Performance Analysis of Machine Learning Models for Object Recognition in Underwater Video Images

2021 ◽  
Author(s):  
Baris Guney Ozdilli ◽  
Mustafa Bora Arslan ◽  
Tilbe Alp ◽  
Ozlem Albayrak ◽  
Perin Unal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Object Recognition ◽  
Performance Analysis ◽  
Underwater Video ◽  
Learning Models ◽  
Video Images ◽  
Machine Learning Models

scholarly journals Vis Quelle: Visual Question based Elementary Learning Companion a system to Facilitate Learning Word Object Associations

2021 ◽  
Vol 11 (1) ◽  
pp. 41-49
Author(s):  
Sandhya Vidyashankar ◽  
◽  
Rakshit Vahi ◽  
Yash Karkhanis ◽  
Gowri Srinivasa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Second Language ◽  
Object Recognition ◽  
Question Answering ◽  
Learning Models ◽  
Text Data ◽  
Visual Question Answering ◽  
Machine Learning Models ◽  
Native Tongue

We present an automated, visual question answering based companion – VisQuelle - to facilitate elementary learning of word-object associations. In particular, we attempt to harness the power of machine learning models for object recognition and the understanding of combined processing of images and text data from visual-question answering to provide variety and nuance in the images associated with letters or words presented to the elementary learner. We incorporate elements such as gamification to motivate the learner by recording scores, errors, etc., to track the learner’s progress. Translation is also provided to reinforce word-object associations in the user’s native tongue, if the learner is using VisQuelle to learn a second language.

scholarly journals A Visualization System for Performance Analysis of Image Classification Models

2020 ◽  
Vol 2020 (1) ◽  
pp. 375-1-375-9
Author(s):  
Chanhee Park ◽  
Hyojin Kim ◽  
Kyungwon Lee
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Image Classification ◽  
Visual Analytics ◽  
Model Performance ◽  
Learning Models ◽  
Classification Problems ◽  
Visualization System ◽  
Wide Range ◽  
Machine Learning Models

Developing machine learning models for image classification problems involves various tasks such as model selection, layer design, and hyperparameter tuning for improving the model performance. However, regarding deep learning models, insufficient model interpretability renders it infeasible to understand how they make predictions. To facilitate model interpretation, performance analysis at the class and instance levels with model visualization is essential. We herein present an interactive visual analytics system to provide a wide range of performance evaluations of different machine learning models for image classification. The proposed system aims to overcome challenges by providing visual performance analysis at different levels and visualizing misclassification instances. The system which comprises five views - ranking, projection, matrix, and instance list views, enables the comparison and analysis different models through user interaction. Several use cases of the proposed system are described and the application of the system based on MNIST data is explained. Our demo app is available at https://chanhee13p.github.io/VisMlic/.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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