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The examination of the drainage system, including drainage density, land use/land cover analysis, slope characteristics, size, and surface morphological features, was conducted using satellite-based remote sensing techniques. This study focused on employing remote sensing and GIS (Geographic Information System) techniques to monitor morphometric parameters. The Digital Elevation Model (DEM) data, with a resolution of 30 meters, was utilized to extract the morphometric parameters for the catchment area of Kanchipuram district. The analysis encompassed various morphometric parameters such as stream distance, stream order, number of streams, bifurcation ratio, drainage density, drainage frequency, drainage texture, elongation ratio, circularity ratio, and form factor, among others. Additionally, slope aspects and contour data were derived from surface tools. A total of 53 morphometric parameters were monitored during this study, employing ArcGIS software for the computation of these parameters. The utilization of the application of remote sensing and GIS methods in this study offers valuable insights for the management of rainwater harvesting, watershed management, and drainage development. By accurately assessing the drainage network and morphometric characteristics, this study provides a foundation for informed decision-making and planning in these areas. The results of this research carry substantial consequences for the strategic development and administration of water resources. The identification of critical areas prone to flooding or water stagnation can inform the implementation of appropriate measures such as rainwater harvesting structures, drainage channels, or reservoirs. Watershed management initiatives can benefit from the understanding of the catchment's characteristics and the prioritization of conservation and restoration efforts. [ABSTRACT FROM AUTHOR]

The study is focused towards the relationship behavior between technology and mathematics among university students. In 21st century both are closely related to each other. Data analysis is done to study the confidence level and attitude of university students in Mathematics and use of technology, Impact of technology on performance in Mathematics. The analysis indicates that the students are more confident in technology as compared to mathematics. None of the students are confident in learning mathematics as a subject and with technology. [ABSTRACT FROM AUTHOR]

Normalized Difference Vegetation Index (NDVI) method was utilized for the of LU/LC change deduction map. Generally land use is the land used by human to obtain product and take the benefits by using the land resources and land cover in other hand is defined as the physical materials that cover the earth surface. In this study NDVI based classification of remote sensing dataset show the land cover changes of the study area for different years from 1995 to 2020 with 5 years interval. Under this study we used Landsat 4-5 TM and Landsat 8 OLI imagery. Landsat 8 imagery available from 2013, so for 2015 and 2020 we takes Landsat 8 images and for other years which are before 2013 for that we take Landsat 4-5 images. NDVI calculation was carried out by raster calculator tool in ArcGIS. From this major changes are found in the vegetation and some minor changes found in land, water body and forest and grassland. In the year 1995 and 2005 the vegetation cover is less than the other years. A major change of vegetation found in these time period. Mostly area of this study area covers with urban land and barren land. [ABSTRACT FROM AUTHOR]

Nowadays, remotely sensed data are continuously used for different purposes in various applications. Due to rapid deforestation, change in land use/land cover (LULC), intensive agriculture and urbanization have major impacts on desertification in many districts of few states such Haryana, Rajasthan, and Punjab etc. of India. This problem is not limited to only in India but world is also facing similar problem from last four decades. The objective was to understand the dynamics of aeolian sand affected wasteland through remotely sensed imagery and change detection analysis method. We have selected Landsat sensor as it have the largest historical archive of the earth resource data hence, we have collected the remotely sensed data of 1977 to 2017 of Sirsa district of Haryana, India. Total nine Landsat satellite images were acquired at five year interval with different dates. The GIS platform was used to analyze output of remote sensing data. Results outline that, aeolian sand affected land decreased in each five year interval of last four decades and its decreasing in progress by 19.01% every five year. Moreover, 3.15 km2 area (or 2.01% area of the 1977) the study area expected to be converted into productive land in next five to ten year under state government plan. These results indicate that conversion of desertification into ossification in the study area. [ABSTRACT FROM AUTHOR]

A large amount of social media data hosted on platforms like Twitter, Instagram, Facebook, etc. are event-based and hold a substantial amount of real-world data. Event-based information can appear on any social media site in the form of news items, images, videos, audio clips, status updates, etc. The task of event detection refers to identifying data relevant to an event and the classification of this relevant data to different event types. Traditional social media event detection techniques focused mainly on a single modality as the data shared were mostly homogenous. However, the current social media data is multimodal and includes text, images, audio, and video clips, and geolocations. Multimodal event detection techniques are essential for handling such heterogeneous data. Among all the social media sites Twitter is the most popular as users share event-related short messages and photos in real-time generating several thousands of tweets very frequently. In this paper, we focus on providing a comprehensive survey of event detection from social media, especially from the widely used platform, Twitter. The survey focuses mainly on research done on event detection using the two main modalities single and multimodality. At the end of the paper, we discuss the relevance of multimodal event detection from social media data which currently spans multiple dimensions. [ABSTRACT FROM AUTHOR]