The project proposes developing an open, decentralized, and automated customer service architecture for Small and Medium Enterprises (SMEs) using AutoML and the Internet of Things. The proposed architecture includes a transparent and automated customer service that utilizes Blockchain technology for secure data exchange, data trading platform for SMEs, and homomorphic encryption to improve system security and performance. The study aims to contribute to the research domain by facilitating the integration of open automated customer services, increasing revenue and customer satisfaction among small businesses. SMEs can benefit from the proposed architecture by automating customer service, enhancing customer satisfaction, and increasing revenue.
Various data masking techniques have been described in the blockchain and machine learning literature, including substitution, shuffling, nulling out or deleting, encryption, and variance of dates and numbers. Due to reduced block sizes, increased consensus times, transient forks, and additional network overheads, current scaling concerns (Ayinala, Choi and Song, 202) result in inefficiencies and performance problems. Even so, without adequate data, today's businesses will not be able to compete, let alone prosper.
Client: React, Redux, TailwindCSS
Server: Node, Express
Auto ML Server: Fast API, Pycaret
Databases: MongoDB, Firebase, BigchainDB
Blockchain: Stellar Blockchain
Clone the project
git clone https://github.com/Shuhaib-Ahamed/DataLan
Set up BigchainDB
docker pull bigchaindb/bigchaindb:all-in-one
docker run \
--detach \
--name bigchaindb \
--publish 9984:9984 \
--publish 9985:9985 \
--publish 27017:27017 \
--publish 26657:26657 \
--volume $HOME/bigchaindb_docker/mongodb/data/db:/data/db \
--volume $HOME/bigchaindb_docker/mongodb/data/configdb:/data/configdb \
--volume $HOME/bigchaindb_docker/tendermint:/tendermint \
bigchaindb/bigchaindb:all-in-one
Install and run the Server
cd server
npm install
npm run dev
Install and run the Client
cd client
npm install
npm start
Install and run the AutoML Server
cd automl
pip install -r requirements.txt
uvicorn main:app --host 0.0.0.0 --port 8000 --reload
If you have any feedback, please reach out to me at shuhaibsamadh@gmail.com