IoT incorporated deep learning model combined with SmartBin technology for real‐time solid waste management

2021 ◽  
Author(s):  
Muthuramalingam Sivakumar ◽  
Perumal Renuka ◽  
Pandian Chitra ◽  
Sundararajan Karthikeyan
Keyword(s):  
Deep Learning ◽  
Waste Management ◽  
Solid Waste ◽  
Real Time ◽  
Solid Waste Management ◽  
Learning Model ◽  
Deep Learning Model

Application of YOLO Deep Learning Model for Real Time Abandoned Baggage Detection

2018 ◽  
Author(s):  
Tossaporn Santad ◽  
Piyarat Silapasupphakornwong ◽  
Worawat Choensawat ◽  
Kingkarn Sookhanaphibarn
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Precision Measurement for Industry 4.0 Standards towards Solid Waste Classification through Enhanced Imaging Sensors and Deep Learning Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Leow Wei Qin ◽  
Muneer Ahmad ◽  
Ihsan Ali ◽  
Rafia Mumtaz ◽  
Syed Mohammad Hassan Zaidi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solid Waste ◽  
Precision Measurement ◽  
Mobile Applications ◽  
Industry 4.0 ◽  
Learning Model ◽  
Edge Computing ◽  
Support Vector ◽  
Waste Classification ◽  
Deep Learning Model

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.

scholarly journals Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

2021 ◽  
Author(s):  
Gaurav Chachra ◽  
Qingkai Kong ◽  
Jim Huang ◽  
Srujay Korlakunta ◽  
Jennifer Grannen ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Learning Model ◽  
Research Community ◽  
Rescue Work ◽  
The Public ◽  
Social Media Platforms ◽  
Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

Insights into deep learning for earthquake magnitude and location estimation

2021 ◽  
Author(s):  
Jannes Münchmeyer ◽  
Dino Bindi ◽  
Ulf Leser ◽  
Frederik Tilmann
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Early Warning ◽  
Source Parameters ◽  
Spectral Composition ◽  
Learning Model ◽  
Location Estimation ◽  
Training Set ◽  
Earthquake Source Parameters ◽  
Deep Learning Model

<p><span>The estimation of earthquake source parameters, in particular magnitude and location, in real time is one of the key tasks for earthquake early warning and rapid response. In recent years, several publications introduced deep learning approaches for these fast assessment tasks. Deep learning is well suited for these tasks, as it can work directly on waveforms and </span><span>can</span><span> learn features and their relation from data.</span></p><p><span>A drawback of deep learning models is their lack of interpretability, i.e., it is usually unknown what reasoning the network uses. Due to this issue, it is also hard to estimate how the model will handle new data whose properties differ in some aspects from the training set, for example earthquakes in previously seismically quite regions. The discussions of previous studies usually focused on the average performance of models and did not consider this point in any detail.</span></p><p><span>Here we analyze a deep learning model for real time magnitude and location estimation through targeted experiments and a qualitative error analysis. We conduct our analysis on three large scale regional data sets from regions with diverse seismotectonic settings and network properties: Italy and Japan with dense networks </span><span>(station spacing down to 10 km)</span><span> of strong motion sensors, and North Chile with a sparser network </span><span>(station spacing around 40 km) </span><span>of broadband stations. </span></p><p><span>We obtained several key insights. First, the deep learning model does not seem to follow the classical approaches for magnitude and location estimation. For magnitude, one would classically expect the model to estimate attenuation, but the network rather seems to focus its attention on the spectral composition of the waveforms. For location, one would expect a triangulation approach, but our experiments instead show indications of a fingerprinting approach. </span>Second, we can pinpoint the effect of training data size on model performance. For example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. <span>Third, the model fails for events with few similar training examples. For magnitude, this means that the largest event</span><span>s</span><span> are systematically underestimated. For location, events in regions with few events in the training set tend to get mislocated to regions with more training events. </span><span>These characteristics can have severe consequences in downstream tasks like early warning and need to be taken into account for future model development and evaluation.</span></p>

Real-Time Estimation of Eye Movement Condition Using a Deep Learning Model

2021 ◽  
pp. 132-143
Author(s):  
Akihiro Sugiura ◽  
Yoshiki Itazu ◽  
Kunihiko Tanaka ◽  
Hiroki Takada
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Eye Movement ◽  
Time Estimation ◽  
Learning Model ◽  
Movement Condition ◽  
Real Time Estimation ◽  
Deep Learning Model

scholarly journals A deep learning model for real-time mortality prediction in critically ill children

Critical Care ◽  
2019 ◽  
Vol 23 (1) ◽  
Author(s):  
Soo Yeon Kim ◽  
Saehoon Kim ◽  
Joongbum Cho ◽  
Young Suh Kim ◽  
In Suk Sol ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Critically Ill ◽  
Learning Model ◽  
Mortality Prediction ◽  
Critically Ill Children ◽  
Deep Learning Model

scholarly journals IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2556
Author(s):  
Liyang Wang ◽  
Yao Mu ◽  
Jing Zhao ◽  
Xiaoya Wang ◽  
Huilian Che
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Test Set ◽  
Non Invasive ◽  
Deep Learning Model

The clinical symptoms of prediabetes are mild and easy to overlook, but prediabetes may develop into diabetes if early intervention is not performed. In this study, a deep learning model—referred to as IGRNet—is developed to effectively detect and diagnose prediabetes in a non-invasive, real-time manner using a 12-lead electrocardiogram (ECG) lasting 5 s. After searching for an appropriate activation function, we compared two mainstream deep neural networks (AlexNet and GoogLeNet) and three traditional machine learning algorithms to verify the superiority of our method. The diagnostic accuracy of IGRNet is 0.781, and the area under the receiver operating characteristic curve (AUC) is 0.777 after testing on the independent test set including mixed group. Furthermore, the accuracy and AUC are 0.856 and 0.825, respectively, in the normal-weight-range test set. The experimental results indicate that IGRNet diagnoses prediabetes with high accuracy using ECGs, outperforming existing other machine learning methods; this suggests its potential for application in clinical practice as a non-invasive, prediabetes diagnosis technology.

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