Mining tourists’ destinations and preferences through LSTM-based text classification and spatial clustering using Flickr data

2021 ◽  
Author(s):  
Hyejin Lee ◽  
Youngok Kang
Keyword(s):  
Text Classification ◽  
Spatial Clustering

Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

1996 ◽  
Vol 54 ◽  
pp. 282-283
Author(s):  
Badrinath Roysam ◽  
Hakan Ancin ◽  
Douglas E. Becker ◽  
Robert W. Mackin ◽  
Matthew M. Chestnut ◽  
...  
Keyword(s):  
Image Analysis ◽  
Structural Changes ◽  
Sampling Methods ◽  
Spatial Clustering ◽  
Three Dimensional ◽  
Field Of View ◽  
Cell Counting ◽  
Depth Of Field ◽  
Tissue Cell ◽  
Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

"Catching" behaviors from our neighbors: Spatial clustering of health behaviors in college residence halls

2014 ◽  
Author(s):  
Helen C. Harton ◽  
Peng Zhang ◽  
Nicholas Terpstra-Schwab ◽  
Brooke Ammerman
Keyword(s):  
Health Behaviors ◽  
Spatial Clustering ◽  
Residence Halls ◽  
College Residence

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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