Assessing multimodal speech-gesture coordination: correlation map analysis for signal comparison

2018 ◽  
Vol 144 (3) ◽  
pp. 1870-1870
Author(s):  
Samantha G. Danner
Keyword(s):  
Map Analysis ◽  
Signal Comparison

How toxicity of nanomaterials towards different species could be simultaneously evaluated: Novel multi-nano-read-across approach

2017 ◽  
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Karolina Jagiello ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski ◽  
...  
Keyword(s):  
Missing Values ◽  
Metal Oxide Nanoparticles ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Related Data ◽  
Mammalian Cell Lines ◽  
Learning Techniques ◽  
Combination Of Methods ◽  
Map Analysis ◽  
Unique Chemical

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been<br>discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

How toxicity of nanomaterials towards different species could be simultaneously evaluated: Novel multi-nano-read-across approach

2017 ◽  
Author(s):  
Natalia Sizochenko ◽  
Alicja Mikolajczyk ◽  
Karolina Jagiello ◽  
Tomasz Puzyn ◽  
Jerzy Leszczynski ◽  
...  
Keyword(s):  
Missing Values ◽  
Metal Oxide Nanoparticles ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Related Data ◽  
Mammalian Cell Lines ◽  
Learning Techniques ◽  
Combination Of Methods ◽  
Map Analysis ◽  
Unique Chemical

Application of predictive modeling approaches is able solve the problem of the missing data. There are a lot of studies that investigate the effects of missing values on qualitative or quantitative modeling, but only few publications have been<br>discussing it in case of applications to nanotechnology related data. Current project aimed at the development of multi-nano-read-across modeling technique that helps in predicting the toxicity of different species: bacteria, algae, protozoa, and mammalian cell lines. In this study, the experimental toxicity for 184 metal- and silica oxides (30 unique chemical types) nanoparticles from 15 experimental datasets was analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis was developed. At the first step, hidden patterns of toxicity among the nanoparticles were identified using a combination of methods. Then the developed model that based on categorization of metal oxide nanoparticles’ toxicity outcomes was evaluated by means of combination of supervised and unsupervised machine learning techniques to find underlying factors responsible for toxicity.

Assessing geography knowledge in primary education with mental map analysis: a Balearic Islands case study

2021 ◽  
pp. 1-22
Author(s):  
Jaume Binimelis Sebastián ◽  
Antoni Ordinas Garau ◽  
Maurici Ruiz Pérez
Keyword(s):  
Primary Education ◽  
Balearic Islands ◽  
Mental Map ◽  
Map Analysis

scholarly journals Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1291
Author(s):  
Seda Camalan ◽  
Hanya Mahmood ◽  
Hamidullah Binol ◽  
Anna Luiza Damaceno Araújo ◽  
Alan Roger Santos-Silva ◽  
...  
Keyword(s):  
Cross Validation ◽  
Clinical Predictors ◽  
Oral Dysplasia ◽  
Novel Technologies ◽  
Photographic Images ◽  
The Uk ◽  
Map Analysis ◽  
Development And Validation ◽  
Fold Cross Validation ◽  
Activation Map

Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as “suspicious” and “normal” by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method’s feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis.

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