Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Puppala, Sai"'
Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized GPT and Context-based Social Media LLM models, utilizing federa
Externí odkaz:
http://arxiv.org/abs/2408.05243
Our paper introduces a novel approach to social network information retrieval and user engagement through a personalized chatbot system empowered by Federated Learning GPT. The system is designed to seamlessly aggregate and curate diverse social medi
Externí odkaz:
http://arxiv.org/abs/2408.05242
Autor:
Puppala, Sai, Hossain, Ismail, Alam, Md Jahangir, Talukder, Sajedul, Talukder, Zahidur, Bahauddin, Syed
Federated Learning (FL) has emerged as a transformative approach for enabling distributed machine learning while preserving user privacy, yet it faces challenges like communication inefficiencies and reliance on centralized infrastructures, leading t
Externí odkaz:
http://arxiv.org/abs/2407.18387
Autor:
Puppala, Sai, Hossain, Ismail, Alam, Md Jahangir, Talukder, Sajedul, Ferdaus, Jannatul, Hasan, Mahedi, Pisupati, Sameera, Mathukumilli, Shanmukh
Federated learning has become a significant approach for training machine learning models using decentralized data without necessitating the sharing of this data. Recently, the incorporation of generative artificial intelligence (AI) methods has prov
Externí odkaz:
http://arxiv.org/abs/2407.18358
User activities can influence their subsequent interactions with a post, generating interest in the user. Typically, users interact with posts from friends by commenting and using reaction emojis, reflecting their level of interest on social media su
Externí odkaz:
http://arxiv.org/abs/2407.09747
Social media platforms are extensively used for sharing personal emotions, daily activities, and various life events, keeping people updated with the latest happenings. From the moment a user creates an account, they continually expand their network
Externí odkaz:
http://arxiv.org/abs/2407.09691
Autor:
Sarker, Shuvra, Biswas, Angona, Nasim, MD Abdullah Al, Ali, Md Shahin, Puppala, Sai, Talukder, Sajedul
The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical diagnosis, and in
Externí odkaz:
http://arxiv.org/abs/2306.02055
Autor:
Biswas, Angona, Nasim, MD Abdullah Al, Imran, Al, Sejuty, Anika Tabassum, Fairooz, Fabliha, Puppala, Sai, Talukder, Sajedul
One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are the
Externí odkaz:
http://arxiv.org/abs/2306.02019
Autor:
Puppala Sai Preetham, Srishti Jain, Tekumatla Abhilash, K.N Sree Priya, Siddamshetty Abhiram, Rakesh Sengupta
Publikováno v:
IBRO Neuroscience Reports, Vol 15, Iss , Pp S891- (2023)
Externí odkaz:
https://doaj.org/article/4c60331cea0f4080891bcad9b550d965
Autor:
Siddamsetty Abhi Ram, Rakesh Sengupta, Puppala Sai Preetham, Tekumatla Abhilash, K.N Sree Priya, Jainn Srishti
Publikováno v:
IBRO Neuroscience Reports, Vol 15, Iss , Pp S807- (2023)
Externí odkaz:
https://doaj.org/article/ec204a043e5c49f7b75a7d3230feb1c8