scholarly journals The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video

2020 ◽  
Vol 12 (18) ◽  
pp. 3020
Author(s):  
Piotr Szymak ◽  
Paweł Piskur ◽  
Krzysztof Naus
Keyword(s):  
Deep Learning ◽  
World War Ii ◽  
Graphics Processing Units ◽  
Object Classification ◽  
Underwater Vehicles ◽  
Training Methods ◽  
Underwater Video ◽  
Deep Learning Neural Network ◽  
Graphics Processing

Video image processing and object classification using a Deep Learning Neural Network (DLNN) can significantly increase the autonomy of underwater vehicles. This paper describes the results of a project focused on using DLNN for Object Classification in Underwater Video (OCUV) implemented in a Biomimetic Underwater Vehicle (BUV). The BUV is intended to be used to detect underwater mines, explore shipwrecks or observe the process of corrosion of munitions abandoned on the seabed after World War II. Here, the pretrained DLNNs were used for classification of the following type of objects: fishes, underwater vehicles, divers and obstacles. The results of our research enabled us to estimate the effectiveness of using pretrained DLNNs for classification of different objects under the complex Baltic Sea environment. The Genetic Algorithm (GA) was used to establish tuning parameters of the DLNNs. Three different training methods were compared for AlexNet, then one training method was chosen for fifteen networks and the tests were provided with the description of the final results. The DLNNs were trained on servers with six medium class Graphics Processing Units (GPUs). Finally, the trained DLNN was implemented in the Nvidia JetsonTX2 platform installed on board of the BUV, and one of the network was verified in a real environment.

scholarly journals Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

2014 ◽  
Vol 2014 ◽  
pp. 1-19
Author(s):  
Liliana Ibeth Barbosa-Santillán ◽  
Inmaculada Álvarez-de-Mon y-Rego
Keyword(s):  
Graphics Processing Units ◽  
Pearson Correlation ◽  
Lexical Entry ◽  
A Value ◽  
Sentiment Lexicon ◽  
Unit Of Measurement ◽  
Time Required ◽  
Graphics Processing ◽  
High Processing

This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral{P,N,Z}depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and −1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and −1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.

scholarly journals APPLICATION OF DEEP LEARNING NEURAL NETWORK FOR CLASSIFICATION OF BIG DATA OF ASTRONOMIC OBSERVATIONS

2017 ◽  
Vol 22 (4) ◽  
pp. 270-275
Author(s):  
A. A. Gorbunov ◽  
◽  
E. A. Isaev ◽  
V. A. Samodurov ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Deep Learning ◽  
Deep Learning Neural Network

scholarly journals Convolving Pre-Trained Convolutional Neural Networks at Various Magnifications to Extract Diagnostic Features for Digital Pathology

2018 ◽  
Author(s):  
John-William Sidhom ◽  
Alexander S. Baras
Keyword(s):  
Deep Learning ◽  
Graphics Processing Units ◽  
Visual Recognition ◽  
Digital Pathology ◽  
Diagnostic Features ◽  
High Level ◽  
Graphics Processing ◽  
Computational Analyses ◽  
Radiology And Pathology

ABSTRACTDeep learning is an area of artificial intelligence that has received much attention in the past few years due to both an increase in computational power with the increased use of graphics processing units (GPU’s) for computational analyses and the performance of these class of algorithms on visual recognition tasks. They have found utility in applications ranging from image search to facial recognition for security and social media purposes. Their continued success has propelled their use across many new domains including the medical field, in areas of radiology and pathology in particular, as these fields are thought to be driven by visual recognition tasks. In this paper, we present an application of deep learning, termed ‘transfer learning’, using ResNet50, a pre-trained convolutional neural network (CNN) to act as a ‘feature-detector’ at various magnifications to identify low and high level features in digital pathology images of various breast lesions for the purpose of classifying them correctly into the labels of normal, benign, in-situ, or invasive carcinoma as provided in the ICIAR 2018 Breast Cancer Histology Challenge (BACH).

