scholarly journals Neural Network Classification of Tan Spot and Stagonospora Blotch Infection Periods in a Wheat Field Environment

2000 ◽  
Vol 90 (2) ◽  
pp. 108-113 ◽  
Author(s):  
E. D. De Wolf ◽  
L. J. Francl
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Tan Spot ◽  
Data Set ◽  
Wheat Field ◽  
Incidence Data ◽  
Significant Difference ◽  
General Regression Neural Networks

Tan spot and Stagonospora blotch of hard red spring wheat served as a model system for evaluating disease forecasts by artificial neural networks. Pathogen infection periods on susceptible wheat plants were measured in the field from 1993 to 1998, and incidence data were merged with 24-h summaries of accumulated growing degree days, temperature, relative humidity, precipitation, and leaf wetness duration. The resulting data set of 202 discrete periods was randomly assigned to 10 modeldevelopment or -validation (n = 50) data sets. Backpropagation neural networks, general regression neural networks, logistic regression, and parametric and nonparametric methods of discriminant analysis were chosen for comparison. Mean validation classification of tan spot incidence was between 71% for logistic regression and 76% for backpropagation models. No significant difference was found between methods of modeling tan spot infection periods. Mean validation prediction accuracy of Stagonospora blotch incidence was 86 and 81% for backpropagation and logistic regression, respectively. Prediction accuracies of other modeling methods were ≤78% and were significantly different (P = 0.01) from backpropagation, but not logistic regression, results. The best backpropagation models of tan spot and Stagonospora blotch incidences correctly classified 82 and 84% of validation cases, respectively. High classification accuracy and consistently good performance demonstrate the applicability of neural network technology to plant disease forecasting.

scholarly journals Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment

1997 ◽  
Vol 87 (1) ◽  
pp. 83-87 ◽  
Author(s):  
E. D. De Wolf ◽  
L. J. Franel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Tan Spot ◽  
Environmental Data ◽  
Outdoor Environment ◽  
Stepwise Logistic Regression ◽  
Data Set ◽  
Logistic Regression Models ◽  
Processing Elements

Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for validation analysis. Neural networks with zero to nine processing elements were evaluated 20 times each to identify the model that most accurately predicted an infection event. The 200 models averaged 74 to 77% accuracy, depending on the number of processing elements and random initialization of coefficients. The most accurate model had five processing elements and correctly predicted 87% of the infection periods in the validation set. In comparison, stepwise logistic regression correctly predicted 69% of the validation cases, and multivariate discriminant analysis distinguished 50% of the validation cases. When wetness-sensor inputs were withheld from the models, both the neural network and logistic regression models declined 6% in prediction accuracy. Thus, neural networks were more accurate than statistical procedures, both with and without wetness-sensor inputs. These results demonstrate the applicability of neural networks to plant disease forecasting.

scholarly journals ON COMPARING MULTI-LAYER PERCEPTRON AND LOGISTIC REGRESSION FOR CLASSIFICATION OF DIABETIC PATIENTS IN FEDERAL MEDICAL CENTER YOLA, ADAMAWA STATE

2021 ◽  
Vol 8 (6) ◽  
pp. 24-40
Author(s):  
H. Ahmed ◽  
M. B. Mohammed ◽  
I. A. Baba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Medical Center ◽  
Oral Glucose ◽  
Diabetic Patients ◽  
Data Set ◽  
Artificial Neural

The logistic regression (LR) and Multi-Layer (MLP) are used to handle regression analysis when the dependent response variable is categorical. Therefore, this study assesses the performance of LR and MLP in terms of classification of object/observations into identified component/groups. A data set consists of 553 cases of diabetes were collected at Federal Medical Center, . The variables measured: Age(years), Mass of a patient(kg/meters), glucose level (plasma glucose concentration, a 2-hour in an oral glucose tolerance test), pressure (Diastolic blood pressure ), insulin (2-hour serum insulin mu U/ml) and class variable (0 or 1) treating 0 as false or negative and 1 treated as true or positive test for diabetes. The method used in the study is Logistic regression analysis and the multi-Layer , a type of Artificial Neural Network, confusion matrix, classification, network algorithm and SPSS version 21 for Windows 10.1. The result of the study showed that LP classifies diabetic patients correctly with 91.8% accuracy. it classifies non-diabetic patients with 89.1% accuracy. MLP classifies diabetic patients with 88.6% accuracy while it classifies non-diabetic patients with 93.2% classification accuracy. Overall, MLP classifies better with 91% accuracy while LR classifies with 90.6% accuracy. This study complements other where MLP, a type Artificial neural network classifies and predicts better than other non-neural network classifiers.

scholarly journals INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA

2017 ◽  
Vol IV-1/W1 ◽  
pp. 115-123 ◽  
Author(s):  
R. Niessner ◽  
H. Schilling ◽  
B. Jutzi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Vehicle Classification ◽  
Support Vector ◽  
Lidar Data ◽  
Data Set

In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks. To study the potential of the Convolutional Neural Network, in this paper three approaches are investigated to train classifiers based on Convolutional Neural Networks. These approaches allow Convolutional Neural Networks to be trained on datasets containing only a few hundred training samples, which results in a successful classification. Two of these approaches are based on the concept of transfer learning. In the first approach features, created by a pretrained Convolutional Neural Network, are used for a classification using a support vector machine. In the second approach a pretrained Convolutional Neural Network gets fine-tuned on a different data set. The third approach includes the design and training for flat Convolutional Neural Networks from the scratch. The evaluation of the proposed approaches is based on a data set provided by the IEEE Geoscience and Remote Sensing Society (GRSS) which contains RGB and LiDAR data of an urban area. In this work it is shown that these Convolutional Neural Networks lead to classification results with high accuracy both on RGB and LiDAR data. Features which are derived by RGB data transferred into LiDAR data by transfer learning lead to better results in classification in contrast to RGB data. Using a neural network which contains fewer layers than common neural networks leads to the best classification results. In this framework, it can furthermore be shown that the practical application of LiDAR images results in a better data basis for classification of vehicles than the use of RGB images.

