International Journal of Hybrid Intelligent Systems
Efficient and accurate early prediction of Alzheimer’s disease (AD) based on the neuroimaging dat... more Efficient and accurate early prediction of Alzheimer’s disease (AD) based on the neuroimaging data has attracted interest from many researchers to prevent its progression. Deep learning networks have demonstrated an optimal ability to analyse large-scale multimodal neuroimaging for AD classification. The most widely used architecture of deep learning is the Convolution neural networks (CNN) that have shown great potential in AD detection. However CNN does not capture long range dependencies within the input image and does not ensure a good global feature extraction. Furthermore, increasing the receptive field of CNN by increasing the kernels sizes can cause a feature granularity loss. Another limitation is that CNN lacks a weighing mechanism of image features; the network doesn’t focus on the relevant features within the image. Recently,vision transformer have shown an outstanding performance over the CNN and overcomes its main limitations. The vision transformer relies on the self-...
Journal of Ambient Intelligence and Humanized Computing
Artificial neural networks (ANNs) are being widely used in supervised machine learning to analyze... more Artificial neural networks (ANNs) are being widely used in supervised machine learning to analyze signals or images for many applications. Using an annotated learning database, one of the main challenges is to optimize the network weights. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively such as gradient-based method, Newton-type method, meta-heuristic method. For the sake of efficiency, regularization is generally used. When non-smooth regularizers are used especially to promote sparse networks, such as the 1 norm, this optimization becomes challenging due to non-differentiability issues of the target criterion. In this paper, we propose an MCMC-based optimization scheme formulated in a Bayesian framework. The proposed scheme solves the above-mentioned sparse optimization problem using an efficient sampling scheme and Hamiltonian dynamics. The designed optimizer is conducted on four (4) datasets, and the results are verified by a comparative study with two CNNs. Promising results show the usefulness of the proposed method to allow ANNs, even with low complexity levels, reaching high accuracy rates of up to 94%. The proposed method is also faster and more robust concerning overfitting issues. More importantly, the training step of the proposed method is much faster than all competing algorithms.
Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. ... more Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy.
The accurate segmentation and counting of the reproductive organs within the herbarium specimen p... more The accurate segmentation and counting of the reproductive organs within the herbarium specimen play an important role in studying the impact of climate change on plant development over time. Recently, the researchers have gained a lot of knowledge about plant phenology owing to herbaria’s digitization efforts, which may help accelerate plant phenology research by making large digitized specimen collections publicly available. Nevertheless, the automatic segmentation and counting of the reproductive organs is a challenging problem. This is because of the high variability of reproductive organs, which vary in size, shape, orientation, and color. The use of machine learning techniques, including deep learning, has recently been shown to be helpful in this endeavor. We proposed in this paper a deep learning method based on the refined Mask Scoring R-CNN approach to segment and count reproductive organs, including buds, flowers, and fruits from specimen images. Our proposed method achie...
International Journal of Environmental Research and Public Health, 2021
The purpose of the present study was to investigate which of two strategies, Video Feedback with ... more The purpose of the present study was to investigate which of two strategies, Video Feedback with Pedagogical Activity (VF-PA) or Video Feedback (VF), would be more beneficial for the remote error correction of the snatch weightlifting technique during the confinement period. Thirty-five school aged children with at least three months of weightlifting experience were randomized to one of three training conditions: VF-PA, VF or the Control group (CONT). Subjects underwent test sessions one week before (T0) and one day after (T1) a six-session training period and a retention test session a week later (T2). During each test session, the Kinovea version 0.8.15 software measured the kinematic parameters of the snatch performance. Following distance learning sessions (T1), the VF-PA improved various kinematic parameters (i.e., barbell horizontal displacements, maximum height, looping and symmetry) compared with T0 (p < 0.5; Cohen’s d = 0.58–1.1). Most of these improvements were maintain...
