scholarly journals FETCH: A deep-learning based classifier for fast transient classification

2020 ◽  
Vol 497 (2) ◽  
pp. 1661-1674 ◽  
Author(s):  
Devansh Agarwal ◽  
Kshitij Aggarwal ◽  
Sarah Burke-Spolaor ◽  
Duncan R Lorimer ◽  
Nathaniel Garver-Daniels
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Radio Frequency ◽  
Real Time ◽  
Machine Learning Algorithms ◽  
Dispersion Measure ◽  
Radio Frequency Interference ◽  
Data Set ◽  
Python Package

ABSTRACT With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their high candidate rate, usage of machine learning algorithms for candidate classification is a necessity. Such algorithms will also play a pivotal role in sending real-time triggers for prompt follow-ups with other instruments. In this paper, we have used the technique of Transfer Learning to train the state-of-the-art deep neural networks for classification of FRB and Radio Frequency Interference (RFI) candidates. These are convolutional neural networks which work on radio frequency-time and dispersion measure-time images as the inputs. We trained these networks using simulated FRBs and real RFI candidates from telescopes at the Green Bank Observatory. We present 11 deep learning models, each with an accuracy and recall above 99.5 per cent on our test data set comprising of real RFI and pulsar candidates. As we demonstrate, these algorithms are telescope and frequency agnostic and are able to detect all FRBs with signal-to-noise ratios above 10 in ASKAP and Parkes data. We also provide an open-source python package fetch (Fast Extragalactic Transient Candidate Hunter) for classification of candidates, using our models. Using fetch, these models can be deployed along with any commensal search pipeline for real-time candidate classification.

scholarly journals Assistant robot through deep learning

2020 ◽  
Vol 10 (1) ◽  
pp. 1053
Author(s):  
Robinson Jiménez-Moreno ◽  
Javier Orlando Pinzón-Arenas ◽  
César Giovany Pachón-Suescún
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Speech Recognition ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Spanish Language ◽  
Assistive Robotics ◽  
The One

This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user, when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reach a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.

scholarly journals Investigating Deep Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

2020 ◽  
Author(s):  
Alisson Hayasi da Costa ◽  
Renato Augusto C. dos Santos ◽  
Ricardo Cerri
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Feedforward Neural Networks ◽  
The State ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Large Application

AbstractPIWI-Interacting RNAs (piRNAs) form an important class of non-coding RNAs that play a key role in the genome integrity through the silencing of transposable elements. However, despite their importance and the large application of deep learning in computational biology for classification tasks, there are few studies of deep learning and neural networks for piRNAs prediction. Therefore, this paper presents an investigation on deep feedforward networks models for classification of transposon-derived piRNAs. We analyze and compare the results of the neural networks in different hyperparameters choices, such as number of layers, activation functions and optimizers, clarifying the advantages and disadvantages of each configuration. From this analysis, we propose a model for human piRNAs classification and compare our method with the state-of-the-art deep neural network for piRNA prediction in the literature and also traditional machine learning algorithms, such as Support Vector Machines and Random Forests, showing that our model has achieved a great performance with an F-measure value of 0.872, outperforming the state-of-the-art method in the literature.

scholarly journals AN APPROACH TO ACCELERATE THE TRAINING OF CONVOLUTIONAL NEURAL NETWORKS BY TUNING THE HYPERPARAMETERS OF LEARNING

2020 ◽  
Vol 2 (2) ◽  
pp. 32-37
Author(s):  
P. RADIUK ◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Video Processing ◽  
Network Architecture ◽  
Random Search ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Data Set ◽  
Training Time

Over the last decade, a set of machine learning algorithms called deep learning has led to significant improvements in computer vision, natural language recognition and processing. This has led to the widespread use of a variety of commercial, learning-based products in various fields of human activity. Despite this success, the use of deep neural networks remains a black box. Today, the process of setting hyperparameters and designing a network architecture requires experience and a lot of trial and error and is based more on chance than on a scientific approach. At the same time, the task of simplifying deep learning is extremely urgent. To date, no simple ways have been invented to establish the optimal values of learning hyperparameters, namely learning speed, sample size, data set, learning pulse, and weight loss. Grid search and random search of hyperparameter space are extremely resource intensive. The choice of hyperparameters is critical for the training time and the final result. In addition, experts often choose one of the standard architectures (for example, ResNets and ready-made sets of hyperparameters. However, such kits are usually suboptimal for specific practical tasks. The presented work offers an approach to finding the optimal set of hyperparameters of learning ZNM. An integrated approach to all hyperparameters is valuable because there is an interdependence between them. The aim of the work is to develop an approach for setting a set of hyperparameters, which will reduce the time spent during the design of ZNM and ensure the efficiency of its work. In recent decades, the introduction of deep learning methods, in particular convolutional neural networks (CNNs), has led to impressive success in image and video processing. However, the training of CNN has been commonly mostly based on the employment of quasi-optimal hyperparameters. Such an approach usually requires huge computational and time costs to train the network and does not guarantee a satisfactory result. However, hyperparameters play a crucial role in the effectiveness of CNN, as diverse hyperparameters lead to models with significantly different characteristics. Poorly selected hyperparameters generally lead to low model performance. The issue of choosing optimal hyperparameters for CNN has not been resolved yet. The presented work proposes several practical approaches to setting hyperparameters, which allows reducing training time and increasing the accuracy of the model. The article considers the function of training validation loss during underfitting and overfitting. There are guidelines in the end to reach the optimization point. The paper also considers the regulation of learning rate and momentum to accelerate network training. All experiments are based on the widespread CIFAR-10 and CIFAR-100 datasets.

