Classification of Plant Somatic Embryos by Using Neural Network Classifiers

1997 ◽  
Vol 13 (6) ◽  
pp. 741-746
Author(s):  
R. Ruan ◽  
J. Xu ◽  
C. Zhang ◽  
C.-M. Chi ◽  
W.-S. Hu
Keyword(s):  
Neural Network ◽  
Somatic Embryos ◽  
Neural Network Classifiers

scholarly journals Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5283 ◽  
Author(s):  
Muhammad Tariq Sadiq ◽  
Xiaojun Yu ◽  
Zhaohui Yuan ◽  
Muhammad Zulkifal Aziz
Keyword(s):  
Neural Network ◽  
Mental Imagery ◽  
Practical Applications ◽  
The Public ◽  
Series Of Experiments ◽  
Neural Network Classifiers ◽  
Public Datasets ◽  
Dependent Tasks ◽  
First Time

The development of fast and robust brain–computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.

An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity

2019 ◽  
Vol 21 (9) ◽  
pp. 681-692
Author(s):  
Luis Francisco Barbosa-Santillán ◽  
María de los Angeles Calixto-Romo ◽  
Juan Jaime Sánchez-Escobar ◽  
Liliana Ibeth Barbosa-Santillán
Keyword(s):  
Neural Network ◽  
Enzymatic Activity ◽  
Real Time ◽  
High Throughput Screening ◽  
Cellulolytic Activity ◽  
Color Patterns ◽  
Cellulolytic Microorganisms ◽  
The Neural Network ◽  
Neural Network Classifiers

Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.

scholarly journals EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING

2015 ◽  
Vol 3 (1) ◽  
pp. 35-41
Author(s):  
Sushil Chandra ◽  
Kundan Lal Verma ◽  
Greeshma Sharma ◽  
Alok Mittal ◽  
Devendra Jha
Keyword(s):  
Neural Network ◽  
Load Balancing ◽  
Cognitive Workload ◽  
Spectral Features ◽  
Scoring Method ◽  
Neural Network Classification ◽  
Neural Network Classifiers

The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification.

scholarly journals Classification of Traffic Density using Three Class Neural Network Classifiers

2016 ◽  
Vol V5 (04) ◽  
Author(s):  
Sapna Ashok Kapse ◽  
Chetana Nandlal Dhokne ◽  
Ruchika Arun Bhoyar ◽  
Pallavi Gayke ◽  
Keyword(s):  
Neural Network ◽  
Traffic Density ◽  
Neural Network Classifiers

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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