Pub Date : 2022-12-06DOI: 10.3991/ijoe.v18i15.34333
N. Jalinus, Ganefri, Mahesi Agni Zaus, R. Wulansari, Rahmat Azis Nabawi, Hendra Hidayat
Engineering education is very important to prepare quality graduates. An alternative hybrid and collaborative networks approach in learning is the current choice. This study aims to explain the online learning integrated project and Kolb learning style in mechanical engineering courses to enhance students' academic achievement which is implemented through a hybrid and collaborative networks approach. The research method used is a quantitative approach with the posttest control group design method. We carry out learning activities using Project and Kolb Learning on higher education students from Mechanical Engineering Education who take part in learning this project. Students who participate are limited to small groups, which are divided according to their learning styles groups, and the implementation is carried out in a hybrid and collaborative through e-learning and face-to-face. Collecting data using Kolb Learning Styles Inventory, and achievement test. While the data analysis used descriptive analysis and one-way Anova with the help of SPSS software. The results of this study indicate that there is a difference in the average academic achievement of students based on the four learning style groups, and the thinker learning style group has the highest average academic achievement among the four. The selection of appropriate learning styles and learning models has an impact on optimal and effective academic achievement.
{"title":"Hybrid and Collaborative Networks Approach: Online Learning Integrated Project and Kolb Learning Style in Mechanical Engineering Courses","authors":"N. Jalinus, Ganefri, Mahesi Agni Zaus, R. Wulansari, Rahmat Azis Nabawi, Hendra Hidayat","doi":"10.3991/ijoe.v18i15.34333","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.34333","url":null,"abstract":"Engineering education is very important to prepare quality graduates. An alternative hybrid and collaborative networks approach in learning is the current choice. This study aims to explain the online learning integrated project and Kolb learning style in mechanical engineering courses to enhance students' academic achievement which is implemented through a hybrid and collaborative networks approach. The research method used is a quantitative approach with the posttest control group design method. We carry out learning activities using Project and Kolb Learning on higher education students from Mechanical Engineering Education who take part in learning this project. Students who participate are limited to small groups, which are divided according to their learning styles groups, and the implementation is carried out in a hybrid and collaborative through e-learning and face-to-face. Collecting data using Kolb Learning Styles Inventory, and achievement test. While the data analysis used descriptive analysis and one-way Anova with the help of SPSS software. The results of this study indicate that there is a difference in the average academic achievement of students based on the four learning style groups, and the thinker learning style group has the highest average academic achievement among the four. The selection of appropriate learning styles and learning models has an impact on optimal and effective academic achievement.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114869949","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 : 2022-12-06DOI: 10.3991/ijoe.v18i15.33639
Abdelhafid Errabih, Mohyeddine Boussarhane, B. Nsiri, A. Sadiq, My Hachem El yousfi Alaoui, R. Thami, Brahim Benaji
Computer-aided diagnosis has the potential to replace or at least support medical personnel in their everyday responsibilities such as diagnosis, therapy, and surgery. In the area of ophthalmology, artificial intelligence approaches have been incorporated in the diagnosis of the most frequent ocular disorders, such as choroidal neovascularization (CNV), diabetic macular oedema (DMO), and DRUSEN; these illnesses pose a significant risk of vision loss. Optical coherence tomography (OCT) is an imaging technology used to diagnose the aforementioned eye disorders. It enables ophthalmologists to see the back of the eye and take various slices of the retina. The goal of this research is to automate the diagnosis of retinopathy, which includes CNV, DME, and DRUSEN. The approach employed is a deep learning-based, and transfer learning technique, applying to a public dataset of OCT pictures and two pertained neural network models VGG16 and InceptionV3, which are trained on the big database "ImageNet." That allows them to be able to extract the main features of millions of images. Furthermore, fine-tuning approaches are applied to outperform the feature extraction method, by modifying the hyperparameters. The findings showed that the VGG16 model performed better in classification than the InceptionV3 architecture, with a 0.93 accuracy.
