Certainty Of Outlier And Boundary Points Processing In Data Mining
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Certainty Of Outlier And Boundary Points Processing In Data Mining
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Author : Elyas Rashno
language : en
Publisher: Infinite Study
Release Date :
Certainty Of Outlier And Boundary Points Processing In Data Mining written by Elyas Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Data certainty is one of the issues in the real-world applications which is caused by unwanted noise in data. Recently, more attentions have been paid to overcome this problem. We proposed a new method based on neutrosophic set (NS) theory to detect boundary and outlier points as challenging points in clustering methods.
Lumen Boundary Detection Using Neutrosophic C Means In Ivoct Images
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Author : Mohammad habibi
language : un
Publisher: Infinite Study
Release Date :
Lumen Boundary Detection Using Neutrosophic C Means In Ivoct Images written by Mohammad habibi and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
In this paper, a novel method for lumen boundary identification is proposed using Neutrosophic c_means. This method clusters pixels of the intravascular optical coherence tomography image into several clusters using indeterminacy and Neutrosophic theory, which aims to detect the boundaries.
Advanced Data Mining And Applications
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Author : Ronghuai Huang
language : en
Publisher: Springer
Release Date : 2009-08-09
Advanced Data Mining And Applications written by Ronghuai Huang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-09 with Computers categories.
This volume contains the proceedings of the International Conference on Advanced Data Mining and Applications (ADMA 2009), held in Beijing, China, during August 17–19, 2009. We are pleased to have a very strong program. Acceptance into the conference proceedings was extremely competitive. From the 322 submissions from 27 countries and regions, the Program Committee selected 34 full papers and 47 short papers for presentation at the conference and inclusion in the proceedings. The c- tributed papers cover a wide range of data mining topics and a diverse spectrum of interesting applications. The Program Committee worked very hard to select these papers through a rigorous review process and extensive discussion, and finally c- posed a diverse and exciting program for ADMA 2009. An important feature of the main program was the truly outstanding keynote spe- ers program. Edward Y. Chang, Director of Research, Google China, gave a talk titled "Confucius and 'Its' Intelligent Disciples". Being right in the forefront of data mining applications to the world's largest knowledge and data base, the Web, Dr. Chang - scribed how Google's Knowledge Search product help to improve the scalability of machine learning for Web-scale applications. Charles X. Ling, a seasoned researcher in data mining from the University of Western Ontario, Canada, talked about his in- vative applications of data mining and artificial intelligence to gifted child education.
Fluid Segmentation In Neutrosophic Domain
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Author : Elyas Rashno
language : un
Publisher: Infinite Study
Release Date :
Fluid Segmentation In Neutrosophic Domain written by Elyas Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Optical coherence tomography (OCT) as retina imaging technology is currently used by ophthalmologist as a non-invasive and non-contact method for diagnosis of agerelated degeneration (AMD) and diabetic macular edema (DME) diseases. Fluid regions in OCT images reveal the main signs of AMD and DME. In this paper, an efficient and fast clustering in neutrosophic (NS) domain referred as neutrosophic C-means is adapted for fluid segmentation. For this task, a NCM cost function in NS domain is adapted for fluid segmentation and then optimized by gradient descend methods which leads to binary segmentation of OCT Bscans to fluid and tissue regions. The proposed method is evaluated in OCT datasets of subjects with DME abnormalities. Results showed that the proposed method outperforms existing fluid segmentation methods by 6% in dice coefficient and sensitivity criteria.
A Convoloutional Neural Network Model Based On Neutrosophy For Noisy Speech Recognition
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Author : Elyas Rashno
language : un
Publisher: Infinite Study
Release Date :
A Convoloutional Neural Network Model Based On Neutrosophy For Noisy Speech Recognition written by Elyas Rashno and has been published by Infinite Study this book supported file pdf, txt, epub, kindle and other format this book has been release on with Mathematics categories.
Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional neural network (CNN) model with data uncertainty handling; referred as NCNN (Neutrosophic Convolutional Neural Network); is proposed for classification task. Here, speech signals are used as input data and their noise is modeled as uncertainty.
Sensors And Actuators In Smart Cities
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Author : Mohammad Hammoudeh
language : en
Publisher: MDPI
Release Date : 2018-05-04
Sensors And Actuators In Smart Cities written by Mohammad Hammoudeh and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-04 with Computers categories.
This book is a printed edition of the Special Issue "Sensors and Actuators in Smart Cities" that was published in JSAN
Bulletin Of The Atomic Scientists
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Author :
language : en
Publisher:
Release Date : 1973-10
Bulletin Of The Atomic Scientists written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1973-10 with categories.
The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.
Annual Report Of The Department Of Mines New South Wales For The Year
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Author : New South Wales. Department of Mines
language : en
Publisher:
Release Date : 1939
Annual Report Of The Department Of Mines New South Wales For The Year written by New South Wales. Department of Mines and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1939 with Mines and mineral resources categories.
Annual Report
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Author : New South Wales. Department of Mines
language : en
Publisher:
Release Date : 1934
Annual Report written by New South Wales. Department of Mines and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1934 with categories.
New Developments In Unsupervised Outlier Detection
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Author : Xiaochun Wang
language : en
Publisher: Springer Nature
Release Date : 2020-11-24
New Developments In Unsupervised Outlier Detection written by Xiaochun Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-24 with Technology & Engineering categories.
This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research. The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.