Pub Date : 2022-07-26DOI: 10.3991/ijoe.v18i10.30879
Syahrul Nizam Junaini, A. Kamal, A. Hashim, Norhunaini Mohd Shaipullah, Liyana Truna
In recent decades, the usage of augmented reality (AR) and virtual reality (VR) games for safety training and rehabilitation has grown exponentially. However, no systematic literature review of the research trends in augmented and virtual reality (AR/VR) for Occupational Safety and Health (OHS) training has been carried out. The authors conducted a comprehensive review of the relevant literature published between 2016 and 2020. This analysis was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The Scopus database contained 1031 records. However, only 12 papers matched the inclusion criteria and were included in this review. According to the findings, the use of augmented and virtual reality for safety training and rehabilitation has been progressively growing. With robust research trends in this field—in the post-pandemic era, the use of augmented reality and virtual reality games has promising potential, especially for safety training and rehabilitation. This study provides critical insights into how augmented reality and virtual reality may impact the future of safety training and rehabilitation at the workplace.
{"title":"Augmented and Virtual Reality Games for Occupational Safety and Health Training: A Systematic Review and Prospects for the Post-Pandemic Era","authors":"Syahrul Nizam Junaini, A. Kamal, A. Hashim, Norhunaini Mohd Shaipullah, Liyana Truna","doi":"10.3991/ijoe.v18i10.30879","DOIUrl":"https://doi.org/10.3991/ijoe.v18i10.30879","url":null,"abstract":"In recent decades, the usage of augmented reality (AR) and virtual reality (VR) games for safety training and rehabilitation has grown exponentially. However, no systematic literature review of the research trends in augmented and virtual reality (AR/VR) for Occupational Safety and Health (OHS) training has been carried out. The authors conducted a comprehensive review of the relevant literature published between 2016 and 2020. This analysis was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The Scopus database contained 1031 records. However, only 12 papers matched the inclusion criteria and were included in this review. According to the findings, the use of augmented and virtual reality for safety training and rehabilitation has been progressively growing. With robust research trends in this field—in the post-pandemic era, the use of augmented reality and virtual reality games has promising potential, especially for safety training and rehabilitation. This study provides critical insights into how augmented reality and virtual reality may impact the future of safety training and rehabilitation at the workplace.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"74 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":"121717900","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.v18i10.31879
Manal Alghieth
Due to the rapid spread of skin diseases among children in school, and the fact that skin disease is the most common contagious disease spreading within students in school, this study investigates the factors that could help in early detection of these skin diseases using AI techniques. The texture and color of the skin can change as a result of the disease. Examples of these diseases are chickenpox, impetigo, scabies, infectious erythema, skin warts, and other infectious skin diseases. Skin disorders are long-term and contagious, it can be detected early and with high accuracy before it become a long-term problem. This research builds a system of skin disease detection using the CNN technique and a pre-trained VGG19 model. In addition, the dataset contains 4500 images that were collected from different sources to train the VGG19 model. Data augmentation technique such as zooming, cropping, and rotating were used. After that, the Adamax optimizer, which is most suitable for the proposed methodology, was used to obtain high accuracy and required results. This study achieved a high accuracy of 99% compared to other similar researchs. It can be concluded that this system is very reliable which can be integrated to smart schools as part of IOT systems.
