scholarly journals Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

2021 ◽  
Author(s):  
Phongsathorn Kittiworapanya ◽  
Kitsuchart Pasupa ◽  
Peter Auer
Keyword(s):  
Particle Size ◽  
Deep Learning ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Input Image ◽  
Size Estimation ◽  
Waste Particles ◽  
Set Up ◽  
Research Students ◽  
Support Research

<div>This work was presented at the 10th Joint Symposium on Computational Intelligence (JSCI10), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.<br></div><div><br></div><div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.<br></div>

scholarly journals Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

2021 ◽  
Author(s):  
Phongsathorn Kittiworapanya ◽  
Kitsuchart Pasupa ◽  
Peter Auer
Keyword(s):  
Computer Vision ◽  
Particle Size ◽  
Deep Learning ◽  
Waste Management ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Input Image ◽  
Size Estimation ◽  
Waste Particles ◽  
Set Up

<div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.<br></div>

scholarly journals Learning from Others: Daily COVID-19 Cases Prediction in India using Ensembles of LSTM-RNNs

2021 ◽  
Author(s):  
Debasrita Chakraborty ◽  
Debayan Goswami ◽  
Susmita Ghosh ◽  
Jonathan H. Chan ◽  
Ashish Ghosh
Keyword(s):  
Computational Intelligence ◽  
Joint Symposium ◽  
Research Students ◽  
Learning From Others ◽  
Young Researchers ◽  
Support Research

This work was presented at the 10th Joint Symposium on Computational Intelligence (JSCI10), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.

scholarly journals Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 73
Author(s):  
Marjan Stoimchev ◽  
Marija Ivanovska ◽  
Vitomir Štruc
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Input Image ◽  
Palmprint Recognition ◽  
Learning Approaches ◽  
Elastic Deformations ◽  
Feature Representations ◽  
Palmar Surface ◽  
Proposed Model ◽  
Visual Artifacts

In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available.

scholarly journals Learning from Others: Daily COVID-19 Cases Prediction in India using Ensembles of LSTM-RNNs

2021 ◽  
Author(s):  
Debasrita Chakraborty ◽  
Debayan Goswami ◽  
Susmita Ghosh ◽  
Jonathan H. Chan ◽  
Ashish Ghosh
Keyword(s):  
Computational Intelligence ◽  
Joint Symposium ◽  
Research Students ◽  
Learning From Others ◽  
Young Researchers ◽  
Support Research

This work was presented at the 10th Joint Symposium on Computational Intelligence (JSCI10), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.

scholarly journals Handwritten Pashto Characters Dataset for Optical Character Recognition

2021 ◽  
Author(s):  
wahidullah mudaser ◽  
Jonathan H. Chan
Keyword(s):  
Computational Intelligence ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Joint Symposium ◽  
Optical Character ◽  
Research Students ◽  
Young Researchers ◽  
Support Research

<div>This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div><div><br></div><div><div>The Pashto character database developed in this work is available at <a href="https://github.com/mudaser37/pashtoCharacterDataset" rel="noreferrer noopener" target="_blank">GitHub - mudaser37/pashtoCharacterDataset</a></div></div>

scholarly journals Hand Foot and Mouth Rash Detection Using Deep Convolution Neural Network

2021 ◽  
Author(s):  
Naqibullah Vakili ◽  
Jonathan H. Chan ◽  
Worarat Krathu ◽  
Nipat Phattarakijtham ◽  
Kazuya Hirata
Keyword(s):  
Neural Network ◽  
Computational Intelligence ◽  
Convolution Neural Network ◽  
Joint Symposium ◽  
Foot And Mouth ◽  
Deep Convolution Neural Network ◽  
Research Students ◽  
Young Researchers ◽  
Support Research

<div>This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div>

scholarly journals Hand Foot and Mouth Rash Detection Using Deep Convolution Neural Network

2021 ◽  
Author(s):  
Naqibullah Vakili ◽  
Jonathan H. Chan ◽  
Worarat Krathu ◽  
Nipat Phattarakijtham ◽  
Kazuya Hirata
Keyword(s):  
Neural Network ◽  
Computational Intelligence ◽  
Convolution Neural Network ◽  
Joint Symposium ◽  
Foot And Mouth ◽  
Deep Convolution Neural Network ◽  
Research Students ◽  
Young Researchers ◽  
Support Research

<div>This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div>

scholarly journals Handwritten Pashto Characters Dataset for Optical Character Recognition

2021 ◽  
Author(s):  
wahidullah mudaser ◽  
Jonathan H. Chan
Keyword(s):  
Computational Intelligence ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Joint Symposium ◽  
Optical Character ◽  
Research Students ◽  
Young Researchers ◽  
Support Research

<div>This work was presented at the 9th Joint Symposium on Computational Intelligence (JSCI9), organized by the IEEE-CIS Thailand Chapter, that aims to support research students and young researchers, to create a place enabling participants to share and discuss on their research prior to publishing their works. The event was open to all researchers who want to broaden their knowledge in the field of computational intelligence.</div><div><br></div><div><div>The Pashto character database developed in this work is available at <a href="https://github.com/mudaser37/pashtoCharacterDataset" rel="noreferrer noopener" target="_blank">GitHub - mudaser37/pashtoCharacterDataset</a></div></div>

scholarly journals Neural Twins Talk and Alternative Calculations

2021 ◽  
Vol 15 (01) ◽  
pp. 93-116
Author(s):  
Zanyar Zohourianshahzadi ◽  
Jugal K. Kalita
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Input Image ◽  
Neural Pathways ◽  
Image Captioning ◽  
Attention Model ◽  
Deep Learning Model ◽  
Previous Image

Inspired by how the human brain employs more neural pathways when increasing the focus on a subject, we introduce a novel twin cascaded attention model that outperforms a state-of-the-art image captioning model that was originally implemented using one channel of attention for the visual grounding task. Visual grounding ensures the existence of words in the caption sentence that are grounded into a particular region in the input image. After a deep learning model is trained on visual grounding task, the model employs the learned patterns regarding the visual grounding and the order of objects in the caption sentences, when generating captions. We report the results of our experiments in three image captioning tasks on the COCO dataset. The results are reported using standard image captioning metrics to show the improvements achieved by our model over the previous image captioning model. The results gathered from our experiments suggest that employing more parallel attention pathways in a deep neural network leads to higher performance. Our implementation of Neural Twins Talk (NTT) is publicly available at: https://github.com/zanyarz/NeuralTwinsTalk .

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