Yazar "Yassin, Riyam Ali" seçeneğine göre listele

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    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.
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    MEMF-Net: A Mega-Ensemble of Multi-Feature CNNs for Classification of Breast Histopathological Images
    (College of Education, Al-Iraqia University, 2025) Abdulaal, Alaa Hussein; Abdulwahhab, Ali H.; Breesam, Aqeel Majeed; Oleiwi, Zahra Hasan; Yassin, Riyam Ali; Valizadeh, Morteza; Mohsin, Saja Nafea
    Pathological anatomical images play a pivotal role in diagnosing diseases, notably breast cancer, which affects women globally. These images, obtained through biopsies or post-mortem examinations, are preserved to maintain their structural integrity. Software tools, like computer-aided diagnosis, aid doctors in early detection and treatment planning, contributing to reduced mortality rates. In this context, convolutional neural networks (CNNs) have emerged as valuable tools for diagnosing benign and malignant breast cancers. This paper introduces a Mega Ensemble Net method, leveraging multi-scale combination features on the breast histopathology dataset. Three fine-tuned deep learning models, namely ResNet-18, ResNet-34, and ResNet-50, are integrated into this method. Techniques such as patch extraction for data augmentation, dataset amalgamation, and transfer learning bolster the method’s capabilities. Fusing extracted patches with primary images enhances the method’s robustness and adaptability, offering diverse perspectives and intricate details for nuanced class distinctions. BACH and BreaKHis datasets have been used to evaluate the Mega Net. During four-fold cross-validation on the test folds, the Mega-Net demonstrates 99% test-set accuracy in the full image and 98% test-set accuracy in patches within the multi-classification BACH dataset and 99% test-set accuracy within the binary classification BreaKHis dataset. Moreover, the MEMF-Net achieved a multi-classification test accuracy of 98.95% across an optimal selected MEMF model in validation testing images.
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    Unsupervised histopathological sub-image analysis for breast cancer diagnosis using variational autoencoders, clustering, and supervised learning
    (Mustansiriyah University College of Engineering, 2024) Abdulaal, Alaa Hussein; Valizadeh, Morteza; Yassin, Riyam Ali; Albaker, Baraa M.; Abdulwahhab, Ali H.; Amirani, Mehdi Chehel; Shah, A. F. M. Shahen
    This paper presents an integrated approach to breast cancer diagnosis that combines unsupervised and supervised learning techniques. The method involves using a pre-trained VGG19 model to process sub-images from the BreaKHis dataset, divided into nine parts for comprehensive analysis. This will be followed by a complete description of the architecture and workings of the variational Autoencoder (VAE) used for unsupervised Learning. The encoder network maps the input features to lower dimensions, capturing the most essential information. VAE learns a compressed representation of sub-images, facilitating a more profound understanding of underlying patterns and structures. For this reason, we then employ k-means clustering on the encoded representation to find naturally occurring clusters in our data set comprising a histopathological image. Every single sub-image is later fed into the VGG19-SVM model for classification purposes. During magnification at 100x, this model has attained a fantastic accuracy rate of 98.56%. Combining unsupervised analysis with VAE/k-means clustering and supervised classification with VGG19/SVM can integrate information from both methods, thereby improving the accuracy and robustness of such a task as sub-image classification in breast cancer histopathology.