{"title":"Identifying Retinal Diseases on OCT Image Based on Deep Learning","authors":"Abdelhafid Errabih, Mohyeddine Boussarhane, B. Nsiri, A. Sadiq, My Hachem El yousfi Alaoui, R. Thami, Brahim Benaji","doi":"10.3991/ijoe.v18i15.33639","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.33639","url":null,"abstract":"Computer-aided diagnosis has the potential to replace or at least support medical personnel in their everyday responsibilities such as diagnosis, therapy, and surgery. In the area of ophthalmology, artificial intelligence approaches have been incorporated in the diagnosis of the most frequent ocular disorders, such as choroidal neovascularization (CNV), diabetic macular oedema (DMO), and DRUSEN; these illnesses pose a significant risk of vision loss. Optical coherence tomography (OCT) is an imaging technology used to diagnose the aforementioned eye disorders. It enables ophthalmologists to see the back of the eye and take various slices of the retina. The goal of this research is to automate the diagnosis of retinopathy, which includes CNV, DME, and DRUSEN. The approach employed is a deep learning-based, and transfer learning technique, applying to a public dataset of OCT pictures and two pertained neural network models VGG16 and InceptionV3, which are trained on the big database \"ImageNet.\" That allows them to be able to extract the main features of millions of images. Furthermore, fine-tuning approaches are applied to outperform the feature extraction method, by modifying the hyperparameters. The findings showed that the VGG16 model performed better in classification than the InceptionV3 architecture, with a 0.93 accuracy.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125754119","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 : 2022-12-06DOI: 10.3991/ijoe.v18i15.35089
Giovanni Romagnoli, Matteo Galli, Davide Mezzogori, Davide Reverberi
In the last decades, Serious Games (SGs) have been implemented more and more in the engineering field, for both educational and professional purposes. The interest in digital SGs has increased even more in the last years of covid-19 pandemic, due to their location-independent availability and to the possibility to use SGs to apply theoretical knowledge and involve the users in a challenging way. Since the beginning of project XXXX in October 2018, the University of Xxxx started to develop a brand-new SG with a strong focus on Operation and Supply Chain Management. The game has been studied as a multiplayer cooperative and competitive game which projects learners in a fictitious universe where multiple companies compete against each other in the same market. The realization of the game started from the definition of the didactical concept, underwent the user acceptance testing phases (alpha and beta tests) up until reach the release and the corresponding final evaluation feedback.
{"title":"Cooperative and Competitive Serious Game for Operations and Supply Chain Management: Didactical Concept and Final Evaluation","authors":"Giovanni Romagnoli, Matteo Galli, Davide Mezzogori, Davide Reverberi","doi":"10.3991/ijoe.v18i15.35089","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.35089","url":null,"abstract":"In the last decades, Serious Games (SGs) have been implemented more and more in the engineering field, for both educational and professional purposes. The interest in digital SGs has increased even more in the last years of covid-19 pandemic, due to their location-independent availability and to the possibility to use SGs to apply theoretical knowledge and involve the users in a challenging way. Since the beginning of project XXXX in October 2018, the University of Xxxx started to develop a brand-new SG with a strong focus on Operation and Supply Chain Management. The game has been studied as a multiplayer cooperative and competitive game which projects learners in a fictitious universe where multiple companies compete against each other in the same market. The realization of the game started from the definition of the didactical concept, underwent the user acceptance testing phases (alpha and beta tests) up until reach the release and the corresponding final evaluation feedback.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133401985","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 : 2022-12-06DOI: 10.3991/ijoe.v18i15.35761
Lafta Raheem Ali, S. A. Jebur, M. M. Jahefer, B. Shaker
Corona virus’s correct and accurate diagnosis is the most important reason for contributing to the treatment of this disease. Radiography is one of the simplest methods to detect virus infection. In this research, a method has been proposed that can diagnose disease based on radiography (X-ray chest) and deep learning techniques. We conducted a comparative study by using three diagnosis models; the first one was developed by using traditional CNN, while the two others are our proposed models (second and third models). The proposed models can diagnose the COVID-19 infection, normal cases, lung opacity, and Viral Pneumonia according to the four categories in the covid19 radiography dataset. The transfer learning technology had used to increase the robustness and reliability of our model, also, data augmentation was used for reducing the overfitting and to increase the accuracy of the model by scaling rotation, zooming, and translation. The third model showed higher training accuracy of 93.18% compared to the two other models that are dependent on using traditional convolution neural networks with an accuracy of 70.28% of the first model, while the accuracy of the second model that uses data augmentation with traditional convolution neural is 90.1%, while the testing accuracy models was 68.27% for the first model, 87.55% for the second model, and 86.03% for the third model.