{"title":"Skin Disease Detection for Kids at School Using Deep Learning Techniques","authors":"Manal Alghieth","doi":"10.3991/ijoe.v18i10.31879","DOIUrl":"https://doi.org/10.3991/ijoe.v18i10.31879","url":null,"abstract":"Due to the rapid spread of skin diseases among children in school, and the fact that skin disease is the most common contagious disease spreading within students in school, this study investigates the factors that could help in early detection of these skin diseases using AI techniques. The texture and color of the skin can change as a result of the disease. Examples of these diseases are chickenpox, impetigo, scabies, infectious erythema, skin warts, and other infectious skin diseases. Skin disorders are long-term and contagious, it can be detected early and with high accuracy before it become a long-term problem. This research builds a system of skin disease detection using the CNN technique and a pre-trained VGG19 model. In addition, the dataset contains 4500 images that were collected from different sources to train the VGG19 model. Data augmentation technique such as zooming, cropping, and rotating were used. After that, the Adamax optimizer, which is most suitable for the proposed methodology, was used to obtain high accuracy and required results. This study achieved a high accuracy of 99% compared to other similar researchs. It can be concluded that this system is very reliable which can be integrated to smart schools as part of IOT systems.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"2 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":"130770527","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.v18i10.26425
Nhu Dinh Dang, V. Pham, Duc-Tan Tran, Van-An Tran, Huu An Nguyen, Anh Duc Nguyen
During the operations, firefighters can be injured or killed because of the smoke and heat emission from the fire area, broken structure elements such as floors, walls, or boiling liquid ejection and gas explosion. Therefore, this paper aims to develop an efficient and portable system to monitor falls and high CO level through integrating a three degrees of freedom accelerometer and an MQ7 sensor to recorded acceleration and measured CO concentration with the embedded fall and high CO detection algorithms. The embedded fall detection algorithm can detect fall events with ultra-high accuracy without mistakenly identifying normal activities such as walking, standing, jogging, and jumping as fall events. The posture recognition and cascade posture recognition after three seconds are proposed in this paper to gain the accuracy of our proposed fall detection system. If a firefighter falls and is unable to stand up, the alert signal message will be sent to their commander outside through the GSM/GPRS module. The embedded high CO detection algorithm used to alert the dangerous CO level to recommend using self-contained breathing apparatuses (SCBA) and saving fresh air with acceptable CO level. We carefully investigated the proposed thresholds and window size before embedding them into the microcontroller. The sensitivity and accuracy achieved were around 96.5% and 93% respectively in our recorded data. Furthermore, the proposed fall detection algorithm also achieved higher geometric mean in comparison with Support Vector Machine classifier (SVM) and a nearest neighbor rule (NN) in the public datasets with the achieved around 99.44%, 98.41% and 95.76% respectively.
{"title":"A Simple and Real-Time Support System for Firefighters Using Low-Cost 3-DOF Accelerometer and CO Sensor","authors":"Nhu Dinh Dang, V. Pham, Duc-Tan Tran, Van-An Tran, Huu An Nguyen, Anh Duc Nguyen","doi":"10.3991/ijoe.v18i10.26425","DOIUrl":"https://doi.org/10.3991/ijoe.v18i10.26425","url":null,"abstract":"During the operations, firefighters can be injured or killed because of the smoke and heat emission from the fire area, broken structure elements such as floors, walls, or boiling liquid ejection and gas explosion. Therefore, this paper aims to develop an efficient and portable system to monitor falls and high CO level through integrating a three degrees of freedom accelerometer and an MQ7 sensor to recorded acceleration and measured CO concentration with the embedded fall and high CO detection algorithms. The embedded fall detection algorithm can detect fall events with ultra-high accuracy without mistakenly identifying normal activities such as walking, standing, jogging, and jumping as fall events. The posture recognition and cascade posture recognition after three seconds are proposed in this paper to gain the accuracy of our proposed fall detection system. If a firefighter falls and is unable to stand up, the alert signal message will be sent to their commander outside through the GSM/GPRS module. The embedded high CO detection algorithm used to alert the dangerous CO level to recommend using self-contained breathing apparatuses (SCBA) and saving fresh air with acceptable CO level. We carefully investigated the proposed thresholds and window size before embedding them into the microcontroller. The sensitivity and accuracy achieved were around 96.5% and 93% respectively in our recorded data. Furthermore, the proposed fall detection algorithm also achieved higher geometric mean in comparison with Support Vector Machine classifier (SVM) and a nearest neighbor rule (NN) in the public datasets with the achieved around 99.44%, 98.41% and 95.76% respectively.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"16 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":"127608291","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.v18i10.31347
M. GeethanjaliT., Minavathi, M. Dinesh
Kidney Cancer is one of the most prevalent diseases that is more common in men than in women. Detecting kidney tumors at an early stage has been found to increase survival rates of patients. It is therefore important to accurately segment tumors in Computed Tomography(CT) images. To assist in early detection of kidney tumors in CT images, we present a method for segmenting kidney tumors using deep convolutional neural networks. Predicted models using U-Net and Attention U-Net architectures are ensemble for effective tumor segmentation. Experimental and visual results obtained using the KiTS2019 dataset clearly demonstrate the enhanced Intersection Over Union(IoU) score of the ensemble model.
