Pub Date : 2024-02-27DOI: 10.3991/ijoe.v20i03.46811
Christian Ovalle, Isaac Leonardo Vallejos García, Franco Rafael Zapata Berrios
Schools have historically been ill-prepared to cater to the needs of deaf students at the elementary and secondary levels. This leads to communication difficulties that impact the learning process for each individual. During the recent COVID-19 pandemic, educational institutions for deaf students faced difficulties in providing effective teaching to children and youth. It is important to emphasize that education is fundamental for all individuals, without exception, as acquiring literacy skills enables them to lead a more fulfilling life. In this context, our research aims to investigate how the use of a computer tool can enhance communication for deaf students in a virtual environment. The methodology used involved the use of a checklist to gather data from each participant’s evaluation. The post-test yielded favorable results, thanks to the statistical analysis employed in the research. In conclusion, it has been determined that a real-time transcriber facilitates learning, leading to improved educational outcomes for deaf students.
{"title":"Real-Time Transcriptionist Based on Artificial Intelligence to Facilitate Learning for People with Hearing Disabilities in Virtual Classes","authors":"Christian Ovalle, Isaac Leonardo Vallejos García, Franco Rafael Zapata Berrios","doi":"10.3991/ijoe.v20i03.46811","DOIUrl":"https://doi.org/10.3991/ijoe.v20i03.46811","url":null,"abstract":"Schools have historically been ill-prepared to cater to the needs of deaf students at the elementary and secondary levels. This leads to communication difficulties that impact the learning process for each individual. During the recent COVID-19 pandemic, educational institutions for deaf students faced difficulties in providing effective teaching to children and youth. It is important to emphasize that education is fundamental for all individuals, without exception, as acquiring literacy skills enables them to lead a more fulfilling life. In this context, our research aims to investigate how the use of a computer tool can enhance communication for deaf students in a virtual environment. The methodology used involved the use of a checklist to gather data from each participant’s evaluation. The post-test yielded favorable results, thanks to the statistical analysis employed in the research. In conclusion, it has been determined that a real-time transcriber facilitates learning, leading to improved educational outcomes for deaf students.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140426536","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 : 2024-02-27DOI: 10.3991/ijoe.v20i03.46101
D.T.P. Yanto, Ganefri, Sukardi, Jelpapo Putra Yanto, Rozalita Kurani, Muslim
The use of augmented reality (AR) technology in the field of education has emerged as a rapidly growing trend. However, there is an urgent need for more comprehensive research to determine the reactions of engineering students and their acceptance of this technology in laboratory learning. This study investigates the acceptance of integrated augmented reality with e-worksheet (IARE-W) among engineering students in the laboratory learning (IARE-W) among engineering students the electrical machines course (EMC). This research empirically uncovers the factors that influence it based on the technology acceptance model (TAM), specifically perceived ease of use (PEU) and perceived usefulness (PU). Acceptance is indicated by students’ attitudes toward the use. A survey-based quantitative research study using questionnaires was conducted to collect data, involving 102 students in the field of industrial electrical engineering. The partial least squares structural equation modeling (PLS-SEM) analysis was used to analyze the research data. The results demonstrated that engineering students had a highly positive attitude toward the use of the IARE-W in the EMC. Additionally, both PEU and PU had a positive and significant direct effect on engineering students’ attitudes toward using IARE-W. Furthermore, PEU also had a significant and positive indirect effect through PU as a mediating variable. These findings have significant implications for the development of engineering education and the integration of AR technology in laboratory learning contexts. The results of this study underscore the importance of taking into account PEU and PU in the design, development, and implementation of the IARE-W.