scholarly journals Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

2020 ◽  
Vol 48 (4) ◽  
pp. 2316-2327
Author(s):  
Caner KOC ◽  
Dilara GERDAN ◽  
Maksut B. EMİNOĞLU ◽  
Uğur YEGÜL ◽  
Bulent KOC ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ensemble Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Performance Criteria ◽  
Gradient Boosting ◽  
Data Set

Classification of hazelnuts is one of the values adding processes that increase the marketability and profitability of its production. While traditional classification methods are used commonly, machine learning and deep learning can be implemented to enhance the hazelnut classification processes. This paper presents the results of a comparative study of machine learning frameworks to classify hazelnut (Corylus avellana L.) cultivars (‘Sivri’, ‘Kara’, ‘Tombul’) using DL4J and ensemble learning algorithms. For each cultivar, 50 samples were used for evaluations. Maximum length, width, compression strength, and weight of hazelnuts were measured using a caliper and a force transducer. Gradient boosting machine (Boosting), random forest (Bagging), and DL4J feedforward (Deep Learning) algorithms were applied in traditional machine learning algorithms. The data set was partitioned into a 10-fold-cross validation method. The classifier performance criteria of accuracy (%), error percentage (%), F-Measure, Cohen’s Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values are provided in the results section. The results showed classification accuracies of 94% for Gradient Boosting, 100% for Random Forest, and 94% for DL4J Feedforward algorithms.

scholarly journals REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS

2017 ◽  
Vol IV-2/W3 ◽  
pp. 41-48 ◽  
Author(s):  
A. Milioto ◽  
P. Lottes ◽  
C. Stachniss
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sugar Beet ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
High Quality ◽  
Sugar Beets ◽  
Quality Classification

UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

scholarly journals Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Diu K. Luu ◽  
Anh T. Nguyen ◽  
Ming Jiang ◽  
Jian Xu ◽  
Markus W. Drealan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Input Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Trade Offs

Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a “pseudo-online” dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.

scholarly journals A Research on Deep Learning Advance for Landslide Classification using Convolutional Neural Networks

2019 ◽  
Vol 8 (6S4) ◽  
pp. 903-906
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Set ◽  
Proposed Model

Landslides can easily be tragic to human life and property. Increase in the rate of human settlement in the mountains has resulted in safety concerns. Landslides have caused economic loss between 1-2% of the GDP in many developing countries. In this study, we discuss a deep learning approach to detect landslides. Convolutional Neural Networks are used for feature extraction for our proposed model. As there was no source of an exact and precise data set for feature extraction, therefore, a new data set was built for testing the model. We have tested and compared this work with our proposed model and with other machine-learning algorithms such as Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors and Support Vector Machine. Our proposed deep learning model produces a classification accuracy of 96.90% outperforming the classical machine-learning algorithms.

scholarly journals Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification

2020 ◽  
pp. 1-9
Author(s):  
Supun Nakandala ◽  
Marta M. Jankowska ◽  
Fatima Tuz-Zahra ◽  
John Bellettiere ◽  
Jordan A. Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Logistic Regression ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Free Living ◽  
Data Set

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

scholarly journals Classification of Rigid and Non-Rigid Objects Using CNN

2021 ◽  
Vol 35 (4) ◽  
pp. 341-347
Author(s):  
Aparna Gullapelly ◽  
Barnali Gupta Banik
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Moving Objects ◽  
Binary Classification ◽  
High Accuracy ◽  
Learning System ◽  
Data Set ◽  
Rigid Objects ◽  
Soft Objects

Classifying moving objects in video surveillance can be difficult, and it is challenging to classify hard and soft objects with high Accuracy. Here rigid and non-rigid objects are limited to vehicles and people. CNN is used for the binary classification of rigid and non-rigid objects. A deep-learning system using convolutional neural networks was trained using python and categorized according to their appearance. The classification is supplemented by the use of a data set, which contains two classes of images that are both rigid and not rigid that differ by illuminations.

scholarly journals Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Real-time Automated Segmentation and Classification of Vocal Cord Leukoplakia

2021 ◽  
Author(s):  
Juanjuan Hu ◽  
Jiawei Luo ◽  
Jia Ren ◽  
Lan Lan ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Vocal Cord ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Pathological Examination ◽  
Automated Segmentation ◽  
Imaging Results ◽  
Surgical Group

Objectives The study was to apply deep learning (DL) with convolutional neural networks (CNNs) to laryngoscopic imaging for assisting in real-time automated segmentation and classification of vocal cord leukoplakia. Methods This was a single-center retrospective diagnostic study included 216 patients who underwent laryngoscope and pathological examination from October 1, 2018 through October 1, 2019. Lesions were classified as nonsurgical group (NSG) and surgical group (SG) according to pathology. All selected images of vocal cord leukoplakia were annotated independently by 2 expert endoscopists and divided into a training set, a validation set, and a test set in a ratio of 6:2:2 for training the model. Results Among the 260 lesions identified in 216 patients, 2220 images from narrow band imaging (NBI) and 2144 images from white light imaging (WLI) were selected. For segmentation, the average intersection-over-union (IoU) value exceeded 70%. For classification, the model was able to classify the surgical group (SG) by laryngoscope with a sensitivity of 0.93 and specificity of 0.94 in WLI, and a sensitivity of 0.99 and specificity of 0.97 in NBI. Moreover, this model achieved a mean average precision (mAP) of 0.81 in WLI and 0.92 in NBI with an IoU> 0.5. Conclusions The study found that a model developed by applying DL with CNNs to laryngoscopic imaging results in high sensitivity, specificity, and mAP for automated segmentation and classification of vocal cord leukoplakia. This finding shows promise for the application of DL with CNNs in assisting in accurate diagnosis of vocal cord leukoplakia from WLI and NBI.

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