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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
👋🏼 Hello World, I'm Chaitanya!
About me
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Posts
Learning Like Machines: Decoding the Different Types of Machine Learning
Published:
Learning Like Machines: Decoding the Different Types of Machine Learning
Delving into the World of Machine Learning: A Beginner’s Guide (Part 2)
Published:
Delving into the World of Machine Learning: A Beginner’s Guide (Part 2)
portfolio
Medicinal Leaf Classification with Transfer Learning and fine tuning
Implementation of Fine tuning pre-trained models along with data parallelism”
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publications
Cyclone Intensity Estimation Using Pre Trained Deep Learning models
Published in International Journal For Innovative Engineering and Management Research, 2023
Cyclones are categorized into various categories based on their intensity which is calculated by various methods and sources. Estimation of the intensity of cyclone helps them to categorize. Based on that the government and people can take precautionary measures to avoid the huge loss of life and property. India meteorological Department uses the Tropical Cyclone Intensity Scale to categorize the cyclones into 7 categories. For this project we are using the INSAT3D Infrared & Raw Cyclone Imagery 2012-2021 from the kaggle. The Raw Data has been sourced from the MOSDAC server. It consists of cyclone images, infrared images of cyclones. It consists of 140 images of cyclones from 2012-2021 that were happened around India. In this project we are trying to estimate the intensity of cyclones using the- state-of-the-art deep learning models as they are pre-trained on the large datasets. We are using Four pre-trained deep learning models to compare the results of this approach.
Recommended citation: Venkata Sai Chaitanya kolliboyina, et al. Cyclone Intensity Estimation Using Pre- Trained Deep Learning Models. International journal for innovative engineering and management research., 2023, pp. 734–51, https://doi.org/ 10.48047/IJIEMR/V12/ISSUE 01/70. http://chaitanya-kolliboyina.github.io/files/Cyclone intensity using pre trained DL.pdf
Fall Prediction of Elder Person Using CCTV Footage and Media Framework
Published in 2023 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), 2023
The elderly population often requires assistance with day-to-day activities and healthcare. The development and use of efficient procedures and systems that would enable affordable clinic care and monitoring services, particularly for the senior population, are receiving more attention. The concept of “ageing in place” refers to the capability of older individuals to live in their own homes and communities comfortably, securely, and independently, without being restricted by their age, financial situation, or physical ability. Moving in with relatives, relocating to a medical facility, or entering an assisted living facility can all cause psychological distress for older adults. This stress can deteriorate their health and lower their quality of life. MediaPipe Body Landmark model employs BlazePose to infer 33 3D body landmarks or 25 upper-body landmarks from RGB video frames to provide high-quality body posture monitoring. It can be configured to focus solely on the upper body, in which case it only estimates the first 25 landmarks, but typically, it identifies landmarks for every body pose. MediaPipe Hands is an effective method for monitoring hands and fingers. A single picture may be used to infer up to 21 3D hand landmarks. Mobile devices can enable a variety of modern life applications by simultaneously detecting human gestures, face landmarks, and hand movements in real-time, such as augmented reality try-on and effects, posture control, sign language recognition, fitness and sport analysis, and posture control.MediaPipe now offers immediate, exact, distinct, yet complementary solutions for these challenging jobs. Combining them all into a real-time, conceptually cohesive edge solution requires the simultaneous inference ofmany, dependent neural networks, which makes it a particularly difficult challenge. We are proposing a low cost and efficient system by using mediapipe body landmark model for extracting the features and PCA-LSTM algorithm to predict the fall in elderly people.
Recommended citation: D. M. Nazeer, V. S. Chaitanya kolliboyina, K. K. Tiruveedula, I. s. H. Punithavathi, C. Shwetha and D. Anusha, "Fall Prediction of Elder Person Using CCTV Footage and Media Framework," 2023 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), Hyderabad, India, 2023, pp. 138-144, doi: 10.1109/ICETCI58599.2023.10331422. https://ieeexplore.ieee.org/abstract/document/10331422
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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