A DYNAMIC IMAGE RETRIEVAL FRAMEWORK BASED ON FUSION-BASED FEATURE EXTRACTION USING DEEP LEARNING Journal of Theoretical and Applied Information Technology, 2025
Utilizing Deep improved ResNet50 for Brain Tumor Classification Based MRI Karrar Neamah, Farhan Mohamed, Safa Riyadh Waheed, Waleed Hadi Madhloom Kurdi, Adil Yaseen Taha, Karrar Abdulameer Kadhim IEEE Open Journal of the Computer Society, 2024 A robust approach for brain tumor classification is being developed using deep convolutional neural networks (CNNs). This study leverages an open-source dataset derived from the MRI Brats2015 brain tumor dataset. Preprocessing included intensity normalization, contrast enhancement, and downsizing. Data augmentation techniques were also applied, encompassing rotations and flipping. The core of our proposed approach lies in the utilization of a modified ResNet-50 architecture for feature extraction. This model integrates transfer learning by replacing the final layer with a spatial pyramid pooling layer, enabling it to leverage pre-trained parameters from ImageNet. Transfer learning from ImageNet aids in countering overfitting. Our model's performance was evaluated with various hyperparameters, including existing methods in terms of accuracy, precision, recall, F1-score, sensitivity, and specificity. This study showcases the potential of deep learning, transfer learning, and spatial pyramid pooling in MRI-based brain tumor classification, providing an effective tool for medical image analysis. Our methodology employs a modified ResNet-50 architecture with transfer learning, integrating a spatial pyramid pooling layer for feature extraction. Systematic evaluation showcases the model's superiority over existing methods, demonstrating remarkable results in accuracy (0.9902), precision (0.9837), recall (0.9915), F1-score (0.9891), sensitivity, and specificity. The comparative analysis against prominent CNN architectures reaffirms its outstanding performance. Our model not only mitigates overfitting challenges but also offers a promising tool for medical image analysis, underlining the combined efficacy of spatial pyramid pooling and transfer learning. The study's optimization parameters, including 25 epochs, a learning rate of 1e-4, and a balanced batch size, contribute to its robustness and real-world applicability, furthering advancements in efficient brain tumor classification within MRI data.
Synergistic Integration of Transfer Learning and Deep Learning for Enhanced Object Detection in Digital Images Safa Riyadh Waheed, Norhaida Mohd Suaib, Mohd Shafry Mohd Rahim, Amjad Rehman Khan, Saeed Ali Bahaj, Tanzila Saba IEEE Access, 2024 Presently, the world is progressing towards the notion of smart and secure cities. The automatic recognition of human activity is among the essential landmarks of smart city surveillance projects. Moreover, classifying group activity and behavior detection is complex and indistinct. Consequently, behavior classification systems reliant on visual data hold expansive utility across a spectrum of domains, including but not limited to video surveillance, human-computer interaction, and the safety infrastructure of smart cities. However, automatic behavior classification poses a significant challenge in the context of live videos captured by the smart city surveillance system. In this regard, the use of pictures with pre-trained convolution neural networks (CNNs)-assisted transfer learning (TL) has emerged as a potential technique for deep neural networks (DNNs) object detection., resulting in increased performance in localization for smart city surveillance. Against this backdrop, this paper explores various strategies to develop advanced synthetic datasets that could enhance accuracy when trained with modern DNNs for object detection (mAP). TL was employed to address the limitation of DL that necessitates a huge dataset. The KITTI datasets were used to train a contemporary DNN single-shot multiple box detector (SSMD) in TensorFlow. A variety of metrics were employed to assess the efficacy of the novel automated Transfer Learning (TL) system within a real-world context, specifically designed for object detection within the DL framework (referred to as OD-SSMD). The results unveiled that this developed system outperformed preceding investigations, demonstrating superior performance. Notably, it exhibited the remarkable capability to autonomously discern and pinpoint various attributes and entities within digital images, effectively identifying and localizing each item present within the images.
