Pub Date : 2022-08-31DOI: 10.3991/ijoe.v18i11.32647
Hakim El Massari, Noreddine Gherabi, Sajida Mhammedi, Hamza Ghandi, M. Bahaj, Muhammad Raza Naqvi
Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an automated system that can identify heart illness. This paper compares and reviews the most prominent machine learning algorithms, as well as ontology-based Machine Learning classification. Random Forest, Logistic regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine were among the classification methods explored. The dataset used consists of 70000 instances and can be downloaded from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Recall, and Precision. The results showed that the ontology outperformed all the machine learning algorithms.
{"title":"The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms","authors":"Hakim El Massari, Noreddine Gherabi, Sajida Mhammedi, Hamza Ghandi, M. Bahaj, Muhammad Raza Naqvi","doi":"10.3991/ijoe.v18i11.32647","DOIUrl":"https://doi.org/10.3991/ijoe.v18i11.32647","url":null,"abstract":"Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an automated system that can identify heart illness. This paper compares and reviews the most prominent machine learning algorithms, as well as ontology-based Machine Learning classification. Random Forest, Logistic regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine were among the classification methods explored. The dataset used consists of 70000 instances and can be downloaded from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Recall, and Precision. The results showed that the ontology outperformed all the machine learning algorithms.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131326969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.3991/ijoe.v18i11.29977
Astrit Hulaj, E. Bytyçi, Veronë Kadriu
This research proposes an efficient algorithm that can be applied to drones to transport materials in indoor environment. This algorithm optimizes the time and reduces energy consumption during sharing and completing tasks between different drones. In this research, the results will be achieved based on the "Earliest Time Algorithm". We have modified this algorithm, where we have reached to get much better results in terms of saving time while performing various tasks from the drone. The performance of the algorithm is tested and analyzed for three different types of tasks and depending on the weight the drone carries.
{"title":"An Efficient Tasks Scheduling Algorithm for Drone Operations in the Indoor Environment","authors":"Astrit Hulaj, E. Bytyçi, Veronë Kadriu","doi":"10.3991/ijoe.v18i11.29977","DOIUrl":"https://doi.org/10.3991/ijoe.v18i11.29977","url":null,"abstract":"This research proposes an efficient algorithm that can be applied to drones to transport materials in indoor environment. This algorithm optimizes the time and reduces energy consumption during sharing and completing tasks between different drones. In this research, the results will be achieved based on the \"Earliest Time Algorithm\". We have modified this algorithm, where we have reached to get much better results in terms of saving time while performing various tasks from the drone. The performance of the algorithm is tested and analyzed for three different types of tasks and depending on the weight the drone carries.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130748041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.3991/ijoe.v18i11.32517
Amina Aboulmira, H. Hrimech, M. Lachgar
Cutaneous disorders are one of the most common burdens world-wide, that affects 30% to 70% of individuals. Despite its prevalence, skin disease diagnosis is highly difficult due to several influencing visual clues, such as the complexities of skin texture, the location of the lesion, and presence of hair. Over 1500 identified skin disorders, ranging from infectious disorders and benign tumors to severe inflammatory diseases and malignant tumors, that often have a major effect on the quality of life. In this paper, several deep CNN architectures are proposed, exploring the potential of Deep Learning trained on “DermNet” dataset for the diagnosis of 23 type of skin diseases. These architectures are compared in order to choose the most performed one. Our approach shows that DenseNet was the most performed one for the skin disease classification using DermNet Dataset with a Top-1 accuracy of 68.97% and Top-5 accuracy of 89.05%.
