Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs

Weather forecasting is an essential task to tackle global climate change. Weather forecasting requires the analysis of multivariate data generated by heterogeneous meteorological sensors. These sensors comprise of ground-based sensors, radiosonde, and sensors mounted on satellites, etc., To analyze the data generated by these sensors we use Graph Neural Networks (GNNs) based weather forecasting model. GNNs are graph learning-based models which show strong empirical performance in many machine learning approaches. In this research, we investigate the performance of weather forecasting using GNNs and traditional Machine learning-based models.Comment: Errors in the Results sections. Experiments are conducted to rectify the error

3D Object Detection in LiDAR Point Clouds using Graph Neural Networks

LiDAR (Light Detection and Ranging) is an advanced active remote sensing technique working on the principle of time of travel (ToT) for capturing highly accurate 3D information of the surroundings. LiDAR has gained wide attention in research and development with the LiDAR industry expected to reach 2.8 billion $ by 2025. Although the LiDAR dataset is of rich density and high spatial resolution, it is challenging to process LiDAR data due to its inherent 3D geometry and massive volume. But such a high-resolution dataset possesses immense potential in many applications and has great potential in 3D object detection and recognition. In this research we propose Graph Neural Network (GNN) based framework to learn and identify the objects in the 3D LiDAR point clouds. GNNs are class of deep learning which learns the patterns and objects based on the principle of graph learning which have shown success in various 3D computer vision tasks.Comment: Errors in the results section. Experiments are carried out to rectify the result

Development of a Nasya fitness form for clinical practice

Introduction: Nasya karma is prime treatment modality for ūrdhvajatrugata vikāra. Though classics clearly mention yogya (arha), ayogya (anarha) criteria for Nasya karma some complications were noticed while practicing. In KLEUS Shri BMK Ayurveda Hospital Belgaum, out of 2867 patients 58 (0.58%) cases reported various complications during and after Nasya karma in the year of 2011 even after taking utmost care in selection of patients as well as drugs. This gave rise to need to develop quick screening criteria to minimize errors. Objective: To develop Nasya fitness form for clinical practice to further minimize unusual complications and thus obtain the maximum result. Materials and Methods: Literature pertaining to Nasya karma, Nāsa śarīra with anatomy of nose, vasculature, innervation, examination of the nose and various anatomical pathologies were considered to develop the fitness form. Results: On the basis of examination of external nose, nasal cavity, concha, nasopharynx and paranasal sinus by anterior and posterior rhinoscopic examination fitness form was developed. Conclusion: Present fitness format will not only help to assess the nasal pathologies, which are obstacles for drug delivery, but also will help to attain optimum results and avoid unusual complications