Int. J. Online Biomed. Eng.最新文献_第3页

Ear Recognition Using Rank Level Fusion of Classifiers Outputs 基于分类器输出秩融合的耳朵识别

Int. J. Online Biomed. Eng. Pub Date : 2023-03-14 DOI: 10.3991/ijoe.v19i03.36831

Resmi K R, S. M. Joseph, Raju G., Debabrata Swain, Om Prakash Das, Biswaranjan Acharya

引用次数: 0

A New Centralized Detection-Based Process for Evaluating Anomalies and Analyzing the First Causes Using Machine Learning and Web Semantic 基于机器学习和Web语义的一种新的基于集中检测的异常评估和第一原因分析过程

Int. J. Online Biomed. Eng. Pub Date : 2023-03-14 DOI: 10.3991/ijoe.v19i03.30079

A. Lasbahani, Rachid Tahri, A. Jarrar, Y. Balouki

{"title":"A New Centralized Detection-Based Process for Evaluating Anomalies and Analyzing the First Causes Using Machine Learning and Web Semantic","authors":"A. Lasbahani, Rachid Tahri, A. Jarrar, Y. Balouki","doi":"10.3991/ijoe.v19i03.30079","DOIUrl":"https://doi.org/10.3991/ijoe.v19i03.30079","url":null,"abstract":"In the last decades, many works have been done to enhance data performances in the computer field. Data performance consists to describe all improvements which can be added to data traffic. More precisely, we are talking about techniques allowing improving the evaluation of big data using machine learning. Data evaluation is composed of several variables such as security, quality of service, data synchronization, scalability, and data structuring. In this work, we complete our proceedings done to supervise the continuity of technological evolution in terms of big data and safety. In other words, we aim to add brick to our previous processes to take into consideration the enhancement of the analysis of the causes generating frauds and intrusions preventing data traffic. To achieve this end, we increase current machine learning techniques with prior knowledge based on data thresholds set by experts in the first place. We also aim to integrate knowledge facilitating the interpretation of the causes causing all kinds of anomalies in the second place. Finally, our process will be endowed with the requirements to improve the rate of detection of anomalies and reduce human involvement operation.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125901421","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}

引用次数: 0

Practical Consideration in using Pre-trained Convolutional Neural Network (CNN) for Finger Vein Biometric 预训练卷积神经网络(CNN)用于手指静脉生物识别的实践思考

Int. J. Online Biomed. Eng. Pub Date : 2023-02-16 DOI: 10.3991/ijoe.v19i02.35273

S. Safie, P. Khalid

{"title":"Practical Consideration in using Pre-trained Convolutional Neural Network (CNN) for Finger Vein Biometric","authors":"S. Safie, P. Khalid","doi":"10.3991/ijoe.v19i02.35273","DOIUrl":"https://doi.org/10.3991/ijoe.v19i02.35273","url":null,"abstract":"Using a pre-trained Convolutional Neural Network (CNN) model for a practical biometric authentication system requires specific procedures for training and performance evaluation. There are two criteria for a practical biometric system studied in this paper. First, the system’s ability to handle identity theft or impersonation attacks. Second, the ability of the system to generate high authentication performance with minimal enrollment period. We propose the use of the Multiple Clip Contrast Limited Adaptive Histogram Equalization (MC-CLAHE) technique to process finger images before being trained by CNN. A pre-trained CNN model called AlexNet is used to extract features as well as classify the MC-CLAHE images. The authentication performance of the pre-trained AlexNet model has increased by a maximum of 30% when using this technique. To ensure that the pre-trained AlexNet model is evaluated based on its ability to prevent impersonation attacks, a procedure to generate the Receiver Operating Characteristics (ROC) curve is proposed. An offline procedure for training the pre-trained AlexNet model is also proposed in this paper. The purpose is to minimize the user enrollment period without compromising the authentication performance. In this paper, this procedure successfully reduces the enrollment time by up to 95% compared to using on-line training.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122669387","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}

引用次数: 1

A New Pyramid Model of Empathy: The Role of ICTs and Robotics on Empathy 一个新的同理心金字塔模型:信息通信技术和机器人技术对同理心的作用

Int. J. Online Biomed. Eng. Pub Date : 2023-02-16 DOI: 10.3991/ijoe.v19i02.33591

A. Drigas, Chara Papoutsi

引用次数: 1

Interaction Multi-Agent Models' Automatic Alignment with MDA Higher Abstraction Level 交互多agent模型与MDA更高抽象层次的自动对齐

Int. J. Online Biomed. Eng. Pub Date : 2023-02-16 DOI: 10.3991/ijoe.v19i02.37047

Nassim Kharmoum, Sara Retal, Karim El Bouchti, Wajih Rhalem, Soumia Ziti

引用次数: 1

Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images 卷积神经网络模型在MRI图像中分割心肌梗死

Int. J. Online Biomed. Eng. Pub Date : 2023-02-16 DOI: 10.3991/ijoe.v19i02.36607

Zakarya Farea Shaaf, M. M. A. Jamil, R. Ambar

引用次数: 0

MRI Brain Scans Classification Using Extreme Learning Machine on LBP and GLCM 使用极限学习机对LBP和GLCM进行MRI脑扫描分类

