scholarly journals Various Techniques Involving Plant Leaf Diseases Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 1544-1550
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Disease Classification ◽  
Biological Research ◽  
Support Vector ◽  
Agricultural Field ◽  
Plant Leaf ◽  
Artificial Neural Network Ann

In agricultural field, paddy development assumes an imperative job. Be that as it may, their developments are influenced by different diseases. There will be diminish in the plant growth, if the illnesses are not recognized at an early arrange. There are several image processing methods we can custom such as Genetic algorithm, Probabilistic Neural Network (NN), Back propagation Neural Network (BNN), Artificial-Neural-Network(ANN), and Support vector machine(SVM). Choosing an organization technique is continuously a tough task since the worth of outcome can differ for unlike input data. Plant leaf infection categorizations have wide-ranging applications in several fields such as in biological research, in Agriculture etc. This survey affords a summary of dissimilar organisation systems used for plant leaf disease classification. Also we have discoursed prevailing segmentation technique beside with classifiers for exposure of plant leaves.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Using Probabilistic Neural Network to Select a Medical Specialist Agent

2010 ◽  
pp. 164-177 ◽  
Author(s):  
Vijay Kumar Mago ◽  
M. Syamala Devi ◽  
Ajay Bhatia ◽  
Ravinder Mehta
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Treatment Plan ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Medical Specialist ◽  
Childhood Disease ◽  
Specialist Doctor ◽  
Novel Approach ◽  
Multi Agent

The authors aim to design the Multi-agent system, in which the software agents interact with each other to diagnose a disease and decide the treatment plan(s). In this chapter, the authors present a novel approach of applying Probabilistic Neural Network (PNN) to classify the childhood disease and their respective medical specialist. Normally this classification is performed by the pediatricians. The system that has been presented here, imitates the behavior of a pediatrician while selecting super specialist doctor. This decision making mechanism will be embedded in an agent called Intelligent Pediatric Agent. To design the PNN, a database consisting of 104 records has been gathered. It includes 17 different sign symptoms and based on their values, one of the five super specialists is selected. A Back propagation Neural Network (BPNN) has also been designed to compare the results produced by the PNN and it is found that PNN is more promising.

Meteorological Tidal Predictions in the Mekong Estuary Using an Evolved ANN Time Series

2019 ◽  
Vol 53 (6) ◽  
pp. 27-34
Author(s):  
Tim Chen ◽  
C.Y.J. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Variation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Systems ◽  
Modeling And Forecasting ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

AbstractThe reproduction of meteorological waves utilizing physically based hydrodynamic models is very difficult in light of the fact that it requires enormous amounts of information, for example, hydrological and water-driven time arrangement limits, stream geometry, and balance coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modeling and forecasting the maximum and time variation of meteorological tsunamis in the Mekong Estuary in Vietnam. The parameters, including both the nearby climatic and breeze field factors, for finding the most extreme meteorological waves are first examined, depending on the preparation of the evolved neural systems. The time series for meteorological tsunamis are used for training and testing the models, and data for three cyclones are used for model prediction. This study finds that the proposed advanced ANN time series model is easy to utilize with display and prediction tools for simulating the time variation of meteorological tsunamis.

ARRHYTHMIA DISEASE DIAGNOSIS USING NEURAL NETWORK, SVM, AND GENETIC ALGORITHM-OPTIMIZED k-MEANS CLUSTERING

2011 ◽  
Vol 11 (04) ◽  
pp. 897-915 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
CHANDAN CHAKRABORTY
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Normal Sinus Rhythm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Back Propagation Neural Network ◽  
Polynomial Kernel ◽  
Support Vector

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms

2005 ◽  
Vol 9 (3) ◽  
pp. 290-296 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
◽  
Teguh P. Arsyad
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Multilayer Perceptron ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Odor Recognition ◽  
Fuzzy Neural ◽  
Odor Mixtures

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

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