{"title":"Semantic Segmentation of Kidney Tumors Using Variants of U-Net Architecture","authors":"M. GeethanjaliT., Minavathi, M. Dinesh","doi":"10.3991/ijoe.v18i10.31347","DOIUrl":"https://doi.org/10.3991/ijoe.v18i10.31347","url":null,"abstract":"Kidney Cancer is one of the most prevalent diseases that is more common in men than in women. Detecting kidney tumors at an early stage has been found to increase survival rates of patients. It is therefore important to accurately segment tumors in Computed Tomography(CT) images. To assist in early detection of kidney tumors in CT images, we present a method for segmenting kidney tumors using deep convolutional neural networks. Predicted models using U-Net and Attention U-Net architectures are ensemble for effective tumor segmentation. Experimental and visual results obtained using the KiTS2019 dataset clearly demonstrate the enhanced Intersection Over Union(IoU) score of the ensemble model.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"1 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":"116321428","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.v18i10.31969
Richa, Karamjit Kaur, Priti Singh
A large percentage of healthcare resources, including imaging tools, like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have been dedicated to the management of affected patients in this pandemic of Coronavirus disease 2019 (COVID-19). The diagnostic modalities in medical research are improving at a rapid pace with an objective to acquire maximum information with as little data as possible without any artifacts. That is where image fusion comes into the picture. It is a technique of merging source medical pictures to maximize the necessary information. CT is generally used for bony structures, whereas MRI is more appropriate for soft tissues. A fusion of MRI and CT images would lead to enhancement of the overall image quality while giving comprehensive information, at the same time artifacts are also eliminated. Image fusion methods are applied in medical science and various other sectors. Several image processing techniques are used in medical diagnostics, like Principal Component analysis (PCA), Intensity-Hue-Saturation, Discrete Wavelet Transform (DWT), and others. This study suggests an image fusion algorithm utilising the principal component averaging and the DWT along with the performance analysis of the fusion of the MRI and CT images of brain. The technique used in our study significantly enhances the image quality in terms of various fusion performance measures that helps the medical practitioners to diagnose any infection and aids in its treatment.
{"title":"A Novel MRI And CT Image Fusion Based on Discrete Wavelet Transform and Principal Component Analysis for Enhanced Clinical Diagnosis","authors":"Richa, Karamjit Kaur, Priti Singh","doi":"10.3991/ijoe.v18i10.31969","DOIUrl":"https://doi.org/10.3991/ijoe.v18i10.31969","url":null,"abstract":"A large percentage of healthcare resources, including imaging tools, like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have been dedicated to the management of affected patients in this pandemic of Coronavirus disease 2019 (COVID-19). The diagnostic modalities in medical research are improving at a rapid pace with an objective to acquire maximum information with as little data as possible without any artifacts. That is where image fusion comes into the picture. It is a technique of merging source medical pictures to maximize the necessary information. CT is generally used for bony structures, whereas MRI is more appropriate for soft tissues. A fusion of MRI and CT images would lead to enhancement of the overall image quality while giving comprehensive information, at the same time artifacts are also eliminated. Image fusion methods are applied in medical science and various other sectors. Several image processing techniques are used in medical diagnostics, like Principal Component analysis (PCA), Intensity-Hue-Saturation, Discrete Wavelet Transform (DWT), and others. This study suggests an image fusion algorithm utilising the principal component averaging and the DWT along with the performance analysis of the fusion of the MRI and CT images of brain. The technique used in our study significantly enhances the image quality in terms of various fusion performance measures that helps the medical practitioners to diagnose any infection and aids in its treatment. \u0000 ","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"14 6 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":"116879924","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.v18i10.31415
