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Bassem Bouaziz

University of Sfax, Higher Institute of Computer Science and Multimedia, Faculty Member

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Bassem BOUAZIZ hold a PhD in computer science from University of Sfax. He is Member of Scientifc council of HICSM. He coordinate international cooperation with Frederish shiller University throught Biodialog DAAD Projet and MAMUDS project from. He was also a Workpackage Leader within Tempus SAGE project from on Serious Game. Bassem has lived and worked in several courtiers including France, Canada and Germany. She has supported his researches by external grants and contract funding from a diverse array of public sources. He spent in 2008 a research stay at Gelog Research Lab-ETS-Montreal. He participated to a CMCU project with Ecole centrale de Lyon from 2002-2005 aiming to create a plateform for indexing and mangment of multimedia documents.His main research areas include Multimedia document indexing and processing, Computer Vision for video analysis, Deep learning, Objects recognition, Image processing for Biodiversity. He coordinate and participate to several research projects in partnership with industry (MESAM Platform, ADIP, Smart Glass, REIVE). He is the author and co-author of several papers and has been a reviewer for international conferences and journals.

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Papers by Bassem Bouaziz

Exploratory study of the acute and mid-term effects of using a novel dynamic meeting environment (Aeris®) on cognitive performance and neurophysiological responses

Frontiers in Human Neuroscience, Nov 22, 2023

Exploratory study of the acute and mid-term effects of using a novel dynamic meeting environment ... more

A new long short-term memory based approach for soil moisture prediction

Journal of Ambient Intelligence and Smart Environments

Water scarcity is becoming more severe around the world as a result of suboptimal irrigation prac... more Water scarcity is becoming more severe around the world as a result of suboptimal irrigation practices. Effective irrigation scheduling necessitates an estimation of future soil moisture content. This study presents deep learning models such as CNN-LSTM, a hybrid Deep Learning model that predicts future soil moisture using climate and soil information, including past soil moisture content. The study also investigates the appropriate number of observations and data sampling rate required to predict the next day’s soil moisture value. In terms of MSE, MAE, RMSE, and R 2 , the hybrid CNN-LSTM model is compared to standalone LSTM and Bi-LSTM models. The LSTM model achieved an MSE of 0.2471, MAE of 0.1978, RMSE of 0.4971, and R 2 of 0.9714. The LSTM model outperformed the Bi-LSTM model, which had an MSE of 0.3036, MAE of 0.3248, RMSE of 0.5510, and R 2 of 0.9614. With an MSE of 0.1348, MAE of 0.1868, RMSE of 0.3672, and R 2 of 0.9838, the hybrid CNN-LSTM model outperformed the LSTM. Our ...

PRECIMED: development of a decision support system (DSS) for precision irrigation in Mediterranean agriculture

Burleigh Dodds series in agricultural science, Jun 11, 2024

Effects of home confinement on mental health and lifestyle behaviours during the COVID-19 outbreak: Insight from the ECLB-COVID19 multicenter study

Biology of Sport, 2021

The user has requested enhancement of the downloaded file.

COVID-19 Home Confinement Negatively Impacts Social Participation and Life Satisfaction: A Worldwide Multicenter Study

International Journal of Environmental Research and Public Health, Aug 27, 2020

Public health recommendations and governmental measures during the new coronavirus disease (COVID... more Public health recommendations and governmental measures during the new coronavirus disease (COVID-19) pandemic have enforced numerous restrictions on daily living including social distancing, isolation, and home confinement. While these measures are imperative to mitigate spreading of COVID-19, the impact of these restrictions on psychosocial health is undefined. Therefore, an international online survey was launched in April 2020 to elucidate the behavioral and lifestyle consequences of COVID-19 restrictions. This report presents the preliminary results from more than one thousand responders on social participation and life satisfaction. Methods: Thirty-five research organizations from Europe, North-Africa, Western Asia, and the Americas promoted the survey through their networks to the general society, in 7 languages (English, German, French, Arabic, Spanish, Portuguese, and Slovenian). Questions were presented in a differential format with questions related to responses "before" and "during" confinement conditions. Results: 1047 participations (54% women) from Asia (36%), Africa (40%), Europe (21%), and others (3%) were included in the analysis. Findings revealed psychosocial strain during the enforced COVID-19 home confinement. Large decreases (p < 0.001) in the amount of social activity through family (−58%), friends/neighbors (−44.9%), or entertainment (−46.7%) were triggered by the enforced confinement. These negative effects on social participation were also associated with lower life satisfaction (−30.5%) during the confinement period. Conversely, the social contact score through digital technologies significantly increased (p < 0.001) during the confinement period with more individuals (+24.8%) being socially connected through digital technology. Conclusion: These preliminary findings elucidate the risk of psychosocial strain during the early COVID-19 home confinement period in 2020. Therefore, in order to mitigate the negative psychosocial effects of home confinement, implementation of national strategies focused on promoting social inclusion through a technology-based solution is strongly suggested.

