Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Temporal topic modeling of scholarly publications for future trend forecasting(Springer Verlag service@springer.de, 2017) Bhopale, A.P.; Kamath S․, S.S.The volume of scholarly articles published every year has grown exponentially over the years. With these growths in both core and interdisciplinary areas of research, analyzing interesting research trends can be helpful for new researchers and organizations geared towards collaborative work. Existing approaches used unsupervised learning methods such as clustering to group articles with similar characteristics for topic discovery, with low accuracy. Efficient and fast topic discovery models and future trend forecasters can be helpful in building intelligent applications like recommender systems for scholarly articles. In this paper, a novel approach to automatically discover topics (latent factors) from a large set of text documents using association rule mining on frequent itemsets is proposed. Temporal correlation analysis is used for finding the correlation between a set of topics, for improved prediction. To predict the popularity of a topic in the near future, time series analysis based on a set of topic vectors is performed. For experimental validation of the proposed approach, a dataset composed of 17 years worth of computer science scholarly articles, published through standard IEEE conferences was used, and the proposed approach achieved meaningful results. © Springer International Publishing AG 2017.Item Water Salinity Assessment Using Remotely Sensed Images—A Comprehensive Survey(Springer Science and Business Media Deutschland GmbH, 2023) Priyadarshini, R.; Sudhakara, B.; Kamath S․, S.; Bhattacharjee, S.; Umesh, P.; Gangadharan, K.V.In the past few years, the problem of growing salinity in river estuaries has directly impacted living and health conditions, as well as agricultural activities globally, especially for those rivers which are the sources of daily water consumption for the surrounding community. Key contributing factors include hazardous industrial wastes, residential and urban wastewater, fish hatchery, hospital sewage, and high tidal levels. Conventional survey and sampling-based approaches for water quality assessment are often difficult to undertake on a large-scale basis and are also labor and cost-intensive. On the other hand, remote sensing-based techniques can be a good alternative to cost-prohibitive traditional practices. In this article, an attempt is made to comprehensively assess various approaches, datasets, and models for determining water salinity using remote sensing-based approaches and in situ observations. Our work revealed that remote sensing techniques coupled with other techniques for estimating the salinity of water offer a clear advantage over traditional practices and also is very cost-effective. We also highlight several observations and gaps that can be beneficial for the research community to contribute further in this significant research domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.