Deep Learning Enabled Classification of Real Time Respiration Signals Acquired by MoSSe Quantum Dots based Flexible Sensor

2021 ◽  
Author(s):  
Naveen Bokka ◽  
JAY KARHADE ◽  
Parikshit Sahatiya
Keyword(s):  
Quantum Dots ◽  
Deep Learning ◽  
Real Time ◽  
Respiration Rate ◽  
Vital Parameter ◽  
Flexible Sensor

: Respiration rate is a vital parameter which is useful for earlier identification of diseases. In this context, various types of devices have been fabricated and developed to monitor different...

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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.

Ransomware Traffic Classification Using Deep Learning Models

2020 ◽  
Vol 12 (1) ◽  
pp. 1-11
Author(s):  
Arivudainambi D. ◽  
Varun Kumar K.A. ◽  
Vinoth Kumar R. ◽  
Visu P.
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Classification Model ◽  
Traffic Classification ◽  
Learning Models ◽  
Learning Methods ◽  
Novel Method

Ransomware is a malware which affects the systems data with modern encryption techniques, and the data is recovered once a ransom amount is paid. In this research, the authors show how ransomware propagates and infects devices. Live traffic classifications of ransomware have been meticulously analyzed. Further, a novel method for the classification of ransomware traffic by using deep learning methods is presented. Based on classification, the detection of ransomware is approached with the characteristics of the network traffic and its communications. In more detail, the behavior of popular ransomware, Crypto Wall, is analyzed and based on this knowledge, a real-time ransomware live traffic classification model is proposed.

Real-Time Classification of Drilled Lithology from Drilling Data Using Deep Learning with Online Calibration

2021 ◽  
Author(s):  
Mikkel Arnø ◽  
John-Morten Godhavn ◽  
Ole Morten Aamo
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Intermediate Step ◽  
Online Calibration ◽  
Proper Actions ◽  
Drilling Operation ◽  
Drilling Parameters ◽  
Bit Wear ◽  
Confusion Matrices

Abstract Decision making to optimize the drilling operation is based on a variety of factors, among them real-time interpretation of drilled lithology. Since logging while drilling (LWD) tools are placed some meters above the bit, mechanical drilling parameters are the earliest indicators, although difficult to interpret accurately. This paper presents a novel deep learning methodology using mechanical drilling parameters for lithology classification. A cascade of multilayered perceptrons (MLPs) are trained on historical data from wells on a field operated by Equinor. Rather than an end-to-end approach, the drilling parameters are utilized to estimate LWD sensor readings in an intermediate step using the first MLPs. This allows continuous updates of the models during operation using delayed LWD data. The second MLP takes the virtual LWD estimates as input to predict currently drilled lithology, similar to manual expert interpretation of logs. This configuration takes into account case dependent (mud, BHA, wellbore geometry) and time varying (bit-wear, wellbore friction) relationships between drilling parameters and LWD readings, while assuming a constant rule when utilizing LWD data to classify lithology. Upon completion of training and validation, the system is tested on a separate, unseen wellbore, for which results are presented. Visualizations for true lithology alongside the estimates are given, along with confusion matrices and model accuracy. The system achieves high accuracy on the test set and presents low confusion between classes, meaning that it distinguishes well between the lithologies present in the wellbore. It can be seen that the borders between successive layers of lithology are detected rapidly, which is crucial seen from an optimization standpoint, so the driller may adjust accordingly immediately. It shows promise as an advisory system, capable of accurately classifying currently drilled lithology by continuously adapting to changing downhole conditions. Although we cannot expect perfect estimates of lithology purely based on drilling parameters, we can obtain a preliminary map of the subsurface this way. This novel configuration gives a real-time interpretation of the currently drilled lithology, allowing the driller to take proper actions to optimize the drilling operation in terms of rate of penetration (ROP) and best practices for different lithologies.

scholarly journals TheLNet270v1 – A Novel Deep-Network Architecture for the Automatic Classification of Thermal Images for Greenhouse Plants

2021 ◽  
Vol 12 ◽  
Author(s):  
Md. Parvez Islam ◽  
Yuka Nakano ◽  
Unseok Lee ◽  
Keinichi Tokuda ◽  
Nobuo Kochi
Keyword(s):  
Deep Learning ◽  
Thermal Radiation ◽  
Surface Temperature ◽  
Real Time ◽  
Network Architecture ◽  
Automatic Segmentation ◽  
Thermal Images ◽  
Learning Technique ◽  
Real Challenge

The real challenge for separating leaf pixels from background pixels in thermal images is associated with various factors such as the amount of emitted and reflected thermal radiation from the targeted plant, absorption of reflected radiation by the humidity of the greenhouse, and the outside environment. We proposed TheLNet270v1 (thermal leaf network with 270 layers version 1) to recover the leaf canopy from its background in real time with higher accuracy than previous systems. The proposed network had an accuracy of 91% (mean boundary F1 score or BF score) to distinguish canopy pixels from background pixels and then segment the image into two classes: leaf and background. We evaluated the classification (segment) performance by using more than 13,766 images and obtained 95.75% training and 95.23% validation accuracies without overfitting issues. This research aimed to develop a deep learning technique for the automatic segmentation of thermal images to continuously monitor the canopy surface temperature inside a greenhouse.

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.

Real-time classification of weeds in organic carrot production using deep learning algorithms

2019 ◽  
Vol 167 ◽  
pp. 105097 ◽  
Author(s):  
Florian J. Knoll ◽  
Vitali Czymmek ◽  
Leif O. Harders ◽  
Stephan Hussmann
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithms ◽  
Real Time Classification
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