PhenoDeep: A Deep Learning-Based Approach for Detecting Reproductive Organs from Digitized Herbarium Specimen Images

Lecture Notes in Computer Science, 2021

Alzheimer’s Disease Prediction Using EfficientNet and Fastai

Lecture Notes in Computer Science, 2021

Automatic Text Regions Location in Video Frames

Signal-Image Technology and Internet-Based Systems, 2005

Deep learning based approach for digitized herbarium specimen segmentation

Multimedia Tools and Applications, Mar 31, 2022

Deep Squeeze and Excitation-Densely Connected Convolutional Network with cGAN for Alzheimer’s Disease Early Detection

Springer eBooks, 2022

AViTExt: Automatic Video Text Extraction; A new Approach for video content indexing Application

In this paper, we propose a spatial temporal video-text detection technique which proceed in two ... more In this paper, we propose a spatial temporal video-text detection technique which proceed in two principal steps: potential text region detection and a filtering process. In the first step we divide dynamically each pair of consecutive video frames into sub block in order to detect change. A significant difference between homologous blocks implies the appearance of an important object which may be a text region. The temporal redundancy is then used to filter these regions and forms an effective text region. The experimentation driven on a variety of video sequences shows the effectiveness of our approach by obtaining a 89,39% as precision rate and 90,19 as recall. Keywords-component; Video indexing and browsing; Spatiotemporal approach; Text extractin ; Non compressed video, multiple-frame I.

Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3

Automatic measurement of functional trait data from digitized herbarium specimen images is of gre... more Automatic measurement of functional trait data from digitized herbarium specimen images is of great interest as traditionally, scientists extract such information manually, which is time-consuming and prone to errors. One challenging task in the automated measurement process of functional traits from specimen images is the existence of other objects such as scale-bar, color pallet, specimen label, envelopes, bar-code and stamp, which are mostly placed at different locations on the herbarium-mounting sheet and require special detection method. To detect automatically all these objects, we train a model based on an improved YOLO V3 full-regression deep neural network architecture, which has gained obvious advantages in both speed and accuracy through capturing deep and high-level features. We made some improvements to adjust YOLO V3 for detecting object from digitized herbarium specimen images. A new scale of feature map is added to the existing scales to improve the detection effect on small targets. At the same time, we adopted the fourth detection layer by a 4* up-sampled layer instead of 2* to get a feature map with higher resolution deeper level. The experimental results indicate that our model performed better with mAP-50 of 93.2% compared to 90.1% mean IoU trained by original YOLO V3 model on the test set.

Epileptic Seizure Detection Using a Convolutional Neural Network

Advances in predictive, preventive and personalised medicine, 2019

The availability of electroencephalogram (EEG) data has opened up the possibility for new interes... more The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy.

