Gopher-Industries/Guardian
Apr 2025 – May 2025
2 Months active
Languages Used
PythonSQL
Technical Skills
Data PreprocessingDeep LearningFeature EngineeringKerasMachine LearningMatplotlib
Darsh Nitinbhai Patel
PROFILE
Darsh Nitinbhai Patel
Developed advanced human activity recognition models for the Guardian repository, focusing on sensor-based analytics and real-time insights. Over two months, delivered two production-ready features by designing deep learning architectures that combined BiLSTM and DenseNet-inspired dense connections with Multi-Head and standard Attention mechanisms. Leveraged Python, TensorFlow, and Keras to implement robust data preprocessing, PCA-based feature engineering, and reproducible evaluation pipelines. Achieved high test accuracy, with models reaching up to 0.9972, and maintained stability with no major bugs reported. The work enhanced Guardian’s ML pipeline, supporting reliable deployment decisions and improving operational efficiency for safety monitoring and user activity analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,305
Activity Months2
Your Network
111 peopleSame Organization
@deakin.edu.au92
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Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary: Delivered a high-precision sensor-based activity recognition model for the Guardian project by combining DenseNet-inspired dense connections with Multi-Head Attention, trained on PCA-preprocessed sensor data. The model achieves a test accuracy of 0.9972, demonstrating strong performance and robustness. The work was integrated into the Guardian ML pipeline with a reproducible evaluation setup, enabling reliable deployment decisions and easier future maintenance. Commit reference for the work: 287ad469dfe00a16e725ce5b759d755c4b5cd144 with message 'Densent+ Attention'.
1 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary: Delivered a high-precision sensor-based activity recognition model for the Guardian project by combining DenseNet-inspired dense connections with Multi-Head Attention, trained on PCA-preprocessed sensor data. The model achieves a test accuracy of 0.9972, demonstrating strong performance and robustness. The work was integrated into the Guardian ML pipeline with a reproducible evaluation setup, enabling reliable deployment decisions and easier future maintenance. Commit reference for the work: 287ad469dfe00a16e725ce5b759d755c4b5cd144 with message 'Densent+ Attention'.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered a high-precision activity recognition model for Guardian with a hybrid BiLSTM + Attention architecture, supported by enhanced feature engineering and streamlined data preprocessing, training, and evaluation. The model achieved test accuracy of 0.9945 and test loss of 0.0256, reflecting strong performance and reliability. No major bugs were reported this month; focus centered on delivering the feature and stabilizing the data pipeline. This work advances real-time analytics capabilities and enables more accurate user/activity insights, contributing to improved safety monitoring and operational efficiency.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered a high-precision activity recognition model for Guardian with a hybrid BiLSTM + Attention architecture, supported by enhanced feature engineering and streamlined data preprocessing, training, and evaluation. The model achieved test accuracy of 0.9945 and test loss of 0.0256, reflecting strong performance and reliability. No major bugs were reported this month; focus centered on delivering the feature and stabilizing the data pipeline. This work advances real-time analytics capabilities and enables more accurate user/activity insights, contributing to improved safety monitoring and operational efficiency.
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data PreprocessingDeep LearningFeature EngineeringHuman Activity RecognitionKerasLSTMMachine LearningMatplotlibModel TrainingMulti-Head AttentionNumpyPCAScikit-learnTensorFlow
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