Supervised machine learning via Hidden Markov Models for accurate classification of plant stress levels & types based on imaged Chlorophyll fluorescence profiles & their rate of change in time

2017 ◽  
Author(s):  
Julie Blumenthal ◽  
Dalila B. Megherbi ◽  
Robert Lussier
Keyword(s):  
Machine Learning ◽  
Chlorophyll Fluorescence ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Plant Stress ◽  
Rate Of Change ◽  
Supervised Machine Learning ◽  
Stress Levels

scholarly journals Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cédric Beaulac ◽  
Fabrice Larribe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Hidden Markov Models ◽  
Real Time ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Estimation ◽  
Supervised Machine Learning ◽  
Real Time Estimation ◽  
Mobile Target

We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.

scholarly journals Early Classification of Intent for Maritime Domains Using Multinomial Hidden Markov Models

2021 ◽  
Vol 4 ◽  
Author(s):  
Logan Carlson ◽  
Dalton Navalta ◽  
Monica Nicolescu ◽  
Mircea Nicolescu ◽  
Gail Woodward
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Rate Of Change ◽  
Maritime Security ◽  
Research Focus ◽  
Intent Recognition ◽  
Early Classification

The need for increased maritime security has prompted research focus on intent recognition solutions for the naval domain. We consider the problem of early classification of the hostile behavior of agents in a dynamic maritime domain and propose our solution using multinomial hidden Markov models (HMMs). Our contribution stems from a novel encoding of observable symbols as the rate of change (instead of static values) for parameters relevant to the task, which enables the early classification of hostile behaviors, well before the behavior has been finalized. We discuss our implementation of a one-versus-all intent classifier using multinomial HMMs and present the performance of our system for three types of hostile behaviors (ram, herd, block) and a benign behavior.

scholarly journals CutProtFam-Pred: Detection and classification of putative structural cuticular proteins from sequence alone, based on profile Hidden Markov Models

2014 ◽  
Vol 52 ◽  
pp. 51-59 ◽  
Author(s):  
Zoi S. Ioannidou ◽  
Margarita C. Theodoropoulou ◽  
Nikos C. Papandreou ◽  
Judith H. Willis ◽  
Stavros J. Hamodrakas
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Profile Hidden Markov Models ◽  
Cuticular Proteins
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