{"title":"Employing Transfer Learning for Diagnosing COVID-19 Disease","authors":"Lafta Raheem Ali, S. A. Jebur, M. M. Jahefer, B. Shaker","doi":"10.3991/ijoe.v18i15.35761","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.35761","url":null,"abstract":"Corona virus’s correct and accurate diagnosis is the most important reason for contributing to the treatment of this disease. Radiography is one of the simplest methods to detect virus infection. In this research, a method has been proposed that can diagnose disease based on radiography (X-ray chest) and deep learning techniques. We conducted a comparative study by using three diagnosis models; the first one was developed by using traditional CNN, while the two others are our proposed models (second and third models). The proposed models can diagnose the COVID-19 infection, normal cases, lung opacity, and Viral Pneumonia according to the four categories in the covid19 radiography dataset. The transfer learning technology had used to increase the robustness and reliability of our model, also, data augmentation was used for reducing the overfitting and to increase the accuracy of the model by scaling rotation, zooming, and translation. The third model showed higher training accuracy of 93.18% compared to the two other models that are dependent on using traditional convolution neural networks with an accuracy of 70.28% of the first model, while the accuracy of the second model that uses data augmentation with traditional convolution neural is 90.1%, while the testing accuracy models was 68.27% for the first model, 87.55% for the second model, and 86.03% for the third model.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133115310","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 : 2022-12-06DOI: 10.3991/ijoe.v18i15.34399
Zahraa Saddi Kadhim, Hasanen S. Abdullah, K. I. Ghathwan
Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid research-ers and data analysts in developing better ML models via identifying the ap-propriate hyperparameter configurations.
{"title":"Artificial Neural Network Hyperparameters Optimization: A Survey","authors":"Zahraa Saddi Kadhim, Hasanen S. Abdullah, K. I. Ghathwan","doi":"10.3991/ijoe.v18i15.34399","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.34399","url":null,"abstract":"Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid research-ers and data analysts in developing better ML models via identifying the ap-propriate hyperparameter configurations.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122498518","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 : 2022-12-06DOI: 10.3991/ijoe.v18i15.33397
Alejandro Boza-Chua, Karen Gabriel-Gonzales, L. Andrade-Arenas
Sight is one of the senses with the greatest sup- port for our daily life because the brain receives 80% of the information visually. However, it is one of the most neglected senses in Latin America, mainly in Peru. This is because since that in this country the sense of sight is related to the second disability with the highest percentage. Thus, 76% of the visually impaired population lost their sight due to lack of treatment or lack of early detection of any visual disease. For this reason, the present research work originated, which has the purpose of designing and implementing an expert web system oriented to the welfare of the vulnerable population regarding visual diseases and the care of the sense of sight. Therefore, the Buchanan methodology was used for the development of this project, which contains 5 phases of development and planning. This methodology allowed the identification of viable requirements for the expert web system, as well as the study of detailed solutions and design. Thus, it allowed the development of an expert web system that complies with the eye care, through early diagnosis. Finally, as a result of the present research work, it was obtained that the expert web system meets 88% of satisfaction, the value obtained through a questionnaire to a sample of 60 people, including patients and specialists of the company called MK Optical Center.
