The confusion matrix obtained with 10-fold cross-validation for the back-propagation neural network model for the studied Alternaria species.

Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network

Alsharaiah, Mohammad A.
H. Baniata , Laith
Al Adwan , Omar
Abu Alghanam, Orieb
Abu-Shareha, Ahmad Adel
Alzboon , Laith
Mustafa, Nedal
Baniata , Mohammad

June 2022

Organism network systems provide a biological data with high complex level. Besides, these data refl...

Confusion matrix indicating the technological application or origin prediction of 172 strains and their predictions as obtained with naïve Bayesian classifier (AUC = 0.70).

Inês Mendes (433938)
Ricardo Franco-Duarte (433939)
Lan Umek (433940)
Elza Fonseca (433941)
João Drumonde-Neves (433942)
Sylvie Dequin (205208)
Blaz Zupan (10031)
Dorit Schuller (181699)

July 2013

Confusion matrix indicating the technological application or origin prediction of 172 strains and...

The confusion matrix shows the accuracy of traditional machine learning algorithms.

Md. Mahadi Hasan (10431872)
Saba Binte Murtaz (13804573)
Muhammad Usama Islam (13804576)
Muhammad Jafar Sadeq (13804579)
Jasim Uddin (1302132)

September 2022

The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...

Confusion matrix obtained with 10-fold cross-validation for the back-propagation neural network model for the studied days after inoculation (dai).

Piotr Baranowski (715522)
Malgorzata Jedryczka (715523)
Wojciech Mazurek (715524)
Danuta Babula-Skowronska (715525)
Anna Siedliska (715526)
Joanna Kaczmarek (715527)

March 2015

The upper number in each entry of the matrix is the average number of actual recognised classes i...

The confusion effect in predatory neural networks

Tosh, Colin R.
Jackson, Andrew L.
Ruxton, Graeme D.

January 2006

A simple artificial neural network model of image reconstruction in sensory maps is presented to exp...

The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network.

Emmanuel Adetiba (832176)
Oludayo O. Olugbara (832177)

December 2015

The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network...

USING ARTIFICIAL NEURAL NETWORKS AS STATISTICAL TOOLS FOR ANALYSIS OF MEDICAL DATA

ANOUSHIRAVAN KAZEMNEZHAD
S.M SADAT HASHEMI
S FAGHIHZADEH
B KAVEHEI

June 2003

Introduction: Artificial neural networks mimic brains behavior. They are able to predict and feature...

Confusion matrix of cell classification task performed by the convolutional neural network using model b.

Ronald Wihal Oei (6454058)
Guanqun Hou (6416799)
Fuhai Liu (6416801)
Jin Zhong (9647)
Jiewen Zhang (5461778)
Zhaoyi An (6416802)
Luping Xu (6454061)
Yujiu Yang (6416805)

March 2019

Confusion matrix of cell classification task performed by the convolutional neural network using mod...

The confusion matrix of the computational method based on the tetrahedron, HOG and MLP neural network.

Emmanuel Adetiba (832176)
Oludayo O. Olugbara (832177)

December 2015

The confusion matrix of the computational method based on the tetrahedron, HOG and MLP neural net...

Artificial neural network properties associated with wiring patterns in the visual projections of vertebrates and arthropods

Tosh, Colin R.
Ruxton, Graeme D.

January 2006

We model the functioning of different wiring schemes in visual projections using artificial neural n...

Confusion matrix showing the results of the testing phase for the AlexNet neural network.

Daniel Motta (6204464)
Alex Álisson Bandeira Santos (6204467)
Ingrid Winkler (6204470)
Bruna Aparecida Souza Machado (657245)
Daniel André Dias Imperial Pereira (6204473)
Alexandre Morais Cavalcanti (6204476)
Eduardo Oyama Lins Fonseca (6204479)
Frank Kirchner (6204482)
Roberto Badaró (6204485)

January 2019

Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p

Neural network based modelling of environmental variables: A systematic approach

Maier, H.
Dandy, G.

January 2001

Chain Graph Explanation of Neural Network Based on Feature-Level Class Confusion

Hyekyoung Hwang
Eunbyung Park
Jitae Shin

January 2022

Despite increasing interest in developing interpretable machine learning methods, most recent studie...

An Improved BP Neural Network Algorithm Based on Factor Analysis

Shifei Ding
Weikuan Jia
Chunyang Su
Xiaoliang Liu
Jinrong Chen

January 2010

Back-Propagation (BP) neural network, as one of the most mature and most widespread algorithms, has ...

Confusion matrix from the execution with the best score (ANN, SNV+D, D2W5 and parameter set 10) with an overall correctly classified value of 87.25% (20 leaves per variety).

Salvador Gutiérrez (830468)
Javier Tardaguila (830469)
Juan Fernández-Novales (830470)
María P. Diago (830471)

November 2015

Each row represents the actual variety and in which one was classified. Bolded values (diagonal o...

Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network

Alsharaiah, Mohammad A.
H. Baniata , Laith
Al Adwan , Omar
Abu Alghanam, Orieb
Abu-Shareha, Ahmad Adel
Alzboon , Laith
Mustafa, Nedal
Baniata , Mohammad

June 2022

Organism network systems provide a biological data with high complex level. Besides, these data refl...

Confusion matrix indicating the technological application or origin prediction of 172 strains and their predictions as obtained with naïve Bayesian classifier (AUC = 0.70).

Inês Mendes (433938)
Ricardo Franco-Duarte (433939)
Lan Umek (433940)
Elza Fonseca (433941)
João Drumonde-Neves (433942)
Sylvie Dequin (205208)
Blaz Zupan (10031)
Dorit Schuller (181699)

July 2013

Confusion matrix indicating the technological application or origin prediction of 172 strains and...