scholarly journals Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification

2020 ◽  
pp. 1-9
Author(s):  
Supun Nakandala ◽  
Marta M. Jankowska ◽  
Fatima Tuz-Zahra ◽  
John Bellettiere ◽  
Jordan A. Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Logistic Regression ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Free Living ◽  
Data Set

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

scholarly journals Detection of Human Facial Expression using CNN Model

2020 ◽  
Vol 9 (5) ◽  
pp. 629-634
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Facial Expression ◽  
High Quality ◽  
Data Set ◽  
Human Desire ◽  
Visualization Techniques ◽  
Historical Past ◽  
Human Facial Expression

Facial expression is the most effective and herbal non verbal emotional conversation method People can range indoors the way they display their expressions Even pics of the same character within the identical countenance can vary in brightness historical past and pose and these variations are emphasized if thinking about particular subjects because of versions in shape ethnicity amongst others Hence countenance recognition remains a challenging trouble in PC vision To advise a solution for expression reputation that uses a combination of Convolutional Neural Network and precise picture prepossessing steps It defined the modern-day solution that has green facial capabilities and deep gaining knowledge of with convolutional neural networks CNN's has achieved high-quality success within the classification of assorted face emotions like glad angry unhappy and impartial Hundreds of neuron smart and layer smart visualization techniques have been applied the usage of a CNN informed with a publicly to be had photo data set So it’s positioned that neural networks can capture the colors and textures of lesions unique to respective emotion upon analysis which resembles human desire making.

scholarly journals Construction of neural network classification of Fisher irises based on JavaScript

2019 ◽  
Author(s):  
A.М. Заяц ◽  
С.П. Хабаров
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Data Set ◽  
Test Dataset ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Multilayer Neural Networks ◽  
Hidden Layer

Рассматривается процедура выбора структуры и параметров нейронной сети для классификации набора данных, известного как Ирисы Фишера, который включает в себя данные о 150 экземплярах растений трех различных видов. Предложен подход к решению данной задачи без использования дополнительных программных средств и мощных нейросетевых пакетов с использованием только средств стандартного браузера ОС. Это потребовало реализации ряда процедур на JavaScript c их подгрузкой в разработанную интерфейсную HTML-страницу. Исследование большого числа различных структур многослойных нейронных сетей, обучаемых на основе алгоритма обратного распространения ошибки, позволило выбрать для тестового набора данных структуру нейронной сети всего с одним скрытым слоем из трех нейронов. Это существенно упрощает реализацию классификатора Ирисов Фишера, позволяя его оформить в виде загружаемой с сервера HTML-страницы. The procedure for selecting the structure and parameters of the neural network for the classification of a data set known as Iris Fisher, which includes data on 150 plant specimens of three different species, is considered. An approach to solving this problem without using additional software and powerful neural network packages using only the tools of the standard OS browser is proposed. This required the implementation of a number of JavaScript procedures with their loading into the developed HTML interface page. The study of a large number of different structures of multilayer neural networks, trained on the basis of the back-propagation error algorithm, made it possible to choose the structure of a neural network with only one hidden layer of three neurons for a test dataset. This greatly simplifies the implementation of the Fisher Iris classifier, allowing it to be formatted as an HTML page downloaded from the server.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

2021 ◽  
Author(s):  
Luke Gundry ◽  
Gareth Kennedy ◽  
Alan Bond ◽  
Jie Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reaction Mechanisms ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Neural Network Classification ◽  
Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

N. Belknap's Four-Valued Logic, Belknap Computer and New Proximity Functions for Comparing Discrete Objects

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Valerii Dmitrienko ◽  
Sergey Leonov ◽  
Mykola Mezentsev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
The Other ◽  
Proximity Function ◽  
Input Information ◽  
Proximity Measures ◽  
The One ◽  
True Values

The idea of ​​Belknap's four-valued logic is that modern computers should function normally not only with the true values ​​of the input information, but also under the conditions of inconsistency and incompleteness of true failures. Belknap's logic introduces four true values: T (true - true), F (false - false), N (none - nobody, nothing, none), B (both - the two, not only the one but also the other).  For ease of work with these true values, the following designations are introduced: (1, 0, n, b). Belknap's logic can be used to obtain estimates of proximity measures for discrete objects, for which the functions Jaccard and Needhem, Russel and Rao, Sokal and Michener, Hamming, etc. are used. In this case, it becomes possible to assess the proximity, recognition and classification of objects in conditions of uncertainty when the true values ​​are taken from the set (1, 0, n, b). Based on the architecture of the Hamming neural network, neural networks have been developed that allow calculating the distances between objects described using true values ​​(1, 0, n, b). Keywords: four-valued Belknap logic, Belknap computer, proximity assessment, recognition and classification, proximity function, neural network.

Classification of heart sound signals with BP neural network and logistic regression

2017 ◽  
Author(s):  
Lina Li ◽  
Xinpei Wang ◽  
Xiaping Du ◽  
Yuanyuan Liu ◽  
Changchun Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Bp Neural Network ◽  
Heart Sound
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