Surya Teja Arvapalli, A. SaiAbhay, D. Mounika, M. VaniPujitha
Autism Spectrum Illness (ASD), a evolution of the brain disorder, is commonly related with sensory difficulties, such as excessive or insufficient sensitivity to sounds, scents, or touch. Autism Spectrum Disorder (ASD) is evolving at a faster rate than ever before. By screening tests autism detection is very expensive and time consuming. With the advancement of Deep Learning (DL),autism can be predicted from a young age.In this paper we are using Convolutional Neural Network (CNN) with Transfer Learning (TL) models to classify the disease and we will suggest the precautions if it is detected as autism. Here we consider the Autism Master Dataset (AMD) from kaggle.com website, which contains two classes (Autism, Non_Autism). By using this models we are obtaining good accuracy
{"title":"Autism Spectrum Disorder Detection Using MobileNet","authors":"Surya Teja Arvapalli, A. SaiAbhay, D. Mounika, M. VaniPujitha","doi":"10.3991/ijoe.v18i10.31415","DOIUrl":"https://doi.org/10.3991/ijoe.v18i10.31415","url":null,"abstract":"Autism Spectrum Illness (ASD), a evolution of the brain disorder, is commonly related with sensory difficulties, such as excessive or insufficient sensitivity to sounds, scents, or touch. Autism Spectrum Disorder (ASD) is evolving at a faster rate than ever before. By screening tests autism detection is very expensive and time consuming. With the advancement of Deep Learning (DL),autism can be predicted from a young age.In this paper we are using Convolutional Neural Network (CNN) with Transfer Learning (TL) models to classify the disease and we will suggest the precautions if it is detected as autism. Here we consider the Autism Master Dataset (AMD) from kaggle.com website, which contains two classes (Autism, Non_Autism). By using this models we are obtaining good accuracy","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"19 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":"124789808","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 : 2022-07-11DOI: 10.3991/ijoe.v18i09.30839
Mohamed Zied Chaari, Rashid Al-Rahimi, O. Aghzout
Wireless Power Collecting (WPC) present the future in powering and energizing intelligent Internet of Things (IoT) electronics devices. This chapter studies and utilizes a circuit to powering wirelessly IoT devices. The WPC offer a best technique to help researchers and engineers of modern societies to build cell blocks. The concept is to energy any IoT devices and sensors wirelessly from Radio Frequency (RF) power strength in the same areas that may be hard to achieve or potentially hazardous. We implemented the RF harvesting technology with IoT devices to increase the efficiency of sensors. The idea of this system is to power up and self-energize any IoT sensors wirelessly. This work studies two different topologies of a rectangular patch antenna and different RF harvesting circuit voltage multiplier configurations using a microwave power station as the input RF source. This work aims to utilize the wireless power transmission technique in the smart house solution. The proposed prototype gets all technical parameters to generate enough electricity to power up the bulbs 5 W wirelessly at a gap distance around a five meters. Finally, we test the RF rectifier circuit coupled with a twin patch antenna that can self-energize the bulb, eventually devices work without batteries.
