scholarly journals Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals

2018 ◽  
Vol 29 (1) ◽  
pp. 799-813 ◽  
Author(s):  
Mohammad Idrees Bhat ◽  
B. Sharada
Keyword(s):  
Graph Embedding ◽  
Feature Space ◽  
Feature Representation ◽  
Graph Representation ◽  
Future Studies ◽  
Spectral Graph ◽  
Statistical Institute ◽  
Indian Statistical ◽  
Effective Representation

Abstract Interpretation of different writing styles, unconstrained cursiveness and relationship between different primitive parts is an essential and challenging task for recognition of handwritten characters. As feature representation is inadequate, appropriate interpretation/description of handwritten characters seems to be a challenging task. Although existing research in handwritten characters is extensive, it still remains a challenge to get the effective representation of characters in feature space. In this paper, we make an attempt to circumvent these problems by proposing an approach that exploits the robust graph representation and spectral graph embedding concept to characterise and effectively represent handwritten characters, taking into account writing styles, cursiveness and relationships. For corroboration of the efficacy of the proposed method, extensive experiments were carried out on the standard handwritten numeral Computer Vision Pattern Recognition, Unit of Indian Statistical Institute Kolkata dataset. The experimental results demonstrate promising findings, which can be used in future studies.

scholarly journals Extending Geodemographics Using Data Primitives: A Review and a Methodological Proposal

2021 ◽  
Vol 10 (6) ◽  
pp. 386
Author(s):  
Jennie Gray ◽  
Lisa Buckner ◽  
Alexis Comber
Keyword(s):  
Social Dynamics ◽  
Feature Space ◽  
Social Processes ◽  
Multiple Time ◽  
Current State ◽  
Cluster Label ◽  
Static Data ◽  
The Social ◽  
Using Data

This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same properties as the cluster centre) and the failure of the static label to describe anything about the underlying neighbourhood processes and dynamics. To address these limitations, this paper proposed a data primitives approach. Data primitives are the fundamental dimensions or measurements that capture the processes of interest. They can be used to describe the current state of an area in a multivariate feature space, and states can be compared over multiple time periods for which data are available, through for example a change vector approach. In this way, emergent social processes, which may be too weak to result in a change in a cluster label, but are nonetheless important signals, can be captured. As states are updated (for example, as new data become available), inferences about different social processes can be made, as well as classification updates if required. State changes can also be used to determine neighbourhood trajectories and to predict or infer future states. A list of data primitives was suggested from a review of the mechanisms driving a number of neighbourhood-level social processes, with the aim of improving the wider understanding of the interaction of complex neighbourhood processes and their effects. A small case study was provided to illustrate the approach. In this way, the methods outlined in this paper suggest a more nuanced approach to geodemographic research, away from a focus on classifications and static data, towards approaches that capture the social dynamics experienced by neighbourhoods.

scholarly journals ArcGIS Urban: an application for plan assessment

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ellen Soward ◽  
Jianling Li
Keyword(s):  
United States ◽  
The United States ◽  
Spatial Context ◽  
Future Studies ◽  
Planning Profession ◽  
Gis Tools ◽  
Comprehensive Plans ◽  
Near Future ◽  
The City

AbstractMost cities in the United States rely on zoning to address important planning-related issues within their jurisdictions. Planners often use GIS tools to analyze these issues in a spatial context. ESRI’s ArcGIS Urban software seeks to provide the planning profession with a GIS-based solution for various challenges, including zoning’s impacts on the built environment and housing capacity.This research explores the use of ArcGIS Urban for assessing the existing zoning and comprehensive plans in meeting the projected residential growth in the near future using the City of Arlington, Texas as a case study. The exploration provides examples and lessons for how ArcGIS Urban might be used by planners to accomplish their tasks and highlights the capabilities and limitations of ArcGIS Urban in its current stand. The paper is concluded with some suggestions for future studies.

scholarly journals The Potential of GRASP/GARRLiC Retrievals for Dust Aerosol Model Evaluation: Case Study during the PreTECT Campaign

2021 ◽  
Vol 13 (5) ◽  
pp. 873
Author(s):  
Dimitra Konsta ◽  
Alexandra Tsekeri ◽  
Stavros Solomos ◽  
Nikolaos Siomos ◽  
Anna Gialitaki ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Dust Concentration ◽  
Saharan Dust ◽  
North Coast ◽  
Concentration Profiles ◽  
Future Studies ◽  
Aerosol Model ◽  
The North ◽  
Sun Photometer

We use the Generalized Retrieval of Aerosol Surface Properties algorithm (GRASP) to compare with dust concentration profiles derived from the NMME-DREAM model for a specific dust episode. The GRASP algorithm provides the possibility of deriving columnar and vertically-resolved aerosol properties from a combination of lidar and sun-photometer observations. Herein, we apply GRASP for analysis of a Saharan dust outburst observed during the “PREparatory: does dust TriboElectrification affect our ClimaTe” campaign (PreTECT) that took place at the North coast of Crete, at the Finokalia ACTRIS station. GRASP provides column-averaged and vertically resolved microphysical and optical properties of the particles. The retrieved dust concentration profiles are compared with modeled concentration profiles derived from the NMME-DREAM dust model. To strengthen the results, we use dust concentration profiles from the POlarization-LIdar PHOtometer Networking method (POLIPHON). A strong underestimation of the maximum dust concentration is observed from the NMME-DREAM model. The reported differences between the retrievals and the model indicate a high potential of the GRASP algorithm for future studies of dust model evaluation.

