The aim of automated electrocardiogram (ECG) delineation system is the reliable detection of fundamental ECG components and from these fundamental measurements, the parameters of diagnostic significance, namely, P-duration, PR-interval, QRS-duration, QTinterval, are to be identified and extracted. In this work, two supervised machine learning algorithms, K-Nearest neighbour (KNN) and Support Ve...

This paper reports our evaluation of k Nearest Neighbor (kNN), Support Vector Machines (SVM), and Adaptive Resonance Associative Map (ARAM) on Chinese web page classi cation. Benchmark experiments based on a Chinese web corpus showed that their predictive performance were roughly comparable although ARAM and kNN slightly outperformed SVM in small categories. In addition, inserting rules into AR...

In order for a robot or a computer to perform tasks, it must recognize what it is looking at. Given an image a computer must be able to classify what the image represents. While this is a fairly simple task for humans, it is not an easy task for computers. Computers must go through a series of steps in order to classify a single image. In this paper, we used a general Bag of Words model in orde...

This research work presents a method for automatic classification of medical images in two classes Normal and Abnormal based on image features and automatic abnormality detection. Our proposed system consists of four phases Preprocessing, Feature extraction, Classification, and Post processing. Statistical texture feature set is derived from normal and abnormal images. We used the KNN classifie...

MedGIFT is a medical imaging research group of the Geneva University Hospitals and the University of Geneva, Switzerland. Since 2004, the medGIFT group has participated in the ImageCLEF benchmark each year, focusing mainly on the medical imaging tasks. For the medical image retrieval task, two existing retrieval engines were used: the GNU Image Finding Tool (GIFT) as image retrieval engine and ...

We investigated how Half-Against-Half Support Vector Machine (HAH-SVM) and Half-Against-Half kNearest Neighbour (HAH-KNN) methods succeed in the classification of the benthic macroinvertebrate images. Automated taxa identification of benthic macroinvertebrates is a slightly researched area and in this paper HAH-KNN was for the first time applied to this application area. The main problem, when ...

We present experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in the collaborative filtering framework using datasets with different properties. While k-Nearest Neighbor is usually used for the collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Since collaborative filtering ca...

An efficient classification framework for mapping agricultural tillage practice using hyperspectral remote sensing imagery is proposed, which has the potential to be implemented practically to provide rapid, accurate, and objective surveying data for precision agricultural management and appraisal from large-scale remote sensing images. It includes a local region filter [i.e., Gaussian low-pass...

In this work to provide the information about an object clinically in terms of its size, shape and type of image for this image segmentation and classification are important tool in medical image processing. Ultrasound imaging is the best way to prediction of which type of thyroid is there. In this paper, images were distinguishing in two groups Benign (non-cancerous) and Malignant (cancerous) ...

In this paper, a zone based features are extracted from handwritten Kannada Vowels and English uppercase Character images for their recognition. A Total of 4,000 handwritten Kannada and English sample images are collected for classifications. The collected images are normalized into 32 x 32 dimensions. Then the normalized images are divided into 64 zones and their pixel densities are calculated...