在教育领域使用增强现实（AR）技术已成为一种迅速发展的趋势。然而，目前急需进行更全面的研究，以确定工科学生的反应及其在实验室学习中对该技术的接受程度。本研究调查了工科学生在电机课程（EMC）的实验学习中对电子作业单集成增强现实技术（IARE-W）的接受程度。本研究根据技术接受模型（TAM），特别是感知易用性（PEU）和感知有用性（PU），实证揭示了影响接受度的因素。接受度由学生对使用的态度来表示。本研究采用问卷调查的方式收集数据，涉及 102 名工业电气工程专业的学生。研究数据采用偏最小二乘结构方程模型（PLS-SEM）分析法进行分析。结果表明，工科学生对在 EMC 中使用 IARE-W 持非常积极的态度。此外，PEU 和 PU 对工科学生使用 IARE-W 的态度有显著的直接影响。此外，PEU 还通过 PU 这一中介变量产生了显著的正向间接影响。这些研究结果对工程教育的发展和将 AR 技术融入实验学习情境具有重要意义。本研究的结果强调了在设计、开发和实施 IARE-W 时考虑 PEU 和 PU 的重要性。
{"title":"Engineering Students' Acceptance of Augmented Reality Technology Integrated with E-Worksheet in The Laboratory Learning","authors":"D.T.P. Yanto, Ganefri, Sukardi, Jelpapo Putra Yanto, Rozalita Kurani, Muslim","doi":"10.3991/ijoe.v20i03.46101","DOIUrl":"https://doi.org/10.3991/ijoe.v20i03.46101","url":null,"abstract":"The use of augmented reality (AR) technology in the field of education has emerged as a rapidly growing trend. However, there is an urgent need for more comprehensive research to determine the reactions of engineering students and their acceptance of this technology in laboratory learning. This study investigates the acceptance of integrated augmented reality with e-worksheet (IARE-W) among engineering students in the laboratory learning (IARE-W) among engineering students the electrical machines course (EMC). This research empirically uncovers the factors that influence it based on the technology acceptance model (TAM), specifically perceived ease of use (PEU) and perceived usefulness (PU). Acceptance is indicated by students’ attitudes toward the use. A survey-based quantitative research study using questionnaires was conducted to collect data, involving 102 students in the field of industrial electrical engineering. The partial least squares structural equation modeling (PLS-SEM) analysis was used to analyze the research data. The results demonstrated that engineering students had a highly positive attitude toward the use of the IARE-W in the EMC. Additionally, both PEU and PU had a positive and significant direct effect on engineering students’ attitudes toward using IARE-W. Furthermore, PEU also had a significant and positive indirect effect through PU as a mediating variable. These findings have significant implications for the development of engineering education and the integration of AR technology in laboratory learning contexts. The results of this study underscore the importance of taking into account PEU and PU in the design, development, and implementation of the IARE-W.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424348","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 : 2024-02-27DOI: 10.3991/ijoe.v20i03.45769
Abdelmalek Makhir, My Hachem El Yousfi Alaoui, Larbi Belarbi
This study addresses the critical issue of classifying cardiac ischemia, a disease with significant global health implications that contributes to the global mortality rate. In our study, we tackle the classification of ischemia using six diverse electrocardiogram (ECG) datasets and a convolutional neural network (CNN) as the primary methodology. We combined six separate datasets to gain a more comprehensive understanding of cardiac electrical activity, utilizing 12 leads to obtain a broader perspective. A discrete wavelet transform (DWT) preprocessing was used to eliminate irrelevant information from the signals, aiming to improve classification results. Focusing on accuracy and minimizing false negatives (FN) in ischemia detection, we enhance our study by incorporating various machine learning models into our base model. These models include multilayer perceptron (MLP), support vector machines (SVM), random forest (RF), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), allowing us to leverage the strengths of each algorithm. The CNN-BiLSTM model achieved the highest accuracy of 99.23% and demonstrated good sensitivity of 98.53%, effectively reducing false negative cases in the overall tests. The CNN-BiLSTM model demonstrated the ability to effectively identify abnormalities, misclassifying only 25 out of 1,673 ischemic cases in the test set as normal. This is due to the BiLSTM’s efficiency in capturing long-range dependencies and sequential patterns, making it suitable for tasks involving time-series data such as ECG signals. In addition, CNNs are well-suited for hierarchical feature learning and complex pattern recognition in ECG data.
