AIEMLA: artificial intelligence enabled machine learning approach for routing attacks on internet of things

2021 ◽  
Author(s):  
Saurabh Sharma ◽  
Vinod Kumar Verma
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Learning Approach ◽  
Routing Attacks ◽  
Machine Learning Approach

scholarly journals Inter-Rater Reliability of Unstructured Text Labeling: Artificially vs. Naturally Intelligent Approaches

2021 ◽  
Author(s):  
Gleb Danilov ◽  
Alexandra Kosyrkova ◽  
Maria Shults ◽  
Semen Melchenko ◽  
Tatyana Tsukanova ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Pulmonary Embolism ◽  
Language Processing ◽  
Learning Approach ◽  
Medical Text ◽  
Unstructured Text ◽  
Textual Data ◽  
Machine Learning Approach ◽  
Rfbr Grant

Unstructured medical text labeling technologies are expected to be highly demanded since the interest in artificial intelligence and natural language processing arises in the medical domain. Our study aimed to assess the agreement between experts who judged on the fact of pulmonary embolism (PE) in neurosurgical cases retrospectively based on electronic health records and assess the utility of the machine learning approach to automate this process. We observed a moderate agreement between 3 independent raters on PE detection (Light’s kappa = 0.568, p = 0). Labeling sentences with the method we proposed earlier might improve the machine learning results (accuracy = 0.97, ROC AUC = 0.98) even in those cases that could not be agreed between 3 independent raters. Medical text labeling techniques might be more efficient when strict rules and semi-automated approaches are implemented. Machine learning might be a good option for unstructured text labeling when the reliability of textual data is properly addressed. This project was supported by the RFBR grant 18-29-22085.

scholarly journals On the Potential for Open-Endedness in Neural Networks

2019 ◽  
Vol 25 (2) ◽  
pp. 145-167 ◽  
Author(s):  
Nicholas Guttenberg ◽  
Nathaniel Virgo ◽  
Alexandra Penn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Gradient Descent ◽  
The Other ◽  
Learning Approach ◽  
Natural Evolution ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Common Barriers

Natural evolution gives the impression of leading to an open-ended process of increasing diversity and complexity. If our goal is to produce such open-endedness artificially, this suggests an approach driven by evolutionary metaphor. On the other hand, techniques from machine learning and artificial intelligence are often considered too narrow to provide the sort of exploratory dynamics associated with evolution. In this article, we hope to bridge that gap by reviewing common barriers to open-endedness in the evolution-inspired approach and how they are dealt with in the evolutionary case—collapse of diversity, saturation of complexity, and failure to form new kinds of individuality. We then show how these problems map onto similar ones in the machine learning approach, and discuss how the same insights and solutions that alleviated those barriers in evolutionary approaches can be ported over. At the same time, the form these issues take in the machine learning formulation suggests new ways to analyze and resolve barriers to open-endedness. Ultimately, we hope to inspire researchers to be able to interchangeably use evolutionary and gradient-descent-based machine learning methods to approach the design and creation of open-ended systems.

scholarly journals Decision Algorithm for Heuristic Donor-Recipient Matching

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 33-40
Author(s):  
Ivars Namatevs ◽  
Ludmila Aleksejeva
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Optimal Allocation ◽  
Prediction Models ◽  
Decision Makers ◽  
Clinical Settings ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Intelligent Computing ◽  
Machine Learning Approach

This paper introduces the application of artificial intelligence paradigm towards precision medicine in renal transplantation. The match of the optimal donor-recipient pair in kidney transplantation in Latvian Transplant Centre (LTC) has been constrained by the lack of prediction models and algorithms. Consequently, LTC seeks for practical intelligent computing solution to assist the clinical setting decision-makers during their search for the optimal donor-recipient match. Therefore, by optimizing both the donor and recipient profiles, prioritizing importance of the features, and based on greedy algorithm approach, advanced decision algorithm has been created. The strength of proposed algorithm lies in identification of suitable donors for a specific recipient based on evaluation of criteria by points principle. Experimental study demonstrates that the decision algorithm for heuristic donor-recipient matching integrated in machine learning approach improves the ability of optimal allocation of renal in LTC. It is an important step towards personalized medicine in clinical settings.

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