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injamul3798/README.md

Hi there, I'm Injamul! 👋

I'm a Machine Learning Engineer specializing in Deep Learning, Computer Vision, and Natural Language Processing. I love solving complex data problems and have a keen interest in mobile development.

  • 🔭 Currently working on various research papers and AI-based projects involving parking systems and medical imaging.
  • 🌱 Always learning something new every day.
  • 👯 Seeking collaboration with fellow developers for hackathons and open-source projects.
  • 💬 Feel free to ask me about coding, machine learning, or mobile development.
  • ⚡ Fun fact: I enjoy playing cricket and exploring new programming challenges.

🛠️ Educational Qualifications

  • B.Sc. in Computer Science & Engineering – Daffodil International University (2020 - 2024)
    CGPA: 3.75/4.00

💼 Professional Experience

  • Machine Learning Engineer – DevTechGuru Limited, Dhaka (February 2024 – Present)
    Working on parking and AI-based surgery projects. Responsibilities include vehicle counting, automated reporting, and performance improvement of machine learning models.

  • Researcher (Member) – Health Informatics Research Lab, DIU (December 2022 – Present)
    Conducting research in NLP, computer vision, and deep learning. Recently submitted a paper on gallbladder cancer and currently working on Graph Neural Networks and GANs.

🚀 Key Projects

  • Semi-automated Parking Management System(DevTechGuru Limited)
    Backend and AI feature development, including vehicle detection, manual entry reports, and revenue tracking using YOLOv8 and OCR for number plate recognition.

  • Avail Ortho - Hip Overlay Project(DevTechGuru Limited)
    Developed backend and AI features to optimize medical imaging for hip replacement surgeries, replacing traditional transparency paper with a digital solution.

  • Web-ML Diabetes Predictor
    Created a Django web app to predict diabetes risk using advanced ML techniques.

🛠️ Skills & Tools

  • Languages: Python, C, C++, Java, Dart, Kotlin, PostgreSQL
  • ML & DL Frameworks: TensorFlow, Keras, PyTorch, Scikit-learn
  • Algorithms: Supervised & Unsupervised Learning, GANs, Transfer Learning (VGG16, ResNet)
  • Computer Vision: OpenCV, YOLOv8
  • NLP: BERT, Text Classification, Sentiment Analysis
  • Web Development: HTML, CSS, JavaScript, Django, MySQL
  • Platforms: Anaconda, JupyterLab, Google Colab, Visual Studio Code

🏆 Competitive Programming

  • HackerRank
    Solved 200+ coding problems using C++ and Python.

  • CodeChef
    Active participant in contests, continually honing competitive programming skills.

  • URI Online Judge
    Participated and submitted solutions, enhancing algorithmic problem-solving skills.

📜 Certificates

  • Intermediate Machine Learning Certificate (Kaggle)
    Gained proficiency in XGBoost, cross-validation, pipelines, and data leakage prevention.

  • Supervised Machine Learning: Classification and Regression (Coursera)
    Covered algorithms like SVM, k-NN, Logistic Regression, and XGBoost through hands-on projects.

📊 Github Stats:

Injamul's Github Stats

🌐 Connect with Me:

Injamul's Website
Injamul on LinkedIn

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  1. Advanced-Threat-Detection-ML-Driven-Intrusion-Detection-Systems-for-Robust-Security Advanced-Threat-Detection-ML-Driven-Intrusion-Detection-Systems-for-Robust-Security Public

    Intrusion Detection and Prevention Systems (IDSs/IPSs) are crucial defenses against evolving network attacks. However, due to a lack of reliable datasets, anomaly-based intrusion detection faces ch…

    Jupyter Notebook 3 1

  2. Fine-Tuning-BERT-for-E-commerce-Text-Classification-A-Multi-category-Approach Fine-Tuning-BERT-for-E-commerce-Text-Classification-A-Multi-category-Approach Public

    In this Kaggle project, we leverage the power of the BERT (Bidirectional Encoder Representations from Transformers) model for fine-tuned multi-category text classification in the context of E-comme…

    Jupyter Notebook 3

  3. Web-ML-Diabetes-Predictor-Empowering-Health-Awareness-Online Web-ML-Diabetes-Predictor-Empowering-Health-Awareness-Online Public

    Our web-based diabetes prediction system utilizes machine learning algorithms to analyze various health indicators and predict the likelihood of an individual developing diabetes. By leveraging a u…

    Jupyter Notebook 1 1

  4. Bangla-Newspaper-Categorization-using-ML-models Bangla-Newspaper-Categorization-using-ML-models Public

    In my Bangla news categorization project, I utilized XGBoost for efficient pattern recognition, SVM for handling non-linear relationships, and an ensemble of Random Forest, AdaBoost, and Logistic R…

    Jupyter Notebook 1

  5. Sentiment-analysis-based-on-Corona-Virus-tweet-using-Word2Vec Sentiment-analysis-based-on-Corona-Virus-tweet-using-Word2Vec Public

    I performed sentiment analysis on coronavirus-related tweets from a Kaggle dataset, utilizing Word2Vec embeddings with ML models like XGBoost, SVM, Random Forest, and Logistic Regression. Model per…

    Jupyter Notebook 2

  6. MelanomaDetector-An-AI-Powered-Melanoma-Skin-Cancer-Detection- MelanomaDetector-An-AI-Powered-Melanoma-Skin-Cancer-Detection- Public

    . The proposed application, "MelanomaDetector," utilizes a Convolutional Neural Network (CNN) model, specifically a modified version of the VGG16 architecture pre-trained on the ImageNet dataset, f…

    Jupyter Notebook