{"title":"Comprehensive Cardiac Ischemia Classification Using Hybrid CNN-Based Models","authors":"Abdelmalek Makhir, My Hachem El Yousfi Alaoui, Larbi Belarbi","doi":"10.3991/ijoe.v20i03.45769","DOIUrl":"https://doi.org/10.3991/ijoe.v20i03.45769","url":null,"abstract":"This study addresses the critical issue of classifying cardiac ischemia, a disease with significant global health implications that contributes to the global mortality rate. In our study, we tackle the classification of ischemia using six diverse electrocardiogram (ECG) datasets and a convolutional neural network (CNN) as the primary methodology. We combined six separate datasets to gain a more comprehensive understanding of cardiac electrical activity, utilizing 12 leads to obtain a broader perspective. A discrete wavelet transform (DWT) preprocessing was used to eliminate irrelevant information from the signals, aiming to improve classification results. Focusing on accuracy and minimizing false negatives (FN) in ischemia detection, we enhance our study by incorporating various machine learning models into our base model. These models include multilayer perceptron (MLP), support vector machines (SVM), random forest (RF), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), allowing us to leverage the strengths of each algorithm. The CNN-BiLSTM model achieved the highest accuracy of 99.23% and demonstrated good sensitivity of 98.53%, effectively reducing false negative cases in the overall tests. The CNN-BiLSTM model demonstrated the ability to effectively identify abnormalities, misclassifying only 25 out of 1,673 ischemic cases in the test set as normal. This is due to the BiLSTM’s efficiency in capturing long-range dependencies and sequential patterns, making it suitable for tasks involving time-series data such as ECG signals. In addition, CNNs are well-suited for hierarchical feature learning and complex pattern recognition in ECG data.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140426863","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 : 2024-02-27DOI: 10.3991/ijoe.v20i03.46287
Mohamed Bellaj, Ahmed Ben Dahmane, Said Boudra, Mohammed Lamarti Sefian
Educational data mining (EDM) is a specialized field within data mining that focuses on extracting valuable insights from academic data across high school and university levels. A common practice in EDM involves predicting students’ grades to identify at-risk individuals and improve the efficiency of academic tasks. This knowledge benefits students, parents, and institutions equally. Early detection enables interventions that improve student performance. The literature presents various prediction strategies, each with its own unique advantages and disadvantages. This study aims to comprehensively evaluate the methods, tools, and applications of machine learning (ML) and data mining (DM) in education. The main goal is to improve the accuracy of predicting academic achievements by employing eight widely recognized ML algorithms: naïve bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), logistic regression (LR), extreme gradient boost (XGBOOST), and ensemble voting classifier (EVC). The focus is on improving data quality by eliminating instances of noise. Performance evaluation involves assessing parameters such as accuracy, precision, F-measure, and recall. Incorporating cross-validation and hyperparameter tuning improves classification accuracy. The ML models outperform other ensemble approaches, providing a valuable tool for predicting student performance and assisting educators in making proactive decisions through timely alerts.
