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MR Brain Image Segmentation Using Spatial Fuzzy C- Means Clustering Algorithm
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conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. 

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Publication Date
Tue Jan 30 2018
Journal Name
Iraqi Journal Of Science
Improve the Spatial Resolution of Multispectral satellite Image using Different Image Sharpening Techniques
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The process of combining the significant information from a series of images into a single image called image sharpening or image fusing, where the resultant fused image will be having more spatial and spectral information than any of the input images. in this research two images of the same place in different spatial resolution have been used the first one was panchromatic and the second image was multispectral with spatial resolution 0.5m and 2 m respectively. These images were captured by world view-2 sensor. This research resent four pan sharpening methods like (HSV, Brovey (color normalizes) , Gram shmidt and PCA)these methods were used to combine the adopted images to get multispectral image

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Publication Date
Tue Feb 05 2019
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
Anemia Blood Cell localization Using Modified K- Means Algorithm
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Publication Date
Wed Oct 06 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Image segmentation by using thresholding technique in two stages
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Publication Date
Mon Jan 10 2022
Journal Name
Iraqi Journal Of Science
Genetic Algorithm based Clustering for Intrusion Detection
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Clustering algorithms have recently gained attention in the related literature since
they can help current intrusion detection systems in several aspects. This paper
proposes genetic algorithm (GA) based clustering, serving to distinguish patterns
incoming from network traffic packets into normal and attack. Two GA based
clustering models for solving intrusion detection problem are introduced. The first
model coined as handles numeric features of the network packet, whereas
the second one coined as concerns all features of the network packet.
Moreover, a new mutation operator directed for binary and symbolic features is
proposed. The basic concept of proposed mutation operator depends on the most
frequent value

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Publication Date
Tue Feb 13 2024
Journal Name
Iraqi Journal Of Science
Fuzzy Linear Discriminant Analysis Clustering With Its Application
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Many fuzzy clustering are based on within-cluster scatter with a compactness measure , but in this paper explaining new fuzzy clustering method which depend on within-cluster scatter with a compactness measure and between-cluster scatter with a separation measure called the fuzzy compactness and separation (FCS). The fuzzy linear discriminant analysis (FLDA) based on within-cluster scatter matrix and between-cluster scatter matrix . Then two fuzzy scattering matrices in the objective function assure the compactness between data elements and cluster centers .To test the optimal number of clusters using validation clustering method is discuss .After that an illustrate example are applied.

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Publication Date
Wed Apr 01 2015
Journal Name
2015 Annual Ieee Systems Conference (syscon) Proceedings
Automatic generation of fuzzy classification rules using granulation-based adaptive clustering
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network
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Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre

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Publication Date
Wed Jan 01 2020
Journal Name
Journal Of Southwest Jiaotong University
Image Segmentation for Skin Detection
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Human skin detection, which usually performed before image processing, is the method of discovering skin-colored pixels and regions that may be of human faces or limbs in videos or photos. Many computer vision approaches have been developed for skin detection. A skin detector usually transforms a given pixel into a suitable color space and then uses a skin classifier to mark the pixel as a skin or a non-skin pixel. A skin classifier explains the decision boundary of the class of a skin color in the color space based on skin-colored pixels. The purpose of this research is to build a skin detection system that will distinguish between skin and non-skin pixels in colored still pictures. This performed by introducing a metric that measu

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Publication Date
Mon Aug 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
User (K-Means) for clustering in Data Mining with application
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  The great scientific progress has led to widespread Information as information accumulates in large databases is important in trying to revise and compile this vast amount of data and, where its purpose to extract hidden information or classified data under their relations with each other in order to take advantage of them for technical purposes.

      And work with data mining (DM) is appropriate in this area because of the importance of research in the (K-Means) algorithm for clustering data in fact applied with effect can be observed in variables by changing the sample size (n) and the number of clusters (K)

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Publication Date
Thu Dec 01 2022
Journal Name
Journal Of Engineering
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 med

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