Abdulbasit Ogundiran
Cybersecurity Analyst | Cloud Security & IT Operations
New to Contra
Abdulbasit is ready for their next project!
This is a project that I titled Low-cost AI Power theft detection that detects electricity theft, bypass of electricity, high electricity usage, with the help of TinyML integrated and trained into the esp32 and alert with the help of the buzzer and shows in a well polished web dashboard, sensor used would be communicated after the project as been finally built and ready for implementation.
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My lab environment, and this is a project giving to me by a client to test my ethical hacking skill cracking an encrypted .rar file open and seeing the content inside, I was authorised and I confirmed it was his before I performed my attacks.
And I successfully hacked into it.
Not to be disclosed cause of what it contains and for privacy but this was when I was working on the project.
So am opened to share my expertise and solving problems in whatever you bring to my table.
Lekk's Forge got you covered.
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A fully functional health website for a project given to me to work on by a client but still on development stage.
It shows a lot of detailed information and a graph to express the blood pressure trends, with the help of AI prediction using various models to make this possible. Will deliver soon and talk more about it when done.
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This is a project I titled: Development of High Blood Pressure (HBP) Prediction and Monitoring System Using CNN-BiLSTM and Fuzzy Logic in Real Time
Still undergoing development:
1. The Flask backend is operational with an integrated CNN-BiLSTM prediction pipeline and a fuzzy logic module for health risk assessment, along with REST API endpoints for communication.
2. A Flutter mobile application has been developed, featuring a dashboard that displays various health metrics and successfully communicates with the backend.
3. Hardware development includes the successful configuration of the ESP32 microcontroller, integration of an OLED display, and testing of the MAX30102 PPG sensor for real-time waveform acquisition.
4. AI integration involves transmitting real PPG data from the MAX30102 sensor to the Flask backend, where the CNN-BiLSTM model generates blood pressure predictions, which are then returned to the ESP32 and backend.
5. The complete communication pipeline from sensor to monitoring dashboard has been established, confirming successful real-time signal processing for blood pressure prediction.
6. Ongoing work focuses on sensor stabilization, hardware integration, synchronization of live updates, alert mechanisms, power management, and final system packaging.
7. The core real-time HBP prediction system using CNN-BiLSTM is implemented and tested, with the project now in the optimization stage before final deployment.
Willing to hear suggestions and getting more gigs to work with and solving of problems
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