AI-CLOUD BASED DIGITAL AFTERLIFE SERIVCE

Authors:

Shravani Kadam, Anuradha Shinge, Samruddhi Shirgave, Sanika Benake, Siddhi Narke, Prof.A.D.Patil

Ashokrao Mane Group Of Institution,Vathar

Department of Computer Science and Engineering

Abstract : Artificial Intelligence and Cloud Computing technologies are rapidly transforming the digital world by enabling intelligent storage, communication, and interaction systems. The proposed system, “AI-Cloud Based Digital Afterlife Service,” aims to preserve a person's digital identity and memories after death through AI-driven conversational systems and cloud infrastructure. The system collects and processes user-approved digital data such as chats, voice recordings, emails, images, and social media interactions to create an intelligent digital personality replica.

The presented system uses Deep Learning, Natural Language Processing (NLP), and cloud-based technologies to simulate personalized conversations and memory interactions. Unlike traditional memorial systems that only preserve static information, the proposed model provides interactive communication with AI-generated responses based on historical user behavior and communication style.

Image, voice, and text processing techniques along with transformer-based conversational models are employed for accurate personality simulation. Cloud infrastructure ensures secure storage, scalability, backup, and real-time accessibility of digital memories. Explainable AI and secure authentication modules are integrated to maintain transparency, privacy, and ethical control over sensitive data.

The developed system aims to support emotional remembrance, digital legacy preservation, and intelligent memory interaction. It can be implemented as a scalable web-based application accessible from anywhere and may assist future digital memorial platforms.

The advanced system also aims to achieve real- time vaticination and can be extended to the scalable perpetration of web interface for availability. The advanced system help medical interpreters for effective identification of diabetic retinopathy at the early stage, reduces the burden of the differentia and increase availability to eye care installations.

INDEX TERMS: Artificial Intelligence, Cloud Computing, Deep Learning, Digital Afterlife, Natural Language Processing, Conversational AI, Digital Legacy, Explainable AI, Memory Preservation, Machine Learning.