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Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze medical images with favorable results. It can help save lives faster and rectify some medical errors. In this study, we look at the most up-to-date methodologies for medical image analytics that use convolutional neural networks on MRI images. There are several approaches to diagnosing and classifying brain cancers. Inside the brain, irregular cells grow so that a brain tumor appears. The size of the tumor and the part of the brain affected impact the symptoms.

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Publication Date
Sun Apr 13 2025
Journal Name
Scientific Reports
Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding
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Abstract<p>Image segmentation using bi-level thresholds works well for straightforward scenarios; however, dealing with complex images that contain multiple objects or colors presents considerable computational difficulties. Multi-level thresholding is crucial for these situations, but it also introduces a challenging optimization problem. This paper presents an improved Reptile Search Algorithm (RSA) that includes a Gbest operator to enhance its performance. The proposed method determines optimal threshold values for both grayscale and color images, utilizing entropy-based objective functions derived from the Otsu and Kapur techniques. Experiments were carried out on 16 benchmark images, which inclu</p> ... Show More
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Publication Date
Wed Feb 01 2017
Journal Name
Journal Of Controlled Release
Surface engineering tumor cells with adjuvant-loaded particles for use as cancer vaccines
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Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
Data Classification using Quantum Neural Network
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In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

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Publication Date
Sun Dec 28 2025
Journal Name
Al–bahith Al–a'alami
Organizational reputation: A descriptive study of concept development and measurement techniques
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The study aimed to monitor the concept of reputation in the previous literature, its relationship to mental image and identity, and to reveal recent trends in its measurement Techniques.

    The study relied on a descriptive approach using library survey and comparative analysis, and the study reached following conclusions:

     Despite the beginning of the first signs of reputation In the fifties of the last century, however, Defining and standardizing the concept with clear and specific dimensions began in the 1990s and the beginning of the third millennium. The concept of reputation refers to the stakeholders’ overall evaluation of organizations, which reflects their perceptions of

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Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Topology-Based Modularity and Modularity Density for Detecting Protein Complexes: A Comparative Study
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     Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solvin

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Publication Date
Sun Sep 02 2012
Journal Name
Baghdad Science Journal
Studying HLA class I polymorphism in brain tumour patients
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The present study aimed at shed light on the association between HLA-class I antigens (A, B and Cw) and brain tumours (meningioma and glioma) in the basis of their individual frequencies or two-locus association A total of 52 brain tumour patients were enrolled in this study, with an age range of 7-68 years. The patients were divided into two clinical groups; meningioma (20 cases) and glioma (22 cases), while the remaining 10 cases represented other types of brain tumour. Control samples included 47 Iraqi Arab apparently healthy blood volunteers, with an age range of 15-50 year. Three HLA antigens showed a significant increased frequency in total patients as compared to controls. They were B13 (34.6 vs. 6.5%), B40 (15.4 vs. 2.2%) and Cw3

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Publication Date
Sat Dec 31 2022
Journal Name
Iraqi Geological Journal
Geochemical Criteria for Discriminating Shallow and Deep Environments in Oligocene-Miocene Succession, Western Iraq
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The geochemical study of the Oligocene-Miocene succession Anah, Euphrates, and Fatha formations, western Iraq, was carried out to discriminate their depositional environments. Different major and trace patterns were observed between these formations. The major elements (Ca, Mg, Fe, Mn, K, and Na) and trace elements (Li, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Zr, Cs, Ba, Hf, W, Pb, Th, and U) are a function of the setting of the depositional environments. The reefal facies have lower concentrations of MgO, Li, Cr, Co, Ni, Ga, Rb, Zr, and Ba than marine and lagoonal facies but have higher concentrations of CaO, V, and Sr than it. Whereas dolomitic limestone facies are enriched V, and U while depletion in Li, Cr, Ni, Ga, Rb, Sr, Zr, Ba, an

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Publication Date
Sun Sep 01 2013
Journal Name
Applied Mechanics And Materials
Artistic Techniques for Developing Creativity in Design
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The design of future will still be the most confusing and puzzling issue and misgivings that arouse worry and leading to the spirit of adventures to make progress and arrive at the ways of reviving, creativity and modernism. The idea of prevailing of a certain culture or certain product in design depends on the given and available techniques, due to the fact that the computer and their artistic techniques become very important and vital to reinforce the image in the design. Thus, it is very necessary to link between these techniques and suitable way to reform the mentality by which the design will be reformed, from what has been said, (there has no utilization for the whole modern and available graphic techniques in the design proce

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Engineering
Database for Baghdad Soil Using GIS Techniques
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teen sites Baghdad are made. The sites are divided into two groups, one in Karkh and the other in Rusafa. Assessing the underground conditions can be occurred by drilling vertical holes called exploratory boring into the ground, obtaining soil (disturbed and undisturbed) samples, and testing these samples in a laboratory (civil engineering laboratory /University of Baghdad). From disturbed, the tests involved the grain size analysis and then classified the soil, Atterberg limit, chemical test (organic content, sulphate content, gypsum content and chloride content). From undisturbed samples, the test involved the consolidation test (from this test, the following parameters can be obtained: initial void ratio eo, compression index cc, swel

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Publication Date
Mon Jun 01 2009
Journal Name
2009 Etp International Conference On Future Computer And Communication
Signal Processing Techniques for Robust Spectrum Sensing
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Cognitive radios have the potential to greatly improve spectral efficiency in wireless networks. Cognitive radios are considered lower priority or secondary users of spectrum allocated to a primary user. Their fundamental requirement is to avoid interference to potential primary users in their vicinity. Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. In addition, reliable sensing creates spectrum opportunities for capacity increase of cognitive networks. One of the key challenges in spectrum sensing is the robust detection of primary signals in highly negative signal-to-noise regimes (SNR).In this paper ,

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