{"title":"Educational Data Mining: Employing Machine Learning Techniques and Hyperparameter Optimization to Improve Students’ Academic Performance","authors":"Mohamed Bellaj, Ahmed Ben Dahmane, Said Boudra, Mohammed Lamarti Sefian","doi":"10.3991/ijoe.v20i03.46287","DOIUrl":"https://doi.org/10.3991/ijoe.v20i03.46287","url":null,"abstract":"Educational data mining (EDM) is a specialized field within data mining that focuses on extracting valuable insights from academic data across high school and university levels. A common practice in EDM involves predicting students’ grades to identify at-risk individuals and improve the efficiency of academic tasks. This knowledge benefits students, parents, and institutions equally. Early detection enables interventions that improve student performance. The literature presents various prediction strategies, each with its own unique advantages and disadvantages. This study aims to comprehensively evaluate the methods, tools, and applications of machine learning (ML) and data mining (DM) in education. The main goal is to improve the accuracy of predicting academic achievements by employing eight widely recognized ML algorithms: naïve bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), logistic regression (LR), extreme gradient boost (XGBOOST), and ensemble voting classifier (EVC). The focus is on improving data quality by eliminating instances of noise. Performance evaluation involves assessing parameters such as accuracy, precision, F-measure, and recall. Incorporating cross-validation and hyperparameter tuning improves classification accuracy. The ML models outperform other ensemble approaches, providing a valuable tool for predicting student performance and assisting educators in making proactive decisions through timely alerts.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424199","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 : 2024-02-27DOI: 10.3991/ijoe.v20i03.45249
M. Baklizi, Issa Atoum, Mohammad Alkhazaleh, Hasan Kanaker, Nibras Abdullah, O. A. Al-wesabi, A. Otoom
Web attacks often target web applications because they can be accessed over a network and often have vulnerabilities. The success of an intrusion detection system (IDS) in detecting web attacks depends on an effective traffic classification system. Several previous studies have utilized machine learning classification methods to create an efficient IDS with various datasets for different types of attacks. This paper utilizes the Canadian Institute for Cyber Security’s (CIC-IDS2017) IDS dataset to assess web attacks. Importantly, the dataset contains 80 attributes of recent assaults, as reported in the 2016 McAfee report. Three machine learning algorithms have been evaluated in this research, namely random forests (RF), k-nearest neighbor (KNN), and naive bayes (NB). The primary goal of this research is to propose an effective machine learning algorithm for the IDS web attacks model. The evaluation compares the performance of three algorithms (RF, KNN, and NB) based on their accuracy and precision in detecting anomalous traffic. The results indicate that the RF outperformed the NB and KNN in terms of average accuracy achieved during the training phase. During the testing phase, the KNN algorithm outperformed others, achieving an average accuracy of 99.4916%. However, RF and KNN achieved 100% average precision and recall rates compared to other algorithms. Finally, the RF and KNN algorithms have been identified as the most effective for detecting IDS web attacks.
{"title":"Web Attack Intrusion Detection System Using Machine Learning Techniques","authors":"M. Baklizi, Issa Atoum, Mohammad Alkhazaleh, Hasan Kanaker, Nibras Abdullah, O. A. Al-wesabi, A. Otoom","doi":"10.3991/ijoe.v20i03.45249","DOIUrl":"https://doi.org/10.3991/ijoe.v20i03.45249","url":null,"abstract":"Web attacks often target web applications because they can be accessed over a network and often have vulnerabilities. The success of an intrusion detection system (IDS) in detecting web attacks depends on an effective traffic classification system. Several previous studies have utilized machine learning classification methods to create an efficient IDS with various datasets for different types of attacks. This paper utilizes the Canadian Institute for Cyber Security’s (CIC-IDS2017) IDS dataset to assess web attacks. Importantly, the dataset contains 80 attributes of recent assaults, as reported in the 2016 McAfee report. Three machine learning algorithms have been evaluated in this research, namely random forests (RF), k-nearest neighbor (KNN), and naive bayes (NB). The primary goal of this research is to propose an effective machine learning algorithm for the IDS web attacks model. The evaluation compares the performance of three algorithms (RF, KNN, and NB) based on their accuracy and precision in detecting anomalous traffic. The results indicate that the RF outperformed the NB and KNN in terms of average accuracy achieved during the training phase. During the testing phase, the KNN algorithm outperformed others, achieving an average accuracy of 99.4916%. However, RF and KNN achieved 100% average precision and recall rates compared to other algorithms. Finally, the RF and KNN algorithms have been identified as the most effective for detecting IDS web attacks.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427166","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 : 2024-02-27DOI: 10.3991/ijoe.v20i03.47101
Dony Novaliendry, Rahmat Febri Yoga Saputra, Novi Febrianti, Doni Tri Putra Yanto, Fadhillah Majid Saragih, Wan Mohd Yusof Rahiman
The world is currently abuzz with the rapid development of technology in the era of Industrial Revolution 4.0. Various technological advancements are facilitating progress and accelerating the development of industrial technology. This evolution has led to automation in production processes, transitioning toward digitalization. With the implementation of sensors that provide real-time data, production processes can now be monitored remotely. However, direct monitoring is still necessary at times to periodically check the condition of each operating machine. Therefore, there is a need for technology that can monitor production processes and reduce high maintenance costs. Currently, numerous new technologies are emerging to enhance the performance and efficiency of production processes in various industries. One such technology is the digital twin. A digital twin is a visual representation that offers insights into the continuous operations of a system. This research focuses on an industrial manufacturing monitoring system that integrates the Internet of Things (IoT) and augmented reality (AR) technologies. The system is composed of an application and a prototype machine in the form of a conveyor, which can simulate a digital twin of the prototype machine. It also transmits sensor data and error notifications to the application in real time. The designed system can serve as a prototype for implementing digital twin technology, combining IoT and AR. This makes it possible to apply the technology to machinery and production tools in various industrial sectors.