The Effect of (Poly)phenol-Rich Interventions on Cognitive Functions and Neuroprotective Measures in Healthy Aging Adults: A Systematic Review and Meta-Analysis

Journal of Clinical Medicine, Mar 19, 2020

Context: As the food industry is continually involved in the development of new attractive altern... more Context: As the food industry is continually involved in the development of new attractive alternative therapeutic agents, the evaluation of the beneficial impact of (poly)phenols on cognitive and brain function during aging has gained increasing interest. Objective: This systematic review and meta-analysis aimed to evaluate the acute and chronic effects of (poly)phenol-rich diet supplementation on cognitive function and brain health in aging adults. Data Sources: PubMed and Web of Science databases were searched for relevant randomized placebo-controlled trials (RCTs) published from inception to July 2019. Study Selection: Two researchers independently screened 4303 records, using the PICOS criteria: Participants were aging adults; Intervention was based on acute and/or chronic (poly)phenols-rich supplementation; Comparator was any comparator; Outcomes included cognitive function and neuroprotective measures; and Study design was RCTs. A third researcher was consulted when discrepancies arose. Fifteen high-quality (mean PEDro score = 8.8 ± 0.56) RCTs (total participants: 918 healthy older adults) were included in the final sample. Data Extraction: Information on study design, employed treatment, characteristics of participants, outcomes, and the correspondent assessing methods were extracted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Data Analysis and Results: A random-effects meta-analysis was used to pool estimates across studies. Effect size (ES) and its 95% confidence interval

A New Video Images Text Localization Approach Based on a Fast Hough Transform

Springer eBooks, 2006

... and Multimedia, Sfax, BP 1030 - Tunisia 69042 [email protected], {walid.mahdi, benh... more

ProgNet: Covid-19 prognosis using recurrent and convolutional neural networks

medRxiv (Cold Spring Harbor Laboratory), May 8, 2020

Humanity is facing nowadays a dramatic pandemic episode with the Coronavirus propagation over all... more Humanity is facing nowadays a dramatic pandemic episode with the Coronavirus propagation over all continents. The Covid-19 disease is still not well characterized, and many research teams all over the world are working on either therapeutic or vaccination issues. Massive testing is one of the main recommendations. In addition to laboratory tests, imagerybased tools are being widely investigated. Artificial intelligence is therefore contributing to the efforts made to face this pandemic phase. Regarding patients in hospitals, it is important to monitor the evolution of lung pathologies due to the virus. A prognosis is therefore of great interest for doctors to adapt their care strategy. In this paper, we propose a method for Covid-19 prognosis based on deep learning architectures. The proposed method is based on the combination of a convolutional and recurrent neural networks to classify multi-temporal chest X-ray images and predict the evolution of the observed lung pathology. When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%.

Measuring Morphological Functional Leaf Traits From Digitized Herbarium Specimens Using TraitEx Software

Biodiversity Information Science and Standards, Jun 13, 2019

Herbarium specimens are of vital importance for understanding biodiversity. There are more than 3... more Herbarium specimens are of vital importance for understanding biodiversity. There are more than 350 million specimens stored in herbaria worldwide (Thiers 2018) Globally, many herbaria have undertaken digitization projects of plant specimens, on a massive scale, to preserve them and to make the images easily accessible to users. Over the past years, with significant advances in the field of computer vision, new potential uses of digitized specimens have emerged, such as automated species identification using qualitative morphological traits. However, due to lack of efficient tools, efforts to extract functional (quantitative) morphological traits from digitized herbarium specimens are lagging behind. Functional trait data is of significant importance to understand the functioning of the ecosystem and interactions between biotic and abiotic factors. It is currently fragmented and initiatives such as TRY Trait database (https://www.try-db.org) are making efforts to fill the gaps in the observed trait matrix (Schrodt et al. 2015). In order to complement the global efforts, we have developed a software tool, TraitEx , which can measure quantitative traits such as the length, area, width and perimeter of leaves along with the petiole length from digitized herbarium specimens.

Ontology Visualization: An Overview

Advances in intelligent systems and computing, 2018

As the main way for knowledge representation for the purpose of completely machine understanding,... more As the main way for knowledge representation for the purpose of completely machine understanding, ontologies are widely used in different application domains. This full machine understanding makes them harder to be easily understood by a human. This necessitates the need to develop ontology visualization tools, which results in the existence of a large number of approaches and visualization tools. Along with this development direction, the number of published research papers related to ontology visualization is largely increasing. To this end, in this paper, we introduce a systemic review on different directions related to ontology visualization. In particular, we start by describing different application domains that make use of ontology visualization. Then, we propose a generic visualization pipeline that incorporates main steps in ontology visualization that could be later used as main criteria during comparing and discussing different visualization tools. By this review, we aim to introduce a general visualization pipeline that is useful when comparing ontology visualization tools and when developing a new visualization technique. Finally, the paper moves into the description of future trends and research issues that still need to be addressed.