USING IDIOBLASTS TO GROUP LAURINOXYLON SPECIES: CASE STUDY FROM THE OLIGO-MIOCENE OF EUROPE

IAWA Journal ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 459-488 ◽  
Author(s):  
Dimitra Mantzouka ◽  
Vasileios Karakitsios ◽  
Jakub Sakala ◽  
Elisabeth A. Wheeler
Keyword(s):  
Fossil Wood ◽  
New Combination ◽  
The Czech Republic ◽  
Axial Parenchyma ◽  
Future Studies ◽  
Ray Parenchyma Cells ◽  
Type 3 ◽  
Of Greece

Several specimens of Lauraceae fossil wood from the Cenozoic of Greece (southern part of Lesbos), the Czech Republic (Kadaň-Zadní Vrch Hill and Jáchymov), and Hungary (Ipolytarnóc) were studied. When considering whether they belonged to the speciose fossil wood genus Laurinoxylon, we reviewed the literature and data from InsideWood on fossil and modern woods. As a result, we propose criteria for excluding a fossil Lauraceae wood from Laurinoxylon and list the species that should be excluded from this genus. The criteria (filters) proposed to exclude a genus from having relationships with Laurinoxylon are: A. Axial parenchyma features: A1. Marginal axial parenchyma, A2. Aliform to aliform-confluent paratracheal parenchyma. B. Ray features: B1. Rays higher than 1 mm, B2. Exclusively homocellular rays, B3. Rays more than 5 cells wide, B4. Rays storied. C. Porosity features: Ring-porous. D. Idioblasts: Absence of idioblasts. Based on the distribution of idioblasts, we recognize four groups in Laurinoxylon (Type 1 - with idioblasts associated only with ray parenchyma cells, Type 2a - with idioblasts associated with both ray and axial parenchyma, Type 2b - with idioblasts associated both with rays and present among the fibres, and Type 3 - with idioblasts associated with ray and axial parenchyma and also among the fibres) and list the extant genera with features of those groups. Such grouping helps with interpreting the relationships of fossil lauraceous woods with extant genera. We discuss the Oligocene–Miocene European species that belong to these Laurinoxylon groups, noting that some warrant reassignment to different genera or even families. Future studies are needed to determine whether new genera should be established to accommodate these species. We propose the new combination Cinnamomoxylon variabile (Privé-Gill & Pelletier) Mantzouka, Karakitsios, Sakala & Wheeler.

scholarly journals Identifying Potential Clinical Syndromes of Hepatocellular Carcinoma Using PSO-Based Hierarchical Feature Selection Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhiwei Ji ◽  
Bing Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Feature Selection ◽  
Clinical Symptoms ◽  
Malignant Tumors ◽  
Feature Space ◽  
Middle Layer ◽  
Active Role ◽  
Feature Representation ◽  
Causal Relationships ◽  
Positive Score

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.

scholarly journals Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

2021 ◽  
Author(s):  
Rogini Runghen ◽  
Daniel B Stouffer ◽  
Giulio Valentino Dalla Riva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Link Prediction ◽  
Graph Embedding ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Large Networks ◽  
Data Set ◽  
Learning Techniques ◽  
Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

scholarly journals Chemical Incident Analysis Involving Storage of Fertilizer Product

2021 ◽  
Vol 38 (1) ◽  
Author(s):  
Rogerio De Medeiros Tocantins ◽  
Bettina Tomio Heckert ◽  
Rafael Salum de Oliveira ◽  
Hélio João Coelho ◽  
Gisele Chibinski Parabocz ◽  
...  
Keyword(s):  
Chemical Reaction ◽  
Water Bodies ◽  
Reaction Products ◽  
Future Studies ◽  
Adjacent Water ◽  
Forensic Engineering ◽  
Fertilizer Material ◽  
The City ◽  
Incident Analysis

A forensic engineering analyses of a chemical incident is presented that was classified as a self-sustaining decomposition (SSD) event, which occurred in a load of 10,000 tons of NK 21-00-21 fertilizer bulk stored inside a warehouse in the city of São Francisco do Sul in Brazil. The chemical reaction developed within the fertilizer mass and took several days to be controlled, resulting in the evacuation of thousands of residents. The water used to fight against the reaction, after having contact with the load of fertilizer material, promoted changes in adjacent water bodies, causing the death of animals (fish, crustaceans, and amphibians). The smoke from the chemical reaction products damaged the incident’s surrounding vegetation. Large SSD events are rare, with an average worldwide frequency of one every three years. Therefore, in addition to presenting a case study of this type of phenomenon, the main objective of this work is to discuss the causes that led to SSD reaction at this event, evaluate its consequences, and motivate future studies.

scholarly journals A Case Study of Chinese Students’ and IEP Faculty Perceptions of a Creativity and Critical Thinking Course

2019 ◽  
Vol 9 (3) ◽  
pp. 34 ◽  
Author(s):  
James Badger
Keyword(s):  
Critical Thinking ◽  
International Students ◽  
Faculty Perceptions ◽  
Chinese Students ◽  
Intensive English ◽  
Future Studies ◽  
Effective Strategies ◽  
Intensive English Programs ◽  
Analytic Skills

The attention to fostering learners’ critical thinking and creativity skills in secondary school and college students is growing in Western and non-Western countries. This study investigated the integration of a creativity and critical thinking course in an Intensive English Programs (IEP) to determine how the same course may contribute to international students’ linguistic skills and analytic abilities in preparation for college. Perry’s (1970) conceptual framework was adopted to analyze Chinese students’ views of problems presented in a creativity and critical thinking course, and how the same knowledge related to the Chinese students’ prior educational experiences as well as connect to their future studies. IEP faculty and administrator’s perceptions provided an additional perspective into the purpose and learning outcomes of the same course. Findings from this research address a gap in the literature that seeks effective strategies and models for IEPs to foster international students’ analytic skills in preparation for college studies.

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