scholarly journals Update-Based Machine Learning Classification of Hierarchical Symbols in a Slowly Varying Two-Way Relay Channel

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2007
Author(s):  
Jakub Kolář ◽  
Jan Sýkora ◽  
Petr Hron
Keyword(s):  
Model Parameters ◽  
Observation Model ◽  
Relay Channel ◽  
Backpropagation Algorithm ◽  
Inference Problem ◽  
Machine Learning Classification ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Stochastic Inference

This paper presents a stochastic inference problem suited to a classification approach in a time-varying observation model with continuous-valued unknown parameterization. The utilization of an artificial neural network (ANN)-based classifier is considered, and the concept of a training process via the backpropagation algorithm is used. The main objective is the minimization of resources required for the training of the classifier in the parametric observation model. To reach this, it is proposed that the weights of the ANN classifier vary continuously with the change of the observation model parameters. This behavior is then used in an update-based backpropagation algorithm. This proposed idea is demonstrated on several procedures, which re-use previously trained weights as prior information when updating the classifier after a channel phase change. This approach successfully saves resources needed for re-training the ANN. The new approach is verified via a simulation on an example communication system with the two-way relay slowly fading channel.

Brazilian Coins Recognition Using Histogram of Oriented Gradients Features

2019 ◽  
Vol 16 (10) ◽  
pp. 4170-4178
Author(s):  
Sheifali Gupta ◽  
Gurleen Kaur ◽  
Deepali Gupta ◽  
Udit Jindal
Keyword(s):  
Region Of Interest ◽  
Initial Step ◽  
Support Vector ◽  
Histogram Of Oriented Gradients ◽  
Lighting Conditions ◽  
Roi Extraction ◽  
Feature Values ◽  
Artificial Neural Network Ann ◽  
Ann Classifier

This paper tends to the issue of coin recognition when dealing with shading and reflection variations under the same lighting conditions. In order to approach the problem, a database containing Brazilian coin images (both front and reverse side of the coin) consisting of five different denominations have been used which is provided by the kaggle-diverse and largest data community in the world. This work focuses on an automatic image classification process for Brazilian coins. The imagebased classification of coins primarily incorporates three stages where the initial step is Region of Interest (ROI) extraction; the subsequent advance is extraction of features and classification. The first step of ROI extraction is accomplished by segmenting the coin region using the proposed segmentation method. In the second step i.e., feature extraction; Histogram of Oriented Gradients (HOG) features are extracted from the image. The image is converted to a vector containing feature values. The third step is where the extracted features are mapped to the class and are known as classification. Three classification algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbour are compared for classification of five coin denominations. With the proposed segmentation methodology, the best classification accuracy of 92% is achieved in the case of ANN classifier.

scholarly journals 5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

2018 ◽  
Vol 1 (1) ◽  
pp. 6 ◽  
Author(s):  
Lubna Farhi ◽  
Razia Zia ◽  
Zain Anwar Ali
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Classification Algorithms ◽  
Mr Images ◽  
Data Set ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Brain Mr Images ◽  
Artificial Neural Network Ann

Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.

Hardware/Software Co-Design of a Vision System for Automatic Classification of Date Fruits

2020 ◽  
Vol 11 (4) ◽  
pp. 21-40
Author(s):  
Lazhar Khriji ◽  
Ahmed Chiheb Ammari ◽  
Medhat Awadalla
Keyword(s):  
Nearest Neighbor ◽  
Vision System ◽  
Texture Feature ◽  
Automatic Classification ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Accuracy Performance

This paper proposes a hardware/software (HW/SW) co-design of an automatic classification system of Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan dates fruit varieties in Oman. Three artificial intelligence (AI) techniques are used for qualitative comparisons: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The accuracy performance of all AI classifiers is characterized for multiple color, shape, size, and texture feature combinations and for different critical parameter settings of the classifiers. In total, 600 date samples (100 dates/variety) are selected and imaged each sample individually. The system starts with preprocessing and segmentation of the colored input images. A total of 19 features are extracted from each image for use in classification models. The ANN classifier is shown to outperform all other classifiers. 97.26% highest classification accuracy is achieved using a combination of 15 color and shape-size features.

scholarly journals 5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

2018 ◽  
Vol 8 (1) ◽  
pp. 6
Author(s):  
Lubna Farhi ◽  
Razia Zia ◽  
Zain Anwar Ali
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Classification Algorithms ◽  
Mr Images ◽  
Data Set ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Brain Mr Images ◽  
Artificial Neural Network Ann

Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

2021 ◽  
Vol 79 ◽  
pp. 52-58
Author(s):  
Arnaldo Stanzione ◽  
Renato Cuocolo ◽  
Francesco Verde ◽  
Roberta Galatola ◽  
Valeria Romeo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Classification

scholarly journals Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06257
Author(s):  
Ennio Idrobo-Ávila ◽  
Humberto Loaiza-Correa ◽  
Rubiel Vargas-Cañas ◽  
Flavio Muñoz-Bolaños ◽  
Leon van Noorden
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Music Information Retrieval ◽  
Machine Learning Classification ◽  
Information Retrieval Methods ◽  
Music Information
Sign in / Sign up

Export Citation Format

Share Document