An IoT‐based human detection system for complex industrial environment with deep learning architectures and transfer learning

2021 ◽  
Author(s):  
Imran Ahmed ◽  
Marco Anisetti ◽  
Gwanggil Jeon
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Detection System ◽  
Human Detection ◽  
Industrial Environment ◽  
Learning Architectures

Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures

Measurement ◽  
2021 ◽  
pp. 109953
Author(s):  
Adhiyaman Manickam ◽  
Jianmin Jiang ◽  
Yu Zhou ◽  
Abhinav Sagar ◽  
Rajkumar Soundrapandiyan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Learning Approach ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Architectures

scholarly journals Towards Image Classification with Machine Learning Methodologies for Smartphones

2019 ◽  
Vol 1 (4) ◽  
pp. 1039-1057 ◽  
Author(s):  
Lili Zhu ◽  
Petros Spachos
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Time Constraints ◽  
Learning Method ◽  
Android Application ◽  
Optimal Model ◽  
Recent Developments ◽  
Learning Architectures

Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies to compare their characteristics by training and testing on a butterfly dataset, and determined the optimal model to deploy in an Android application. The application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from the mobile gallery.

scholarly journals Computational Prediction of Disease Detection and Insect Identification using Xception model

2021 ◽  
Author(s):  
Lidia Cleetus ◽  
Raji Sukumar ◽  
Hemalatha N
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Computational Prediction ◽  
Detection Model ◽  
Insect Identification ◽  
Detection And Identification ◽  
Efficient Detection ◽  
Improved Performance ◽  
Learning Architectures ◽  
Base Data

In this paper, a detection tool has been built for the detection and identification of the diseases and pests found in the crops at its earliest stage. For this, various deep learning architectures were experimented to see which one of those would help in building a more accurate and an efficient detection model. The deep learning architectures used in this study were Convolutional Neural Network, VGG16, InceptionV3, and Xception. VGG16, InceptionV3, and Xception are categorized as the pre-trained models based on CNN architecture. They follow the concept of transfer learning. Transfer learning is a technique which makes use of the learnings of the models previously trained on a base data and applies it to the present dataset. This is an efficient technique which gives us rapid results and improved performance. Two plant datasets have been used here for disease and insects. The results of the algorithms were then compared. Most successful one has been the Xception model which obtained 82.89 for disease and 77.9 for pests.

scholarly journals A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face

2021 ◽  
Vol 9 (1) ◽  
pp. 115
Author(s):  
Faisal Dharma Adhinata ◽  
Diovianto Putra Rakhmadani ◽  
Merlinda Wibowo ◽  
Akhmad Jayadi
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Face Detection ◽  
Detection System ◽  
Video Data ◽  
Training Data ◽  
Public Places ◽  
Learning Techniques ◽  
The Face ◽  
F Measure

The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.

scholarly journals Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Mark Christopher ◽  
Akram Belghith ◽  
Christopher Bowd ◽  
James A. Proudfoot ◽  
Michael H. Goldbaum ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Optic Neuropathy ◽  
Glaucomatous Optic Neuropathy ◽  
Fundus Photographs ◽  
Learning Architectures

scholarly journals Accelerating the update of a DL-based IDS for IoT using deep transfer learning

2021 ◽  
Vol 23 (2) ◽  
pp. 1059
Author(s):  
Idriss Idrissi ◽  
Mostafa Azizi ◽  
Omar Moussaoui
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Detection Rate ◽  
Detection System ◽  
Behavior Changes ◽  
Small Data ◽  
Learning Technique ◽  
Multiple Metrics ◽  
And Behavior ◽  
Fine Tune

<p>Deep learning (DL) models are nowadays broadly applied and have shown outstanding performance in a variety of fields, including our focus topic of "IoTcybersecurity". Deep learning-based intrusion detection system (DL-IDS) models are more fixated and depended on the trained dataset. This poses a problem for these DL-IDS, especially with the known mutation and behavior changes of attacks, which can render them undetected. As a result, the DL-IDShas become outdated. In this work, we present a solution for updating DL-ID Semploying a transfer learning technique that allows us to retrain and fine-tune pre-trained models on small datasets with new attack behaviors. In our experiments, we built CNN-based IDS on the Bot-IoT dataset and updated it on small data from a new dataset named TON-IoT. We obtained promising results in multiple metrics regarding the detection rate and the training between the initial training for the original model and the updated one, in the matter of detecting new attacks behaviors and improving the detection rate for some classes by overcoming the lack of their labeled data.</p>

MAPPING IN DYNAMIC ENVIRONMENTS USING DEEP LEARNING-BASED HUMAN DETECTION

2019 ◽  
Author(s):  
João Carlos Virgolino Soares ◽  
Marcelo Gattass ◽  
Marco Antonio Meggiolaro
Keyword(s):  
Deep Learning ◽  
Dynamic Environments ◽  
Human Detection
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