Deep Learning With PyTorch

2020 ◽  
pp. 61-95
Author(s):  
Anmol Chaudhary ◽  
Kuldeep Singh Chouhan ◽  
Jyoti Gajrani ◽  
Bhavna Sharma
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Machine Translation ◽  
Exponential Growth ◽  
Graphics Processing Units ◽  
New Technologies ◽  
The Internet ◽  
Computational Power ◽  
Practical Applications ◽  
Graphics Processing

In the last decade, deep learning has seen exponential growth due to rise in computational power as a result of graphics processing units (GPUs) and a large amount of data due to the democratization of the internet and smartphones. This chapter aims to throw light on both the theoretical aspects of deep learning and its practical aspects using PyTorch. The chapter primarily discusses new technologies using deep learning and PyTorch in detail. The chapter discusses the advantages of using PyTorch compared to other deep learning libraries. The chapter discusses some of the practical applications like image classification and machine translation. The chapter also discusses the various frameworks built with the help of PyTorch. PyTorch consists of various models that increases its flexibility and accessibility to a greater extent. As a result, many frameworks built on top of PyTorch are discussed in this chapter. The authors believe that this chapter will help readers in getting a better understanding of deep learning making neural networks using PyTorch.

scholarly journals Detection of COVID-19 from CT scan images: A spiking neural network-based approach

2021 ◽  
Author(s):  
Avishek Garain ◽  
Arpan Basu ◽  
Fabio Giampaolo ◽  
Juan D. Velasquez ◽  
Ram Sarkar
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Graphics Processing Units ◽  
Early Stage ◽  
Medical Image Analysis ◽  
Global Pandemic ◽  
Radiological Images ◽  
Chest X Ray ◽  
Computer Based ◽  
Graphics Processing

AbstractThe outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

scholarly journals On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java

MATEMATIKA ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 83-90
Author(s):  
Nita Cahyani ◽  
Kartika Fithriasari ◽  
Irhamah Irhamah ◽  
Nur Iriawan
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Deep Learning ◽  
Binary Logistic Regression ◽  
Training Data ◽  
Regression Methods ◽  
Logistic Regression Method ◽  
Validation Procedure ◽  
Deep Learning Neural Network

Neural Network and Binary Logistic Regression are modern and classical data mining analysis tools that can be used to classify data on Bidikmisi scholarship acceptance in East Java Province, Indonesia. One form of Neural Network model available for various applications is the Resilient Backpropagation Neural Network (Resilient BPNN). This study aims to compare the performance of the Resilient BPNN method as a Deep Learning Neural Network and Binary Logistic Regression method in determining the classification of Bidikmisi scholarship acceptance in East Java Province. After preprocessing data and dividing them into two parts, i.e. sets of testing and training data, with 10-foldcross-validation procedure, the Resilient BPNN and Binary Logistic Regression methods are implemented. The result shows that Resilient BPNN with two hidden layers is the best platformnetwork model. The classificationG-mean resulted by these both methods is that Resilient BPNN with two hidden layers is more representative with better performance than Binary Logistic Regression. The Resilient BPNN is recommended to be used topredict acceptance of Bidikmisi applicants yearly.

scholarly journals Chiron: Translating nanopore raw signal directly into nucleotide sequence using deep learning

2017 ◽  
Author(s):  
Haotian Teng ◽  
Minh Duc Cao ◽  
Michael B. Hall ◽  
Tania Duarte ◽  
Sheng Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Graphics Processing Units ◽  
State Of The Art ◽  
Electrical Signal ◽  
Desktop Computer ◽  
Small Set ◽  
Unseen Species ◽  
Graphics Processing ◽  
Deep Learning Model

ABSTRACTSequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology which offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling: directly translating the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4000 reads, we show that our model provides state-of-the-art basecalling accuracy even on previously unseen species. Chiron achieves basecalling speeds of over 2000 bases per second using desktop computer graphics processing units.

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