognition are multi-model, multi-pose, complicated background, and depth variations. Recently developed methods have dealt with these challenges and have reported remarkable results for 3D objects. In this paper, a comprehensive overview of recent advances in 3D object recognition using Convolutional Neural Networks (CNN) has been presented. Along with the latest progress in 3D images, general overview of object recognition of 2D, 2.5D, and 3D images is presented.

SDG 3 is interwoven with, and draws complementarity from SDG 1 (Poverty), SDG 2 (Zero Hunger), SDG 4 (Quality Education), SDG 5 (Gender Equality), SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), SDG 14 (Life under Water), SDG 15 (Life on Land) and SDG 17 (Partnerships for Goals)[2]. By the sheer nature of its preventive, curative, rehabilitative, restorative and health-promoting role, functional health systems are indispensable for any individual, community, society and the country not only for the physical and mental well-being of the people but also for the incrementally increasing overall economic productivity and human development. Any disruption in access to quality service delivery is not only detrimental to human health but can also be responsible for loss of life and substantial economic losses. Institutional deliveries of newborn, care of mother and baby, protection of children from malnutrition and infectious diseases through vaccinations, health promotion, management of various acute and chronic diseases, surgical interventions for saving lives or restoring essential senses such as vision and hearing and responding to acute emergencies including trauma are some of the services that are at the core of the health system.

In the current millennium, as the threat of a pandemic with avian flu due to Influenza H5N1 virus emerged and subsequent Influenza H1N1 (2009) pandemic hit the world, major recommendations made by the WHO[6] were public education, social distancing, home quarantine and travel restrictions. While developing CRCS for communities, especially in countries such as India with diversity in culture, social norms, educational status, language, faiths and beliefs as well as demography, it is essential to identify and appropriately address the common practical concerns of the communities in responding to a pandemic. The WHO guidelines on ethical considerations in developing a public health response to influenza pandemiccan be adopted to develop comprehensive risk communication strategies in the local context and with relevant cultural values[10].

Some of the barriers hampering implementation in countries include fragmented and disconnected governance of health, animal health and environment, lack of clarity about the definition, concept and scope of One Health approach, under-recognition of its economic benefits, absence of an agreement between health, veterinary and environment professionals on way forward and inadequate training activities[9]. Framework for effective implementation of One Health incorporates political commitment, policy formulation, sustainable financing, programme development, knowledge sharing, institutional collaboration, capacity enhancement, engagement of civil society and active participation of the communities. Research needs to be promoted and encouraged for a better understanding of risk management, economic implications, improving intersectoral coordination and translating global best practices into national action plans to provide policy inputs and help in the development of cost-effective interventions.