{"title":"Comparative Study of Multiple CNN Models for Classification of 23 Skin Diseases","authors":"Amina Aboulmira, H. Hrimech, M. Lachgar","doi":"10.3991/ijoe.v18i11.32517","DOIUrl":"https://doi.org/10.3991/ijoe.v18i11.32517","url":null,"abstract":"Cutaneous disorders are one of the most common burdens world-wide, that affects 30% to 70% of individuals. Despite its prevalence, skin disease diagnosis is highly difficult due to several influencing visual clues, such as the complexities of skin texture, the location of the lesion, and presence of hair. Over 1500 identified skin disorders, ranging from infectious disorders and benign tumors to severe inflammatory diseases and malignant tumors, that often have a major effect on the quality of life. In this paper, several deep CNN architectures are proposed, exploring the potential of Deep Learning trained on “DermNet” dataset for the diagnosis of 23 type of skin diseases. These architectures are compared in order to choose the most performed one. Our approach shows that DenseNet was the most performed one for the skin disease classification using DermNet Dataset with a Top-1 accuracy of 68.97% and Top-5 accuracy of 89.05%.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.3991/ijoe.v18i11.29563
N. Tahat, R. Shaqbou'a, M. Abu-Dalu, Ala Qadomi
A new fast public key cryptosystem is proposed, which is based on two dissimilar number-theoretic hard problems, namely the simultaneous chaotic maps (CM) problem and quadratic residue (QR) problem. The adversary has to solve the two hard problems simultaneously to recover the plaintext according to their knowledge about the public keys and the cipher-text. Cryptographic quadratic residue and chaotic system are employed to enhance the security of our cryptosystem scheme. The encryption, and decryption are discussed in details. Several security attacks are proposed to illustrate the system shield through chaotic maps and quadratic residue problems. The performance analysis of the proposed scheme show a much improved performance over existing techniques.
{"title":"Development of a New Chaotic Maps Cryptosystem with Quadratic Residue Problem","authors":"N. Tahat, R. Shaqbou'a, M. Abu-Dalu, Ala Qadomi","doi":"10.3991/ijoe.v18i11.29563","DOIUrl":"https://doi.org/10.3991/ijoe.v18i11.29563","url":null,"abstract":"A new fast public key cryptosystem is proposed, which is based on two dissimilar number-theoretic hard problems, namely the simultaneous chaotic maps (CM) problem and quadratic residue (QR) problem. The adversary has to solve the two hard problems simultaneously to recover the plaintext according to their knowledge about the public keys and the cipher-text. Cryptographic quadratic residue and chaotic system are employed to enhance the security of our cryptosystem scheme. The encryption, and decryption are discussed in details. Several security attacks are proposed to illustrate the system shield through chaotic maps and quadratic residue problems. The performance analysis of the proposed scheme show a much improved performance over existing techniques.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130832906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.3991/ijoe.v18i11.30415
Nur Alisa Ali, S. Radzi, Abd Shukur Jaafar, N. Nor
As a neurodevelopmental disability, Autism Spectrum Disorder (ASD) is classified as a spectrum disorder. The availability of an automated technology system to classify the ASD trait would have a significant impact on paediatricians, as it would assist them in diagnosing ASD in children using a quantifiable method. In this paper, we propose a novel autism diagnosis method that is based on a hybrid of the deep learning algorithms. This hybrid consists of a convolutional neural network (ConVnet) architecture that merges two LSTM blocks (BiLSTM) with the other direction of propagation to classify the output state on the brain signal data from electroencephalogram (EEG) on individuals; typically development (TD) and autism (ASD) obtained from the Simon Foundation Autism Research Initiative (SFARI) database to classify the output state. For a 70:30 data distribution, an accuracy of 97.7 percent was achieved. Proposed methods outperformed the current state-of-the art in terms of autism classification efficiency and have the potential to make a significant contribution to neuroscience research, as demonstrated by the results.
