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Ola Najah Kadhim

Lectuer

Medical Instruments Techniques

Al-Mussaib Technical Institute

Biography

.Name: Ola Najah Kadhim .Title: Lecture in Technical Institute of Al Mussaib .Degree: PhD in Computer Science .General Specialization: Computer Science .Specialization: Artificial Intelligence and Image Processing .Published Researches: 15 research .Email : [email protected] Research interests include optimizing deep learning architectures for real-world applications, including biometric recognition and security systems. In addition to technical skills, she has experience in microprocessor systems, including assembly language programming and signal processing applications.

Rsearch Interests

  • .Research in the field of artificial intelligence and image processing

Qualifications

  • B.Sc in a Computer Science from College of Science, Babylon University, Babylon, Iraq
  • M.Sc in Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Al-Najaf, Iraq,
  • Ph.D. Computer Science, Department of Computer Science, Faculty of Computer Science and Mathematics University of Kufa, Najaf, Iraq

Experiences

  • Programming: Python, TensorFlow, Keras, NumPy, OpenCV Machine Learning: CNNs, Scattering Wavelet Transform, Feature Extraction Microprocessor Systems: Assembly Language, Signal Processing Tools: MATLAB, Scikit-learn, Pandas

Scientific Research

  • Information hiding using chaotic-address steganography
  • Simulate a first-order Bézier curve in image encoding
  • Digital filters windowing for data transmission enhancement in communication channel
  • Histogram features extraction for edge detection approach‏
  • Soft edge detection by mamdani fuzzy inference of color image
  • Evaluating the Effectiveness of E-learning: Based on Academic Staff Evaluation‏
  • Simulation Secure MQTT Protocol Based on TLS in IoT-Fog Computing Environment‏
  • Face Recognition approach via Deep and Machine Learning‏
  • Detection of COVID-19 in X-Rays by convolutional neural networks‏
  • Hybrid Multimodal Biometric Identification System: Integrating Face and Palmprint Traits Through Feature-Level Fusion
  • Secure Image Encryption using E-Fractal-Based Non-Commutative Group and Hash Function
  • Ant Colony Optimization Based Edge Detection Algorithm
  • Biometric Identification Advances: Unimodal to Multimodal Fusion of Face, Palm, and Iris Features
  • A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion
  • A multimodal biometric database and case study for face recognition based deep learning

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