Rakshith Mahishi

Xiaoyin Pan, Christian R. Shelton, Rakshith Mahishi et al.

We study generative modeling of spatial point processes (SPP), where both the number of points and their spatial configuration are governed by a joint distribution. While diffusion models have achieved strong performance in modeling complex distributions, extending them to variable-cardinality SPP remains challenging. Existing approaches either decouple the modeling of cardinality and spatial structure, or rely on discrete trans-dimensional operations to modify the number of points, resulting in inflexible and asymmetric generative dynamics. We propose the existence-field diffusion model (EFDM) for spatial point processes modeling, where each potential point is associated with an existence variable representing its degree of presence. This enables a unified diffusion process that jointly models both spatial locations and cardinality without requiring explicit discrete transitions. We demonstrate that our approach provides a flexible and general framework for generative modeling of spatial point processes, achieving improved modeling capability on datasets with varying cardinality.

Praneeth Kumar T, Nidhi Srivastava, Rakshith Mahishi et al.

In the current technological era, the medical profession has emerged as one of the researchers' favorite subject areas, and cancer is one of them. Because there is now no effective treatment for this illness, it is a matter of concern. Only if this disease is discovered early may patients be rescued (stage I and stage II). The likelihood of survival is quite low if it is discovered in later stages (stages III and IV). The application of machine learning, deep learning, and data mining techniques in the medical industry has the potential to address current issues and bring benefits. Numerous symptoms of cancer exist, including tumors, unusual bleeding, increased weight loss, etc. It is not necessary for all tumor types to be cancerous. There are two sorts of tumors: benign and malignant. To give patients, the right care, symptoms must be carefully examined, and an automated system is to distinguish between benign and malignant tumors. Most data produced in today's online environment comes from websites related to healthcare or social media. Using data mining techniques, it is possible to extract symptoms from this vast amount of data, which will be helpful for identifying or classifying cancer. This research classifies bacteria cells as benign or cancerous using various deep-learning Algorithms. To get the best and most reliable results for the classification, a variety of methodologies and models are trained and improved.