{"title":"ConVnet BiLSTM for ASD Classification on EEG Brain Signal","authors":"Nur Alisa Ali, S. Radzi, Abd Shukur Jaafar, N. Nor","doi":"10.3991/ijoe.v18i11.30415","DOIUrl":"https://doi.org/10.3991/ijoe.v18i11.30415","url":null,"abstract":"As a neurodevelopmental disability, Autism Spectrum Disorder (ASD) is classified as a spectrum disorder. The availability of an automated technology system to classify the ASD trait would have a significant impact on paediatricians, as it would assist them in diagnosing ASD in children using a quantifiable method. In this paper, we propose a novel autism diagnosis method that is based on a hybrid of the deep learning algorithms. This hybrid consists of a convolutional neural network (ConVnet) architecture that merges two LSTM blocks (BiLSTM) with the other direction of propagation to classify the output state on the brain signal data from electroencephalogram (EEG) on individuals; typically development (TD) and autism (ASD) obtained from the Simon Foundation Autism Research Initiative (SFARI) database to classify the output state. For a 70:30 data distribution, an accuracy of 97.7 percent was achieved. Proposed methods outperformed the current state-of-the art in terms of autism classification efficiency and have the potential to make a significant contribution to neuroscience research, as demonstrated by the results.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129330936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.3991/ijoe.v18i11.32329
Roberto Muñoz Villacorta, Carlos Oscco Agüero, L. Andrade-Arenas
Over time, human beings are attacked by different venereal diseases, which cause serious consequences. Not all people know exactly if they suffer from a venereal disease, so they look for possible treatments based on their symptoms in different media, not all of them are reliable. The objective of the research is to implement an expert system, which through a web page, provides a correct diagnosis based on the symptoms registered by the user, as well as a possible treatment for the identified disease. This was achieved based on the knowledge tree that was developed in Python so that when a user completes the symptom record, the expert system continues with the process. All this procedure was carried out using the Common kads methodology. which is related based on knowledge topics. The result was the development of the application, which was validated by different specialists in expert systems, giving a total average of 4 as a response, which was qualified as a high-quality level, on the other hand, the system brings an improvement in the acquisition of information through the web, providing diagnoses and possible treatments, in addition, it provides a facility to people who do not wish to attend a health establishment, as well as to specialists in the health sector, which allows them to provide diagnoses and treatments.
{"title":"Implementation of an Intelligent System for the Diagnosis and Treatment of Venereal Diseases","authors":"Roberto Muñoz Villacorta, Carlos Oscco Agüero, L. Andrade-Arenas","doi":"10.3991/ijoe.v18i11.32329","DOIUrl":"https://doi.org/10.3991/ijoe.v18i11.32329","url":null,"abstract":"Over time, human beings are attacked by different venereal diseases, which cause serious consequences. Not all people know exactly if they suffer from a venereal disease, so they look for possible treatments based on their symptoms in different media, not all of them are reliable. The objective of the research is to implement an expert system, which through a web page, provides a correct diagnosis based on the symptoms registered by the user, as well as a possible treatment for the identified disease. This was achieved based on the knowledge tree that was developed in Python so that when a user completes the symptom record, the expert system continues with the process. All this procedure was carried out using the Common kads methodology. which is related based on knowledge topics. The result was the development of the application, which was validated by different specialists in expert systems, giving a total average of 4 as a response, which was qualified as a high-quality level, on the other hand, the system brings an improvement in the acquisition of information through the web, providing diagnoses and possible treatments, in addition, it provides a facility to people who do not wish to attend a health establishment, as well as to specialists in the health sector, which allows them to provide diagnoses and treatments.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130401661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.3991/ijoe.v18i11.29899
Seun O. Olukanmi, F. Nelwamondo, N. Nwulu
This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delays in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for a finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions.
