Application Research of Neural Expert System on Diseases and Insects of Fruit Tree

2014 ◽  
Vol 543-547 ◽  
pp. 4161-4164
Author(s):  
Hong Juan Li ◽  
Shu Mei Zhang
Keyword(s):  
Neural Networks ◽  
Expert System ◽  
Prediction Models ◽  
Growth Management ◽  
Fruit Trees ◽  
Disease Diagnosis ◽  
Fruit Tree ◽  
Learning Ability ◽  
Diagnostic Process ◽  
Management Decision

Information technology includes neural networks, ontology technology, expert system, and so on, and the growth model can predict and manage growth conditions of fruit trees. The traditional expert system has shortcomings of poor self-learning ability, so the improved expert system is used to perform diagnosis of diseases and insects of fruit tree. Firstly the ontology is used to collect related symptoms of diseases and insects of fruit trees, the expert system and neural network are combined to build the prediction model of diseases and insects of fruit tree, then the conclusions of the diagnostic process are regarded as the input neurons and output neurons of neural networks, and are diagnosed by expert, so the prediction models of disease diagnosis of fruit trees are made. The models can implement the function of expert diagnosis and prediction, and provide technical support and management decision for the growth management of fruit tree, greatly improving the diagnosis efficiency of diseases and insects of fruit tree.

Application Research of Neural Expert System on Tree Growth Model

2014 ◽  
Vol 513-517 ◽  
pp. 3728-3731
Author(s):  
Wen Qing Zhang
Keyword(s):  
Expert System ◽  
Tree Growth ◽  
Growth And Development ◽  
Production Management ◽  
Environmental Control ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Management ◽  
Management Decision ◽  
Factors Affecting

In order to simulate growth and development process of tree, then provide services for production management and scientific research, all kinds of tree growth models are constructed. The paper firstly considers a variety of factors affecting the growth and development of tree, then studies artificial intelligence knowledge such as neural network and expert system, uses the neural expert system to solve the acquisition and management of tree growth parameters, and design and develop tree growth management and expert system based on growth models, the models combine morphogenesis model of tree and knowledge model to provide comprehensive environmental control and management decision-making. Practice has indicated that the growth models of tree can reflect the growth of trees under different physiological and ecological conditions, and visual effect is very good.

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT SALURAN PERNAFASAN DENGAN METODE FUZZY TSUKAMOTO

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Dhevi Dadi Kusumaningtyas ◽  
Muhammad Hasbi ◽  
Hendro Wijayanto
Keyword(s):  
Expert System ◽  
Respiratory Disease ◽  
Respiratory Diseases ◽  
Good Description ◽  
Disease Diagnosis ◽  
Diagnostic Process ◽  
Good Information ◽  
Disease Information ◽  
Binary Tree Structure ◽  
User Friendly

Respiratory diseases are one of the most common diseases in Indonesia. Respiratory diseases increase the risk of fatal if not treated immediately. However, it is unfortunate that knowledge about the risk of respiratory disease is still lacking. The search method used in making this expert system is forward chaining with binary tree structure. Namely doing the processing of a set of data, then conducted inference in accordance with the rules applied to find the optimal conclusion. Experts provide rules for determining symptoms and illness. While the calculation and ranking of diseases that may suffer patients using the method fuzzy tsukamoto to provide the results of calculations that are certain based on the parameters. Then the patient's diagnostic process is done by the system. The Diagnostic Expert System for Respiratory Disease has been successfully established and can be used to assist in estimating the illness suffered by the patient as the result of the developed system is not much different from the running system. Based on the comparison of disease diagnosis result in expert system with manual system then the system accuracy level is 90,9%. Based on the website view has the largest percentage of 71.42 in good description, for user friendly / ease of respiratory system experts get the largest percentage of 85.71 in good information, to assist in the process of disease information and treatment get the largest percentage of 57.14 in a good description, to help the diagnosis process becomes easier to get the largest percentage of 71.42 in good information, for this expert system provides information on respiratory disease treatment accurately get the largest percentage of 57.14 in either.

scholarly journals Protein–protein interaction prediction based on ordinal regression and recurrent convolutional neural networks