Charts are frequently embedded objects in digital documents and are used to convey a clear analys... more Charts are frequently embedded objects in digital documents and are used to convey a clear analysis of research results or commercial data trends. These charts are created through different means and may be represented by a variety of patterns such as column charts, line charts and pie charts. Chart recognition is as important as text recognition to automatically comprehend the knowledge within digital document. Chart recognition consists on identifying the chart type and decoding its visual contents into computer understandable values. Previous work in chart image identification has relied on hand crafted features which often fails when dealing with a large amount of data that could contain significant varieties and less common char types. Hence, as a first step towards this goal, in this paper we propose to use a deep learning-based approach that automates the feature extraction step. We present an improved version of the LeNet [LeCu 89] convolutional neural network architecture f...
As the main way for knowledge representation for the purpose of machine understanding, ontologies... more As the main way for knowledge representation for the purpose of machine understanding, ontologies are widely used in different application domains. This requires more and more domain specific information to be inserted into ontologies, making them harder to be easily understood by a human and there is a growing need to develop ontology visualization tools. However, most of existing tools focus on either a specific user requirement or ontology-specific features. To this end, in this demo, we introduce a new generic and user-friendly ontology visualization tool, called BioOntoVis, for visualizing and editing ontologies. We present the general architecture of the tool focusing on the web-based user interface and different ontology visualization schemes. Through the demonstration of BioOntoVis, we introduce the tool’s capabilities and highlight its effectiveness and usability.
This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmer... more This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
ECLB-COVID19 Consortium INTRODUCTION Coronavirus disease 2019 (COVID-19) is an infectious disease... more ECLB-COVID19 Consortium INTRODUCTION Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The disease was first identified in December 2019 in Wuhan, the capital of China's Hubei province, and has since spread globally to affect around 6 million people (as of the 4 th week of May 2020), including nearly 350 000 deaths in more than 220 countries [2]. Due to the consistently growing number of confirmed cases and to avoid overwhelming health systems, WHO and public health authorities around the world have been acting to contain the rapid spread of the COVID-19 outbreak, with primary measures focusing on social distancing, self-isolation, and nationwide lockdowns.
International Journal of Hybrid Intelligent Systems
Efficient and accurate early prediction of Alzheimer’s disease (AD) based on the neuroimaging dat... more Efficient and accurate early prediction of Alzheimer’s disease (AD) based on the neuroimaging data has attracted interest from many researchers to prevent its progression. Deep learning networks have demonstrated an optimal ability to analyse large-scale multimodal neuroimaging for AD classification. The most widely used architecture of deep learning is the Convolution neural networks (CNN) that have shown great potential in AD detection. However CNN does not capture long range dependencies within the input image and does not ensure a good global feature extraction. Furthermore, increasing the receptive field of CNN by increasing the kernels sizes can cause a feature granularity loss. Another limitation is that CNN lacks a weighing mechanism of image features; the network doesn’t focus on the relevant features within the image. Recently,vision transformer have shown an outstanding performance over the CNN and overcomes its main limitations. The vision transformer relies on the self-...
Journal of Ambient Intelligence and Humanized Computing
Artificial neural networks (ANNs) are being widely used in supervised machine learning to analyze... more Artificial neural networks (ANNs) are being widely used in supervised machine learning to analyze signals or images for many applications. Using an annotated learning database, one of the main challenges is to optimize the network weights. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively such as gradient-based method, Newton-type method, meta-heuristic method. For the sake of efficiency, regularization is generally used. When non-smooth regularizers are used especially to promote sparse networks, such as the 1 norm, this optimization becomes challenging due to non-differentiability issues of the target criterion. In this paper, we propose an MCMC-based optimization scheme formulated in a Bayesian framework. The proposed scheme solves the above-mentioned sparse optimization problem using an efficient sampling scheme and Hamiltonian dynamics. The designed optimizer is conducted on four (4) datasets, and the results are verified by a comparative study with two CNNs. Promising results show the usefulness of the proposed method to allow ANNs, even with low complexity levels, reaching high accuracy rates of up to 94%. The proposed method is also faster and more robust concerning overfitting issues. More importantly, the training step of the proposed method is much faster than all competing algorithms.
Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. ... more Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy.
The accurate segmentation and counting of the reproductive organs within the herbarium specimen p... more The accurate segmentation and counting of the reproductive organs within the herbarium specimen play an important role in studying the impact of climate change on plant development over time. Recently, the researchers have gained a lot of knowledge about plant phenology owing to herbaria’s digitization efforts, which may help accelerate plant phenology research by making large digitized specimen collections publicly available. Nevertheless, the automatic segmentation and counting of the reproductive organs is a challenging problem. This is because of the high variability of reproductive organs, which vary in size, shape, orientation, and color. The use of machine learning techniques, including deep learning, has recently been shown to be helpful in this endeavor. We proposed in this paper a deep learning method based on the refined Mask Scoring R-CNN approach to segment and count reproductive organs, including buds, flowers, and fruits from specimen images. Our proposed method achie...
International Journal of Environmental Research and Public Health, 2021
The purpose of the present study was to investigate which of two strategies, Video Feedback with ... more The purpose of the present study was to investigate which of two strategies, Video Feedback with Pedagogical Activity (VF-PA) or Video Feedback (VF), would be more beneficial for the remote error correction of the snatch weightlifting technique during the confinement period. Thirty-five school aged children with at least three months of weightlifting experience were randomized to one of three training conditions: VF-PA, VF or the Control group (CONT). Subjects underwent test sessions one week before (T0) and one day after (T1) a six-session training period and a retention test session a week later (T2). During each test session, the Kinovea version 0.8.15 software measured the kinematic parameters of the snatch performance. Following distance learning sessions (T1), the VF-PA improved various kinematic parameters (i.e., barbell horizontal displacements, maximum height, looping and symmetry) compared with T0 (p < 0.5; Cohen’s d = 0.58–1.1). Most of these improvements were maintain...
Charts are frequently embedded objects in digital documents and are used to convey a clear analys... more Charts are frequently embedded objects in digital documents and are used to convey a clear analysis of research results or commercial data trends. These charts are created through different means and may be represented by a variety of patterns such as column charts, line charts and pie charts. Chart recognition is as important as text recognition to automatically comprehend the knowledge within digital document. Chart recognition consists on identifying the chart type and decoding its visual contents into computer understandable values. Previous work in chart image identification has relied on hand crafted features which often fails when dealing with a large amount of data that could contain significant varieties and less common char types. Hence, as a first step towards this goal, in this paper we propose to use a deep learning-based approach that automates the feature extraction step. We present an improved version of the LeNet [LeCu 89] convolutional neural network architecture f...
As the main way for knowledge representation for the purpose of machine understanding, ontologies... more As the main way for knowledge representation for the purpose of machine understanding, ontologies are widely used in different application domains. This requires more and more domain specific information to be inserted into ontologies, making them harder to be easily understood by a human and there is a growing need to develop ontology visualization tools. However, most of existing tools focus on either a specific user requirement or ontology-specific features. To this end, in this demo, we introduce a new generic and user-friendly ontology visualization tool, called BioOntoVis, for visualizing and editing ontologies. We present the general architecture of the tool focusing on the web-based user interface and different ontology visualization schemes. Through the demonstration of BioOntoVis, we introduce the tool’s capabilities and highlight its effectiveness and usability.
This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmer... more This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
ECLB-COVID19 Consortium INTRODUCTION Coronavirus disease 2019 (COVID-19) is an infectious disease... more ECLB-COVID19 Consortium INTRODUCTION Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. The disease was first identified in December 2019 in Wuhan, the capital of China's Hubei province, and has since spread globally to affect around 6 million people (as of the 4 th week of May 2020), including nearly 350 000 deaths in more than 220 countries [2]. Due to the consistently growing number of confirmed cases and to avoid overwhelming health systems, WHO and public health authorities around the world have been acting to contain the rapid spread of the COVID-19 outbreak, with primary measures focusing on social distancing, self-isolation, and nationwide lockdowns.
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