Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4

k-nearest neighbor Related Abstracts

4 Development of the Academic Model to Predict Student Success at VUT-FSASEC Using Decision Trees

Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

Abstract:

The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: Machine Learning, Decision trees, support vector machine, FSASEC, academic environment model, k-nearest neighbor, popularity index

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3 Principle Component Analysis on Colon Cancer Detection

Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti

Abstract:

Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.

Keywords: Lymphoma, colorectal cancer, carcinoma, Principle Component Analysis, k-nearest neighbor

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2 Lung Cancer Detection and Multi Level Classification Using Discrete Wavelet Transform Approach

Authors: G. Narendra Kumar, V. Veeraprathap, G. S. Harish

Abstract:

Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: Artificial Neural Networks, roi, ANN, discrete wavelet transform, DWT, kNN, region of interest, GLCM, k-nearest neighbor, gray-level co-occurrence matrix

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1 Convolutional Neural Networks for Feature Extraction and Automated Target Recognition in Synthetic Aperture Radar Images

Authors: Ying Zhao, John Geldmacher, Christopher Yerkes

Abstract:

Advances in the development of deep neural networks and other machine learning algorithms combined with ever more powerful hardware and the huge amount of data available on the internet has led to a revolution in ML research and applications. These advances present massive potential and opportunity for the military applications such as the analysis of Synthetic Aperture Radar (SAR) imagery. Synthetic Aperture Radar imagery is a useful tool capable of capturing high resolution images regardless of cloud coverage and at night. However, there is a limited amount of publicly available SAR data to train a machine learning model. This paper shows how to successfully dissect, modify, and re-architect cross-domain object recognition models such as the VGG-16 model, transfer learning models from the ImageNet, and the k-nearest neighbor (kNN) classifier. The paper demonstrates that the combinations of these factors can significantly and effectively improve the automated object recognition (ATR) of SAR clean and noisy images. The paper shows a potentially inexpensive, accurate, transfer and unsurpervised learning SAR ATR system when data labels are scarce and data are noisy, simplifying the whole recognition for the tactical operation requirements in the area of SAR ATR.

Keywords: Deep learning, Transfer Learning, kNN, SAR images, k-nearest neighbor, synthetic aperture radar images, VGG-16

Procedia PDF Downloads 1