GROWTH OF INVASIVE AQUATIC MACROPHYTES OVER TAPI RIVER

Aquatic macrophytes are important elements of freshwater ecosystems, fulfilling a pivotal role in the ecological functions of these environments and biogeochemical cycles. Although aquatic macrophytes are beneficial, some species can hinder human activity. They can clog reservoirs and reduce water availability for human needs. Surveys of macrophytes are hindered by logistic problems, and remote sensing represents a powerful alternative, allowing comprehensive assessment and monitoring. The objectives of this study was to map temporal changes in the macrophytes using time series multispectral dataset over Tapi River, Surat. The field trip was conducted over the Tapi River on 22nd June 2018, where in-situ spectral response dataset were acquired using ASD Spectroradiometer. Water samples were also collected over three locations, one before entering the city (Kamrej), second at the Sarthana water treatment plant and third at the outer end (causeway). The nutrient concentration was less before entering the city (Ammonical Nitrogen 0.056 mg/L and phosphate 0.0145 mg/l), while higher concentration (Ammonical Nitrogen 0.448 mg/l and phosphate 0.05 mg/l) was observed within the city. Maps of aquatic macrophytes fractional cover were produced using Resourcesat-2/2A (LISS-III) dataset covering a period of 2012–2018. Maximum extent was observed in February-March of every year. Although during monsoon, lot of agriculture run-off and nutrients will come into the river, but main flow of water will dilute its concentration. During summer, the same nutrient concentration will boost these macrophytes due to less availability of stream water. Within the area of 16 km2 between Kamrej and causeway, 3.35 % was covered by macrophytes during March 2013. This area coverage increase to 36.41 % in March 2018. Based on these maps, we discuss how remote sensing could support monitoring strategies and provide insight into spatial variability, and by identifying hotspot areas where invasive species could become a threat to ecosystem functioning

Privacy enhanced key management protocol for handling remote data using deep learning and evolutionary models

AI is becoming more common because of expert automated systems and modern technology's ability to process information. This makes our lives easier and more exciting. It's better for privacy, reliability, and network resource use if AI inference algorithms run on a smart device rather than in the cloud, so this is how most smart devices use them. For a long time, on-device intelligence has been more common [1]. Cloud and big data have become important tools for pooling resources and training equipment data on the device [2]. Using the important technologies, AI spreads and humans become better at their jobs. Basic Communication technology like gesture recognition is based on data and uses ANN (Artificial Neural Network) algorithms to do periodic tracking and 3D hand modelling [3]. It has been used to run multimedia apps as well as handheld devices by gestures. Security in touch-enabled devices is also important because of authenticating users, which is provided by a learned display contact data set and classifier based on the K-means algorithm. AI-based algorithms are also used in industrial applications to combine live tracking, predictive maintenance, virtual support, and ground truth. This is done by using the cloud and AI-based algorithms