Yazar "Myderrizi, Indrit" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • [ X ]
    Öğe
    A review on medical image applications based on deep learning techniques
    (University of Portsmouth, 2024) Abdulwahhab, Ali H.; Mahmood, Noof T.; Mohammed, Ali Abdulwahhab; Myderrizi, Indrit; Al-Jumaili, Mustafa Hamid
    The integration of deep learning in medical image analysis is a transformative leap in healthcare, impacting diagnosis and treatment significantly. This scholarly review explores deep learning’s applications, revealing limitations in traditional methods while showcasing its potential. It delves into tasks like segmentation, classification, and enhancement, highlighting the pivotal roles of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Specific applications, like brain tumor segmentation and COVID-19 diagnosis, are deeply analyzed using datasets like NIH Clinical Center’s Chest X-ray dataset and BraTS dataset, proving invaluable for model training. Emphasizing high-quality datasets, especially in chest X-rays and cancer imaging, the article underscores their relevance in diverse medical imaging applications. Additionally, it stresses the managerial implications in healthcare organizations, emphasizing data quality and collaborative partnerships between medical practitioners and data scientists. This review article illuminates deep learning’s expansive potential in medical image analysis, a catalyst for advancing healthcare diagnostics and treatments.
  • Küçük Resim
    Öğe
    BCI-DRONE CONTROL BASED ON THE CONCENTRATION LEVEL AND EYE BLINK SIGNALS USING A NEUROSKY HEADSET
    (University of Kufa, 2025) Mohammed, Ali Abdulwahhab; Abdulwahhab, Ali H.; Abdulaal, Alaa Hussein; Mahmood, Musaria Karim; Myderrizi, Indrit; Yassin, Riyam Ali; Abdulridha, Taha Talib; Valizadeh, Morteza
    Brain neurons activate Human movements by producing electrical bio-signals. Neuron activity is used in several technologies by operating their applications based on mind waves. The Brain-Computer Interface (BCI) technology enables a processor to connect with the brain using a signal received from the brain. This study proposes a drone controlled using EEG signals acquired by a Neurosky device based on the BCI system. Two active signals are adapted for controlling the drone motions: concentration brain signals portrayed by attention level and the eye blinks as an integer value. A dynamic classification method is implemented via a Linear Regression algorithm for attention-level code. The eye blinking generates a binary code to control the drone's motions. The accuracy of this code is improved through Artificial Neural Networks and Machine Learning techniques. These codes (attention level and eye blink codes) drive two controlling layers and manipulate nine possible drone movements. The experiment was evaluated with several users and showed high performance for the classification methods and developed algorithm. The experiment shows a 90.37% accuracy control that outperforms most existing experiments. Also, the experiment can support 16 commands, making the algorithm appropriate for various applications.
  • [ X ]
    Öğe
    PAFWF-EEGC Net: parallel adaptive feature weight fusion based on EEG-dynamic characteristics using channels neural network for driver drowsiness detection
    (Springer Science and Business Media Deutschland GmbH, 2025) Abdulwahhab, Ali H.; Myderrizi, Indrit; Yurdakul, Muhammet Mustafa
    Drowsy driving is considered one of the most dangerous causes of road accidents and deaths worldwide. Drivers’ concentration is directly affected by fatigue, which affects their reaction time, reducing their attention and decision-making ability on the road. This can often lead to dangerous situations. With the development of Human Computer Interface systems and the rise of intelligent transportation systems, examining the effects of driver fatigue has become more critical, and research aimed at reducing the risk of fatigue-related accidents has gained importance. For this purpose, this study proposes a Parallel Adaptive Feature Weight Fusion based on EEG-Dynamic Characteristics using Channels Neural Network (PAFWF-EEGC Net) to detect the driver drowsiness condition. Two signal processing techniques are used to extract EEG dynamic features: first, Continuous Wavelet Transform (CWT) to capture the spectral-temporal features by accurately estimating both time and frequency localizations, and second, Fast Fourier Transform (FFT)—Power Spectrum Density (PSD) to convert the signals from the time domain to the frequency domain and show the distribution of signal power over frequency. These extracted dynamic features are passed to Attention channels and Parallel Adaptive Feature Fusion to integrate the most relevant feature channels to detect mental state. Furthermore, three processing dataset scenarios and cross-validation techniques are used to validate the Net. The Net showed excellent performance through ninefold/3rd scenario by achieving 98% detection accuracy, and 84%, 88.75%, 93.8% average detection accuracy through 1st, 2nd, 3rd scenarios respectively.