{"title":"Leveraging Google Search Data and Artificial Intelligence Methods for Provincial-level Influenza Forecasting: A South African Case Study","authors":"Seun O. Olukanmi, F. Nelwamondo, N. Nwulu","doi":"10.3991/ijoe.v18i11.29899","DOIUrl":"https://doi.org/10.3991/ijoe.v18i11.29899","url":null,"abstract":"This paper investigates the usefulness of Google search patterns with Artificial Intelligence (AI) techniques for timely influenza-like illness (ILI) forecasting for each of the nine South African provinces. Traditional surveillance methods are limited by delays in reporting. Existing digital disease surveillance studies that employ alternative online data have scarcely explored sub-Saharan African countries. In South Africa, Google search data has only been recently studied for ILI surveillance at the national level. Meanwhile, the differences in socio-economic and technological conditions across provinces call for a finer spatial investigation. We perform correlation analysis between Google trends (GT) data for 21 ILI-related terms and real-life ILI surveillance data for each province. Next, we develop models to assess the predictive performance of these GT data for forecasting ILI rates, using time series, machine learning, and deep learning methods. We observe sufficient correlation for only two of the nine provinces: Gauteng and Western Cape. Thus, GT data could only be used to forecast ILI in these two provinces. Interestingly, these two provinces are regarded as the most economically developed. In the other seven provinces, LSTM, a deep learning technique, gives more accurate predictions than a baseline autoregressive model when only past ILI data are used for forecasting future ILI trends. The results reveal that, for provinces for which GT data is sufficiently available, it is not only free and fast, but is an effective predictor on its own as well as when added to past ILI data for forecasting future ILI infection rates. The correlation analysis suggests an association between provincial socio-economic development and the use of digital platforms for disease surveillance. Overall, the study established the need for finer scale ILI forecasting which will inform targeted planning for disease surveillance and interventions.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123211103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.3991/ijoe.v18i11.31055
M. I. H. Umam, B. Santosa, N. Siswanto
Emergency medical services are an essential element in the modern healthcare system. Health care services are the most important because they play an important role in saving people's lives and reducing rates of mortality and morbidity. Especially during the covid-19 pandemic and the new normal era makes this problem very interesting to discuss. For this reason, this study tries to overcome the problem location and allocation of MES by using a combination of metaheuristics and simulation. The approach taken to overcome these challenges is developing Symbiotic Organisms Search algorithm and then use the simulation method to validation the result. The transition of the ambulance system from a centralized to decentralized system by using the M-SOS algorithm, found that to shorten the response time to 9 minutes, need to combine the 5 core bases with about 12 potential bases. From the simulation scenarios tested, the total number of ambulances involved in the proposed system is 16 units. So it can be concluded that involving several potential bases can produce a short response time.
{"title":"Simulation Optimization for Location and Allocation of Emergency Medical Service","authors":"M. I. H. Umam, B. Santosa, N. Siswanto","doi":"10.3991/ijoe.v18i11.31055","DOIUrl":"https://doi.org/10.3991/ijoe.v18i11.31055","url":null,"abstract":"Emergency medical services are an essential element in the modern healthcare system. Health care services are the most important because they play an important role in saving people's lives and reducing rates of mortality and morbidity. Especially during the covid-19 pandemic and the new normal era makes this problem very interesting to discuss. For this reason, this study tries to overcome the problem location and allocation of MES by using a combination of metaheuristics and simulation. The approach taken to overcome these challenges is developing Symbiotic Organisms Search algorithm and then use the simulation method to validation the result. The transition of the ambulance system from a centralized to decentralized system by using the M-SOS algorithm, found that to shorten the response time to 9 minutes, need to combine the 5 core bases with about 12 potential bases. From the simulation scenarios tested, the total number of ambulances involved in the proposed system is 16 units. So it can be concluded that involving several potential bases can produce a short response time.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115058069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-26DOI: 10.3991/ijoe.v18i10.32845
Kisno, B. Wibawa, Khaerudin
One of the important activities in children's learning that is rarely explored is creativity. This is an important concept for the successful implementation of early childhood education programs. Every child's creative talent must be developed. Opportunities and learning resources in the form of an environment to explore sources and media need to be given to children in realizing their creative potential. The growing digital world and children's interest in devices such as smartphones are an opportunity for teachers to take advantage of interactive and interesting ICT-based learning resources and media through audio, visual and audio visual media. Utilization through the wise use of digital resources and media for children is an important part of learning. Digital Storytelling in coloring activities through the “Quiver 3-D Coloring based on augmented reality technology” application offers the integration of virtual objects into a real environment forming three-dimensional animations on smartphones, color pictures for children, presenting digital stories at the end of the activity, so that children have an interest so that their creativity will develop. The purpose of this research is to spread "augmented reality technology with Quiver-3D coloring application in developing children's creativity" by presenting digital stories. The approach used in this research is descriptive analysis method through a qualitative-quantitative approach. The data in this research were obtained by direct observation and research-related questions to informants. The results of the research show that: "children's creativity develops well through digital storytelling learning with 3-D Coloring based on Augmented Reality applications".
