Image Recognition Technology Based on Artificial Intelligence

2013 ◽  
Vol 347-350 ◽  
pp. 3537-3540
Author(s):  
Hai Yun Lin ◽  
Yu Jiao Wang ◽  
Jian Chun Cai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Semantic Feature ◽  
Current Image ◽  
Semantic Feature Extraction

In respect of the classification of current image retrieval technology and the existing issues, the paper put forward a method designed for image semantic feature extraction based on artificial intelligence. The new method has solved the tough problem of image semantic feature extraction, by fusing fuzzy logic, genetic algorithm and artificial neural network altogether, which greatly improved the efficiency and accuracy of image retrieval.

scholarly journals Types of classifier in artificial intelligence

2015 ◽  
Vol 15 (1) ◽  
pp. 6436-6443
Author(s):  
Hadis Askarifard
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Supervised Classification ◽  
Machine Intelligence ◽  
Fuzzy Logics ◽  
Information Recovery ◽  
Correct Information

Artificial intelligence or machine intelligence should be considered as the vast domain of junction of many knowledge, sciences and old and new technics. Today, classification of documents is adopted extensively in information recovery for organizing documents. In the method of document supervised classification some correct information about documents that previously have been classified are available for us and based on these information we classify these documents. Thus, we will examine methods such as: expert systems, artificial neural network, Genetic algorithm and fuzzy logics and so on. In this project we examine documents thematically and then using existing algorithms we predict a theme for a new document.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Christopher-John L. Farrell
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Computer Simulation ◽  
Clinical Chemistry ◽  
Human Interaction ◽  
Final Decision ◽  
P Values ◽  
Laboratory Staff

Abstract Objectives Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors. Methods De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%. Laboratory staff volunteers reviewed the AI model’s predictions, and the EUC results on which they were based, before making a final decision regarding the presence or absence of a WBIT error. The performance of this approach was compared to the performance of the AI model operating without human supervision. Results Laboratory staff supervised the classification of 510 sets of EUC results. This workflow identified WBIT errors with an accuracy of 81.2%, sensitivity of 73.7% and specificity of 88.6%. However, the AI model classifying these samples autonomously was superior on all metrics (p-values<0.05), including accuracy (92.5%), sensitivity (90.6%) and specificity (94.5%). Conclusions Human interaction with AI models can significantly alter their performance. For computationally complex tasks such as WBIT error identification, best performance may be achieved by autonomously functioning AI models.

scholarly journals Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach

2010 ◽  
Author(s):  
Sergio Davalos ◽  
Richard Gritta ◽  
Bahram Adrangi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Local Optima ◽  
Air Carrier ◽  
Genetic Algorithm Approach ◽  
Genetic Algorithm Model

Statistical and artificial intelligence methods have successfully classified organizational solvency, but are limited in terms of generalization, knowledge on how a conclusion was reached, convergence to a local optima, or inconsistent results. Issues such as dimensionality reduction and feature selection can also affect a model's performance. This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy.

Methodology

2018 ◽  
pp. 81-113
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Swarm Intelligence ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Artificial Neural ◽  
Heart Disorders ◽  
Intelligent Methods

This chapter uses intelligent methods based on swarm intelligence and artificial neural network to detect heart disorders based on electrocardiogram signals. This chapter has introduced the methodology undertaken in the denoising, feature extraction, and classification of ECG signals to four heart disorders including the normal heartbeat. It also presents denoising using intelligent methods.

scholarly journals Diagnosis of Plant Diseases using Artificial Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 1464-1467
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Plant Diseases ◽  
Economic Losses ◽  
Fruit Crops ◽  
Agricultural Business ◽  
Complex Foundation

Pomegranate is one of India's most commonly cultivated fruit crops. manual expert observations are being used to detect leaf diseases that take longer time for further prevention. Fruit diseases are causing devastating disadvantages in worldwide agricultural business economic losses in production .in this journal, the answer is proposed and valid by experiment for the identification and classification of fruit disorders. The objective of proposed work is to analyze the illness utilizing picture preparing and artificial intelligence techniques on pictures of pomegranate plant leaf. In the proposed framework, pomegranate leaf picture with complex foundation is taken as input. Then pomegranate leaf ailment division is finished utilizing K-means clustering. The infected segment from portioned pictures is recognized. Best results have been seen when neural networks with a RBFN is used for a classification.

scholarly journals Classification of Erythemato-squamous diseases using Artificial Neural Network and Genetic algorithm

2020 ◽  
pp. 208-212
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Experimental Result ◽  
Mining System ◽  
Statistical Measures ◽  
Specificity And Sensitivity ◽  
Artificial Neural

This paper introduces a hybrid model using artificial neural network (ANN) and genetic algorithm (GA) to develop an efficient classification technique for classification of different categories of Erythemato-squamous diseases. Neural network has been extensively used in many applications like classification, regression, web mining, system identification and pattern recognition. Weight optimization in neural network has been a matter of concern for researchers in the field of soft computing. In this paper the weights of ANN are optimized with GA. The proposed hybrid model is applied on the Erythemato-squamous dataset taken from UCI machine learning repository. The dataset contains six different categories: psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris of Erythemato-squamous diseases. The main aim of this paper is to determine the type of Eryhemato-Squamous disease using the hybrid model. The performance of the hybrid model is evaluated using statistical measures like accuracy, specificity and sensitivity. The accuracy of the proposed model is found to be 99.34% on test dataset. The experimental result shows the effectiveness of the hybrid model in classification of Erythematosquamous diseases.

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