{"title":"Expert Web System: Diagnosis of Visual Diseases","authors":"Alejandro Boza-Chua, Karen Gabriel-Gonzales, L. Andrade-Arenas","doi":"10.3991/ijoe.v18i15.33397","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.33397","url":null,"abstract":"Sight is one of the senses with the greatest sup- port for our daily life because the brain receives 80% of the information visually. However, it is one of the most neglected senses in Latin America, mainly in Peru. This is because since that in this country the sense of sight is related to the second disability with the highest percentage. Thus, 76% of the visually impaired population lost their sight due to lack of treatment or lack of early detection of any visual disease. For this reason, the present research work originated, which has the purpose of designing and implementing an expert web system oriented to the welfare of the vulnerable population regarding visual diseases and the care of the sense of sight. Therefore, the Buchanan methodology was used for the development of this project, which contains 5 phases of development and planning. This methodology allowed the identification of viable requirements for the expert web system, as well as the study of detailed solutions and design. Thus, it allowed the development of an expert web system that complies with the eye care, through early diagnosis. Finally, as a result of the present research work, it was obtained that the expert web system meets 88% of satisfaction, the value obtained through a questionnaire to a sample of 60 people, including patients and specialists of the company called MK Optical Center.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"594 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123189673","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 : 2022-12-06DOI: 10.3991/ijoe.v18i15.28245
H. H. Muljo, A. S. Perbangsa, T. W. Cenggoro, Kartika Purwandari, D. Sudigyo, B. Pardamean
The high prevalence of Tuberculosis (TB) in Indonesia puts Indonesia in the second-highest national TB prevalence in the world after India. This high prevalence can cause a failure to deliver medical treatments to TB patients, which is exacerbated by the disproportionate distribution of doctors in Indonesia. To address this issue, an AI system is necessary to help doctors in screening a large number of patients in a short time. However, to develop a robust AI for this purpose, we need a large dataset. This study aims to develop a database system for storing TB sputum sample images, which can be used as the dataset to train an AI for TB detection. The developed system can help doctors and health workers to manage the images during their daily job. After a period of time, the stored images can be utilized as the dataset to train AI.
{"title":"Database System for Storing Tuberculosis Sputum Sample Images as an AI Training Dataset","authors":"H. H. Muljo, A. S. Perbangsa, T. W. Cenggoro, Kartika Purwandari, D. Sudigyo, B. Pardamean","doi":"10.3991/ijoe.v18i15.28245","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.28245","url":null,"abstract":"The high prevalence of Tuberculosis (TB) in Indonesia puts Indonesia in the second-highest national TB prevalence in the world after India. This high prevalence can cause a failure to deliver medical treatments to TB patients, which is exacerbated by the disproportionate distribution of doctors in Indonesia. To address this issue, an AI system is necessary to help doctors in screening a large number of patients in a short time. However, to develop a robust AI for this purpose, we need a large dataset. This study aims to develop a database system for storing TB sputum sample images, which can be used as the dataset to train an AI for TB detection. The developed system can help doctors and health workers to manage the images during their daily job. After a period of time, the stored images can be utilized as the dataset to train AI.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126714761","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 : 2022-12-06DOI: 10.3991/ijoe.v18i15.34397
Baraa M. Abed, Wesam M. Jasim
Path planning algorithms is the most significant area in the robotics field. Path Planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Optimization of path planning refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOOs present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. The purpose of this paper is to present an overview of mobile robot navigation strategies employed to find the path that has the minimum number of criteria (shortest, smoothness, and safest) so far. Here, multi objective approaches are discussed in detail in order to identify research gaps. In addition, it is important to understand how path planning strategies are developed under various environmental circumstances.
{"title":"Multi Objective Optimization Algorithms for Mobile Robot Path Planning: A Survey","authors":"Baraa M. Abed, Wesam M. Jasim","doi":"10.3991/ijoe.v18i15.34397","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.34397","url":null,"abstract":"Path planning algorithms is the most significant area in the robotics field. Path Planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Optimization of path planning refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOOs present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. The purpose of this paper is to present an overview of mobile robot navigation strategies employed to find the path that has the minimum number of criteria (shortest, smoothness, and safest) so far. Here, multi objective approaches are discussed in detail in order to identify research gaps. In addition, it is important to understand how path planning strategies are developed under various environmental circumstances.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129423484","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}
Diabetes Mellitus (DM) is a chronic disease that occurs when the body cannot effectively use the insulin it produces. The use of artificial intelligence (AI) can provide a means to diagnose. This study aims to obtain the best classification of the Naïve Bayes (NB) and K-Nearest Neighbors (KNN) methods so that accurate results are obtained in diagnosing DM disease using a dataset originating from The Abdul Moeis Hospital, Samarinda, East Kalimantan, Indonesia. The results showed that the KNN performed better in accuracy, precision, and specificity with an Area Under the Curve (AUC) value 10% higher than NB. Overall, KNN obtained a better recall compared to the NB in order to DM diagnosis.