Positioning Optimization of UAV (Drones) Base Station in Communication Networks Mustafa Qahtan Alsudani, Mushtaq Talb Tally, Israa Fayez Yousif, Ali Abdullhussein Waad, Safa Riyadh Waheeda, Myasar Mundher Adnan Malaysian Journal of Fundamental and Applied Sciences, 2023 Unmanned aerial vehicles (UAV) and cellular networks are growing closer to being integrated in the realm of wireless communications, which will improve service quality even further. In this study, we investigate a wireless communication system in which two types of base stations—in the air and on the ground—serve separate groups of users. We analyze the effect of the aerial base station (ABS) height and transmit power on the system's downlink and uplink data rates while accounting for the reciprocal interference between the Aerial and terrestrial communication lines. The findings demonstrate that in many cases the best ABS altitude and transmit Power are either the highest or lowest values attainable. The distance between the ABS, the ABS user (AU), and the terrestrial base station user, among other factors, may affect how well they all communicate (TU). In this article we will discuss the following topics: unmanned aerial vehicle (UAV), terrestrial base station (BTS), transmit power optimization (TPO), interference (I), downlink (DL), and uplink (UL).
Coronavirus Classification based on Enhanced X-ray Images and Deep Learning Fallah H. Najjar, Safa Riyadh Waheed, Duha Amer Mahdi Malaysian Journal of Fundamental and Applied Sciences, 2023 In light of the fact that the global pandemic of Coronavirus Disease 2019 (COVID-19) is still having a significant impact on the health of people all over the world, there is a growing need for testing diagnosis and treatment that can be completed quickly. The primary imaging modalities used in the respiratory disease diagnostic process are the Chest X-ray (CXR) and the computed tomography scan. In this context, this paper aims to design a new Convolutional Neural Network (CNN) to diagnose COVID-19 in patients based on CXR images and determine whether they are COVID or healthy. We have tested the performance of our CNN on the COVID-19 Radiography Database with three classes (COVID, Pneumonia, and Normal). Also, we proposed a new enhancement technique to enhance the CXR image using the Laplacian kernel with Delta Function and Contrast-Limited Adaptive Histogram Equalization. The proposed CNN has been trained and tested on 15153 enhanced and original images, COVID (3616), Pneumonia (1345), and Normal (10192). Our enhancement technique increased the performance metrics scores of the proposed CNN. Hence, the proposed method obtained better results than the state-of-the-art methods in accuracy, sensitivity, precision, specificity, and F measure.
Automatic Car Number Plate Detection using Morphological Image Processing Mustafa Qahtan Alsudani, Safa Riyadh Waheed, Karrar A Kadhim, Myasar Mundher Adnan, Ameer Al-khaykan Malaysian Journal of Fundamental and Applied Sciences, 2023 One of the most common uses of computer vision, automatic number plate recognition (ANPR) is also a pretty well-explored subject with numerous effective solutions. Due to regional differences in license plate design, however, these solutions are often optimized for a specific setting. Number plate recognition algorithms are often dependent on these aspects, making a universal solution unlikely due to the fact that the image analysis methods used to develop these algorithms cannot guarantee a perfect success rate. In this research, we offer an algorithm tailor-made for use with brand-new license plates in Iraq. The method employs edge detection, Feature Detection, and mathematical morphology to find the plate; it was developed in C++ using the OpenCV library. When characters were found on the plate, they were entered into the Easy OCR engine for analysis.
A novel medical image enhancement technique based on hybrid method Kifah T Khudhair, Fallah H Najjar, Safa Riyadh Waheed, Hassan M Al-Jawahry, Haneen H Alwan, Ameer Al-khaykan Journal of Physics Conference Series, 2023 Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm’s efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics.
Design a Crime Detection System based Fog Computing and IoT Safa Riyadh Waheed, Ammar AbdRoba Sakran, Mohd Shafry Mohd Rahim, Norhaida Mohd Suaib, Fallah H Najjar, Karrar A Kadhim, Ali Aqeel Salim, Myasar Mundher Adnan Malaysian Journal of Fundamental and Applied Sciences, 2023
Review of Intrusion Detection Systems Based on Machine Learning Mohammed Hasan Ali, Karrar Al-Jawaheri, Myasar Mundher Adnan, Safa Riyadh Waheed, Karrar Abdulameer Kadhim, Mohd Shafry Mohd Rahim 4th International Iraqi Conference on Engineering Technology and their Applications Iiceta 2021, 2021
A survey and analysis on image annotation Myasar Mundher Adnan, Mohd Shafry Mohd Rahim, Kerrar Al-Jawaheri, Mohammed Hasan Ali, Safa Riyadh Waheed, A. Hussien Radie 2020 3rd International Conference on Engineering Technology and Its Applications Iiceta 2020, 2020