在工业革命 4.0 时代，全世界都在热议科技的飞速发展。各种技术进步促进了工业技术的进步和加速发展。这种演变导致生产流程自动化，向数字化过渡。随着可提供实时数据的传感器的应用，现在可以远程监控生产流程。不过，有时仍需要直接监控，以定期检查每台运行机器的状况。因此，需要能够监控生产过程并降低高昂维护成本的技术。目前，许多新技术不断涌现，以提高各行业生产流程的性能和效率。数字孪生技术就是其中之一。数字孪生是一种可视化的表现形式，能让人深入了解系统的持续运行情况。本研究的重点是集成了物联网（IoT）和增强现实（AR）技术的工业制造监控系统。该系统由应用程序和传送带形式的原型机组成，可以模拟原型机的数字孪生。它还能向应用程序实时传输传感器数据和错误通知。所设计的系统可作为实施数字孪生技术的原型，将物联网和 AR 结合在一起。这使得将该技术应用于各工业部门的机械和生产工具成为可能。
{"title":"Development of a Digital Twin Prototype for Industrial Manufacturing Monitoring System Using IoT and Augmented Reality","authors":"Dony Novaliendry, Rahmat Febri Yoga Saputra, Novi Febrianti, Doni Tri Putra Yanto, Fadhillah Majid Saragih, Wan Mohd Yusof Rahiman","doi":"10.3991/ijoe.v20i03.47101","DOIUrl":"https://doi.org/10.3991/ijoe.v20i03.47101","url":null,"abstract":"The world is currently abuzz with the rapid development of technology in the era of Industrial Revolution 4.0. Various technological advancements are facilitating progress and accelerating the development of industrial technology. This evolution has led to automation in production processes, transitioning toward digitalization. With the implementation of sensors that provide real-time data, production processes can now be monitored remotely. However, direct monitoring is still necessary at times to periodically check the condition of each operating machine. Therefore, there is a need for technology that can monitor production processes and reduce high maintenance costs. Currently, numerous new technologies are emerging to enhance the performance and efficiency of production processes in various industries. One such technology is the digital twin. A digital twin is a visual representation that offers insights into the continuous operations of a system. This research focuses on an industrial manufacturing monitoring system that integrates the Internet of Things (IoT) and augmented reality (AR) technologies. The system is composed of an application and a prototype machine in the form of a conveyor, which can simulate a digital twin of the prototype machine. It also transmits sensor data and error notifications to the application in real time. The designed system can serve as a prototype for implementing digital twin technology, combining IoT and AR. This makes it possible to apply the technology to machinery and production tools in various industrial sectors.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425540","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 : 2024-02-27DOI: 10.3991/ijoe.v20i03.45681
Syifaul Fuada, Mariella Särestöniemi, Marcos Katz
Implantable medical devices (IMDs) play a crucial role in improving individuals’ well-being and ensuring their safety by providing real-time health data monitoring for recovery. The use of energy harvesting (EH) technology has become increasingly popular among researchers because it offers the potential to extend the battery life of IMDs and reduce their weight. This study successfully examined the expansion of EH in the field of IMDs, the distribution of publications across different countries, and the identification of the most influential authors for potential research collaborations. A bibliometric analysis was conducted to evaluate two metrics: performance and science mapping. Data was collected from the Scopus database from the initial publications until October 2023, encompassing 250 articles published in Englishlanguage journals. The titles, keywords, and abstracts of these publications were analyzed and interpreted using VOS Viewer (version 1.6.19). Furthermore, network analysis using VOS Viewer enabled the identification of key research clusters. The findings reveal a continuous increase in EH for research on infectious and parasitic diseases over the 15-year period from 2008 to 2023. The United States and the University of Bern are recognized as the leading contributors to this field, based on their country and institutional contributions, respectively. The author with the most published papers and citations hails from China. Additionally, this study identifies several opportunities for collaboration with countries, institutions, authors, and research hotspots in EH for IMDs that benefit the reader.
