scholarly journals Through-Wall Radar Classification of Human Posture Using Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Alper Kılıç ◽  
İsmail Babaoğlu ◽  
Ahmet Babalık ◽  
Ahmet Arslan
Keyword(s):  
Deep Learning ◽  
Continuous Wave ◽  
Human Posture ◽  
Learning Techniques ◽  
Military Applications ◽  
Radar Signals ◽  
Traditional Approaches ◽  
Term Data

Through-wall detection and classification are highly desirable for surveillance, security, and military applications in areas that cannot be sensed using conventional measures. In the domain of these applications, a key challenge is an ability not only to sense the presence of individuals behind the wall but also to classify their actions and postures. Researchers have applied ultrawideband (UWB) radars to penetrate wall materials and make intelligent decisions about the contents of rooms and buildings. As a form of UWB radar, stepped frequency continuous wave (SFCW) radars have been preferred due to their advantages. On the other hand, the success of classification with deep learning methods in different problems is remarkable. Since the radar signals contain valuable information about the objects behind the wall, the use of deep learning techniques for classification purposes will give a different direction to the research. This paper focuses on the classification of the human posture behind the wall using through-wall radar signals and a convolutional neural network (CNN). The SFCW radar is used to collect radar signals reflected from the human target behind the wall. These signals are employed to classify the presence of the human and the human posture whether he/she is standing or sitting by using CNN. The proposed approach achieves remarkable and successful results without the need for detailed preprocessing operations and long-term data used in the traditional approaches.

Attention-Based Deep Recurrent Model for Survival Prediction

2021 ◽  
Vol 2 (4) ◽  
pp. 1-18
Author(s):  
Zhaohong Sun ◽  
Wei Dong ◽  
Jinlong Shi ◽  
Kunlun He ◽  
Zhengxing Huang
Keyword(s):  
Risk Factors ◽  
Survival Analysis ◽  
Deep Learning ◽  
Service Management ◽  
Short Term Memory ◽  
Survival Prediction ◽  
Learning Techniques ◽  
Proposed Model ◽  
Traditional Approaches

Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attention-based deep recurrent model, named AttenSurv , for clinical survival analysis. Specifically, a global attention mechanism is proposed to extract essential/critical risk factors for interpretability improvement. Thereafter, Bi-directional Long Short-Term Memory is employed to capture the long-term dependency on data from a series of visits of patients. To further improve both the prediction performance and the interpretability of the proposed model, we propose another model, named GNNAttenSurv , by incorporating a graph neural network into AttenSurv, to extract the latent correlations between risk factors. We validated our solution on three public follow-up datasets and two electronic health record datasets. The results demonstrated that our proposed models yielded consistent improvement compared to the state-of-the-art baselines on survival analysis.

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

2021 ◽  
Author(s):  
Hamdi Altaheri ◽  
Ghulam Muhammad ◽  
Mansour Alsulaiman ◽  
Syed Umar Amin ◽  
Ghadir Ali Altuwaijri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Techniques ◽  
Electroencephalogram Eeg

Classification of Dermoscopy Skin Images with the Application of Deep Learning Techniques

2020 ◽  
pp. 73-81
Author(s):  
Pablo David Minango Negrete ◽  
Yuzo Iano ◽  
Ana Carolina Borges Monteiro ◽  
Reinaldo Padilha França ◽  
Gabriel Gomes de Oliveira ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Techniques

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

scholarly journals Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

Deep learning techniques for classification of brain MRI

2020 ◽  
Vol 19 (6) ◽  
pp. 571
Author(s):  
Arnab Kumar Maji ◽  
Imayanmosha Wahlang ◽  
Goutam Saha ◽  
Sugata Sanyal ◽  
Pallabi Sharma
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Learning Techniques

scholarly journals Using deep learning to predict solar explosions using magnetograms

2019 ◽  
Author(s):  
Matheus Evers Rodrigues Fernandes ◽  
Andre Leon Sampaio Gradvohl
Keyword(s):  
Deep Learning ◽  
Energy Transmission ◽  
Transmission Networks ◽  
Learning Techniques

Solar activities, especially solar explosions, have a significant impact on some important technologies used on Earth, e.g., energy transmission networks and communications. Depending on the class of the explosion, the consequences can be hazardous. Therefore, when forecasting of solar explosions, earlier we can take actions to mitigate their impact on the affected technologies on Earth. In this work, we applied Deep Learning techniques to classify solar magnetograms, which indicate the class of a solar explosion. The classification of such images may help to anticipate the phenomenon. The results show a 97% of accuracy for the magnetograms classification.

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