scholarly journals An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

2022 ◽  
Vol 70 (3) ◽  
pp. 4393-4410
Author(s):  
Abeer D. Algarni ◽  
Walid El-Shafai ◽  
Ghada M. El Banby ◽  
Fathi E. Abd El-Samie ◽  
Naglaa F. Soliman
Keyword(s):  
Segmentation Approach ◽  
Hybrid Classification

HYBRID CLASSIFICATION SCHEMES FOR HEART MURMUR DETECTION TO ASSIST PHONOCARDIOGRAM BASED SIGNAL ACQUISITION

2017 ◽  
Vol 13 (9) ◽  
pp. 6480-6488 ◽  
Author(s):  
A.D. Jeyarani ◽  
Reena Daphne ◽  
Solomon Roach
Keyword(s):  
Neural Network ◽  
Signal Acquisition ◽  
Heart Murmur ◽  
Classification Techniques ◽  
Classification Schemes ◽  
Neural Network Classification ◽  
Nonlinear Classification ◽  
Processing Step ◽  
System Characterization ◽  
Hybrid Classification

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying system’s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.

XRCE: Hybrid Classification for Aspect-based Sentiment Analysis

2014 ◽  
Author(s):  
Caroline Brun ◽  
Diana Nicoleta Popa ◽  
Claude Roux
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Classification

Analysis of the Nascar Hall of Fame Exhibition Event: A Generation-Based Market Segmentation Approach

2019 ◽  
Vol 23 (6) ◽  
pp. 913-926
Author(s):  
Kakyom Kim ◽  
Giri Jogaratnam
Keyword(s):  
Generation X ◽  
Market Segmentation ◽  
Generation Y ◽  
Generational Differences ◽  
The Past ◽  
The Us ◽  
Segmentation Approach ◽  
Research Findings ◽  
Hall Of Fame ◽  
Southeastern Region

Research findings on generations have been becoming useful for event organizers and destination developers over the past decades. The current study investigated generational differences in exhibition dimensions, satisfaction, and future intentions along with trip characteristics of visitors to the NASCAR Hall of Fame Exhibition event held in a medium-sized city in the southeastern region of the US. Analysis confirmed the existence of six exhibition dimensions labeled as "exhibits," "staff," "facility," "concessions," "audio tours," and "hard cards" on the event. As part of the most substantial results, there were both dissimilarities and similarities in the exhibition dimensions across four generations including "Matures," "Baby Boomers," "Generation X," and "Generation Y." Analysis also suggested significant differences in exhibition visitors' overall satisfaction, future intentions, and trip characteristics across the generations. Some useful implications are discussed for exhibition event managers and organizers.

Motives underlying water sport tourist behaviour: a segmentation approach

2021 ◽  
Vol 63 (1) ◽  
pp. 109-127
Author(s):  
Kinga Zsuzsanna Nagy ◽  
Kata Tóth ◽  
Noémi Gyömbér ◽  
László Tóth ◽  
Miklós Bánhidi
Keyword(s):  
Segmentation Approach ◽  
Water Sport ◽  
Tourist Behaviour

A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images

2021 ◽  
Author(s):  
Ahmed A. Sleman ◽  
Ahmed Soliman ◽  
Mohamed Elsharkawy ◽  
Guruprasad Giridharan ◽  
Mohammed Ghazal ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optical Coherence ◽  
3D Segmentation ◽  
Retinal Layers ◽  
Segmentation Approach

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

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