{"title":"Analyzing the Trends and Global Growth of Energy Harvesting for Implantable Medical Devices (IMDs) Research—A Bibliometric Approach","authors":"Syifaul Fuada, Mariella Särestöniemi, Marcos Katz","doi":"10.3991/ijoe.v20i03.45681","DOIUrl":"https://doi.org/10.3991/ijoe.v20i03.45681","url":null,"abstract":"Implantable medical devices (IMDs) play a crucial role in improving individuals’ well-being and ensuring their safety by providing real-time health data monitoring for recovery. The use of energy harvesting (EH) technology has become increasingly popular among researchers because it offers the potential to extend the battery life of IMDs and reduce their weight. This study successfully examined the expansion of EH in the field of IMDs, the distribution of publications across different countries, and the identification of the most influential authors for potential research collaborations. A bibliometric analysis was conducted to evaluate two metrics: performance and science mapping. Data was collected from the Scopus database from the initial publications until October 2023, encompassing 250 articles published in Englishlanguage journals. The titles, keywords, and abstracts of these publications were analyzed and interpreted using VOS Viewer (version 1.6.19). Furthermore, network analysis using VOS Viewer enabled the identification of key research clusters. The findings reveal a continuous increase in EH for research on infectious and parasitic diseases over the 15-year period from 2008 to 2023. The United States and the University of Bern are recognized as the leading contributors to this field, based on their country and institutional contributions, respectively. The author with the most published papers and citations hails from China. Additionally, this study identifies several opportunities for collaboration with countries, institutions, authors, and research hotspots in EH for IMDs that benefit the reader.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140427966","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 : 2024-02-27DOI: 10.3991/ijoe.v20i03.46769
Lesly Velezmoro-Abanto, Rocío Cuba-Lagos, Bryan Taico-Valverde, Orlando Iparraguirre-Villanueva, M. Cabanillas-Carbonell
This paper analyzes the application of artificial intelligence (AI) techniques in lean construction (LC) and their potential to enhance project management (PM) for improved cost and schedule efficiency. The PRISMA methodology is used to select relevant articles in four steps. Furthermore, a bibliometric analysis of keywords and their occurrences is conducted. The study emphasizes the different methods of utilizing lean tools and AI techniques to attain optimal results in the construction industry. By combining a variety of tools and techniques, it is possible to create an environment that fosters improved project outcomes while minimizing risks and inefficiencies. According to the articles reviewed, the LC methodology and its tools are becoming increasingly relevant in general practice (GP). Machine learning (ML) techniques, particularly artificial neural networks (ANN), have been extensively researched as a tool to enhance construction projects by minimizing delays, fostering collaboration, cutting costs, saving time, and boosting productivity. Combining LC with ML can enhance profitability and align with lean principles, leading to successful outcomes for construction projects.