2021 ◽  
Vol 22 (S6) ◽  
Author(s):  
Weixia Xu ◽  
Yangyun Gao ◽  
Yang Wang ◽  
Jihong Guan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Computational Methods ◽  
Protein Sequence ◽  
Protein Pair ◽  
Prediction Models ◽  
Confidence Score ◽  
Disease Diagnosis ◽  
Ordinal Regression ◽  
Sequence Pair

Abstract Background Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug design etc. As the amount of protein data is growing fast in the post genomic era, high-throughput experimental methods are expensive and time-consuming for the prediction of PPIs. Thus, computational methods have attracted researcher’s attention in recent years. A large number of computational methods have been proposed based on different protein sequence encoders. Results Notably, the confidence score of a protein sequence pair could be regarded as a kind of measurement to PPIs. The higher the confidence score for one protein pair is, the more likely the protein pair interacts. Thus in this paper, a deep learning framework, called ordinal regression and recurrent convolutional neural network (OR-RCNN) method, is introduced to predict PPIs from the perspective of confidence score. It mainly contains two parts: the encoder part of protein sequence pair and the prediction part of PPIs by confidence score. In the first part, two recurrent convolutional neural networks (RCNNs) with shared parameters are applied to construct two protein sequence embedding vectors, which can automatically extract robust local features and sequential information from the protein pairs. Based on it, the two embedding vectors are encoded into one novel embedding vector by element-wise multiplication. By taking the ordinal information behind confidence score into consideration, ordinal regression is used to construct multiple sub-classifiers in the second part. The results of multiple sub-classifiers are aggregated to obtain the final confidence score. Following that, the existence of PPIs is determined by the confidence score. We set a threshold $$\theta$$ θ , and say the interaction exists between the protein pair if its confidence score is bigger than $$\theta$$ θ . Conclusions We applied our method to predict PPIs on data sets S. cerevisiae and Homo sapiens. Through experimental verification, our method outperforms state-of-the-art PPI prediction models.

Diagnose Expert System of Engine Based on Fuzzy Neural Network

2012 ◽  
Vol 588-589 ◽  
pp. 1472-1475
Author(s):  
Miao Tian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural Networks ◽  
Learning Ability ◽  
Fuzzy Neural ◽  
Enhance Efficiency ◽  
Self Learning

Engine has a high chance of failure, it usually accounts for about 40% of vehicle failures. Study expert system of engine fault diagnosises that it can locate fault timely and accurately, and enhance efficiency. However, the traditional expert system has shortcomings so as inefficient inference and poor self-learning capability. The fuzzy logic and traditional neural networks are combined to form fuzzy neural networks, they are established a model of fuzzy neural network (FNN) of fault diagnosis, and that the model is applied to engine fault diagnosis, complementary advantages, to effectively enhance efficiency of inference and self-learning ability, its performance is higher than the traditional BP network.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

scholarly journals S2K Guideline for Diagnosis of Idiopathic Pulmonary Fibrosis

Respiration ◽  
2021 ◽  
pp. 1-34
Author(s):  
Jürgen Behr ◽  
Andreas Günther ◽  
Francesco Bonella ◽  
Julien Dinkel ◽  
Ludger Fink ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Disease Diagnosis ◽  
Diagnostic Process ◽  
High Resolution Computed Tomography ◽  
Cellular Analysis ◽  
Golden Standard ◽  
Technical Advances ◽  
Transbronchial Lung Cryobiopsy ◽  
Effective Diagnosis

Idiopathic pulmonary fibrosis (IPF) is a severe and often fatal disease. Diagnosis of IPF requires considerable expertise and experience. Since the publication of the international IPF guideline in the year 2011 and the update 2018 several studies and technical advances have occurred, which made a new assessment of the diagnostic process mandatory. The goal of this guideline is to foster early, confident, and effective diagnosis of IPF. The guideline focusses on the typical clinical context of an IPF patient and provides tools to exclude known causes of interstitial lung disease including standardized questionnaires, serologic testing, and cellular analysis of bronchoalveolar lavage. High-resolution computed tomography remains crucial in the diagnostic workup. If it is necessary to obtain specimens for histology, transbronchial lung cryobiopsy is the primary approach, while surgical lung biopsy is reserved for patients who are fit for it and in whom a bronchoscopic diagnosis did not provide the information needed. After all, IPF is a diagnosis of exclusion and multidisciplinary discussion remains the golden standard of diagnosis.

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