scholarly journals Simulated Despondency for Robots in Distress

Leonardo ◽  
2019 ◽  
Vol 52 (1) ◽  
pp. 71-72
Author(s):  
Vicky Isley ◽  
Paul Smith
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Life ◽  
Environmental Issues ◽  
Human Interaction ◽  
Natural Systems ◽  
Bioinspired Robotics ◽  
Current Thinking ◽  
Artificial Neural

It is widely accepted that increased human interaction with natural systems is responsible for complex environmental issues, with most current thinking centered on the provision of advanced technological solutions. One response emerging from current bioinspired robotics research proposes artificial neural networks (ANNs) enhanced with artificial hormones for increased performance and efficiency. Here the authors discuss their artistic project concept, developed in collaboration with a bioinspired artificial life lab, considering the affordance of emotional robotics to develop despondency in the field.

scholarly journals Wound image segmentation using clustering based algorithms

2019 ◽  
Vol 2 (1) ◽  
pp. 570-578
Author(s):  
Ihor Farmaha ◽  
Marian Banaś ◽  
Vasyl Savchyn ◽  
Bohdan Lukashchuk ◽  
Taras Farmaha
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Artificial Neural Networks ◽  
Clustering Algorithms ◽  
Human Interaction ◽  
Clustering Methods ◽  
Insufficient Amount ◽  
Improve Measurement ◽  
Artificial Neural ◽  
Model Training

Abstract Classic methods of measurement and analysis of the wounds on the images are very time consuming and inaccurate. Automation of this process will improve measurement accuracy and speed up the process. Research is aimed to create an algorithm based on machine learning for automated segmentation based on clustering algorithms Methods. Algorithms used: SLIC (Simple Linear Iterative Clustering), Deep Embedded Clustering (that is based on artificial neural networks and k-means). Because of insufficient amount of labeled data, classification with artificial neural networks can't reach good results. Clustering, on the other hand is an unsupervised learning technique and doesn't need human interaction. Combination of traditional clustering methods for image segmentation with artificial neural networks leads to combination of advantages of both of them. Preliminary step to adapt Deep Embedded Clustering to work with bio-medical images is introduced and is based on SLIC algorithm for image segmentation. Segmentation with this method, after model training, leads to better results than with traditional SLIC.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural
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