本文分析了人工智能（AI）技术在精益建造（LC）中的应用及其在加强项目管理（PM）以提高成本和进度效率方面的潜力。采用 PRISMA 方法分四个步骤筛选相关文章。此外，还对关键词及其出现情况进行了文献计量分析。本研究强调了利用精益工具和人工智能技术的不同方法，以在建筑行业取得最佳成果。通过将各种工具和技术相结合，有可能创造出一种环境，促进项目成果的改善，同时最大限度地降低风险和低效率。根据所查阅的文章，LC 方法及其工具在全科实践（GP）中的作用越来越大。机器学习（ML）技术，特别是人工神经网络（ANN），已被广泛研究作为一种工具，通过最大限度地减少延误、促进协作、降低成本、节省时间和提高生产率来改进建筑项目。将 LC 与 ML 相结合可以提高盈利能力，并与精益原则保持一致，从而为建筑项目带来成功的结果。
{"title":"Lean Construction Strategies Supported by Artificial Intelligence Techniques for Construction Project Management—A Review","authors":"Lesly Velezmoro-Abanto, Rocío Cuba-Lagos, Bryan Taico-Valverde, Orlando Iparraguirre-Villanueva, M. Cabanillas-Carbonell","doi":"10.3991/ijoe.v20i03.46769","DOIUrl":"https://doi.org/10.3991/ijoe.v20i03.46769","url":null,"abstract":"This paper analyzes the application of artificial intelligence (AI) techniques in lean construction (LC) and their potential to enhance project management (PM) for improved cost and schedule efficiency. The PRISMA methodology is used to select relevant articles in four steps. Furthermore, a bibliometric analysis of keywords and their occurrences is conducted. The study emphasizes the different methods of utilizing lean tools and AI techniques to attain optimal results in the construction industry. By combining a variety of tools and techniques, it is possible to create an environment that fosters improved project outcomes while minimizing risks and inefficiencies. According to the articles reviewed, the LC methodology and its tools are becoming increasingly relevant in general practice (GP). Machine learning (ML) techniques, particularly artificial neural networks (ANN), have been extensively researched as a tool to enhance construction projects by minimizing delays, fostering collaboration, cutting costs, saving time, and boosting productivity. Combining LC with ML can enhance profitability and align with lean principles, leading to successful outcomes for construction projects.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425915","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 : 2024-02-14DOI: 10.3991/ijoe.v20i02.44845
Wafae Abbaoui, Sara Retal, Soumia Ziti, Brahim El Bhiri, Hassan Moussif
This study presents a comprehensive exploration of deep learning models for precise brain ischemic stroke classification using medical data from Morocco. Following the OSEMN approach, our methodology leverages transfer learning with the VGG-16 architecture and employs data augmentation techniques to enhance model performance. Our developed model achieved a remarkable validation accuracy of 90%, surpassing alternative state-of-theart models (ResNet50: 87.0%, InceptionV3: 82.0%, VGG-19: 81.0%). Notably, all models were rigorously evaluated on the same meticulously curated dataset, ensuring fair and consistent comparisons. The investigation underscores VGG-16’s superior performance in distinguishing stroke cases, highlighting its potential as a robust tool for accurate diagnosis. Comparative analyses among popular deep learning architectures not only demonstrate our model’s efficacy but also emphasize the importance of selecting the right architecture for medical image classification tasks. These findings contribute to the growing evidence supporting advanced deep learning techniques in medical imaging. Achieving a validation accuracy of 90%, our model holds significant promise for real-world healthcare applications, showcasing the critical role of cutting-edge technologies in advancing diagnostic accuracy and the transformative potential of deep learning in the medical field.