Distance Motor Learning during the COVID-19 Induced Confinement: Video Feedback with a Pedagogical Activity Improves the Snatch Technique in Young Athletes

International Journal of Environmental Research and Public Health, Mar 16, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more

Mild cognitive impairment classification based on a deep learning-based approach using EEG data

Exploratory study of the acute and mid-term effects of using a novel dynamic meeting environment (Aeris®) on cognitive performance and neurophysiological responses

Frontiers in Human Neuroscience, Nov 22, 2023

Exploratory study of the acute and mid-term effects of using a novel dynamic meeting environment ... more

A new long short-term memory based approach for soil moisture prediction

Journal of Ambient Intelligence and Smart Environments

Water scarcity is becoming more severe around the world as a result of suboptimal irrigation prac... more Water scarcity is becoming more severe around the world as a result of suboptimal irrigation practices. Effective irrigation scheduling necessitates an estimation of future soil moisture content. This study presents deep learning models such as CNN-LSTM, a hybrid Deep Learning model that predicts future soil moisture using climate and soil information, including past soil moisture content. The study also investigates the appropriate number of observations and data sampling rate required to predict the next day’s soil moisture value. In terms of MSE, MAE, RMSE, and R 2 , the hybrid CNN-LSTM model is compared to standalone LSTM and Bi-LSTM models. The LSTM model achieved an MSE of 0.2471, MAE of 0.1978, RMSE of 0.4971, and R 2 of 0.9714. The LSTM model outperformed the Bi-LSTM model, which had an MSE of 0.3036, MAE of 0.3248, RMSE of 0.5510, and R 2 of 0.9614. With an MSE of 0.1348, MAE of 0.1868, RMSE of 0.3672, and R 2 of 0.9838, the hybrid CNN-LSTM model outperformed the LSTM. Our ...

PRECIMED: development of a decision support system (DSS) for precision irrigation in Mediterranean agriculture

Burleigh Dodds series in agricultural science, Jun 11, 2024

Effects of home confinement on mental health and lifestyle behaviours during the COVID-19 outbreak: Insight from the ECLB-COVID19 multicenter study

Biology of Sport, 2021

The user has requested enhancement of the downloaded file.

COVID-19 Home Confinement Negatively Impacts Social Participation and Life Satisfaction: A Worldwide Multicenter Study

International Journal of Environmental Research and Public Health, Aug 27, 2020

Public health recommendations and governmental measures during the new coronavirus disease (COVID... more Public health recommendations and governmental measures during the new coronavirus disease (COVID-19) pandemic have enforced numerous restrictions on daily living including social distancing, isolation, and home confinement. While these measures are imperative to mitigate spreading of COVID-19, the impact of these restrictions on psychosocial health is undefined. Therefore, an international online survey was launched in April 2020 to elucidate the behavioral and lifestyle consequences of COVID-19 restrictions. This report presents the preliminary results from more than one thousand responders on social participation and life satisfaction. Methods: Thirty-five research organizations from Europe, North-Africa, Western Asia, and the Americas promoted the survey through their networks to the general society, in 7 languages (English, German, French, Arabic, Spanish, Portuguese, and Slovenian). Questions were presented in a differential format with questions related to responses "before" and "during" confinement conditions. Results: 1047 participations (54% women) from Asia (36%), Africa (40%), Europe (21%), and others (3%) were included in the analysis. Findings revealed psychosocial strain during the enforced COVID-19 home confinement. Large decreases (p < 0.001) in the amount of social activity through family (−58%), friends/neighbors (−44.9%), or entertainment (−46.7%) were triggered by the enforced confinement. These negative effects on social participation were also associated with lower life satisfaction (−30.5%) during the confinement period. Conversely, the social contact score through digital technologies significantly increased (p < 0.001) during the confinement period with more individuals (+24.8%) being socially connected through digital technology. Conclusion: These preliminary findings elucidate the risk of psychosocial strain during the early COVID-19 home confinement period in 2020. Therefore, in order to mitigate the negative psychosocial effects of home confinement, implementation of national strategies focused on promoting social inclusion through a technology-based solution is strongly suggested.

