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Hi there 👋

  • 🔭 Currently Working as:
    • Machine Learning Engineer
  • 🔭 Most recent research:
    • My PhD about Countermeasure Algorithms Against Subterfuge in Mobile Biometric Systems
    • Published papers Google Scholar
  • 🌱 Currently Learning:
    • Explainable AI.
    • Data Analysis.
    • Deep models for image recognition.
  • 👯 Looking to Collaborate On:
    • Writing a proposal to secure funds for machine translation and AI in education projects.
  • 🤔 Seeking Help With:
    • Starting a startup.
  • 💬 Let's Talk About:
    • Machine Learning.
    • Python.
    • Machine Translation.
    • AI in Education.
    • Biometrics.
  • 📫 How to Reach Me:
  • 👨‍🏫 Current Project:
    • Considering the establishment of a startup for AI in education to address the educational needs of Turk people in Iran who lack schools teaching in the Azerbaijani language.

Jalil Nourmohammadi Khiarak's Projects

kartalol-fluent-python-2022 icon kartalol-fluent-python-2022

I am going to read Fluent Python SECOND EDITION Clear, Concise, and Effective Programming and publish what I am learning from new version of the book.

kartalol-nir-isl2021031301 icon kartalol-nir-isl2021031301

Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2 deep neural network method. We employ the pre-trained weights given with MobileNetV2 for the ImageNet dataset and fine-tune it on the iris recognition and localization domain. Further, we have introduced a new dataset, called KaratolOl, to better evaluate detectors in iris recognition scenarios. To provide domain adaptation, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. We also augment the data by performing left-right flips, rotation, zoom, and brightness. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed method is tested and trained in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KaratolOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://github.com/Jalilnkh/KartalOl-NIR-ISL2021031301 .

kubernates-for-learning icon kubernates-for-learning

I have get from my boss a video entitled "Kubernetes Course - Full Beginners Tutorial (Containerize Your Apps!)"

laser icon laser

Language-Agnostic SEntence Representations

mu-net icon mu-net

U-Net Model for Sclera Segmentation in the Mobile Environment using a Transfer Learning approach on MobileNetV2

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