{"title":"Ischemic Stroke Classification Using VGG-16 Convolutional Neural Networks: A Study on Moroccan MRI Scans","authors":"Wafae Abbaoui, Sara Retal, Soumia Ziti, Brahim El Bhiri, Hassan Moussif","doi":"10.3991/ijoe.v20i02.44845","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.44845","url":null,"abstract":"This study presents a comprehensive exploration of deep learning models for precise brain ischemic stroke classification using medical data from Morocco. Following the OSEMN approach, our methodology leverages transfer learning with the VGG-16 architecture and employs data augmentation techniques to enhance model performance. Our developed model achieved a remarkable validation accuracy of 90%, surpassing alternative state-of-theart models (ResNet50: 87.0%, InceptionV3: 82.0%, VGG-19: 81.0%). Notably, all models were rigorously evaluated on the same meticulously curated dataset, ensuring fair and consistent comparisons. The investigation underscores VGG-16’s superior performance in distinguishing stroke cases, highlighting its potential as a robust tool for accurate diagnosis. Comparative analyses among popular deep learning architectures not only demonstrate our model’s efficacy but also emphasize the importance of selecting the right architecture for medical image classification tasks. These findings contribute to the growing evidence supporting advanced deep learning techniques in medical imaging. Achieving a validation accuracy of 90%, our model holds significant promise for real-world healthcare applications, showcasing the critical role of cutting-edge technologies in advancing diagnostic accuracy and the transformative potential of deep learning in the medical field.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778468","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 : 2024-02-14DOI: 10.3991/ijoe.v20i02.45377
L. Andrade-Arenas, Cesar Yactayo-Arias, Félix Pucuhuayla-Revatta
In the context of advancing technological development, chatbots have emerged as an innovative tool in the field of mental health, offering new possibilities to provide therapy and emotional support in an accessible and convenient manner. The aim of this study was to develop and evaluate a chatbot implemented in a web application designed to provide emotional support to an adult population, specifically targeting young people and adults over the age of 18. The research focused on user satisfaction with the chatbot experience. Using a qualitative approach and non-random convenience sampling, we collected feedback on the chatbot’s performance from 15 users through an online questionnaire. The results showed a positive assessment, with an average satisfaction score of 4.09 on a scale of 1 to 5. The participants expressed their approval of the emotional support provided by the chatbot, emphasizing the sense of understanding and trust generated by the therapeutic interventions and emotional support. In conclusion, this study successfully assessed user satisfaction with the emotional support chatbot, emphasizing its significance in the realm of digital mental health. The scope of this study was solely focused on user satisfaction. For future research, it is recommended to expand the scope to investigate the correlation between user satisfaction and therapeutic outcomes. Additionally, there is a need to tailor these systems to meet the specific emotional requirements of diverse user groups and enhance the efficacy of mental health patient care.
{"title":"Therapy and Emotional Support through a Chatbot","authors":"L. Andrade-Arenas, Cesar Yactayo-Arias, Félix Pucuhuayla-Revatta","doi":"10.3991/ijoe.v20i02.45377","DOIUrl":"https://doi.org/10.3991/ijoe.v20i02.45377","url":null,"abstract":"In the context of advancing technological development, chatbots have emerged as an innovative tool in the field of mental health, offering new possibilities to provide therapy and emotional support in an accessible and convenient manner. The aim of this study was to develop and evaluate a chatbot implemented in a web application designed to provide emotional support to an adult population, specifically targeting young people and adults over the age of 18. The research focused on user satisfaction with the chatbot experience. Using a qualitative approach and non-random convenience sampling, we collected feedback on the chatbot’s performance from 15 users through an online questionnaire. The results showed a positive assessment, with an average satisfaction score of 4.09 on a scale of 1 to 5. The participants expressed their approval of the emotional support provided by the chatbot, emphasizing the sense of understanding and trust generated by the therapeutic interventions and emotional support. In conclusion, this study successfully assessed user satisfaction with the emotional support chatbot, emphasizing its significance in the realm of digital mental health. The scope of this study was solely focused on user satisfaction. For future research, it is recommended to expand the scope to investigate the correlation between user satisfaction and therapeutic outcomes. Additionally, there is a need to tailor these systems to meet the specific emotional requirements of diverse user groups and enhance the efficacy of mental health patient care.","PeriodicalId":507997,"journal":{"name":"International Journal of Online and Biomedical Engineering (iJOE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836843","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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