{"title":"Naïve Bayes and K-Nearest Neighbor Algorithms Performance Comparison in Diabetes Mellitus Early Diagnosis","authors":"Haviluddin, N. Puspitasari, Aji Ery Burhandenny, Andi Dhiya Awalia Nurulita, Dinnuhoni Trahutomo","doi":"10.3991/ijoe.v18i15.34143","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.34143","url":null,"abstract":"Diabetes Mellitus (DM) is a chronic disease that occurs when the body cannot effectively use the insulin it produces. The use of artificial intelligence (AI) can provide a means to diagnose. This study aims to obtain the best classification of the Naïve Bayes (NB) and K-Nearest Neighbors (KNN) methods so that accurate results are obtained in diagnosing DM disease using a dataset originating from The Abdul Moeis Hospital, Samarinda, East Kalimantan, Indonesia. The results showed that the KNN performed better in accuracy, precision, and specificity with an Area Under the Curve (AUC) value 10% higher than NB. Overall, KNN obtained a better recall compared to the NB in order to DM diagnosis.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125420275","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 : 2022-12-06DOI: 10.3991/ijoe.v18i15.33805
Ayeh Arabiat, Mohammad Matahen, Omar Abu Zaid, M. Zgoul
Exoskeletons are powered robotic devices designed to be worn by humans to provide physical assistance or power augmentation. In this work, a control system for a powered exoskeleton is designed. This exoskeleton is aimed at aiding in the rehabilitation of Spinal Bifidas. Spinal Bifida is the most common disability in childhood after Cerebral Palsy, it is a defective development of the spinal cord during conception. Two phases for this work are presented: system identification and control using ANFIS. While it is difficult to attain an accurate dynamical model of complex system, this work employed ANFIS to help control and stabilize the system. Gait trajectories were obtained by modeling the system as a linear inverted pendulum, a simulation was performed with a traditional controller. Afterwards, trajectory data was obtained and used to train and test ANFIS to create the model and controller. One, two and three inputs were investigated to train the ANFIS. Results showed that the one-input model visibly failed to follow the trajectory. The average RMSE for the two-input model was 0.096, and for the three-inputs, the RMSE on average was higher; 0.19, making it worse, however the knee model contrastingly showed improvement, as the RMSE was lower by 2% for the knee specifically.
{"title":"Control of an Exoskeleton for Lower Limb Rehabilitation Using ANFIS","authors":"Ayeh Arabiat, Mohammad Matahen, Omar Abu Zaid, M. Zgoul","doi":"10.3991/ijoe.v18i15.33805","DOIUrl":"https://doi.org/10.3991/ijoe.v18i15.33805","url":null,"abstract":"Exoskeletons are powered robotic devices designed to be worn by humans to provide physical assistance or power augmentation. In this work, a control system for a powered exoskeleton is designed. This exoskeleton is aimed at aiding in the rehabilitation of Spinal Bifidas. Spinal Bifida is the most common disability in childhood after Cerebral Palsy, it is a defective development of the spinal cord during conception. Two phases for this work are presented: system identification and control using ANFIS. While it is difficult to attain an accurate dynamical model of complex system, this work employed ANFIS to help control and stabilize the system. Gait trajectories were obtained by modeling the system as a linear inverted pendulum, a simulation was performed with a traditional controller. Afterwards, trajectory data was obtained and used to train and test ANFIS to create the model and controller. One, two and three inputs were investigated to train the ANFIS. Results showed that the one-input model visibly failed to follow the trajectory. The average RMSE for the two-input model was 0.096, and for the three-inputs, the RMSE on average was higher; 0.19, making it worse, however the knee model contrastingly showed improvement, as the RMSE was lower by 2% for the knee specifically.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122918229","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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