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S. Niveditha

Bio: S. Niveditha is an academic researcher. The author has contributed to research in topics: Oceanography & Salinity. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper , the salinity gradient and oxygen zonation were studied in the Ulhas Estuary, and the dominance of diatoms (>70%) was observed in the euhaline-oxic region.

4 citations

Journal ArticleDOI
TL;DR: This paper aims at discovering the number of diverse user’s search goal for giving a query and for each goal a keyword is associated automatically and put forward a fuzzy similarity-based self-constructing algorithm for feature clustering.
Abstract: Different users have different search goals when they submit a query to a search engine. In this paper we aim at discovering the number of diverse user’s search goal for giving a query and for each goal a keyword is associated automatically. We initially derive user's search goal for a query by clustering our proposed feedback conclave. Then the feedback conclave is mapped to pseudo-documents so that the user's needs are retrieved efficiently. Finally, these pseudo documents are then clustered to deduce user search goals and depict them with some keywords. Though K means clustering is used in the existing system sometimes queries may not exactly represent user specific information needs. This method only finds whether a pair of query is belonging to the same set of goal and does not look into goal in detail. Hence we put forward a fuzzy similarity-based self-constructing algorithm for feature clustering. Our method works efficiently and will return provide better inferred properties than any other method. General Terms Data mining, Information retrieval

2 citations

TL;DR: An active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest by defining a novel critical social density value and showing that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value.
Abstract: : Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value.

Cited by
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Journal ArticleDOI
TL;DR: In this article , the authors used the pressure-state-response (PSR) approach and nonlinear methods to assess the trophic state of macrotidal estuaries.

2 citations

Journal ArticleDOI
TL;DR: In this article , a calibrated ocean chlorophyll 2-band (OC-2) model was used to retrieve chlorophyLL-a (chl-a) concentration in Ha Long Bay from 2019 to 2021, and the results showed that the OC-2 model was calibrated well to the conditions of the study areas.
Abstract: Chlorophyll-a is one of the most important water quality parameters that can be observed by satellite imagery. It plays a significant function in the aquatic environments of rapidly developing coastal cities such as Ha Long City, Vietnam. Urban population growth, coal mining, and tourist activities have affected the water quality of Ha Long Bay. This work uses Sentinel-2/Multispectral Instrument (MSI) imagery data to a calibrated ocean chlorophyll 2-band (OC-2) model to retrieve chlorophyll-a (chl-a) concentration in the bay from 2019 to 2021. The variability of chlorophyll-a during seasons over the study area was inter-compared. The chlorophyll-a concentration was mapped by analyzing the time series of water cover on the Google Earth Engine platform. The results show that the OC-2 model was calibrated well to the conditions of the study areas. The calibrated model accuracy increased nearly double compared with the uncalibrated OC-2 model. The seasonal assessment of chl-a concentration showed that the phytoplankton (algae) developed well in cold weather during fall and winter. Spatially, algae grew densely inside and in the surroundings of aquaculture, urban, and tourist zones. In contrast, coal mining activities did not result in algae development. We recommend using the Sentinel-2 data for seawater quality monitoring and assessment. Future work might focus on model calibration with a longer time simulation and more in situ measured data. Moreover, manual atmospheric correction of optical remote sensing is crucial for coastal environmental studies.

2 citations

Journal ArticleDOI
TL;DR: In this article , the sediment and surface water from 8 stations each from Dhamara and Paradeep estuarine areas were sampled for investigation of heavy metals, Cd, Cu, Pb, Mn, Ni, Zn, Cr, and Cu showing permissible (0 ≤ Ised ≤ 1, IEn ˂ 2, IEcR ≤ 150) to moderate (1 ≤ IEn ≤ 2, 40 ≤ Rf ≤ 80) contamination.

1 citations

Journal ArticleDOI
TL;DR: In this paper , the sediment and surface water from 8 stations each from Dhamara and Paradeep estuarine areas were sampled for investigation of heavy metals, Cd, Cu, Pb, Mn, Ni, Zn, Cr, and Cu showing permissible (0.31-26.56) in off-shore stations of the estuary.
Abstract: The sediments and surface water from 8 stations each from Dhamara and Paradeep estuarine areas were sampled for investigation of heavy metals, Cd, Cu, Pb, Mn, Ni, Zn, Fe, and Cr contamination. The objective of the sediment and surface water characterization is to find the existing spatial and temporal intercorrelation. The sediment accumulation index (Ised), enrichment index (IEn), ecological risk index (IEcR) and probability heavy metals (p-HMI) reveal the contamination status with Mn, Ni, Zn, Cr, and Cu showing permissible (0 ≤ Ised ≤ 1, IEn ˂ 2, IEcR ≤ 150) to moderate (1 ≤ Ised ≤ 2, 40 ≤ Rf ≤ 80) contamination. The p-HMI reflects the range from excellent (p-HMI = 14.89-14.54) to fair (p-HMI = 22.31-26.56) in off shore stations of the estuary. The spatial patterns of the heavy metals load index (IHMc) along the coast lines indicate that the pollution hotspots are progressively divulged to trace metals pollution over time. Heavy metal source analysis coupled with correlation analysis and principal component analysis (PCA) was used as a data reduction technique, which reveals that the heavy metal pollution in marine coastline might originate from redox reactions (FeMn coupling) and anthropogenic sources.

1 citations

Journal Article
TL;DR: This paper is clustering the feedback session by using Fuzzy c-means algorithm and uses this method to map feedback sessions to pseudo-documents which can efficiently reflect required data.
Abstract: When a query is submitted to search engine, user have in mind a fixed goal. Search engine gives thousands of results for such a query. Most of them are not useful for user so time and energy is wasted. For increasing retrieval precision, some new method provides manually verified answers to Frequently Asked Queries (FAQs). In this paper we are clustering the feedback session by using Fuzzy c-means algorithm. Also we use method to map feedback sessions to pseudo-documents which can efficiently reflect required data. Then, we evaluate the "Classified Average Precision (CAP)" of restructured web search results. In the area of web mining, more importance is given to fast and accurate extraction of information. Query suggestions provided by the search engine will help to find the user needs. But it may cover broad topics, so this may not be solution for achieving a better search result. Also same queries have different goals for different users. The analysis of user search goal improves the relevance and user satisfaction of the search engine. This method analyzes the user query and restructure the search results.