Performance Comparison for EMD Based Classification of Unstructured Acoustic Environments Using GMM and k-NN Classifiers

2016 ◽  
pp. 123-135
Author(s):  
Sujay D. Mainkar ◽  
S. P. Mahajan
Keyword(s):  
Performance Comparison ◽  
Acoustic Environments

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

scholarly journals Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6940 ◽  
Author(s):  
Vincenzo Taormina ◽  
Donato Cascio ◽  
Leonardo Abbene ◽  
Giuseppe Raso
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fluorescence Intensity ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Fine Tuning ◽  
Tuning Method ◽  
Effective Performance ◽  
Training Modalities

The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

Enhance Wi-Fi Fingerprinting Indoor-Positioning by Error Flag Framework

2014 ◽  
Vol 931-932 ◽  
pp. 942-946
Author(s):  
Shutchon Premchaisawatt ◽  
Nararat Ruangchaijatupon
Keyword(s):  
Machine Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Limited Information ◽  
Machine Learning Classifiers ◽  
Wireless Device ◽  
Learning Data

This research aims to purpose the new method, which is called Error Flag Framework (EFF) to enhance accuracy fingerprinting indoor positioning of wireless device by using machine learning algorithms. EFF is compared with well-known machine learning classifiers; i.e. Decision Tree, Naive Bayes, and Artificial Neural Networks, by exploiting the signal strength from limited information. The performance comparison is done in terms of accuracy of classification of positions, precision of distance classified, and effects of classification of positions on results from quantity of learning data. The result of this study can suggest that EFF can increase performance for indoor positioning of every well-known classifier, especially when the quantity of learning data is large enough. Hence, EFF is the alternate way for implementing in positioning software by using the fingerprinting method.

A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images

2019 ◽  
Vol 53 ◽  
pp. 3-19 ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Antonio Brunetti ◽  
Andrea Guerriero ◽  
Gianpaolo Francesco Trotta ◽  
Michele Telegrafo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Supervised Classification ◽  
Performance Comparison ◽  
Breast Lesions ◽  
A Performance
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