Signal Processing and Pattern Recognition in Electronic Tongues

2020 ◽  
pp. 84-108
Author(s):  
Jersson X. Leon-Medina ◽  
Maribel Anaya Vejar ◽  
Diego A. Tibaduiza
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Signal Processing ◽  
Learning Algorithm ◽  
Electronic Tongue ◽  
Sensor Arrays ◽  
Practical Implementation ◽  
Machine Learning Algorithm ◽  
Use Of Data ◽  
Processing Strategies

This chapter reviews the development of solutions related to the practical implementation of electronic tongue sensor arrays. Some of these solutions are associated with the use of data from different instrumentation and acquisition systems, which may vary depending on the type of data collected, the use and development of data pre-processing strategies, and their subsequent analysis through the development of pattern recognition methodologies. Most of the time, these methodologies for signal processing are composed of stages for feature selection, feature extraction, and finally, classification or regression through a machine learning algorithm.

Signal Processing and Machine‐learning Algorithm to classify anesthesia depth

2020 ◽  
Vol 34 (S1) ◽  
pp. 1-1
Author(s):  
Dario Reyes-Cruz ◽  
Oscar Leonardo Mosquera ◽  
Daniel Alfonso Botero-Rosas ◽  
John Jairo Gallego-Correa ◽  
Henry H. Leon-Ariza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Anesthesia Depth

scholarly journals A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771988160 ◽  
Author(s):  
Jersson X Leon-Medina ◽  
Leydi J Cardenas-Flechas ◽  
Diego A Tibaduiza
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Data Analysis ◽  
Nearest Neighbor ◽  
Electronic Tongue ◽  
Sensor Arrays ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
K Nearest Neighbor

Electronic tongue-type sensor arrays are devices used to determine the quality of substances and seek to imitate the main components of the human sense of taste. For this purpose, an electronic tongue-based system makes use of sensors, data acquisition systems, and a pattern recognition system. Particularly, in the latter, machine learning techniques are useful in data analysis and have been used to solve classification and regression problems. However, one of the problems in the use of this kind of device is associated with the development of reliable pattern recognition algorithms and robust data analysis. In this sense, this work introduces a taste recognition methodology, which is composed of several steps including unfolding data, data normalization, principal component analysis for compressing the data, and classification through different machine learning models. The proposed methodology is tested using data from an electronic tongue with 13 different liquid substances; this electronic tongue uses multifrequency large amplitude pulse signal voltammetry. Results show that the methodology is able to perform the classification accurately and the best results are obtained when it includes the use of K-nearest neighbor machine in terms of accuracy compared with other kinds of machine learning approaches. Besides, the comparison to evaluate the methodology is made with different classification performance measures that show the behavior of the process in a single number.

scholarly journals Fire incidents visualization and pattern recognition using machine learning algorithms

2021 ◽  
Vol 22 (3) ◽  
pp. 1427
Author(s):  
Jonardo R. Asor ◽  
Jefferson L. Lerios ◽  
Sherwin B. Sapin ◽  
Jocelyn O. Padallan ◽  
Chester Alexis C. Buama
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Learning Algorithm ◽  
The Philippines ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Fire Occurrence ◽  
The Past ◽  
Fire Incident ◽  
Analyze Data

A fire incident is a devastating event that can be avoided with enough knowledge on how and when it may occur. For the past years, fire incidents have become a big problem for the Philippines, since it affects the socio-economic growth of the country. Machine learning algorithm is a well-known technique to predict and analyze data. It can also be used to recognize pattern and develop models for artificial intelligence. Pattern recognition through machine learning algorithm is already established and have proven itself accurate in different fields such as education, crime, health and many others including fire incidents. This paper aims to develop a model for recognizing patterns of fire incidents in the province of Laguna, Philippines implementing a machine learning algorithm. With the foregoing project, it is found out that a recurrent neural network shows an astonishing result in terms of pattern recognition. Further, it is also found that Calamba City is the most vulnerable area in case of fire occurrence in the Province of Laguna.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery
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