{"title":"Energized IOT Sensor through RF Harvesting Energy","authors":"Mohamed Zied Chaari, Rashid Al-Rahimi, O. Aghzout","doi":"10.3991/ijoe.v18i09.30839","DOIUrl":"https://doi.org/10.3991/ijoe.v18i09.30839","url":null,"abstract":"Wireless Power Collecting (WPC) present the future in powering and energizing intelligent Internet of Things (IoT) electronics devices. This chapter studies and utilizes a circuit to powering wirelessly IoT devices. The WPC offer a best technique to help researchers and engineers of modern societies to build cell blocks. The concept is to energy any IoT devices and sensors wirelessly from Radio Frequency (RF) power strength in the same areas that may be hard to achieve or potentially hazardous. We implemented the RF harvesting technology with IoT devices to increase the efficiency of sensors. The idea of this system is to power up and self-energize any IoT sensors wirelessly. This work studies two different topologies of a rectangular patch antenna and different RF harvesting circuit voltage multiplier configurations using a microwave power station as the input RF source. This work aims to utilize the wireless power transmission technique in the smart house solution. The proposed prototype gets all technical parameters to generate enough electricity to power up the bulbs 5 W wirelessly at a gap distance around a five meters. Finally, we test the RF rectifier circuit coupled with a twin patch antenna that can self-energize the bulb, eventually devices work without batteries.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121113722","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 : 2022-07-11DOI: 10.3991/ijoe.v18i09.30075
S. Geetha, Mansi Parashar, JS Abhishek, Raj Vishal Turaga, I. A. Lawal, Seifedine Kadry
Diabetic Retinopathy is a serious complication arising in diabetes afflicted patients. Its effective treatment depends on early detection, and the course of action varies decisively with the intensity of the affliction. Computer-aided diagnosis helps to detect not only the presence or absence of the disease but also the severity, making it easier for ophthalmologists to construct a treatment plan. Diabetic retinopathy grading is the task of classifying images of the eye's fundus of diabetic patients into 5 different grades ranging from 0-4 based on the severity of the disease. In this work, we propose a deep neural network architecture to address the grading problem. The method utilizes an additional attention layer in the neural network model to capture the spatial relationship between the region of interest in the images during the training process to better discriminate between the different severity stages of the disease. Also, we analyze the impact of different image processing techniques on the classification results. We assessed the performance of our proposed method using a dataset of eye fundus images and obtained a classification accuracy of 89.20% on average. This performance surpasses that reported for other state-of-the-art methods on the same dataset. The effectiveness of the proposed method will facilitate the procedural workflow of identifying severe cases of diabetic retinopathy
{"title":"Diabetic Retinopathy Grading with Deep Visual Attention Network","authors":"S. Geetha, Mansi Parashar, JS Abhishek, Raj Vishal Turaga, I. A. Lawal, Seifedine Kadry","doi":"10.3991/ijoe.v18i09.30075","DOIUrl":"https://doi.org/10.3991/ijoe.v18i09.30075","url":null,"abstract":"Diabetic Retinopathy is a serious complication arising in diabetes afflicted patients. Its effective treatment depends on early detection, and the course of action varies decisively with the intensity of the affliction. Computer-aided diagnosis helps to detect not only the presence or absence of the disease but also the severity, making it easier for ophthalmologists to construct a treatment plan. Diabetic retinopathy grading is the task of classifying images of the eye's fundus of diabetic patients into 5 different grades ranging from 0-4 based on the severity of the disease. In this work, we propose a deep neural network architecture to address the grading problem. The method utilizes an additional attention layer in the neural network model to capture the spatial relationship between the region of interest in the images during the training process to better discriminate between the different severity stages of the disease. Also, we analyze the impact of different image processing techniques on the classification results. We assessed the performance of our proposed method using a dataset of eye fundus images and obtained a classification accuracy of 89.20% on average. This performance surpasses that reported for other state-of-the-art methods on the same dataset. The effectiveness of the proposed method will facilitate the procedural workflow of identifying severe cases of diabetic retinopathy","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133610730","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 : 2022-07-11DOI: 10.3991/ijoe.v18i09.29691
S. Fuada, Akhmad Alfaruq, T. Adiono
In recent times, electronic money (e-money) has gained significant popularity in the form of smartphone applications, barcodes, and smart card systems. In previous research, we have developed a smartcard-based device equipped with a dual interface to facilitate electronic transactions using contact or contactless technology. These methods involve initiating a connection with a cloud server, where all transaction data are recorded with full encryption. This device also supports payment activities, as well as checking or topping up account balances by utilizing contact and contactless smart cards, which are produced in Indonesia by PT. Xirka Silicon Technology. The aforementioned research did not explain the significance of a database which actually plays a vital role in the system’s data storage. Therefore, this research aims to provide detailed information on the design of this database. Furthermore, five web pages are designed including, token generator, token list, partner list, payments, and transactions to provide specific services requested by users via a web URL through the database that stores transaction activities carried out by the device.
