Automatic Detection and Classification of Diabetes Using Artificial Intelligence

2021 ◽  
Vol 8 (1) ◽  
pp. 01-05
Author(s):  
V. Nithyalakshmi ◽  
Dr.R. Sivakumar ◽  
Dr.A. Sivaramakrishnan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Diabetes Diagnosis ◽  
Artificial Intelligence Technique ◽  
Classification Rate ◽  
Neural Network Algorithm ◽  
Health Complications

Diabetes is characterized as a chronic disease that may cause many health complications. Artificial intelligence techniques are adopted diagnose diabetes more accurately. This paper presents an artificial intelligence technique for diabetes diagnosis. Efficacy of the technique is evaluated using diabetes database. Experimental results show that the back propagation neural network algorithm yields the highest classification rate compared to k-nearest neighbourhood classifier. Additionally, the back propagation neural network provides error with the highest area under curve of 90 %.

scholarly journals Intracardiac Mass Detection and Classification Using Double Convolutional Neural Network Classifier

2021 ◽  
Author(s):  
Manikandan A ◽  
◽  
M.Ponni Bala ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Mass Detection ◽  
Neural Network Algorithm ◽  
Definition Of ◽  
Intracardiac Masses ◽  
Intracardiac Thrombi

Intracardiac masses identification in the images of echocardiogram images in one of the most essential tasks in making the diagnosis of cardiac disease. For making the improvement in accuracy over the diagnosis as a new complete method of classifying the echocardiogram images automatically which is based on robust back propagation neural network algorithm in being proposed for distinguishing intracardiac thrombi and tumor. Initially, the cropping over the specific region is done in order to make the definition of the mass area. Later on, as the second step the processing of globally unique denoising technique is being implied for the removal of speckle and in order to make the preservation of anatomical structured component in the image. This is defined in terms of preprocessing and it is carried out by Patch-based sparse representation. Subsequently the description of the mass contour and its interconnected wall of the artery are being done by the segmentation mechanism denoted as Linear Iterative Vessel Segmentation model. As the prefinal stage, the processing of boundary, texture and the motion features are being carried out through the processing by double convolutional neural network (DCNN) classifier in order to determine the classification of two different masses. Totally 108 cardiac masses images are being collected for accessing the effectiveness of the classifier. It is also realized with the various state of the art classifiers as projected the demonstration of the greatest performance that has been disclosed with an achievement of 98.98% of accuracy, 98.89% of sensitivity and 99.16% of specificity that has been resulted for DCNN classifier. It determines the explication that the proposed method is capable of performing the classification of intracardiac thrombi and tumors in the echocardiography and ensures for potentially assisting the medical doctors who are in the clinical practice.

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

2016 ◽  
Vol 7 (1) ◽  
pp. 33-49 ◽  
Author(s):  
Suruchi Chawla
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Web Search ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Information Need ◽  
Data Set ◽  
User Query ◽  
User Queries

In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK

2013 ◽  
Vol 37 (3) ◽  
pp. 459-465
Author(s):  
Chih-Ta Yen ◽  
Ing-Jr Ding ◽  
Zong-Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh–Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First method, we used a low-pass filter and median filter to remove noise interferences. The second one, we used a back-propagation neural network algorithm to suppress noises. We removed nearly all noise and recovered the originally embedded watermarks of Walsh–Hadmard codes.

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