Irrigation System Automation Using Finite State Machine Model and Machine Learning Techniques

2020 ◽  
pp. 495-501
Author(s):  
H. K. Pradeep ◽  
Prabhudev Jagadeesh ◽  
M. S. Sheshshayee ◽  
Desai Sujeet
Keyword(s):  
Machine Learning ◽  
Finite State Machine ◽  
Irrigation System ◽  
State Machine ◽  
Machine Learning Techniques ◽  
Machine Model ◽  
Learning Techniques ◽  
Finite State

scholarly journals Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 110 ◽  
Author(s):  
Gadelhag Mohmed ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Neural Networks ◽  
Human Activities ◽  
Finite State Machine ◽  
Fuzzy Rule ◽  
State Machine ◽  
Machine Learning Techniques ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
Finite State ◽  
Learning Capabilities

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

Reliable Web Services Selection Based on Finite State Machine Model

2011 ◽  
Vol 10 (9) ◽  
pp. 1662-1672 ◽  
Author(s):  
Hongjie Shen ◽  
Zhijun Ding ◽  
Hongzhong Chen
Keyword(s):  
Web Services ◽  
Finite State Machine ◽  
State Machine ◽  
Machine Model ◽  
Finite State

First aid simulation game with finite state machine model

2015 ◽  
Author(s):  
Rosalina Eka Dianty ◽  
Alfian M. Azhari ◽  
M. Faris Al Hakim ◽  
Imam Kuswardayan ◽  
Anny Yuniarti ◽  
...  
Keyword(s):  
Finite State Machine ◽  
First Aid ◽  
State Machine ◽  
Simulation Game ◽  
Machine Model ◽  
Finite State

Morphology

2017 ◽  
Author(s):  
Kemal Oflazer
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Morphological Processing ◽  
Machine Learning Techniques ◽  
Processing Application ◽  
Computational Approaches ◽  
Learning Techniques ◽  
Finite State ◽  
Basic Concepts

Morphology is the study of the structure of words and how words are forme3d by combining smaller units of linguistic information called morphemes. Any natural language processing application will need to computationally process the words in a language before any of the more complex processing is done. This is especially a must for morphologically complex languages. After a compact overview of the basic concepts in morphology, this chapter presents the state-of-the-art computational approaches to morphology, concentrating on two-level morphology and cascaded-rules and describing how morphographemics and morphotactics are handled in a finite-state setting. The chapter then summarizes recent approaches to how machine learning techniques are applied in morphological processing.

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