{"title":"Design and Implementation of Database Prototype for A Portable Electronic Transaction Device","authors":"S. Fuada, Akhmad Alfaruq, T. Adiono","doi":"10.3991/ijoe.v18i09.29691","DOIUrl":"https://doi.org/10.3991/ijoe.v18i09.29691","url":null,"abstract":"In recent times, electronic money (e-money) has gained significant popularity in the form of smartphone applications, barcodes, and smart card systems. In previous research, we have developed a smartcard-based device equipped with a dual interface to facilitate electronic transactions using contact or contactless technology. These methods involve initiating a connection with a cloud server, where all transaction data are recorded with full encryption. This device also supports payment activities, as well as checking or topping up account balances by utilizing contact and contactless smart cards, which are produced in Indonesia by PT. Xirka Silicon Technology. The aforementioned research did not explain the significance of a database which actually plays a vital role in the system’s data storage. Therefore, this research aims to provide detailed information on the design of this database. Furthermore, five web pages are designed including, token generator, token list, partner list, payments, and transactions to provide specific services requested by users via a web URL through the database that stores transaction activities carried out by the device.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129684386","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 : 2022-07-11DOI: 10.3991/ijoe.v18i09.29847
Md. Ashikul Aziz Siddique, J. Ferdouse, Md. Tarek Habib, Md. Jueal Mia, Mohammad Shorif Uddin
The eye is an important sensing organ of the human body, as it reacts to light and allows vision of humans. Many Bangladeshi people become nearsighted when it comes to the awareness of vision loss due to eye disease. Many Bangladeshis people are more concerned about losing their money than getting nearsighted or blind, due to a combination of poverty and illiteracy. With this view, this paper proposes an osteopathic expert system that can deal with an image of the eye and recognize the disease. Here, we have focused on the three most common eye diseases in Bangladesh, namely cataract, chalazion, and squint. We have modeled six convolutional neural networks (CNN’s), namely VGG16, VGG19, MobileNet, Xception, InceptionV3, and DenseNet121 to recognize the diseases. We have reached the best configuration of each of these CNN models after adequate investigation. After performing satisfactory experimentation, we have found that the MobileNet model gives the best performance based on accuracy, precision, recall, and F1-score. At last, we have compared our findings with the recently reported relevant works to show their efficacy.
{"title":"Convolutional Neural Network Modeling for Eye Disease Recognition","authors":"Md. Ashikul Aziz Siddique, J. Ferdouse, Md. Tarek Habib, Md. Jueal Mia, Mohammad Shorif Uddin","doi":"10.3991/ijoe.v18i09.29847","DOIUrl":"https://doi.org/10.3991/ijoe.v18i09.29847","url":null,"abstract":"The eye is an important sensing organ of the human body, as it reacts to light and allows vision of humans. Many Bangladeshi people become nearsighted when it comes to the awareness of vision loss due to eye disease. Many Bangladeshis people are more concerned about losing their money than getting nearsighted or blind, due to a combination of poverty and illiteracy. With this view, this paper proposes an osteopathic expert system that can deal with an image of the eye and recognize the disease. Here, we have focused on the three most common eye diseases in Bangladesh, namely cataract, chalazion, and squint. We have modeled six convolutional neural networks (CNN’s), namely VGG16, VGG19, MobileNet, Xception, InceptionV3, and DenseNet121 to recognize the diseases. We have reached the best configuration of each of these CNN models after adequate investigation. After performing satisfactory experimentation, we have found that the MobileNet model gives the best performance based on accuracy, precision, recall, and F1-score. At last, we have compared our findings with the recently reported relevant works to show their efficacy.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127495858","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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