{"title":"Digital Storytelling for Early Childhood Creativity: Diffusion of Innovation \"3-D Coloring Quiver Application Based on Augmented Reality Technology in Children's Creativity Development\"","authors":"Kisno, B. Wibawa, Khaerudin","doi":"10.3991/ijoe.v18i10.32845","DOIUrl":"https://doi.org/10.3991/ijoe.v18i10.32845","url":null,"abstract":"One of the important activities in children's learning that is rarely explored is creativity. This is an important concept for the successful implementation of early childhood education programs. Every child's creative talent must be developed. Opportunities and learning resources in the form of an environment to explore sources and media need to be given to children in realizing their creative potential. The growing digital world and children's interest in devices such as smartphones are an opportunity for teachers to take advantage of interactive and interesting ICT-based learning resources and media through audio, visual and audio visual media. Utilization through the wise use of digital resources and media for children is an important part of learning. Digital Storytelling in coloring activities through the “Quiver 3-D Coloring based on augmented reality technology” application offers the integration of virtual objects into a real environment forming three-dimensional animations on smartphones, color pictures for children, presenting digital stories at the end of the activity, so that children have an interest so that their creativity will develop. The purpose of this research is to spread \"augmented reality technology with Quiver-3D coloring application in developing children's creativity\" by presenting digital stories. The approach used in this research is descriptive analysis method through a qualitative-quantitative approach. The data in this research were obtained by direct observation and research-related questions to informants. The results of the research show that: \"children's creativity develops well through digital storytelling learning with 3-D Coloring based on Augmented Reality applications\".","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132718086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-26DOI: 10.3991/ijoe.v18i10.31531
Youssef Ouassit, S. Ardchir, M. Y. E. Ghoumari, M. Azouazi
Semantic Segmentation is the process of assigning a label to every pixel in the image that share same semantic properties and stays a challenging task in computer vision. In recent years, and due to the large availability of training data the performance of semantic segmentation has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed. However, in some crucial fields we can't assure sufficient data to learn a deep model and achieves high accuracy. This paper aims to provide a brief survey of research efforts on deep-learning-based semantic segmentation methods on limited labeled data and focus our survey on weakly-supervised methods. This survey is expected to familiarize readers with the progress and challenges of weakly supervised semantic segmentation research in the deep learning era and present several valuable growing research points in this field.
{"title":"A Brief Survey on Weakly Supervised Semantic Segmentation","authors":"Youssef Ouassit, S. Ardchir, M. Y. E. Ghoumari, M. Azouazi","doi":"10.3991/ijoe.v18i10.31531","DOIUrl":"https://doi.org/10.3991/ijoe.v18i10.31531","url":null,"abstract":"Semantic Segmentation is the process of assigning a label to every pixel in the image that share same semantic properties and stays a challenging task in computer vision. In recent years, and due to the large availability of training data the performance of semantic segmentation has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed. However, in some crucial fields we can't assure sufficient data to learn a deep model and achieves high accuracy. This paper aims to provide a brief survey of research efforts on deep-learning-based semantic segmentation methods on limited labeled data and focus our survey on weakly-supervised methods. This survey is expected to familiarize readers with the progress and challenges of weakly supervised semantic segmentation research in the deep learning era and present several valuable growing research points in this field.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129282173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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