Fine-tuning deep belief networks using cuckoo search

2016 ◽  
pp. 47-59 ◽  
Author(s):  
D. Rodrigues ◽  
X.-S. Yang ◽  
J.P. Papa
Keyword(s):  
Cuckoo Search ◽  
Fine Tuning ◽  
Belief Networks ◽  
Deep Belief Networks

scholarly journals Quaternion-based Deep Belief Networks fine-tuning

2017 ◽  
Vol 60 ◽  
pp. 328-335 ◽  
Author(s):  
João Paulo Papa ◽  
Gustavo H. Rosa ◽  
Danillo R. Pereira ◽  
Xin-She Yang
Keyword(s):  
Fine Tuning ◽  
Belief Networks ◽  
Deep Belief Networks

A Novel Method for Classification of ECG Arrhythmias Using Deep Belief Networks

2016 ◽  
Vol 15 (04) ◽  
pp. 1650021 ◽  
Author(s):  
Zhiyong Wu ◽  
Xiangqian Ding ◽  
Guangrui Zhang
Keyword(s):  
Feature Learning ◽  
Feature Representation ◽  
Fine Tuning ◽  
Classification Model ◽  
Training Algorithms ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Ecg Signals ◽  
Contrastive Divergence

In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph (ECG) arrhythmias classification is proposed. The construction process of ECG classification model consists of two steps: features learning for ECG signals and supervised fine-tuning. In order to deeply extract features from continuous ECG signals, two types of restricted Boltzmann machine (RBM) including Gaussian–Bernoulli and Bernoulli–Bernoulli are stacked to form DBN. The parameters of RBM can be learned by two training algorithms such as contrastive divergence and persistent contrastive divergence. A suitable feature representation from the raw ECG data can therefore be extracted in an unsupervised way. In order to enhance the performance of DBN, a fine-tuning process is carried out, which uses backpropagation by adding a softmax regression layer on the top of the resulting hidden representation layer to perform multiclass classification. The method is then validated by experiments on the well-known MIT-BIH arrhythmia database. Considering the real clinical application, the inter-patient heartbeat dataset is divided into two sets and grouped into four classes (N, S, V, F) following the recommendations of AAMI. The experiment results show our approach achieves better performance with less feature learning time than traditional hand-designed methods on the classification of ECG arrhythmias.

scholarly journals Deep Belief Networks for Recognizing Handwriting Captured by Leap Motion Controller

2018 ◽  
Vol 8 (6) ◽  
pp. 4693 ◽  
Author(s):  
Abas Setiawan ◽  
Reza Pulungan
Keyword(s):  
Fine Tuning ◽  
Input Device ◽  
Leap Motion ◽  
Belief Networks ◽  
Multi Layer Perceptron ◽  
Deep Belief Networks ◽  
Motion Controller ◽  
Leap Motion Controller ◽  
Gaming Environment ◽  
Better Than

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>Leap Motion controller is an input device that can track hands and fingers position quickly and precisely. In some gaming environment, a need may arise to capture letters written in the air by Leap Motion, which cannot be directly done right now. In this paper, we propose an approach to capture and recognize which letter has been drawn by the user with Leap Motion. This approach is based on Deep Belief Networks (DBN) with Resilient Backpropagation (Rprop) fine-tuning. To assess the performance of our proposed approach, we conduct experiments involving 30,000 samples of handwritten capital letters, 8,000 of which are to be recognized. Our experiments indicate that DBN with Rprop achieves an accuracy of 99.71%, which is better than DBN with Backpropagation or Multi-Layer Perceptron (MLP), either with Backpropagation or with Rprop. Our experiments also show that Rprop makes the process of fine-tuning significantly faster and results in a much more accurate recognition compared to ordinary Backpropagation. The time needed to recognize a letter is in the order of 5,000 microseconds, which is excellent even for online gaming experience.</p></div></div></div>

scholarly journals Diagnosis of Breast Hyperplasia and Evaluation of RuXian-I Based on Metabolomics Deep Belief Networks

2019 ◽  
Vol 20 (11) ◽  
pp. 2620 ◽  
Author(s):  
Mingyang Jiang ◽  
Yanchun Liang ◽  
Zhili Pei ◽  
Xiye Wang ◽  
Fengfeng Zhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Animal Model ◽  
Close Association ◽  
Fine Tuning ◽  
Cancer Type ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Metabolomic Data ◽  
Proposed Model ◽  
Breast Hyperplasia

Breast cancer is estimated to be the leading cancer type among new cases in American women. Core biopsy data have shown a close association between breast hyperplasia and breast cancer. The early diagnosis and treatment of breast hyperplasia are extremely important to prevent breast cancer. The Mongolian medicine RuXian-I is a traditional drug that has achieved a high level of efficacy and a low incidence of side effects in its clinical use. However, for detecting the efficacy of RuXian-I, a rapid and accurate evaluation method based on metabolomic data is still lacking. Therefore, we proposed a framework, named the metabolomics deep belief network (MDBN), to analyze breast hyperplasia metabolomic data. We obtained 168 samples of metabolomic data from an animal model experiment of RuXian-I, which were averaged from control groups, treatment groups, and model groups. In the process of training, unlabelled data were used to pretrain the Deep Belief Networks models, and then labelled data were used to complete fine-tuning based on a limited-memory Broyden Fletcher Goldfarb Shanno (L-BFGS) algorithm. To prevent overfitting, a dropout method was added to the pretraining and fine-tuning procedures. The experimental results showed that the proposed model is superior to other classical classification methods that are based on positive and negative spectra data. Further, the proposed model can be used as an extension of the classification method for metabolomic data. For the high accuracy of classification of the three groups, the model indicates obvious differences and boundaries between the three groups. It can be inferred that the animal model of RuXian-I is well established, which can lay a foundation for subsequent related experiments. This also shows that metabolomic data can be used as a means to verify the effectiveness of RuXian-I in the treatment of breast hyperplasia.

scholarly journals Fine-tuning Deep Belief Networks using Harmony Search

2016 ◽  
Vol 46 ◽  
pp. 875-885 ◽  
Author(s):  
João Paulo Papa ◽  
Walter Scheirer ◽  
David Daniel Cox
Keyword(s):  
Harmony Search ◽  
Fine Tuning ◽  
Belief Networks ◽  
Deep Belief Networks

Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

2020 ◽  
Vol 16 (9) ◽  
pp. 1059-1073
Author(s):  
Vidhusha Srinivasan ◽  
N. Udayakumar ◽  
Kavitha Anandan
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Language Processing ◽  
Brain Connectivity ◽  
Primary Auditory Cortex ◽  
Autism Spectrum ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
High Functioning ◽  
Low Functioning

Background: The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research. Objective: This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA. Results: Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge

2017 ◽  
Vol 16 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Tianming Zhan ◽  
Yi Chen ◽  
Xunning Hong ◽  
Zhenyu Lu ◽  
Yunjie Chen
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Brain Tumor Segmentation
Sign in / Sign up

Export Citation Format

Share Document