Origins-AI and Samsung Partnership: Advancing IoT Development and User Verification
The partnership between Origins-AI and Samsung R&D Institute India was established to enhance IoT solutions and ensure robust user verification mechanisms. Working as a Software Development Firm for Samsung’s IoT team , Origins-AI played a vital role in developing tools, designing models, and leveraging machine learning to analyze user data for fraud detection. Key contributions included plugin development for Tizen Studio and server and web tools to streamline data visualization and improve IoT security.
About Samsung
Samsung India, a subsidiary of South Korea’s Samsung Electronics, has been a significant player in India’s electronics market since its establishment in 1995. The company operates two major manufacturing plants: one near Chennai, producing home appliances like refrigerators and washing machines, and another in Uttar Pradesh, recognized as the world’s largest mobile factory since 2018. In the smartphone segment, Samsung holds a substantial market share, producing most devices domestically. The company also leads India’s smart TV market and operates three R&D facilities in Noida and Bengaluru.
Our Partnership with Samsung
Industry
Technology & Electronics
Services
IoT Data Analysis and Model Development
Business Type
Research & Development
Technologies Used
Python
Machine Learning
Server Development
IoT Sensors
Data Visualization Tools
The Challenges
IDV (IoT Data Validation)
Ensuring the authenticity of smartwatch user data was a critical issue, especially with data coming from various sensors such as accelerometers and gyroscopes. The challenge was to filter out unreliable data that could potentially skew user identification accuracy.
MCC (Model Calibration Complexity)
Calibrating the machine learning model for detecting fraudulent behavior required extensive tuning due to variations in user walking patterns and sensor data accuracy. The challenge intensified as the model had to differentiate subtle behavioral nuances between legitimate and fraudulent users while adapting to new data continuously.
PLD (Plugin Development Delay)
Developing plugins for Tizen Studio to support both Eclipse and Visual Studio Code environments introduced significant integration challenges. Differences in IDE architectures and their handling of TypeScript led to compatibility issues, causing delays and impacting developer productivity.
SRL (Server Reliability):
The server handling data uploads and model retrieval faced performance bottlenecks due to high data volumes and varied user access patterns. Ensuring stable, uninterrupted service while managing concurrent requests from multiple devices posed a major reliability challenge.
The Solutions
Data Validation Pipeline
We implemented a robust data validation pipeline that pre-processed data from smartwatches, filtering out anomalies and detecting inconsistencies.
Model Refinement
Leveraging advanced machine learning techniques, we calibrated the model using diverse data sets that represented various user profiles and behaviors.
Server Optimization
Server optimization included restructuring code for efficiency, implementing caching strategies, and using load balancers to distribute incoming requests evenly.
Multi-IDE Plugin Integration
To address compatibility issues, we implemented a unified plugin structure in TypeScript and added an automated testing framework to catch IDE-specific issues early in development.
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What Our Partners Say?
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