scholarly journals A PRELIMARY APPROACH FOR THE AUTOMATED RECOGNITION OF MALIGNANT MELANOMA

2011 ◽  
Vol 23 (2) ◽  
pp. 121 ◽  
Author(s):  
Ezzeddine Zagrouba ◽  
Walid Barhoumi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Color Image ◽  
Skin Lesions ◽  
Benign Lesions ◽  
Data Set ◽  
Pigmented Lesions ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural

In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.

scholarly journals Document Classification Using Artificial Neural Network

2019 ◽  
Vol 8 (2) ◽  
pp. 55-58
Author(s):  
Kshitij Tripathi ◽  
Rajendra G. Vyas ◽  
Anil K. Gupta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Document Classification ◽  
Vector Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Document Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

The Document classification system is the field of data mining in which the format of data is based on bag of words (BoW) or document vector model and the task is to build a machine which after successfully learn the characteristic of given data set, predicts the category of the document to which the word vector belongs. In this approach document is represented by BoW where every single word is used as feature which occurs in a document. The proposed article presents artificial neural network approach which is hybrid of n-fold cross validation and training-validation-test approach for classification of data.

BRIGHTNESS PRESERVING HISTOGRAM EQUALIZATION (BPHE) TECHNIQUE FOR CLASSIFICATION OF DATES

2013 ◽  
pp. 200-202
Author(s):  
MANICKAVASAGAN. A ◽  
GABRIEL THOMAS ◽  
AL-YAHYAI, R ◽  
HEMA, M
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classification Accuracy ◽  
Histogram Equalization ◽  
Neural Network Classifier ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural ◽  
Hidden Neurons ◽  
Classification Efficiency

Brightness preserving histogram equalization (BPHE) technique was used to enhance the features to discriminate three dates varieties (Khalas, Fard and Madina). Mean, entropy and kurtosis features were computed from the enhanced images and used in an Artificial Neural Network classifier. The classification efficiency of 4 sets of hidden neurons (5, 10, 20, and 30) was tested and the network with 5 neurons yielded the highest classification accuracy of 95.2%.

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 Ultrasonic Acoustic Health Monitoring of Ball Bearings Using Neural Network Pattern Classification of Power Spectral Density

2010 ◽  
Author(s):  
William Kirchner ◽  
Steve Southward ◽  
Mehdi Ahmadian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Health Monitoring ◽  
Training Data ◽  
Ultrasonic Frequency ◽  
Ball Bearings ◽  
Frequency Range ◽  
Data Set ◽  
Artificial Neural

This work presents a generic passive non-contact based acoustic health monitoring approach using ultrasonic acoustic emissions (UAE) to facilitate classification of bearing health via neural networks. This generic approach is applied to classifying the operating condition of conventional ball bearings. The acoustic emission signals used in this study are in the ultrasonic range (20–120 kHz), which is significantly higher than the majority of the research in this area thus far. A direct benefit of working in this frequency range is the inherent directionality of microphones capable of measurement in this range, which becomes particularly useful when operating in environments with low signal-to-noise ratios that are common in the rail industry. Using the UAE power spectrum signature, it is possible to pose the health monitoring problem as a multi-class classification problem, and make use of a multi-layer artificial neural network (ANN) to classify the UAE signature. One major problem limiting the usefulness of ANN’s for failure classification is the need for large quantities of training data. This becomes a particularly important issue when considering applications involving higher value components such as the turbo mechanisms and traction motors on diesel locomotives. Artificial training data, based on the statistical properties of a significantly smaller experimental data set is created to train the artificial neural network. The combination of the artificial training methods and ultrasonic frequency range being used results in an approach generic enough to suggest that this particular method is applicable to a variety of systems and components where persistent UAE exist.

AN INTEGER RECURRENT ARTIFICIAL NEURAL NETWORK FOR CLASSIFYING FEATURE VECTORS

2000 ◽  
Vol 14 (03) ◽  
pp. 339-355 ◽  
Author(s):  
ROELOF K. BROUWER
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Connection Matrix ◽  
Training Set ◽  
Data Set ◽  
Feature Vectors ◽  
Application Data ◽  
Binary Input ◽  
Artificial Neural

The main contribution of this paper is the development of an Integer Recurrent Artificial Neural Network (IRANN) for classification of feature vectors. The network consists both of threshold units or perceptrons and of counters, which are non-threshold units with binary input and integer output. Input and output of the network consists of vectors of natural numbers that may be used to represent feature vectors. For classification purposes, representatives of sets are stored by calculating a connection matrix such that all the elements in a training set are attracted to members of the same training set. The class of its attractor then classifies an arbitrary element if the attractor is a member of one of the original training sets. The network is successfully applied to the classification of sugar diabetes data, credit application data, and the iris data set.

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