Comparison of Deep Learning Techniques for Semantic Classification of Ramps in Search and Rescue Arenas

2020 ◽  
Author(s):  
Kaya Turgut ◽  
Burak Kaleci
Keyword(s):  
Deep Learning ◽  
Search And Rescue ◽  
Semantic Classification ◽  
Learning Techniques

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 Review article: Detection of informative tweets in crisis events

2020 ◽  
Author(s):  
Anna Kruspe ◽  
Jens Kersten ◽  
Friederike Klan
Keyword(s):  
Social Media ◽  
Review Article ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Semantic Classification ◽  
Crisis Events ◽  
Learning Techniques ◽  
Media Data ◽  
Crisis Situations

Abstract. Messages on social media can be an important source of information during crisis situations, be they short-term disasters or longer-term events like COVID-19. They can frequently provide details about developments much faster than traditional sources (e.g. official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks. One challenge for utilizing social media in crisis situations is the reliable detection of informative messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowd-sourced methods. Lately, approaches have shifted towards an automatic analysis of messages. In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, as they can serve very different purposes. This is followed by an overview of existing, crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning techniques with regard to their focus, their data requirements, their technical prerequisites, their efficiency and accuracy, and their time scales. These factors determine the suitability of the approaches for different expectations, but also their limitations. We identify which aspects each of them can contribute to the detection of informative tweets, and which areas can be improved upon in the future.We point out particular challenges, such as the linguistic issues concerning this kind of data. Finally, we suggest future avenues of research, and show connections to related tasks, such as the subsequent semantic classification of tweets.

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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