PhenoDeep: A Deep Learning-Based Approach for Detecting Reproductive Organs from Digitized Herbarium Specimen Images

Lecture Notes in Computer Science, 2021

Alzheimer’s Disease Prediction Using EfficientNet and Fastai

Lecture Notes in Computer Science, 2021

Automatic Text Regions Location in Video Frames

Signal-Image Technology and Internet-Based Systems, 2005

Deep learning based approach for digitized herbarium specimen segmentation

Multimedia Tools and Applications, Mar 31, 2022

Deep Squeeze and Excitation-Densely Connected Convolutional Network with cGAN for Alzheimer’s Disease Early Detection

Springer eBooks, 2022

AViTExt: Automatic Video Text Extraction; A new Approach for video content indexing Application

In this paper, we propose a spatial temporal video-text detection technique which proceed in two ... more In this paper, we propose a spatial temporal video-text detection technique which proceed in two principal steps: potential text region detection and a filtering process. In the first step we divide dynamically each pair of consecutive video frames into sub block in order to detect change. A significant difference between homologous blocks implies the appearance of an important object which may be a text region. The temporal redundancy is then used to filter these regions and forms an effective text region. The experimentation driven on a variety of video sequences shows the effectiveness of our approach by obtaining a 89,39% as precision rate and 90,19 as recall. Keywords-component; Video indexing and browsing; Spatiotemporal approach; Text extractin ; Non compressed video, multiple-frame I.

Objects Detection from Digitized Herbarium Specimen based on Improved YOLO V3

Automatic measurement of functional trait data from digitized herbarium specimen images is of gre... more Automatic measurement of functional trait data from digitized herbarium specimen images is of great interest as traditionally, scientists extract such information manually, which is time-consuming and prone to errors. One challenging task in the automated measurement process of functional traits from specimen images is the existence of other objects such as scale-bar, color pallet, specimen label, envelopes, bar-code and stamp, which are mostly placed at different locations on the herbarium-mounting sheet and require special detection method. To detect automatically all these objects, we train a model based on an improved YOLO V3 full-regression deep neural network architecture, which has gained obvious advantages in both speed and accuracy through capturing deep and high-level features. We made some improvements to adjust YOLO V3 for detecting object from digitized herbarium specimen images. A new scale of feature map is added to the existing scales to improve the detection effect on small targets. At the same time, we adopted the fourth detection layer by a 4* up-sampled layer instead of 2* to get a feature map with higher resolution deeper level. The experimental results indicate that our model performed better with mAP-50 of 93.2% compared to 90.1% mean IoU trained by original YOLO V3 model on the test set.

Epileptic Seizure Detection Using a Convolutional Neural Network

Advances in predictive, preventive and personalised medicine, 2019

The availability of electroencephalogram (EEG) data has opened up the possibility for new interes... more The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy.

The Effect of (Poly)phenol-Rich Interventions on Cognitive Functions and Neuroprotective Measures in Healthy Aging Adults: A Systematic Review and Meta-Analysis

Journal of Clinical Medicine, Mar 19, 2020

Context: As the food industry is continually involved in the development of new attractive altern... more Context: As the food industry is continually involved in the development of new attractive alternative therapeutic agents, the evaluation of the beneficial impact of (poly)phenols on cognitive and brain function during aging has gained increasing interest. Objective: This systematic review and meta-analysis aimed to evaluate the acute and chronic effects of (poly)phenol-rich diet supplementation on cognitive function and brain health in aging adults. Data Sources: PubMed and Web of Science databases were searched for relevant randomized placebo-controlled trials (RCTs) published from inception to July 2019. Study Selection: Two researchers independently screened 4303 records, using the PICOS criteria: Participants were aging adults; Intervention was based on acute and/or chronic (poly)phenols-rich supplementation; Comparator was any comparator; Outcomes included cognitive function and neuroprotective measures; and Study design was RCTs. A third researcher was consulted when discrepancies arose. Fifteen high-quality (mean PEDro score = 8.8 ± 0.56) RCTs (total participants: 918 healthy older adults) were included in the final sample. Data Extraction: Information on study design, employed treatment, characteristics of participants, outcomes, and the correspondent assessing methods were extracted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Data Analysis and Results: A random-effects meta-analysis was used to pool estimates across studies. Effect size (ES) and its 95% confidence interval

