159,702 research outputs found
Illustration of Medical Image Segmentation based on Clustering Algorithms
Image segmentation is the most basic and crucial process remembering the true objective to facilitate the characterization and representation of the structure of excitement for medical or basic images. Despite escalated research, segmentation remains a challenging issue because of the differing image content, cluttered objects, occlusion, non-uniform object surface, and different factors. There are numerous calculations and techniques accessible for image segmentation yet at the same time there requirements to build up an efficient, quick technique of medical image segmentation. This paper has focused on K-means and Fuzzy C means clustering algorithm to segment malaria blood samples in more accurate manner
Quality Assessment of Linked Datasets using Probabilistic Approximation
With the increasing application of Linked Open Data, assessing the quality of
datasets by computing quality metrics becomes an issue of crucial importance.
For large and evolving datasets, an exact, deterministic computation of the
quality metrics is too time consuming or expensive. We employ probabilistic
techniques such as Reservoir Sampling, Bloom Filters and Clustering Coefficient
estimation for implementing a broad set of data quality metrics in an
approximate but sufficiently accurate way. Our implementation is integrated in
the comprehensive data quality assessment framework Luzzu. We evaluated its
performance and accuracy on Linked Open Datasets of broad relevance.Comment: 15 pages, 2 figures, To appear in ESWC 2015 proceeding
Automated Protein Structure Classification: A Survey
Classification of proteins based on their structure provides a valuable
resource for studying protein structure, function and evolutionary
relationships. With the rapidly increasing number of known protein structures,
manual and semi-automatic classification is becoming ever more difficult and
prohibitively slow. Therefore, there is a growing need for automated, accurate
and efficient classification methods to generate classification databases or
increase the speed and accuracy of semi-automatic techniques. Recognizing this
need, several automated classification methods have been developed. In this
survey, we overview recent developments in this area. We classify different
methods based on their characteristics and compare their methodology, accuracy
and efficiency. We then present a few open problems and explain future
directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront
FRIOD: a deeply integrated feature-rich interactive system for effective and efficient outlier detection
In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the detection process. A user-friendly interactive mechanism is developed to allow easy and intuitive user interaction in all the major stages of the underlying outlier detection algorithm which includes dense cell selection, location-aware distance thresholding, and final top outlier validation. By doing so, we can mitigate the major difficulty of the competitive outlier detection methods in specifying the key parameter values, such as the density and distance thresholds. An innovative optimization approach is also proposed to optimize the grid-based space partitioning, which is a critical step of FRIOD. Such optimization fully considers the high-quality outliers it detects with the aid of human interaction. The experimental evaluation demonstrates that FRIOD can improve the quality of the detected outliers and make the detection process more intuitive, effective, and efficient
Text categorization and similarity analysis: similarity measure, architecture and design
This research looks at the most appropriate similarity measure to use for a document classification problem. The goal is to find a method that is accurate in finding both semantically and version related documents. A necessary requirement is that the method is efficient in its speed and disk usage. Simhash is found to be the measure best suited to the application and it can be combined with other software to increase the accuracy. Pingar have provided an API that will extract the entities from a document and create a taxonomy displaying the relationships and this extra information can be used to accurately classify input documents. Two algorithms are designed incorporating the Pingar API and then finally an efficient comparison algorithm is introduced to cut down the comparisons required
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