Int. J. Online Biomed. Eng. Pub Date : 2023-02-16 DOI: 10.3991/ijoe.v19i02.33987

Jhan Yahya Rbat Al-Awadi, Hadeel K. Aljobouri, A. M. Hasan

{"title":"MRI Brain Scans Classification Using Extreme Learning Machine on LBP and GLCM","authors":"Jhan Yahya Rbat Al-Awadi, Hadeel K. Aljobouri, A. M. Hasan","doi":"10.3991/ijoe.v19i02.33987","DOIUrl":"https://doi.org/10.3991/ijoe.v19i02.33987","url":null,"abstract":"The primary goal of this study is to predict the presence of a brain tumor using MRI brain images. These images are first pre-processed to remove the boundary borders and the undesired regions. Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern method (LBP) approaches are mixed for extracting multiple local and global features. The best features are selected using the ANOVA statistical approach, which is based on the largest variance. Then, the selected features are applied to many state of arts classifiers as well as to Extreme Learning Machine (ELM) neural network model, where the weights are optimized via the regularization of RELM using a suitable ratio of Cross Validation (CV) for the images' classification into one of two classes, namely normal (benign) and abnormal (malignant). The proposed ELM algorithm was trained and tested with 800 images of BRATS 2015 datasets types, and the experimental results demonstrated that this approach has better performance on several evaluation criteria, including accuracy, stability, and speedup. It reaches to 98.87% accuracy with extremely low classification time. ELM can improve the classification performance by raising the accuracy more than 2% and reducing the number of processes needed by speeding up the algorithm by a factor of 10 for an average of 20 trials.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129478429","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}

引用次数: 1

Remote Lab Experiments in Mechanic: The Compound Pendulum 机械远程实验室实验:复合摆

Int. J. Online Biomed. Eng. Pub Date : 2023-02-16 DOI: 10.3991/ijoe.v19i02.37061

Zineb Laouina, Lynda Ouchaouka, M. Moussetad, S. Mordane, M. Radid

引用次数: 0

Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems 电子健康智能系统的数据安全机制、方法和挑战

Int. J. Online Biomed. Eng. Pub Date : 2023-02-16 DOI: 10.3991/ijoe.v19i02.37069

Hamza Rafik, A. Maizate, Abdelaziz Ettaoufik

{"title":"Data Security Mechanisms, Approaches, and Challenges for e-Health Smart Systems","authors":"Hamza Rafik, A. Maizate, Abdelaziz Ettaoufik","doi":"10.3991/ijoe.v19i02.37069","DOIUrl":"https://doi.org/10.3991/ijoe.v19i02.37069","url":null,"abstract":"In the new era, the trend of using wearable devices and smart accessories gained considerable popularity and become a necessity utility for human life due to their major role to keep monitoring health conditions and providing healthcare services. The combination of IoT networks with edge computing paradigms develops an intelligent e-health system that aims to monitor different real-time scenarios. The deployment of an e-health system exposes several challenges regarding the security and privacy aspects, particularly in the case of dealing with an enormous quantity of medical data and the risk presented by exchanging operations with external entities. In this paper a comprehensive presentation covered the basic topics of e-health system layers thus the advantages and limitations in terms of existing challenges has been mentioned, subsequently, adapted to the exposed cyber risk through the traditional systems in exchanging medical data, a discussion of the blockchain technology come over for new application opportunities, where this approach efficiently ensure the security of data transactions over the network, in addition, an overview outlined the main research works related to this technology. Therefore, a presentation study of diverse works reveals different security framework solutions related to e-health system’s layers, furthermore, uncovering the benefits of integrating intelligent technologies such as Machine Learning (supervised, and unsupervised types), Deep Learning, and Reinforcement Learning as well as introducing a comparison analysis of multiple AI algorithm models based on their efficiency for future deployment related security purposes to provide a smart healthcare monitoring system that meets patient needs. The end of this review highlighted further research directions and the actual open challenges regarding the e-Health system’s limitations.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121129427","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}

引用次数: 0

Clinical Decision Support Systems' Usage Continuance Intentions by Health Care Providers in Jordan: Toward an Integrated Model 临床决策支持系统的使用延续意图由卫生保健提供者在约旦:迈向一个综合模型

Int. J. Online Biomed. Eng. Pub Date : 2023-02-16 DOI: 10.3991/ijoe.v19i02.37239

Jehad Imlawi

{"title":"Clinical Decision Support Systems' Usage Continuance Intentions by Health Care Providers in Jordan: Toward an Integrated Model","authors":"Jehad Imlawi","doi":"10.3991/ijoe.v19i02.37239","DOIUrl":"https://doi.org/10.3991/ijoe.v19i02.37239","url":null,"abstract":"Health organizations in Jordan has just started adopting a nationwide health information system [Hakeem] including useful tools such as the clinical decision support system [CDSS]. Adopting CDSS by health care providers is not mandatory; However, the fruitful results of these tools can only be gained after adopted by the health care providers, and when they have the intentions to continue use it in the future. \u0000The current study proposes a model that integrates factors from tow important theories of technology acceptance; Technology Acceptance Model [TAM], and Information Systems Success Model [ISSM] to predict the health care providers’ usage continuance intentions of CDSS in future. The study also checks if gender, experience, and CDSS alerts’ frequency has any moderation effects on the proposed research model. To assess the research model, data were collected from 218 participants via an online survey. \u0000The proposed model has strongly predicted the CDSS usage continuance intentions [R2=0.486]. However, the moderators; gender, experience, and CDSS alerts’ frequency, partially moderate the proposed relationships. Conclusions: This research extends the growing literature on health information systems' adoption by building an integrated model that integrates factors from two well-established technology acceptance models, TAM and ISSM. The findings proved a significant impact of ISSM's factors [system quality, information quality, and satisfaction] on CDSS usage continuance intentions.","PeriodicalId":247144,"journal":{"name":"Int. J. Online Biomed. Eng.","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114915108","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}

引用次数: 0