A New Video Images Text Localization Approach Based on a Fast Hough Transform

Springer eBooks, 2006

... and Multimedia, Sfax, BP 1030 - Tunisia 69042 [email protected], {walid.mahdi, benh... more

ProgNet: Covid-19 prognosis using recurrent and convolutional neural networks

medRxiv (Cold Spring Harbor Laboratory), May 8, 2020

Humanity is facing nowadays a dramatic pandemic episode with the Coronavirus propagation over all... more Humanity is facing nowadays a dramatic pandemic episode with the Coronavirus propagation over all continents. The Covid-19 disease is still not well characterized, and many research teams all over the world are working on either therapeutic or vaccination issues. Massive testing is one of the main recommendations. In addition to laboratory tests, imagerybased tools are being widely investigated. Artificial intelligence is therefore contributing to the efforts made to face this pandemic phase. Regarding patients in hospitals, it is important to monitor the evolution of lung pathologies due to the virus. A prognosis is therefore of great interest for doctors to adapt their care strategy. In this paper, we propose a method for Covid-19 prognosis based on deep learning architectures. The proposed method is based on the combination of a convolutional and recurrent neural networks to classify multi-temporal chest X-ray images and predict the evolution of the observed lung pathology. When applied to radiological time-series, promising results are obtained with an accuracy rates higher than 92%.

Measuring Morphological Functional Leaf Traits From Digitized Herbarium Specimens Using TraitEx Software

Biodiversity Information Science and Standards, Jun 13, 2019

Herbarium specimens are of vital importance for understanding biodiversity. There are more than 3... more Herbarium specimens are of vital importance for understanding biodiversity. There are more than 350 million specimens stored in herbaria worldwide (Thiers 2018) Globally, many herbaria have undertaken digitization projects of plant specimens, on a massive scale, to preserve them and to make the images easily accessible to users. Over the past years, with significant advances in the field of computer vision, new potential uses of digitized specimens have emerged, such as automated species identification using qualitative morphological traits. However, due to lack of efficient tools, efforts to extract functional (quantitative) morphological traits from digitized herbarium specimens are lagging behind. Functional trait data is of significant importance to understand the functioning of the ecosystem and interactions between biotic and abiotic factors. It is currently fragmented and initiatives such as TRY Trait database (https://www.try-db.org) are making efforts to fill the gaps in the observed trait matrix (Schrodt et al. 2015). In order to complement the global efforts, we have developed a software tool, TraitEx , which can measure quantitative traits such as the length, area, width and perimeter of leaves along with the petiole length from digitized herbarium specimens.

Ontology Visualization: An Overview

Advances in intelligent systems and computing, 2018

As the main way for knowledge representation for the purpose of completely machine understanding,... more As the main way for knowledge representation for the purpose of completely machine understanding, ontologies are widely used in different application domains. This full machine understanding makes them harder to be easily understood by a human. This necessitates the need to develop ontology visualization tools, which results in the existence of a large number of approaches and visualization tools. Along with this development direction, the number of published research papers related to ontology visualization is largely increasing. To this end, in this paper, we introduce a systemic review on different directions related to ontology visualization. In particular, we start by describing different application domains that make use of ontology visualization. Then, we propose a generic visualization pipeline that incorporates main steps in ontology visualization that could be later used as main criteria during comparing and discussing different visualization tools. By this review, we aim to introduce a general visualization pipeline that is useful when comparing ontology visualization tools and when developing a new visualization technique. Finally, the paper moves into the description of future trends and research issues that still need to be addressed.

Distance Motor Learning during the COVID-19 Induced Confinement: Video Feedback with a Pedagogical Activity Improves the Snatch Technique in Young Athletes

International Journal of Environmental Research and Public Health, Mar 16, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more

Mild cognitive impairment classification based on a deep learning-based approach using EEG data