The confusion matrix shows the accuracy of traditional machine learning algorithms.

Md. Mahadi Hasan (10431872)
Saba Binte Murtaz (13804573)
Muhammad Usama Islam (13804576)
Muhammad Jafar Sadeq (13804579)
Jasim Uddin (1302132)

September 2022

The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...

Confusion matrix obtained with 10-fold cross-validation for the back-propagation neural network model for the studied days after inoculation (dai).

Piotr Baranowski (715522)
Malgorzata Jedryczka (715523)
Wojciech Mazurek (715524)
Danuta Babula-Skowronska (715525)
Anna Siedliska (715526)
Joanna Kaczmarek (715527)

March 2015

The upper number in each entry of the matrix is the average number of actual recognised classes i...

The confusion effect in predatory neural networks

Tosh, Colin R.
Jackson, Andrew L.
Ruxton, Graeme D.

January 2006

A simple artificial neural network model of image reconstruction in sensory maps is presented to exp...

The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network.

Emmanuel Adetiba (832176)
Oludayo O. Olugbara (832177)

December 2015

The confusion matrix of the computational method based on the Z-curve, HOG and MLP neural network...

USING ARTIFICIAL NEURAL NETWORKS AS STATISTICAL TOOLS FOR ANALYSIS OF MEDICAL DATA

ANOUSHIRAVAN KAZEMNEZHAD
S.M SADAT HASHEMI
S FAGHIHZADEH
B KAVEHEI

June 2003

Introduction: Artificial neural networks mimic brains behavior. They are able to predict and feature...

Confusion matrix of cell classification task performed by the convolutional neural network using model b.

Ronald Wihal Oei (6454058)
Guanqun Hou (6416799)
Fuhai Liu (6416801)
Jin Zhong (9647)
Jiewen Zhang (5461778)
Zhaoyi An (6416802)
Luping Xu (6454061)
Yujiu Yang (6416805)

March 2019

Confusion matrix of cell classification task performed by the convolutional neural network using mod...

The confusion matrix of the computational method based on the tetrahedron, HOG and MLP neural network.

Emmanuel Adetiba (832176)
Oludayo O. Olugbara (832177)

December 2015

The confusion matrix of the computational method based on the tetrahedron, HOG and MLP neural net...

Artificial neural network properties associated with wiring patterns in the visual projections of vertebrates and arthropods

Tosh, Colin R.
Ruxton, Graeme D.

January 2006

We model the functioning of different wiring schemes in visual projections using artificial neural n...

Confusion matrix showing the results of the testing phase for the AlexNet neural network.

Daniel Motta (6204464)
Alex Álisson Bandeira Santos (6204467)
Ingrid Winkler (6204470)
Bruna Aparecida Souza Machado (657245)
Daniel André Dias Imperial Pereira (6204473)
Alexandre Morais Cavalcanti (6204476)
Eduardo Oyama Lins Fonseca (6204479)
Frank Kirchner (6204482)
Roberto Badaró (6204485)

January 2019

Confusion matrix showing the results of the testing phase for the AlexNet neural network.</p

Neural network based modelling of environmental variables: A systematic approach

Maier, H.
Dandy, G.

January 2001

Chain Graph Explanation of Neural Network Based on Feature-Level Class Confusion

Hyekyoung Hwang
Eunbyung Park
Jitae Shin

January 2022

Despite increasing interest in developing interpretable machine learning methods, most recent studie...

An Improved BP Neural Network Algorithm Based on Factor Analysis

Shifei Ding
Weikuan Jia
Chunyang Su
Xiaoliang Liu
Jinrong Chen

January 2010

Back-Propagation (BP) neural network, as one of the most mature and most widespread algorithms, has ...

Confusion matrix from the execution with the best score (ANN, SNV+D, D2W5 and parameter set 10) with an overall correctly classified value of 87.25% (20 leaves per variety).

Salvador Gutiérrez (830468)
Javier Tardaguila (830469)
Juan Fernández-Novales (830470)
María P. Diago (830471)

November 2015

Each row represents the actual variety and in which one was classified. Bolded values (diagonal o...

Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network

Alsharaiah, Mohammad A.
H. Baniata , Laith
Al Adwan , Omar
Abu Alghanam, Orieb
Abu-Shareha, Ahmad Adel
Alzboon , Laith
Mustafa, Nedal
Baniata , Mohammad

June 2022

Organism network systems provide a biological data with high complex level. Besides, these data refl...

Confusion matrix indicating the technological application or origin prediction of 172 strains and their predictions as obtained with naïve Bayesian classifier (AUC = 0.70).

Inês Mendes (433938)
Ricardo Franco-Duarte (433939)
Lan Umek (433940)
Elza Fonseca (433941)
João Drumonde-Neves (433942)
Sylvie Dequin (205208)
Blaz Zupan (10031)
Dorit Schuller (181699)

July 2013

Confusion matrix indicating the technological application or origin prediction of 172 strains and...

The confusion matrix shows the accuracy of traditional machine learning algorithms.

Md. Mahadi Hasan (10431872)
Saba Binte Murtaz (13804573)
Muhammad Usama Islam (13804576)
Muhammad Jafar Sadeq (13804579)
Jasim Uddin (1302132)

September 2022

The correctly classified data is reflected along the diagonal regions. The misclassified is reflecte...

The confusion matrix obtained with 10-fold cross-validation for the back-propagation neural network model for the studied <i>Alternaria</i> species.

Abstract

Extracted data

Topics

The confusion matrix obtained with 10-fold cross-validation for the back-propagation neural network model for the studied <i>Alternaria</i> species.

Abstract

Extracted data

Topics

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