DataBytes-Organisation/Intelligent-IoT-Data-Management
Apr 2026 – May 2026
2 Months active
Languages Used
JavaScriptPython
Technical Skills
Backend DevelopmentData AnalysisDockerFrontend DevelopmentMachine LearningPython
Saransenthikumar
PROFILE
Saransenthikumar
Worked on the Intelligent-IoT-Data-Management repository to develop and enhance real-time anomaly detection for IoT time-series data. Built the VolatilityShiftAD detector, leveraging machine learning and Python to identify shifts in data volatility, and refined its output for improved integration by returning structured dictionaries. Enhanced the detector’s documentation, parameter handling, and detection logic to increase reliability within the data science pipeline. Introduced the InterQuartileRangeAD detector, broadening anomaly detection coverage for sensor data. Established a benchmarking suite to quantify detector performance and inform production decisions. Focused on backend development, data analysis, and time-series analysis, with an emphasis on maintainability and usability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
215,977
Activity Months2
Your Network
124 peopleSame Organization
@deakin.edu.au99
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Shared Repositories
25
Work History
May 2026
2 Commits • 1 Features
May 1, 2026Concise monthly summary focusing on key achievements and business value for May 2026, centered on time-series anomaly detection enhancements in the IoT data pipeline.
2 Commits • 1 Features
May 1, 2026Concise monthly summary focusing on key achievements and business value for May 2026, centered on time-series anomaly detection enhancements in the IoT data pipeline.
May 2026
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary focusing on key accomplishments in the Intelligent-IoT-Data-Management repo. Delivered VolatilityShiftAD detector for real-time IoT time-series anomaly detection, with a dedicated detector class and ML-based approach. Output was refined to a structured dictionary for clearer usability; fixed evaluator output format to remove DataFrame dependency. These changes improved real-time monitoring, reduced integration friction, and demonstrated solid ML, data processing, and code quality improvements.
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary focusing on key accomplishments in the Intelligent-IoT-Data-Management repo. Delivered VolatilityShiftAD detector for real-time IoT time-series anomaly detection, with a dedicated detector class and ML-based approach. Output was refined to a structured dictionary for clearer usability; fixed evaluator output format to remove DataFrame dependency. These changes improved real-time monitoring, reduced integration friction, and demonstrated solid ML, data processing, and code quality improvements.
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage35.0%
Skills & Technologies
Programming Languages
JavaScriptPython
Technical Skills
Backend DevelopmentData AnalysisDockerFrontend DevelopmentMachine LearningPythonanomaly detectiondata analysisdata sciencemachine learningtime-series analysis
Repositories Contributed To
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