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Full-Text Articles in Computer Sciences
Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu
Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu
Applied Mathematics & Information Sciences
In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …
Using Deep Neural Networks To Classify Astronomical Images, Andrew D. Macpherson
Using Deep Neural Networks To Classify Astronomical Images, Andrew D. Macpherson
Honors Projects
As the quantity of astronomical data available continues to exceed the resources available for analysis, recent advances in artificial intelligence encourage the development of automated classification tools. This paper lays out a framework for constructing a deep neural network capable of classifying individual astronomical images by describing techniques to extract and label these objects from large images.
Artificial Intelligence For Para Rubber Identification Combining Five Machine Learning Methods, Chairote Yaiprasert Ph.D.
Artificial Intelligence For Para Rubber Identification Combining Five Machine Learning Methods, Chairote Yaiprasert Ph.D.
Karbala International Journal of Modern Science
This study aims to identify Para rubber species using a combination of five machine learning techniques to classify leaf images. The learning process is defined using a dataset for each classification method. Approximately 1,472 leaf images are prepared consisting of various sizes, shapes, quality provided for the model. The classification indicators are defined with the help of an algorithm to identify at least three of the top five potential classification outcomes. The algorithm accurately predicts 100% of the five classification methods. Methods can provide precise and rapid classification of large quantities, without the need for image preprocessing prior to classification.
A Systematic Mapping Study On The Risk Factors Leading To Type Ii Diabetes Mellitus, Karar N. J Musafer, Fahrul Zaman Huyop, Mufeed J Ewadh, Eko Supriyanto, Mohammad Rava
A Systematic Mapping Study On The Risk Factors Leading To Type Ii Diabetes Mellitus, Karar N. J Musafer, Fahrul Zaman Huyop, Mufeed J Ewadh, Eko Supriyanto, Mohammad Rava
Karbala International Journal of Modern Science
Diabetes is one of the most common diseases that has had devastating effects on the general population. It is also among the most popular research trends in modern medicine. Thus, due to the complexity and desirability of this particular affliction, there is a lot of demand towards understanding this disease better, so that it can pave the way towards better solutions in combating diabetes. The aim of this review is to provide a categorization of the risk factors leading to Type II Diabetes. In order to provide a justification for the type of diabetes, an explanation is provided which covers …
Automated Species Classification Methods For Passive Acoustic Monitoring Of Beaked Whales, John Lebien
Automated Species Classification Methods For Passive Acoustic Monitoring Of Beaked Whales, John Lebien
University of New Orleans Theses and Dissertations
The Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive …