http://localhost:8081/jspui/handle/123456789/15627 Sat, 17 Oct 2026 03:34:53 GMT 2026-10-17T03:34:53Z http://localhost:8081/jspui/handle/123456789/21509 Title: Evaluation of Machine Learning and Deep Learning Techniques For Extraction Of Flood Extents Using Synthetic Aperture Radar Authors: Dev, Sachin Abstract: Flooding is a worldwide issue, and floods are getting increasingly frequent and severe around the world . Floods account for 40% of natural disasters in the world (“Weather-related disasters” 2021) . Flood inundation extent is crucial information for flood mitigation, readiness, planning, and response actions (“Flood mitigation measures | Department of Infrastructure, Planning and Logistics” 2018) . Hence it is important to model and anticipate floods. Flood inundation extent estimates that are accurate and updated in real time can help people comprehend flood risk and damage, as well as aid flood mitigation and planning. Using intelligent systems and unique communication platforms, flood inundation maps can be employed in flood risk communication. Climate change, urbanisation, and other human activities disturb the hydrological cycle across the world, resulting in water pollution, floods, and droughts (Li, Zhang, and Xu, n.d.). "An accumulation of water over places that are typically not submerged" is what a flood is defined as . It is a natural event that can result in fatalities, environmental damage, and economic growth in a town. The extent of the deluge's destruction is largely determined by the actions taken by local and global authorities. In this case, flood management is crucial, with a focus on flood mapping, monitoring, forecasting, warning, and floodplain management can be classified based on the speed of the water, the location of the flooding, or the source of the flooding. Thu, 01 Jun 2023 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/21509 2023-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21508 Title: Development of flood assistance system using social media and deep learning methods Authors: Goje, Satyam Abstract: This thesis investigates the application of Twitter data and deep learning natural language processing (NLP) models for identifying tweets in urban areas during flood events where individuals require assistance. The research aims to leverage the real-time nature of social media to facilitate prompt and targeted aid delivery in urban flood situations. The study focuses on the development of deep learning NLP models capable of accurately classi fying flood-related tweets as help-seeking or non-help-seeking. By harnessing the power of these models, the thesis aims to enable efficient rescue operations by identifying and prioritizing areas and individuals in urban settings that are most in need of assistance. Ad ditionally, geocoding techniques are utilized to extract location information from identified help-seeking tweets, aiding in the geospatial targeting of rescue efforts. The findings of the study highlight the effectiveness of combining Twitter data, deep learning NLP models, and geocoding techniques to improve response times and optimize resource allocation in urban flood situations. The research contributes to the field of disaster management by show casing the potential of social media analytics and advanced NLP techniques in enhancing flood response strategies in urban environments. Thu, 01 Jun 2023 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/21508 2023-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21507 Title: ENHANCING FACE RECOGNITION SECURITY: A COMPREHENSIVE STUDY ON ANTI-SPOOFING TECHNIQUES AND A NOVEL APPROACH BASED ON MTCNN WITH EMOTION AND POSTURE DETECTION Authors: H S, Sumanth Abstract: Face recognition is a prominent technology with numerous applications, ranging from security systems to personalized user experiences. However, questions concerning the security and dependability of facial recognition systems have been raised due to their susceptibility to spoofing attacks. This thesis presents a comprehensive study on face recognition, including a literature survey on different approaches, their accuracy, and the challenges associated with them. The thesis specifically focuses on spoofing attacks on Face recognition system, such as photo attacks, video replay attacks, and 3D face mask attacks, which can deceive face recognition systems. Various anti-spoofing techniques, including liveness detection, motion detection, and analysis of facial reflectance and texture properties, are explored to mitigate the risks associated with spoofing attacks. Furthermore, a novel anti-spoofing model based on the Multi-task Cascaded Convolutional Networks (MTCNN) architecture is proposed. This model leverages the capabilities of MTCNN for face detection and recognition, and incorporates real time emotion detection and posture detection to enhance the anti-spoofing capabilities of the system. The proposed model is tested and evaluated, and its performance is compared with various other PAD models, demonstrating its effectiveness in mitigating spoofing attacks. The work in this thesis advances face recognition security by shedding light on various strategies, highlighting difficulties, and putting forth a fresh anti-spoofing model. In order to improve the robustness and accuracy of face recognition systems in the face of spoofing attempts, the results emphasize the need of combining various cues and real-time detection procedures. Thu, 01 Jun 2023 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/21507 2023-06-01T00:00:00Z http://localhost:8081/jspui/handle/123456789/21506 Title: Spatiotemporal prediction of dynamic particulate matter using deep learning methods Authors: Gupta, Vineet Abstract: With the unprecedented increase in the world population, there is an increase in the number of vehicles and industries, which results in a worldwide increase in air pollution. Currently, the world population touched the mark of 8 billion. Vehicular emission is the main reason for the increase in air pollution. Thus, measuring and modeling air pollution and taking preventive actions efficiently becomes extremely important. For modeling, traffic characteristics including traffic volume and density near fixed monitoring sites, play an important role. Other factors such as meteorological data like Relative Humidity (RH), Atmospheric Temperature (AT), Wind Speed (WS), and Barometric Pressure (BP) are also used. The pollution decreases during summers as the temperature increases, wind speed increases in summers, and the humidity is less. Due to all these reasons air pollution decreases as winds blow away the pollution. As the winter approaches at the start of November, the pollutants accumulate in the air due to high humidity and wind speeds. There are only 36 monitoring stations located in Delhi. There is a large need of monitoring stations, but it is not feasible to install new monitoring stations as there is a high cost of setting and maintenance of the static stations. So, there is a need to develop a prediction model for the prediction of the pollutants at the locations that are away from the monitoring stations. The study mainly takes four types of features for model development. These features are: Meteorological features, Traffic flow features, Point of Interest (POI) features, and historical data on pollution gathered from the monitoring stations. The research study aims to develop a model to minimize the error between the actual values and the predicted values. Thu, 01 Jun 2023 00:00:00 GMT http://localhost:8081/jspui/handle/123456789/21506 2023-06-01T00:00:00Z